Moving Beyond Walled Gardens: The Shift in Digital Advertising
TL;DR
- Walled Gardens Defined: Walled gardens are closed ecosystems where platforms control user data and ad inventory, limiting advertisers' access to comprehensive performance insights.
- Market Shift: Recent trends indicate a decline in the dominance of walled gardens, with advertisers increasingly turning to first-party measurement solutions and retail media networks for greater transparency and control.
- Cost and Trust Issues: Rising advertising costs and trust concerns related to major platforms have prompted brands to explore alternative advertising avenues beyond walled gardens.
- Advantage of First-Party Data: Leveraging first-party data allows brands to gain deeper customer insights, enhance targeting accuracy, and improve return on investment without relying solely on large platforms.
Moving Beyond Walled Gardens: The Shift in Digital Advertising
It’s no secret that a small handful of platforms have historically controlled the lion's share of information when it comes to digital advertising. It has been an incongruous game of chess between tech giants and brands trying to scale. But, the winds may finally be changing.
In the ever-evolving landscape of e-commerce, brands are increasingly taking control of their advertising decisions by leveraging their own data and first-party measurement solutions, like Northbeam. According to recent reporting by Adweek, this shift marks a significant departure from the traditional reliance on the walled gardens operated by tech giants such as Meta and Google.
The Decline of Walled Gardens
Adweek's conversations with four digital advertising firms reveal a growing trend: clients are moving away from the walled gardens of major platforms and turning to first-party services. This move raises the question: Are walled gardens withering away? While it's premature to declare their demise, the advertising industry is certainly experiencing a transformation.
Historically, walled gardens—digital ecosystems where the platform controls the user data and ad inventory—were seen as gold mines for platforms, media outlets, and hardware companies. These entities leveraged their substantial user data to attract advertisers and drive revenue. However, the landscape is shifting.
Financial Milestones and Market Share
Despite forecasts predicting that ‘US walled garden programmatic digital display ad spending’ will surpass $100 billion this year, there are signs of a change. For the first time since tracking began in 2017, walled gardens lost market share in programmatic ad spending last year, and this decline is expected to continue.
One significant factor contributing to this shift is the rise of retail media networks (RMNs). RMNs allow advertisers to spread their messages across various digital storefronts, reaching a broader audience. This flexibility and reach are increasingly appealing to brands, making the restricted nature of walled gardens less attractive.
Retail media's success underscores a growing distaste for the limitations imposed by walled gardens. Advertisers seek more open-ended approaches, driven by distrust of major ad platforms and a desire for greater control and transparency.
The Cost Factor
The financial aspect is also a crucial consideration. The cost of running ads on major platforms like Google and Meta has surged, prompting budget-conscious brands to reassess their strategies. These rising costs, coupled with various controversies that have plagued these platforms, are driving brands to explore alternatives.
Trust Issues
Over the past year, both Google and Meta have faced numerous challenges, including scandals that have cast doubt over their quality control, brand safety, and return on investment. These issues have further eroded trust in the ad duopoly, pushing brands to seek more reliable and transparent solutions.
For instance, Meta’s Advantage+, an AI-powered tracking solution designed to mitigate signal loss caused by Apple’s AppTrackingTransparency change, has faced criticism. Accusations of inflated metrics and budget-draining practices have not helped Meta's cause, contributing to the growing disillusionment with walled gardens.
As the advertising industry evolves, a more open-ended approach is gaining traction. Brands are increasingly relying on their own data and first-party measurement solutions, reducing their dependence on these major platforms. This shift signifies a move towards greater autonomy and control over advertising strategies.
Embracing First-Party Solutions & Diversification
While walled gardens are hardly disappearing entirely, one of the key trends in shifting away from them is the adoption of first-party measurement solutions. Northbeam, for example, is platform agnostic and provides brands with more accurate and transparent metrics, helping them make more informed, more nuanced decisions, daily. By relying on first-party data, brands can avoid the potential biases and limitations associated with walled gardens and create more personalized and targeted advertising campaigns, enhancing their effectiveness and ROI. Brands such as HexClad and Kizik have been able to leverage Northbeam in this precise way. Achieving significant growth that would not have been possible through the gated and biased channels of in platform metrics only.
Diversification is another strategy brands are adopting. Rather than relying solely on major platforms, brands are exploring a variety of advertising channels. This includes social media platforms, influencer marketing, programmatic advertising, and retail media networks. Through this method, a broader audience can be reached and any previous reliance on one single platform to drive conversions can be spread across multiple audience pools.
Emphasizing Transparency and Trust
Transparency and trust are becoming increasingly important in the advertising industry. Brands are seeking partners and platforms that prioritize these values, providing clear and honest metrics. This emphasis on transparency helps build stronger relationships between brands and their customers, fostering loyalty and trust.
Conclusion
The advertising industry, and e-commerce in general, are at a crossroads with a decision needing to be made. While walled gardens have long been the dominant force, a shift is clearly underway. With the advent of solutions like Northbeam’s MTA tool and MMM+ offering, brands are taking control of their advertising strategies, leveraging their own data and first-party solutions to make more informed decisions. The rise of retail media networks, increasing costs of major platforms, and distrust of gatekept data are all contributing to this transformation and transition.
As the industry continues to evolve, transparency, control, and cost-effectiveness will remain key priorities for brands. While walled gardens may not disappear entirely, their share of ad spending is likely to diminish. The future of advertising lies in a more open-ended approach, where brands have greater autonomy and flexibility to reach their target audiences effectively.
In this new landscape, the ability to adapt and innovate will be crucial. Brands that embrace these changes and prioritize transparency and trust will be well-positioned to succeed in the ever-evolving world of digital advertising.
Set up a demo with Northbeam today to see how you can evolve past in-platform metrics, and stop leaving money on the table.
Probabilistic vs. Deterministic Approaches in a Cookieless Future
The potential deprecation of third-party cookies has sent shockwaves across the industry. Marketers are hustling to adapt as the tools and methodologies they’ve relied on for years are at risk of losing their potency. Whether that cookieless future is a year away or five years away, the best marketers are taking steps today to set themselves up for success in the long run.
This upcoming adaptation has also fed an ongoing debate about probabilistic vs. deterministic approaches in marketing strategy. In this guide, we’ll talk about both approaches, discuss why they matter, and cover how a probabilistic approach can help marketers prepare for a cookieless future.
The cookie crisis
Before we get into the nitty-gritty, let’s take a step back to discuss the potential deprecation of third party cookies. While Google Chrome won’t be deprecating cookies just yet, there are indications that we’re moving in that direction sooner rather than later. User preferences and governmental regulations are indicating that privacy is and will remain top-of-mind in the coming years.
This shift poses a threat to how digital advertising has traditionally been measured and managed. Deterministic measurement, which relies heavily on cookie data, is particularly vulnerable to this change.
According to eMarketer, almost 90% of browsers could become cookieless long term. In this potential future, fewer than 20% of users will opt in to sharing cookies when browsing online. This drastic reduction in available data may lead to diminished performance for campaigns that rely solely on deterministic models.
For marketers, this could be the difference between thriving in a competitive environment and seeing their performance decline.
Probabilistic vs. deterministic measurement
Let’s dive into definitions.
Deterministic measurement relies on direct user actions and data points to track and measure behavior. It bases outputs on actual user behavior and builds its measurements off of cookie data, login information, and device IDs. While this method is highly accurate, it is also highly dependent on the availability of direct data, which will become increasingly scarce as security measures proliferate.
Probabilistic measurement, on the other hand, uses algorithms and statistical models to infer user behavior based on aggregated data points. Instead of relying on exact matches, it identifies patterns and correlations to estimate outcomes. While it might not offer the same level of precision as deterministic measurement, it is far more resilient in a world where direct data points are dwindling.
The growth of probabilistic measurement
As cookies disappear, so too does the feasibility of relying solely on deterministic measurement. Marketing intelligence platforms like Northbeam have taken this inevitable shift to heart and built their analytics platforms off of probabilistic models.
By leveraging advanced machine learning models, probabilistic measurement can continue to provide valuable insights even when direct data is limited. It’s adaptable, scalable, and, most importantly, future-proof.
The best thing about machine learning-based modeling is that it gets more accurate over time as it trains on massive amounts of available marketing touchpoints — we’re talking billions of data points a day about how users engage with your product and behave online. With a longer view, we can expect probabilistic measurement to grow in accuracy and gain even more value as a go-to tool.
The digital vs. traditional divide
Compounding the challenge of accurate measurement is the growing divide between digital and traditional media spending. According to recent projections by eMarketer, U.S. total media spending in 2024 is expected to reach $389.49 billion, with digital accounting for a staggering $302.77 billion of that total. In contrast, traditional media is expected to bring in $86.72 billion.
This shift from traditional to digital media underscores the importance of effective digital measurement. As more dollars flow into digital channels, the stakes for getting measurement right are higher than ever. Marketers can’t afford to rely solely on deterministic models as digital spend continues to rise — a marketing strategy built on unreliable or diminishing data is not scalable in the long- or even medium-term.
It’s time to adapt
What does all of this mean for you as a marketer? Now more than ever, you need tools and platforms that are built to not just survive but thrive in a cookieless world. Unlike traditional MTA solutions that rely heavily on deterministic data, Northbeam’s approach is and has always been rooted in probabilistic measurement. This ensures that your campaigns remain effective, even as the data landscape shifts beneath your feet.
By educating yourself on probabilistic measurement and anticipating the deprecation of cookies, you can stay ahead of the curve and remain competitive in tomorrow’s marketing ecosystem. This isn’t just about improving performance today, it's about setting yourself and your organization up for success in the long term. A future-proof strategy is the best strategy.
What is Pixel Tracking?
In the dynamic world of digital marketing, understanding customer behavior online is paramount. Pixel tracking is a crucial technology for sophisticated marketers that enables them to unlock detailed insights about users and measure their campaign performance with ease.
How do pixels work?
Pixels, or “tracking pixels,” are tiny, often-invisible images or pieces of code embedded on a webpage or email. When a user visits a website or opens an email, the pixel sends a signal back to the server. This signal, or “ping,” carries data about the user’s interaction, such as the time of their visit, the pages they viewed, and any specific actions they took.
How are pixels different from cookies?
There are a lot of ways to glean user information. While tracking pixels, first party cookies, and third party cookies all serve similar purposes, they do so in different ways. Understanding these differences is crucial for marketers who want to leverage the right tools in the right way to maintain compliance and gather the data they need.
Use this table below to differentiate between tracking pixels, first party cookies, and third party cookies.
Why use tracking pixels?
Pixel tracking offers several vital benefits to marketers, including but not limited to:
- Performance measurement - by tracking user actions, like email opens, clicks, and time-on-page, marketers can measure the effectiveness of their campaigns with high precision.
- Audience segmentation - data from tracking pixels can help create detailed user profiles and segment a given audience based on behavior. This enables more personalized and targeted marketing efforts
Pixels also have various use cases when it comes to digital marketing. Some of the big ones are:
- Website analytics - use pixels to understand how users navigate a website, which pages they visit, and how much time they spend on each page
- Ad campaigns - use pixels to measure the performance of display ads, social media ads, and other paid campaigns to optimize spend and improve ROI
- Email marketing - use pixels to track open rates, click-through rates, and user engagement with your email content
- E-commerce - user pixels to monitor how users interact with your products and website to refine your sales strategies. Pixels can surface browsing patterns, add-to-cart actions, and other significant clicks
What is the Northbeam Pixel?
Northbeam takes advantage of pixels and pixel tracking to generate actionable insights and near-real-time information about user behavior.
Like pixels in general, the Northbeam Pixel is a snippet of code that allows Northbeam to collect important behavioral information about your website visitors. This information then feeds into Northbeam’s backend device graphic, allowing us to track customer journeys from site visit to purchase, along with all the other marketing touchpoints in between.
Read more in our Knowledge Base.
What’s the future of pixel tracking?
As digital marketing technologies continue to evolve, pixel tracking is expected to become even more sophisticated. Technologies like artificial intelligence and machine learning will enhance our ability to predict user behavior and personalize marketing efforts in unprecedented ways. It’s key to stay ahead of privacy concerns and regulations to understand how they will affect pixel data.
With its advanced features and ease-of-use, the Northbeam Pixel represents the next generation of tracking technologies, offering enhanced accuracy, comprehensive data collection, and near-real-time reporting so marketers can optimize their strategies and drive better results.
2025 Marketing Trends to Watch Out For: Part 1
Can you believe we’re already over halfway through the year? Before you know it, it’ll be Labor Day, then Halloween, Thanksgiving; I mean it’s basically almost Christmas if you think about it. As we get closer to 2025, the marketing landscape continues to undergo a transformative journey, driven by new technologies, evolving social media platforms and heightened awareness of data privacy, among several other factors. GenAI has emerged as a truly exciting innovation, enabling marketers to create personalized content at an unprecedented scale and speed. Imagine having a virtual assistant who not only understands your brand but also crafts compelling campaigns while you sit back and enjoy your coffee.
Meanwhile, social media platforms are starting to see glimmers of a world where Facebook no longer dominates the advertising landscape. Just like in Formula 1, platforms are constantly jockeying for pole position to fight for marketer’s advertising dollars. Platforms including LinkedIn and YouTube are making big pushes for relevance as user behavior shifts to adapt to new features and algorithm changes that challenge us to keep up and stay agile. In the past few years, we’ve seen some seismic changes in social media including the rebranding of Twitter to X (plus a variety of other changes instituted since the acquisition led by Elon Musk), a potential US ban of TikTok, and the introduction of Threads to rival X to name just a few. Beyond social, new search methodologies (voice, social) show much promise as opportunities to gain an edge over the competition for the savviest marketers.
Data privacy is also taking center stage, reshaping how marketers approach targeting and other strategies. Although Google has yet again postponed the phasing out of third-party cookies to early 2025, there is no doubt that the average consumer is increasingly wary of their personal information being used by marketers. In this two-part article, we’ll cover the top marketing trends to keep an eye on going into 2025.
The Rise of LinkedIn
Believe it or not, the platform best known for uploading your resume and job hunting has been one of the hottest movers and shakers since the pandemic upended our lives. In 2023, the platform reached 1 billion global users (including an impressive 211M+ in the United States) and 500 million newsletter subscribers. LinkedIn doesn’t share daily or monthly average users, but the company reported that in the spring of 2023, users shared 41% more content than they had in the same period in 2021. That kind of organic growth is hard to fathom for a 22-year-old platform, but LinkedIn has managed to position itself as one of the last places to have professional and increasingly personal conversations due to the changes at X (more on this later). Users who previously would have posted on Facebook, Instagram, or Twitter are gravitating towards LinkedIn. Why? Terrified of TikTok eating their lunch, Meta has prioritized their products to emphasize interests instead of friends, moving personal sharing to semiprivate realms such as Stories or direct messages. X on the other hand, has simply become too unstable for many advertisers. Algorithmic changes, the introduction of the paid blue badge, amongst several other unpopular changes have caused brands to flee, bringing their ad dollars with them (a year after the acquisition, X’s ad revenues were down 55% YOY). LinkedIn also saw a huge boost during the pandemic when our personal and work lives became increasingly blurred. Workers felt more comfortable posting vacation pictures and emotional stories, content that previously would’ve been less socially acceptable, often captioned with a hail-mary, vague mention of something work-related (in case the boss sees). The product team at LinkedIn has ridden this wave, introducing new tools for creators and pushing “knowledge-based” content to great success.
Advertisers have taken note. While B2B marketers have long utilized LinkedIn in their campaigns, B2C marketers are increasing their spending, driving up ad prices and revenues (in some cases by as much as 30%). Business Insider expects LinkedIn’s US ad revenues to increase +14.1% to almost $5B by the end of 2024. While that’s only a small fraction of Meta and Google’s market share, it’s more than double that of Snap and around 4 times that of X. Despite the sky-high prices of impressions on LinkedIn, marketers report very impressive ROI on their campaigns (as much as 20% ROI). Penry Price, LinkedIn’s VP of Marketing Solutions, said that brands are investing more ad spend into LinkedIn because the platform is filled with decision-makers and key stakeholders in buying processes. We expect this trend to continue through the rest of 2024 and into 2025; as Bloomberg so eloquently put it, LinkedIn is cool now.
Stay up to date with product announcements and exclusive insights by following Northbeam’s Linkedin page here.
The Race to Replace Twitter
In October 2022, Elon Musk officially acquired Twitter and immediately began dramatically remolding the company to fit his vision. He started by firing several senior executives at Twitter including CEO Parag Agrawal and CFO Ned Segal, eventually laying off 80% of Twitter’s 7,500+ employees. He famously declared that only employees who wanted to engage in “maniacal work” should stay, leading to the resignations of hundreds of staffers (including a large chunk of the trust and safety team, spurring the flight of several large advertisers from the platform). Musk also took Twitter private, officially delisting it from the New York Stock Exchange in November ‘22, giving him the freedom to make changes as he saw fit without having to report to a board or shareholders. He went on to overhaul the verified system by launching a paid $ 8-per-month subscription, allowing anyone to obtain a blue check mark. This led to a wave of impersonations of public figures and brands, leading to mass confusion and misinformation. Musk also controversially reinstituted accounts of several notorious accounts that had previously been banned, including former president Donald Trump. The biggest change came in the summer of 2023 when Musk rebranded Twitter to X seemingly overnight in an attempt to turn the platform into more of an “everything” app similar to WhatsApp. Although it remains to be seen if these changes will benefit X in the long run, there’s no doubt that advertisers have fled the platform in droves. Reports that brand ads were being shown next to controversial and offensive content made marketing teams uncomfortable with continued spending on X (perhaps because the brand safety team had been axed). A year after Musk bought Twitter, ad revenue was down 55% year over year. Musk later named Linda Yaccarino, a former executive at NBCUniversal, as the new CEO of X in a bid to reassure and woo advertisers back to the platform.
This fall from grace for X has prompted a race to provide an alternative space for users fleeing “Twitter 2.0” for greener pastures. Meta, always keen to take competitor market share, launched Threads in July of 2023 as their take on a text-based social media platform. Threads started hot with a reported 30 million sign-ups within 24 hours of release before hitting 100 million members within the 1-week mark. Members aren’t the same thing as active users, but the latest update from Meta shows Threads has 130 million monthly active users. X currently has 550 million monthly active users, so Threads still has a way to go before dethroning the king, but Meta has made remarkable progress in just one year. Leadership has stated many times that they believe Threads has the potential to become the next billion-user platform. Although there are several other players in the space such as Mastodon and Bluesky, Meta’s expertise in monetizing social media platforms and partnering with advertisers makes them the smart bet to eventually come out on top of this race.
Check out Northbeam’s twitter account here.
Bye Bye TikTok?
During the pandemic, TikTok emerged as a powerhouse and social media darling as the platform raced to over a billion monthly active users worldwide by September 2021. TikTok is largely responsible for the popularity of short-form video content in the US, primarily based on its powerful recommendation algorithm that’s adept at showing users more of what they enjoy. Owned by Beijing-based ByteDance, TikTok has courted plenty of controversy in the past as politicians have accused of it being a tool for propaganda and a national security risk. Many American lawmakers are worried that the Chinese regime could “weaponize” TikTok and spy on US citizens including military and government personnel. Many universities and government agencies at the local, state, and federal levels began enacting bans on the app from official work devices. The Trump administration attempted to force a sale, but efforts were largely unsuccessful until April 2024 when President Biden signed the “Protecting Americans from Foreign Adversary Controlled Applications Act.” The bill stipulated that ByteDance has 9 months (until January 2025) to find a buyer for TikTok with the possibility of a 3-month extension if meaningful progress is made toward a sale. As of the writing of this article (summer 2024), TikTok has responded by suing the US government on First Amendment grounds. The case will almost certainly take more than a year to resolve, but as marketers, we can’t afford to be caught without a contingency plan in case TikTok does get banned by early January of next year. Creators are already beefing up their Instagram Reels presences in response, but YouTube Shorts has emerged as a promising contender to the big two. In early 2023 Shorts crossed the 50 billion daily views mark, which although still short of Meta’s combined 140 billion daily views across Facebook and Instagram, is starting to garner significant traction with advertisers. Interestingly enough, YouTube seems committed to longer-form content with the intent of using Shorts to drive engagement towards that content. The company is intent on chasing TV ad dollars instead of just digital ones given that YouTube owns more than 50% of ad-supported streaming watch time on TVs. This runs counter to the fact that Shorts isn’t designed for a TV experience (it’s optimized for mobile), but it’s useful for creators to have the option to create shorter content that can boost traffic to longer videos. While we’ll have to see who comes out on top of this short-form video war, this signals that YouTube believes its core value is in the longer video content that’s historically been its bread and butter.
The Elephant in the Room: GenAI
We would be remiss to talk about marketing trends without mentioning GenAI. Ever since ChatGPT was launched in late 2022, marketers have been salivating over the potential of using GenAI to create text, images, video, and audio. With GenAI, marketers can create personalized content at scale, producing tailored ads, social media posts, email campaigns, etc. that will resonate more deeply with target audiences. These AI-driven tools can analyze vast amounts of customer data to generate insights into behavior, preferences, and trends, allowing for more precise segmentation and messaging. Beyond that, GenAI can significantly streamline the content creation process, reducing the need to hire expensive agencies for good old-fashioned photo and video shoots. It can write decent copy, design graphics, and even generate videos, freeing up human resources to focus on strategy and creative oversight. GenAI has even empowered us to conduct A/B testing and optimize campaigns faster and more effectively than before. However, while content creation was the first widely adopted use case, another development could be more impactful to a wider range of users. In May 2024, Google rolled out AI Overviews: its AI-powered search engine (formerly known as Search Generative Experience). AI Overviews is designed to improve user interaction and engagement by offering conversational and contextually relevant search results in a UI similar to ChatGPT’s prompt system. When users search on Google using AI Overviews, they will no longer see highly-ranked links first. Instead, they will first get a summarized overview of the prompt before scrolling down to see which links contributed to that answer. As a result, AI Overviews tends to prioritize summarized content over publisher links which now get placements below the Overviews chatbox. Publishers have seen a substantial decline of up to 60% in organic search traffic, translating to an estimated $2 billion loss in ad revenue within Google’s pilot version of AI Overviews. It’s too soon to say for sure, but the release of Overviews may upend all of our traditional SEO strategies if we’re to stay relevant in this new era. Since GenAI can generate large amounts of low-quality content, we’ll likely need to focus on high-quality content that provides clear and valuable information to get our content featured in Contextual Summaries. It remains to be seen if SEO tactics will be as effective if getting highly-ranked pages is not nearly as impactful due to AI Overviews eating up most of the real estate on screen.
2024 has shaped up to be an exciting year already, and we can’t wait to see what 2025 brings us. Is your team keeping an eye on these trends? If so, watch out for Part 2 of our 2025 Marketing Trends deep dive, coming soon!
Unlocking the Truth Behind Incremental Performance: Why & How Attribution Models Can Make Or Break Your Business
In the ever-evolving landscape of direct to consumer (DTC) ecommerce, understanding the true impact of your marketing efforts is paramount. Investing a little here and a little there without a clear cut grasp of what’s actually driving net new revenue growth (and profit) for your business can quickly lead you into a ditch. As marketers, and leaders, we need to be able to understand how to best slice the data we have available to us in a way that helps us reach our revenue targets & profitability goals. Enter attribution models. Attribution models are not just tools—they are the lenses through which savvy marketers can observe the nuanced behaviors of their marketing campaigns. At Northbeam, we are committed to equipping you with these critical insights, ensuring that every dollar spent is an investment toward measurable success. Over the next few sections, we will dive into the specifics of attribution modeling, things to consider first before analyzing your marketing data and finally, the best way to select the model most appropriate for your business.
What Is An Attribution Model?
At a high level, an “attribution model” is simply a formula for dividing up credit across the various touch points in a user’s journey that led to a transaction on your website.
When we talk about “credit”, we are typically referring to revenue & transactions associated with a specific marketing touch point.
Attribution models can come in many sizes, shapes and forms. Not all are created equal. For example, most marketers are typically familiar with a “Last Click” model which is usually what most ad platforms like Meta and analytics solutions such as GA4 will use to assign credit to an individual campaign. Models like Last Click look to give all the credit to the last click a user made prior to visiting your website and purchasing a product. Yet, other models like “Time Decay” look to increase the amount of credit given to a marketing touch point the closer in time it occurred to the user purchasing a product on your website. Many models are plagued by a lack of consistent results across business models, verticals and customer audience segments. Luckily, at Northbeam, we understand that the results live in the nuance of the data. As such, we’ve built our attribution models with this at the forefront of our minds. We will discuss the specific models Northbeam offers in a later section, but before we do, let’s discuss some more pitfalls of most attribution models.
Common Pitfalls of Most Common Attribution Models
Attribution models are essential for marketers to evaluate the effectiveness of their campaigns, but they come with inherent limitations. Especially if you’re relying solely on a model offered by the ad platform themselves as they are often incentivized to over inflate the performance of their own campaigns. It’s important to first understand the lens a model looks to take on your data before leaping into making crucial decisions based off of the insights. Here’s an overview of some general pitfalls associated with the most commonly used attribution models:
Bias Towards Certain Touchpoints
Models like Last Click attribution often disproportionately credit the last click interaction a customer has with the brand. This can lead to a skewed understanding of what is truly driving conversions, often either overvaluing or undervaluing the significance of middle or initial interactions in the customer journey.
Oversimplification of Customer Interactions
Linear Attribution treats all touchpoints as equally influential, which can dilute the strategic value of critical interactions that may play a more decisive role in influencing customer decisions. This equal weighting can mask the true effectiveness of specific campaigns or channels.
Neglect of Long-Term Impact
Models like Time Decay focus primarily on the actions that occur closest to the time of conversion. This approach tends to overlook the foundational interactions that occur earlier in the sales cycle, which may have been vital in setting the stage for eventual sales, thus potentially leading to short-term planning over long-term strategy effectiveness.
Lack of Contextual Flexibility
Position-Based Attribution may assign arbitrary values to certain interactions based on their position in the conversion path, which might not accurately reflect their actual impact on different types of customer behaviors or purchase processes. This lack of flexibility can result in inefficient resource allocation and misguided strategic decisions.
Inadequate Representation of Complex Buyer Journeys
Many common attribution models do not account for the multifaceted and often non-linear nature of modern consumer journeys. This can lead to incomplete or inaccurate insights, as these models fail to capture the complex interplay of multiple channels and touchpoints over time.
Understanding these general pitfalls is crucial for marketers to carefully consider which model, or combination of models, might best align with their specific objectives and the nuances of their target market. It’s also vital to adapt and refine attribution strategies continually as new data becomes available and as consumer behavior evolves.
A Quick Overview Of Each Model Northbeam Offers
At Northbeam, our platform harnesses several cutting-edge attribution models, each serving unique business needs.
Last Touch Attribution: Ideal for short, decisive campaigns where the last interaction amongst your marketing channels is most likely to convert prospects.
First Touch Attribution: Gives credit to the first interaction, valuable for understanding which marketing channels were the initial attractors.
Linear Attribution: Distributes credit evenly across all touchpoints, useful for long-term engagement campaigns with a high number of touchpoints. This model can also be useful for businesses with very even spending across all channels, putting them all on a level playing field.
Last Non-Direct Touch: Gives credit to the last touchpoint in the user journey excluding direct traffic. Great for matching closely to how the ad platforms view things and only focusing in on your paid marketing channels.
Clicks + Views: The most robust MTA model that Northbeam offers. Clicks + Views leverages our proprietary view model in tandem with hard factual click data. It looks to distribute credit in a weighted fashion amongst your marketing touchpoints based on which touchpoints helped drive the conversion initially. This model is best for understanding which channels & campaigns are driving the most incremental revenue to your business.
Clicks Only: A close cousin to Clicks + Views but with some stark differences. For one, as the name suggests, clicks only looks to measure hard factual click data amongst your marketing touch points. It's a conservative lens on things that ignores view data and distributes credit linearly to all click based marketing touchpoints that resulted in a purchase. This is great to use in tandem with Clicks + Views to cross validate the direction Clicks + Views is saying you should go.
Each model offers its strengths, and by leveraging them businesses can illuminate different aspects of the customer journey.
Stop Thinking Like A Marketer - Think Like A Model
Marketers have inherent bias. No matter how immune we think we are, at the end of the day, we all have a subconscious or even conscious tendency to try and draw conclusions in data that either aren’t there or only validate a half truth.
Attribution models on the other hand, especially ones like clicks + views, do not have irrational biases that prevent them from delivering unfiltered insights that can help you make better decisions with your marketing channels.
As marketers, we need to position ourselves in a place of objectivity. Whereby, we rely on the data and the models behind them to lead us down the right path in our efforts to continuously improve our campaigns and drive profitable business growth.
“Thinking like a model” involves understanding what we thought might happen by scaling a certain “good” campaign may have actually had the opposite effect. Maybe it caused over inflated numbers to occur because it was not actually as good at driving incremental first time customers as we might have initially thought or even assumed.
At any rate, level setting your expectations will allow you to be more tactical in your decision making and provide a unique cutting edge lens into your day to day campaign optimizations.
How to Think About Sales Cycles vs. Payback Periods
Understanding your sales cycle's duration and aligning it with the correct payback period is critical when choosing an attribution model. A longer sales cycle might benefit from a data-driven model like Clicks + Views in Northbeam, ensuring that all influential interactions are considered. Conversely, shorter cycles might align better with a narrow model like Clicks Only, capturing the rapid decision-making process typical of impulse purchases.
Payback periods on the other hand, refer to the following question, “After I acquire a customer, how long does it take for that customer to become profitable for me taking into account my acquisition costs & expenses?” Answering this question relies on having an attribution model that can show which marketing touchpoints deserve credit for transactions that happen later in time. This ties into accounting modes on Northbeam, AKA, whether revenue is assigned to the day the marketing touchpoints themselves occurred, or the day the transaction occurred.
Generally, when using an accrual accounting mode in tandem with Northbeam’s proprietary Clicks + Views model, customers are able to see precisely when, where and how much revenue is being attributed to each marketing touchpoint along the user journey even past just the initial transaction. In fact, unlike many other attribution tools out there, Northbeam has an indefinite attribution window. That means, if a marketing touchpoint, say, a Meta ad were to be clicked on today and that user converts outside of the normal attribution window set within your ad account (typically anywhere between 7-30 days), Northbeam still sees this user as having first come from this Meta ad. Northbeam will then attribute the appropriate amount of revenue back to that marketing touchpoint taking into account the weighted contribution it had that led to the conversion.
Choosing the Right Model & Window for Your Business & Data
Selecting the right attribution model and window hinges on understanding your specific business context: Start by identifying your measurement goals: are you optimizing for short-term conversions, attempting to understand longer sales cycles, or seeking insights into the most effective incremental marketing channels? Your goals will significantly influence the choice of model.
Next, consider the length of your sales cycle carefully. Businesses with longer sales cycles benefit from a comprehensive view of all influential interactions, making models like Clicks + Views more appropriate. Conversely, shorter sales cycles might align better with models like Clicks Only, which focus on immediate, decisive interactions. Additionally, evaluate the diversity and complexity of your marketing channels. A wide range of channels with varying degrees of influence may necessitate a specific model to capture the full spectrum of touchpoints.
Analyzing historical data is crucial to understanding trends and patterns in customer journeys. This analysis helps identify which touchpoints consistently drive conversions and which models provide the most accurate insights. Aligning your attribution model with your accounting practices is also vital. For instance, using an accrual accounting mode that accounts for revenue on the day the marketing touchpoints occurred, with a model like Northbeam's Clicks + Views, will yield a more precise financial picture.
Attribution is not a one-size-fits-all solution, so it's essential to regularly test different models and attribution windows to find the most actionable insights. Continuous iteration and adaptation ensure your approach stays aligned with evolving market dynamics and consumer behaviors. Leveraging the expertise of attribution experts here at Northbeam can further guide you through the complexities of attribution modeling, ensuring you select the most effective models and windows for your unique business needs.
To wrap this up, Attribution isn’t just about tracing sales back to their sources—it’s about understanding how each marketing element contributes to the overarching goal of profitable revenue growth. Northbeam’s platform provides an unparalleled view into these dynamics, empowering marketers to make informed decisions that drive success.
Navigating the complex world of attribution is no small feat, but with Northbeam, clarity isn’t just an outcome—it’s a guarantee. Attribution is only one piece of the equation. To dive deeper into how our MMM+ tool can revolutionize your approach by maximizing every ad dollar, start by visiting our product page or contacting our support team for a personalized demo.
This article was written by Northbeam Senior Media Strategist, Brayden Cruz. Check out his Linkedin here!
Why You Should Care About MTA, MMM, and Incrementality
How they Differ, and How they Work Together
If you’re up-to-date on your alphabet soup, you may know that multi-touch attribution (MTA), media mix modeling (MMM) and incrementality work together to give you a full picture of your campaign performance. They all provide useful answers as you assess what’s working and what’s not — just to different types of questions.
And if you’re not as familiar with these terms, let’s talk about it. Read on for more information about MTA, MMM, and incrementality so you can understand how they work in concert to give you the information you need to make informed marketing decisions.
Multi-Touch Attribution (MTA)
MTA is major; we have an entire guide dedicated to it.
The TL;DR is this: MTA is a method of marketing attribution that accounts for all of the different touchpoints and activities in a customer’s journey — not just the first or last touch.
While first or last touch attribution gives all of the credit to a single touch on the customer journey, MTA understands that the customer’s path to a conversion isn’t linear, and aims to distribute credit (and attribution) across all relevant touchpoints.
MTA is your go-to tool for the day-to-day. It gives you attribution information on a granular level about campaign performance so you can make decisions on where to spend and adjust your budget on a given day in order to optimize for profitability.
What worked last week may not work any more; market and consumer variables are constantly shifting and making your ad performance fluctuate. MTA lets you stay on top of those shifts across all of your digital channels.
Checking out your MTA on a regular basis helps you keep your finger on the pulse so you can make regular adjustments and know what’s happening based on hourly or real-time performance data.
The cons of MTA? It’s primarily focused on digital touchpoints, so if you do a lot of traditional marketing, it may not capture attribution for all of your activities. That’s where MMM comes in.
You should use MTA if:
- You want to know the contribution of each touch point to a final conversion
- You want granular, real-time data
- You want a go-to resource to check daily to inform your spend decisions
Media Mix Modeling (MMM)
While MTA goes deep on individual touchpoints, Media Mix Modeling (MMM) goes deep on individual channels and tactics. MMM gives you a top-down look at how each channel is performing so you can fill in the gaps of your MTA reporting.
MMM goes beyond the day-to-day to incorporate all of your past and present data as well as seasonality, market trends, and more to inform your strategy and help you allocate resources.
Rather than a tool to help you make everyday decisions, MMM can be thought of as a powerful forecasting and budgeting tool in the longer term. It helps you understand the marginal efficiency of each channel, and where you might start to see diminishing returns.
Let’s say that one of your channels is performing at an 8x ROAS (return on ad spend). With just that data point, you might scale your ads indefinitely and pour more money into that channel. But proper MMM will tell you that if you spend more than a certain amount per day there, you’ll stop seeing returns.
Both pieces of data are needed to make the best decision possible: MMM puts integral parameters around your MTA insights.
A major pro of MMM: it can give you information about both digital and traditional channels, both online and offline efforts. Not only can you see how your ads and influencers are performing, but you’ll get data about your billboards and commercials as well.
Another pro: MMM shows you the limits of what’s possible with the resources you have available — regardless of what your boss says.
Marketing teams are often given budgets that are difficult to work with. With MMM, you can input your budget and see if your goals are possible based on historical channel performance so you can set realistic goals and make your case using data.
The cons? MMM tools are advanced, and you may need support to set up or interpret your outputs. Depending on your provider, they may also take longer to run — while Northbeam provides regular MMM reports, other providers can often only deliver these insights on a quarterly basis.
Still, MMM is powerful. The best MMM tools will give you suggestions on how to shift your budget to optimize for performance based on your goals. You can do multiple MMMs by regions or marketplaces, metrics, and more. You can rework the numbers to see how your channels are performing against new customer acquisition, total revenue, or any other metric you want to measure. MMM is a flexible and dynamic way to understand your marketing attribution.
You should use MMM if:
- You want to understand when you’ll start seeing diminishing returns and/or room to scale in a given channel
- You want to establish and track a long-term strategy that involves a variety of different marketing channels
- You want to capture offline channel performance
Incrementality
Incrementality can be thought of as a calibration tool — a sanity check. It provides a more controlled environment in which you can test and control for different variables, like spend, that may affect how your campaigns or channels are performing.
Here’s an example: let’s say that you run a subway ad and receive 5,000 new users that week. But what if 50% of those users would have signed up anyway, regardless of whether or not they saw the ad?
Incrementality can help you answer the question of organic cannibalization by better controlling for variables and measuring the actual impact of each activity on your core metrics.
While attribution matches two events, like a touch and a conversion, incrementality quantifies the relationship: what is that relationship actually worth?
But measuring incrementality can be time- and resource-intensive to do consistently. It involves a test group and a control group, and a variable you want to test for, like geography. When you compare the performance of both groups, you’ll be able to quantify the incremental lift provided by each campaign.
Because they capture a moment in time, incrementality tests get outdated quite quickly. The tests you ran in January will yield results in February or March, and by then, they may already be outdated.
And because incrementality involves a test group, you will have to turn off marketing for a subset of your audience. This could result in lower sales and marketing performance — maybe that’s worth it to gain a deeper level of understanding, but maybe it isn’t. Keep in mind that a channel has to be large enough to begin with to garner statistically significant results, so this decrease in marketing will not be insignificant.
When running an incrementality test, you also need to be hands off and let things run for weeks or months at a time. That means very little room for changes, tweaks, and adapting on the fly if you want to get clear and accurate results.
It’s important to note that MTA and MMM, when used together, can provide a similar or much higher level of detail than incrementality without the hands-off requirement. MTA can show you all the different touches that happened on the way to a conversion, and MMM can add a layer of strategy and insight about where you should funnel budget to see incremental returns.
Bonus: incrementality results can be incorporated into your MMM model; Northbeam offers this service to train your MMM on your specific and unique data set.
You should use incrementality if:
- You’re worried that your paid channels are cannibalizing your organic growth
- You want to improve your MMM tool by feeding it incrementality test data
- Your marketing team is not in a time-sensitive or resource-sensitive phase of growth
What do we suggest?
The best marketing strategies create room for all three of the tools above at different stages of a company’s growth and maturity. The data that each tool provides is different in key ways, and they each have their own distinct pros and cons, so you can best assess which makes the most sense for your current marketing priorities.
What to Expect When Switching to Northbeam
You’ve done your research, compared your options, and made the decision to switch to Northbeam — congratulations! In our reasonably biased opinion, you’re on the right path.
But, onboarding onto a new platform can take a minute. Let’s talk through what you can expect when switching to Northbeam — there may be some surprises, but only the good kind.
Why do my metrics look different?
Northbeam’s data is different, and there are a lot of great reasons for that: we wrote a whole blog post about it.
The TL;DR is this: Northbeam uses its own proprietary machine learning models to generate first-party attribution data that is more accurate than traditional platform or third party data.
If you’re used to getting data directly from platforms, third party pixels, UTM tracking, or even other marketing intelligence platforms that have their own algorithms, your data will look a little different when you migrate to Northbeam.
But we’re scrupulous about our data and the AI behind it — if you’re curious, you can read more about how our AI works.
Another major benefit of switching to Northbeam: because we don’t rely on third party pixels, your data is future-proofed. Algorithm or operating system updates such as the changes we saw Apple make to iOS within the last few years won’t affect the accuracy of our data, and neither will the deprecation of cookies.
Our model gets better over time as it learns your various channels and data points, so you can also expect accuracy and insight improvements as the tool learns you (and you learn the tool).
Ad Performance
Your ad performance may look different in Northbeam — in fact, it probably should. Ad performance differences are expected as you move to a more accurate platform. These differences come from Northbeam’s attribution model, attribution window, and how we conceptualize the customer journey.
Northbeam’s attribution model and attribution window are different from legacy models that platforms like Meta, Google, Pinterest, and others use. This creates a difference in how revenue is allocated to different channels, and will result in your performance looking different across campaigns.
Whereas individual platforms lean towards taking credit for conversions, Northbeam takes into account the entire customer journey to look at every touch that resulted in a conversion, not just the last or first touch.
Northbeam’s multi-touch attribution (MTA) model divides credit between touchpoints to give you a holistic picture of what is really driving sales. You can read more about our MTA models here.
Differences in Total Revenue
When comparing Northbeam’s Total Revenue and Orders to your internal source of truth (like a Shopify Dashboard) it’s very common to see differences in the data. Each platform has a slightly different revenue calculation.
Want to make sure that shipping, taxes, and/or refunds are accounted for in your total revenue? Our team can make adjustments so you see the data you need to see to make informed decisions.
Some technical tips:
- Check to make sure you are in the right accounting mode (more below)
- Check to make sure your filters are set correctly based on what you want to include and exclude in your total revenue calculation
- Check to make sure Northbeam is set to the same timezone as your internal order table
Accounting Modes
Northbeam has two accounting modes for looking at your data: Cash Snapshot and Accrual Performance. Each accounting mode has a different way of allocating revenue and transactional credit, so they’re important to understand when you switch to Northbeam.
In the Cash Snapshot mode, revenue and transactional credit are given when the transaction occurs, or when an order is placed. This is useful for understanding the money that comes in on any given day — aka, cash flow.
Note that if you’re in Cash Snapshot mode, your return on ad spend (ROAS) metric will be shown as marketing efficiency ratio (MER). This is a semantic difference and the two metrics can be used similarly. MER can be most accurately described as a blended ROAS.
In the Accrual Performance mode, revenue and transactional credit are given when the contributory marketing touchpoints occur. Contributory marketing touchpoints include any interaction that results in a website visit, such as clicks from an ad, direct visits, or clicks from an influencer link.
Accrual Performance is mainly used to understand the direct return of your marketing dollars and the full impact of marketing channels on your business. It is more popular among Northbeam users because it gives credit to each touch and lets you get an accurate assessment of ad performance.
TL;DR: Use Cash Snapshot mode to understand on which days revenue was created, and use Accrual Performance to understand which touchpoints contributed to that final revenue.
What are all these new metrics?
If you’re coming from in-platform analytics, there may be some metrics on Northbeam that you’ve never seen before. While you don’t have to take full advantage of each and every data point that Northbeam serves up, it’s useful to have a cursory understanding of the different numbers you have at your disposal.
Here are some of the big ones:
% of New Visits
This metric shows you the percentage of visits to your site that come from new versus returning customers. You may be able to see this number on other platforms, but Northbeam gives you some added functionality.
Whereas you may be able to see % of New Visits on a UTM basis or broadly across your website with other tools, Northbeam lets you see it for each campaign or even each unique ad. You can also use Northbeam to see your revenue per new visit at different levels of granularity: revenue per new visit per channel, revenue per new visit per campaign, etcetera. There are loads of ways to serve up this data in a way that helps you make decisions.
New Customer %
What percentage of orders come from new versus recurring customers? This is a great metric for understanding how effectively different channels are driving new customers.
Returning customers are cheaper and easier to acquire, so platforms are often incentivized to include them in campaigns in order to skew conversions upwards. Even if you target a campaign entirely at new customer acquisition, there will typically be some leakage. Some channels do a much better job of targeting new users than others, and traditional ad platforms don’t show you this data.
New Customer % is a way to look under the hood and find out which channel campaigns are actually serving your acquisition goals — and which ones are relying on returning customers to drive performance.
First Time Customer Return on Ad Spend (ROAS)
How well are your ads bringing in new customers? First Time Customer ROAS removes returning customers from your ROAS calculation so you can see how your ads are performing in terms of new customer acquisition only.
First Time Customer ROAS also adds useful color to New Customer %. It takes that New Customer % and puts dollars and cents behind it so you can make informed spend decisions based on real and targeted ROI.
Lifetime Value (LTV) ROAS
While many platforms offer ad attribution windows from 1-day to 7-days, they cannot show you the fully realized value of your ads on a longer attribution window without heavy modeling.
How is the ad you launched two weeks ago doing? Thirty days ago? Three months ago? An LTV attribution window gives you complete information on the full impact of your ads, and Northbeam can even bridge the gap if a given user changes devices or phone numbers during that period.
LTV ROAS gives ROAS an indefinite attribution window to see the effects of your campaign and how long those effects last; it shows you the lifetime value of your marketing campaigns and efforts without time constraints.
Return on Ad Spend (ROAS) Lift
ROAS Lift takes your LTV ROAS and divides it by your ROAS. But what does this mean?
Let’s say your 1-day ROAS for an ad is 3x, meaning that you generated three times what you spent on ads. If you were to broaden that attribution window to look at the entire lifetime of an ad (LTV), perhaps that ROAS goes up to 4.5x as it gains more conversions over time.
ROAS Lift would then be calculated as 4.5 / 3 = 1.5x. Your ROAS “lifts” by 1.5x when you look at it from an lifetime attribution perspective.
ROAS Lift shows any efficacy that might be missed on top-of-funnel channels like TikTok when looking at 1-day or shorter term ROAS. ROAS Lift can help you validate your brand plays by showing how ROAS improves over time beyond initial measurements — and help you make tactical adjustments if your upper funnel media isn’t seeing a growing impact over time.
Ecommerce Conversion Rate (ECR)
Everyone should be familiar with the concept of a conversion rate, but Northbeam’s ECR metric is a little bit different. Because we can hook up to your Shopify and add a layer of MTA, conversion rate can be broken down by channel, by ad and campaign, and even by new versus returning customers.
Is your conversion rate good because it’s all repeat customers? Is it lower than expected but still acceptable given its high percentage of new customers?
Like the other metrics on this list, ECR unlocks critical context that marketing decision makers aren’t used to having at their disposal with other tools.
Get in touch with our team for more information on what you can expect when switching to Northbeam.
What is Multi-Touch Attribution (MTA)?
Multi-Touch Attribution, or MTA, is a method of marketing attribution that accounts for all of the different digital touchpoints and activities in the customer’s journey. Different MTA models will assign a different weight or “credit” to different touches to give you an idea of the value that each is adding on the path towards a final conversion.
But let’s break it down even further. In this guide, we’ll cover the ins and outs of MTA and attribution in general so you can ace this particular marketing acronym.
What is marketing attribution?
Attribution is, at its core, the act of assigning an effect to a certain cause. If you’re satiated, you can attribute that to the meal you just ate. If you’re cold, you can attribute that to the wintery weather. Easy.
But if someone just made a purchase on your website, what can you attribute that to?
The answer isn’t as straightforward when it comes to marketing attribution, but it’s highly important: if you can’t do attribution properly, you can’t optimize your spend towards the channels and activities that work.
Many different attribution models exist to give you data on what’s leading to that final conversion. The Northbeam platform hosts six different attribution models (including two MTA models) so you can visualize what your data looks like from different angles.
What are the different attribution models?
There are straightforward attribution models, and then there are more complex ones like MTA in all its variations. Northbeam measures all of them, and our proprietary machine learning models are trained to cut through the complexity and deliver industry-leading MTA accuracy.
On the simpler side of things, you have the following attribution models:
- First touch attribution models give all the credit to the first interaction a customer has before a conversion. This is useful for understanding how leads enter the top of your funnel.
- Last touch attribution models give all the credit to the last interaction a customer has before a conversion. This is useful for understanding which bottom-of-the-funnel activities are driving success.
- Last non-direct touch attribution models are a variation of last-touch models that exclude direct traffic. Direct traffic means that someone directly visited your website. Imagine that someone sees your Instagram ad today, and then in three days they type in your website to make a purchase. A last non-direct touch attribution model would still attribute that conversion to Instagram, while a typical last touch model would call it direct traffic.
Things get more complex — and more interesting — when we look at MTA models.
The classic MTA model is linear: it spreads credit and revenue evenly across all the touchpoints in a customer’s journey to a conversion.
But that doesn’t reflect reality. If you see an Instagram ad, a Google ad, and a marketing email in the week before making a purchase, it’s likely that each of these touches had a different and distinct effect on your final buying decision.
Northbeam has two proprietary models to help allocate credit and revenue where its due:
- Our Clicks Only MTA model specifically excludes certain touchpoints like direct visits, organic search, paid branded search, and email/SMS if there are other touchpoints in the customer’s journey in order to put more weight on activities where a customer actively engages with your campaigns.
- Our Clicks and Views MTA model builds on the Clicks Only model by also giving credit to campaign views at any point during the customer’s journey; it includes both active and passive customer engagement with campaigns.
In addition to the different types of attribution models, there are also attribution windows to take into account. Northbeam can offer you a variety of attribution windows, from 1-day to 90-day all the way to an indefinite attribution window so you can recognize campaign revenue at different cut-off points as well as total customer lifetime value (LTV).
Why MTA?
There are dozens and dozens of ways to reach a potential customer in today’s marketing environment, and you as a marketer have to be able to make informed decisions on where to spend your limited budget.
MTA lets you assess which channels are responsible for which revenue so you can stay on top of performance and make the best marketing decisions with the best data available.
But that doesn’t mean that other attribution models aren’t helpful. We recommend regularly referencing other attribution models as well as MTA so you can get a full picture of performance.
Having multiple ways to visualize your data lets you continue to optimize and test to find what works best for you and your organization. Data is power, after all.
What Makes Northbeam Data Different?
In this era of big data, the amount of tools that can help you surface marketing insights is nearly limitless. But not all insights are created equally, and there is a major gap when it comes to execution, a wide valley between having access to large amounts of data and being able to use it.
Part of this gap has to do with the limitations of a given data source: every platform and tool has its own way of gathering and visualizing data, so you have to be discerning when you decide which tool to go with. There's also the inherent difficulty of fully understanding a buyer’s journey — what influences what and how cause and effect can be attributed — combined with growing limits on ad platform data.
Not all marketing intelligence software navigates the gap between data and execution successfully.
Let’s cover the ways that the best marketers gather data and talk about what makes Northbeam data different.
Platform Data
Nearly all ecommerce platforms — X, Facebook, Youtube, Google, TikTok, etc. — have their own in-platform analytics suites. These suites are easy to read and access but are often catered towards individual users, not billion-dollar enterprises, and can therefore lack the sophistication needed to inform spending decisions.
If you spend a lot of your time on a given platform, native analytics suites can feel convenient, but they’re only going to give you part of the picture. What if someone saw one of your YouTube ads and later Googled your company and made a purchase? What if someone saw your product on a Pinterest board on their computer and later looked up your company’s Instagram on their phone?
This sounds obvious, but it’s not. Platform-specific analytics suites are handy and straightforward, but fail to reflect the complicated nature of the buyer’s journey. They’re also susceptible to inherent biases: Google doesn’t benefit from telling you that LinkedIn or Twitter are performing well; they want to keep your money on-platform.
For these reasons and others, great marketers know that platform data can only ever be a piece of a greater puzzle — not an end within itself.
Third Party Pixels
Third party pixels or cookies are bite-sized (pun intended) pieces of code embedded in a site to track user behavior. They provide more data than native platform analytics, and are able to give you information like someone’s email address, location, and more. Because they provide personal consumer information, there is growing vigilance surrounding their use, and consumers are more and more wary of allowing cookies as they navigate online.
While third party pixels are powerful, they can’t be consistently relied on, and their usage will be increasingly monitored and restricted over time.
Mozilla, for example, has accessible offerings that block all third party pixels by default, as does Google Chrome. Another limitation is that third party pixels only work on desktop web — not apps, and not mobile.
Third party pixels or cookies are easy to set up and gather data with, but limited in the actual data that they can gather. Like platform analytics, they are part of a greater marketing intelligence picture. Cookies are fine, but only in moderation (pun intended).
Read more about how cookie depreciation will affect marketing analytics on our blog. Spoiler: it won’t affect Northbeam’s data.
UTM Tracking
UTMs (urchin tracking modules) are custom URLs that transmit source information easily and for free. All you have to do is create a distinct URL for different ads and content to start gathering data about how much of your traffic is coming from different sources.
While UTMs are free to create and implement, the data they provide is limited. You can know which campaigns are sending traffic your way, but you are left with little understanding of the full buyer’s journey. UTMs are only available on browsers and can’t track offline conversions.
They are also easy to spot in a navigation bar, and, to be frank, unattractive. This may deter click rate, and savvy consumers may purposely avoid or even amend UTM links. UTMs also require regular, manual creation and maintenance, which can be time-intensive.
UTMs are another piece of the marketing intelligence puzzle. What they lack, like the other data sources above, is a holistic understanding that the buyer’s journey is multi-touch, multi-channel, and more complex than a single click and conversion — at least for most types of purchases. That’s where marketing intelligence platforms come in.
Marketing Intelligence
Today there are more channels than ever before. Pre-digital modes like print, radio, TV, mail, outdoor, and experiential marketing are still around, while new digital contenders like TikTok and YouTube and dozens of others have gained and maintained prominence in today’s marketing landscape.
More channels equals more data. We’re talking about near-infinite amounts of data points that need to come together to help you understand what’s working, what’s not, and what you can do better to improve your marketing strategy. Northbeam alone processes over 300 terabytes of data in a single day — that’s billions of data points — through its machine learning models.
Marketing intelligence platforms aggregate data across channels and visualize it in a way that is not just intuitive but actionable. They help close the gap between insights and execution, and bring disparate parts of your marketing mix together to form a bigger picture that tells a story. These platforms acknowledge the wider buyer’s journey and try to piece the puzzle together for you — saving you hours and eliminating human error.
That being said, they are not immune from error. Each marketing intelligence platform has its own unique algorithms and formulas for understanding and representing attribution to different channels. With infinite data points at play, even minor differences in accuracy between providers can result in millions of dollars lost or gained.
And many marketing intelligence platforms draw data directly from platforms themselves, leaving them open to volatility if these platforms have service issues, interruptions, or changes in their own algorithms. Data could be there today and gone tomorrow, leaving some marketing intelligence providers scrambling to keep up.
That’s why it's crucially important that you understand how each marketing intelligence software works so you can decide what makes the most sense for you and your company.
What makes Northbeam's data different?
Northbeam uses proprietary machine learning models to generate first-party multi-touch attribution data. This data is more accurate, more actionable, and more insightful than any amalgamation of in-platform or third-party data you could get as a marketer. With this data, you can run experiments faster, attribute ad results more accurately, and compete more efficiently in an expensive performance marketing environment.
This isn't just all your ad platform data collected in one place: it's new data, polished with machine learning. Northbeam’s in-house PhDs built its proprietary machine learning model in 2019 and have been fine-tuning and improving it ever since, training it on massive amounts of data every day to improve its performance over time.
While some marketing intelligence platforms succeed in providing actionable insights and making connections between touchpoints, this data is often retroactive — a week or even a month old. Making decisions based on yesterday’s data is fine, but it’s not great, and today’s marketer wants to be great. This is where Northbeam stands out.
With first-party data, you can attribute conversion to ads in near-real-time. Northbeam’s machine learning models allow it to not just present but predict how various customer touch points will play out in the future, letting you run simulations and automatically shift spending in informed ways in order to succeed.
It's also future-proof: Northbeam doesn’t rely on third party pixels, protecting you from volatility, bugs, and the ever-changing compliance landscape. A new iOS update or algorithm change doesn’t affect your data, and neither would the deprecation of cookies altogether.
Unlike other marketing intelligence solutions, Northbeam’s machine learning models are built with privacy and security in mind, keeping data anonymized and aggregated so you can be GDPR compliant today instead of worrying about it in the future.
The best part is that Northbeam’s machine learning model gets better over time. As it learns your unique data and channel performance, its predictions become more fine-tuned, delivering more value and accuracy with use.
“You can get the most up-to-date insights based on what you’re trying to understand, whether it’s revenue, spend, or attribution. We get you the depth of the information you’re looking for as quickly as possible,” said Steven Yampolsky, Head of Data Engineering at Northbeam.
“We go to a great depth to ensure that our data is as accurate as it can be. There’s a high push for accuracy to make sure we’re not biased and we’re using all the information we have to the best fidelity possible. That’s what makes Northbeam’s data different.”
But you don’t have to be a machine learning expert like Steven to use Northbeam. Our software is built with marketers in mind, with easy-to-use and customizable dashboards to get you the right data at the right time so you can turn insight into action.
Questions to Ask When You're Shopping for Marketing Intelligence Software:
As you’re choosing the marketing analytics platform that works best for you and your organization, ask the following questions so you can ensure you’re getting the best bang for your buck:
- How real-time is your platform’s data? How often is it updated? Can you forecast?
- How robust is your platform’s algorithm for calculating attribution and touches?
- How accurate is your attribution model? What is your margin of error?
- How resilient is your data when it comes to social media platform bugs and issues?
- How future-proof is your data when it comes to changes in the compliance and privacy landscape?
- How secure is your data when it comes to encryption and compliance regulations?
How The Ridge Used Tactical Media Buying to hit $300 Million in Wallet Sales
“If you don’t have Northbeam, you just don’t know what happens between your spend and the revenue generated. There’s immense business value in understanding the steps between, as well as having the accurate data and clean UI needed to see how the whole marketing mix plays together.”
– Connor MacDonald, CMO, The Ridge
The Ridge launched with a simple goal: make wallets better. Two kickstarters, nine years, and 3 million wallets later, the now well-known company is applying the same innovative design to even more everyday items – and with the help of a new attribution tool. Knowing that expansion would mean more data and more complex customer journeys, the company knew it needed to move on from the limited platform analytics to stay on top of consumer behaviors.
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In-Platform Biases and Customer Journey Blind Spots
As a premium accessories brand, The Ridge often struggled with one very specific problem: unsustainably high customer acquisition costs (CAC). They simply didn’t have enough insights to understand what was happening between spend and conversion.
“Google Analytics (GA) is a good starter tool because it’s simple and easy to use, but that also means it’s rigid,” said Connor MacDonald, CMO at The Ridge. “For example, Google prioritizes its own search function, so we saw our GA display performance lose attribution to branded search, which created holes in our reports that made it difficult to know which campaign or which customer path was having the greatest impact.”
Moreover, The Ridge’s marketing team faced issues with in-platform data, like Facebook Analytics, where they saw as much as a 70% discrepancy between it and GA. Without baseline first-party data to measure against, it’s impossible to determine where ad platforms overstate their own performance metrics.
The Flexibility of Custom Labeling Lead = Profitability
Once The Ridge integrated Northbeam, its first order of business was to improve their CAC. They started by using the Automatic Label Rules to organize their data and to quickly identify which top-of-funnel efforts were driving the most awareness with new customers.
“Custom labeling makes it possible to instill your intent in your various campaigns and structure your reporting in a very intricate way,” explained MacDonald. “We created landing page labels, and labels based on things like ad concepts rather than targeting or platform, which gave an unbiased view into interest and helped quickly identify where it made the most sense to pause or increase spend.”
Northbeam’s deep ML capabilities enable it to learn and automatically apply labels to future content as well – a feature The Ridge was quick to leverage. Once labeling and reporting was on autopilot, the company was able to understand which awareness tactics captured the highest-intent audiences. By doubling down on these tactics, it boosted performance across the rest of the marketing funnel.
“Most people are looking at campaign performance within the various platforms they use,” added MacDonald. “That can be useful, but you can’t overstate the value in an intricate and unbiased view of all channels in one place. When you have both a top level and granular view at the same time, you can actually understand the nuances in your customer journeys and make really lucrative decisions.”
Onward and Upward (Post iOS14.5)
Using Northbeam’s custom labeling and proprietary attribution models deeper insights into customer journeys, audience cohorts, and media spend for The Ridge. The company improved ROAS by +24% and MER by +15% across all channels. As far as CAC goes, they improved their spend efficiency by 24%.
Not only that, but as the competition floundered in the wake of iOS14.5, The Ridge increased marketing spend +147% in 2021, increasing site visits +205% and ultimately growing revenue +239% year over year.
“We went from looking at Northbeam once a week to every day after the iOS update,” said MacDonald. “The first-party data focus gives us the accurate baseline we need, and we’re able to continuously give credit to the channels that created the intent rather than the ones that captured the conversion.”
Today, The Ridge is exploring additional functionality within the Northbeam platform to continue optimizing its ad buying strategies. This includes taking a deep dive into accrual vs. cash spend reporting in order to better understand how much new customer revenue is attributable to day-1 spend–this has helped us distill exactly what we want to be getting and making that replicable.
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Targeting High-Value Customers and Subscriptions for Sustainable Growth at PetMeds
“Northbeam has been instrumental in making sure we find and recruit high-value customers who are likely to repurchase in the future for growth. We’re able to have visibility - by channel - into acquisition costs and lifetime value to make sure we’re keeping them at healthy levels.”
–Dan Pingree, CMO, PetMeds
Founded in 1996, PetMeds makes buying prescription medication online easy and convenient for pet owners in the US and Canada. As pet ownership and spending continue to rise, PetMeds found it difficult to navigate complex customer journeys that spanned dozens of touchpoints to identify customers who could drive recurring revenue with subscriptions or repeat purchases. To complicate matters, competition has only intensified with other brands over the past several years. The team needed a new solution to identify which marketing initiatives were profitably acquiring new customers who would be more likely to return.
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Data Silos and the Limitations of Platform-Native Data
Prior to Northbeam, PetMeds relied on a variety of tools including SAP Commerce and Google Analytics across their marketing and sales teams, which meant much of their data lived in separate, siloed databases. The accuracy and quality of this data was also unreliable due to inconsistent implementation, resulting in an incomplete and fractured view of commercial performance. Due to these data silos, the team struggled to identify which marketing strategies and customer touchpoints were actually driving impact and revenue, and couldn’t effectively leverage all of their data to make opportunistic media-buying decisions. As a result, PetMeds often overspent on their top channels, leading to wasted marketing dollars that could’ve been better allocated elsewhere.
Furthermore, these solutions provided mostly blended metrics and didn’t distinguish between first-time or returning customers. The team had extremely limited visibility into performance by customer type, obscuring their ability to identify actionable insights into what was actually driving repeat purchases.
“We couldn’t figure out how to efficiently recruit new customers, and were losing ground to our competitors,” said Dan Pingree, CMO at PetMeds. “Keeping acquisition costs at healthy levels is crucial for our business, but we were struggling to find a solution to help us do that.”
How PetMeds uses Northbeam to Find High-Value Customers
Using native integrations to many popular ecommerce tools and platforms, PetMeds was able to break down their data silos by using Northbeam for all of their digital marketing channels. The team took full advantage of Northbeam’s proprietary pixel to start collecting and utilizing first-party data, instead of relying on ineffective third-party data in a post iOS14.5 world. With all of their marketing data consolidated on a single platform, PetMeds suddenly had a much clearer picture and understanding of campaign performance across all channels.
Next, the team focused on keeping CAC healthy by acquiring valuable customers more likely to purchase again in the future. They used Northbeam’s Clicks Only attribution model to compare acquisition costs and revenue across different channels side-by-side to identify the strategies that were driving the most success at the top of the funnel. Ultimately, the team identified the biggest opportunity on Google. Instead of spreading budget between multiple channels, they scaled their Google spend because it accounted for 36% of purchases from new customers.
Finally, PetMeds looked to increase customer retention by honing in on subscriptions and repeat purchases that could sustainably drive lifetime value. The team used Northbeam’s Customer Type feature to split out key KPIs such as ROAS by first-time and returning customers, revealing clear insights into what campaigns were converting throughout the funnel. This feature also allowed PetMeds to uncover previously hidden opportunities that didn’t look promising at first, but were highly impactful at driving return purchases. For example, paid retargeting campaigns only saw a +6% increase in first-time ROAS, but a +74% increase in returning ROAS.
Fueling Sustainable and Profitable Growth
Using Northbeam’s advanced attribution-modeling, PetMeds gained a deeper understanding into which prospecting campaigns and strategies were performing well and which weren’t. By identifying the exact touchpoints and customer journeys that were driving traffic from new customers, PetMeds decreased overall CAC -21% while increasing the number of orders by +26%.
In addition, PetMeds increased attributed revenue by +21% and ROAS +74% despite decreasing spend -24%. Unlike previous campaigns where decreased budget would lead to a decline in performance, PetMeds was able to identify what was working and focused on those campaigns and ads.
PetMeds continues to explore new ways of using Northbeam to grow their brand – including using the LTV tab to track customer lifetime value by the first product a customer purchased. This data not only ensures that customers are coming back and repurchasing, but also helps inform the team on which product(s) to push during key selling periods.
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Improving ROAS +84% With Data Driven Decisions at MyHD
“I had a slight feeling that Google Ads was the key to my financial success, but I wasn’t so sure until Northbeam made it clear that I needed to invest more in Google to see a better long-term ROI.”
- Juan, MyHD DJ Store
Juan, an entrepreneur and small business owner based in Chile, had become accustomed to relying on instinct to make ad spend decisions. Amidst an economic crisis, his ecommerce business finds itself among the lucky few with profits increasing. Juan often juggles a myriad of daily tasks as an individual running his own business. So, swift decision-making to ensure his time and budget are utilized efficiently was of utmost importance.
After only two months on Northbeam, Juan was surprised by how quickly he was able to identify the best channels for finding new customers, reallocate his limited media budget, and drive growth – all of which were dependent on accurate data.
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Unhelpful Data Aggregation and Vanity Metrics
Juan accepted that data to assess the performance of his digital ads was limited and ambiguous. He often questioned the data provided by ad platforms like Meta and Google – the revenue number the ad platforms claimed to realize was always more than he realized during that same period. It was clear that if he made media buying decisions based solely on Google’s data, then he would spend more on advertising than they were bringing in through sales.
Ultimately, Juan’s only choice was to make decisions based on his instinct, which wasn’t a risk worth taking for a small business amidst an economic crisis.
How First-Party Data Reduces Spend to Increase Profitability
Juan knew there had to be a better way – so he searched for a tool that could provide him with accurate marketing data to really learn what was driving revenue. Northbeam’s ability to collect and aggregate first-party data allowed Juan to access insights generated from a complete data set that wasn’t limited by privacy laws placed on 3rd party data providers (i.e., Google and Meta). A dashboard that is easy to navigate and interpret–with the ability to dig more granularly, which was crucial for Juan since his day-to-day as a business owner required him to wear many hats. “Northbeam makes it clear on any of the gaps you find on other platforms," Juan said.
Since Juan implemented Northbeam, he uses the platform as a single source of truth to ensure that he continues to realize more revenue than he is spending. After only 67 days on Northbeam, Juan was able to quickly compare Google versus Facebook’s contribution to sales. Northbeam’s data demonstrated that CAC was significantly higher on Facebook compared to Google–but ROAS had the potential to be higher since his prospective customers were finding him mostly through Google search. He re-allocated spending based on new insights, increasing ROAS on Google from 7.72 to 18.68.
The Growth Continues
Since using Northbeam to inform decisions, Juan’s total ad spend decreased – making room in his budget to continue using and spending on Northbeam. After reducing spend, Juan improved blended ROAS by +84% and reduced CAC by -21%. He refers to the platform as a free tool because his returns are far more than the cost of Northbeam. Aside from data fidelity and ease of use and decision-making, Juan feels supported by the regular Data Reviews conducted by his dedicated customer success manager and continues to find the Northbeam dashboard and the customer success team as invaluable.
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How HexClad uses Northbeam's Indefinite Lookback Windows for Global Scale
“Northbeam is our one stop shop for all things data–from overall business performance to performance by channel, Northbeam has us covered. We even use Northbeam to understand where we need to be at in-platform in order to hit our blended business goals. This has been a huge problem since iOS14 muddied the relationship between in-platform and blended metrics, but Northbeam makes it a non-issue."
–Connor Rolain, Head of Growth, HexClad Cookware'
HexClad’s ultimate goal is to provide high-performing cookware for home chefs. Even though multi-Michelin celebrity Chef Gordon Ramsay endorsed HexClad as “the Rolls Royce of pans,” the cookware brand still faced challenges growing their business. HexClad needed to find the ideal ad spend to acquire quality customers despite the long consideration process on premium cookware. With the help of a new universal attribution tool, the company was able to refine their brand communications, revamp their performance marketing strategy, and execute high converting funnels.
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Finding an Efficient Way to Fill Customer Funnels
HexClad had a record year in 2021, bolstered by excellent results from their traditional performance marketing channels. But the company felt the pressure to keep up the meteoric pace of growth, so at the start of 2022 HexClad went back to the (marketing) drawing board. The team realized they needed to introduce a new mix of campaign messaging, increase the number of assets into their ad accounts, and prioritize email and SMS in their media mix. And most importantly, they needed to identify how their previous media buying spend was contributing to tomorrow’s revenue as they dealt with a long customer consideration period.
In addition to devising a new marketing strategy focused primarily on growth, HexClad needed a solution to see how past ad spend can contribute to future revenue. Since the cookware brand has a 1-3 month consideration period, customers would see a large variety of HexClad’s ads before purchase, and purchases would usually happen outside traditional ad lookback windows.
Understanding the influence of past spend on future purchases is critical for both adding new customers to the funnel, as well as ensuring that customers are properly moving through the various stages of the purchase journey.
The Accuracy of Cash Snapshot Streamlines Operations & Guarantees Profitability
As HexClad set up Northbeam, they first worked with Northbeam’s Customer Success team to activate their dashboard with Breakdown Labels in order to better organize their marketing data by country, creative type, ad placement, funnel stage, and campaign objectives. With the addition of Northbeam’s deep machine learning capabilities on top of custom labeling, Head of Growth Connor Rolain quickly honed in on which marketing touchpoints were generating revenue by connecting top of funnel touchpoints to bottom of funnel conversions.
Next HexClad looked at Northbeam’s accounting modes to identify the relationship between today’s media buying initiatives with tomorrow’s return on ad spend. The Cash Snapshot and Accrual Performance models helped them forecast total revenue against total spend across specific time periods. Figuring out the relationship between their MER goal and contribution margin was crucial for HexClad’s operational planning. “You can make that connection between performance today and connect that with what it’s going to happen 60, 90 days from now,” Rolain said.
Finally, the HexClad team was looking to accelerate their growth feedback loop, which required making day-to-day decisions to scale - or cut back - specific media buying initiatives. This meant having to break the silos between the finance, operations, and marketing departments. Making media buying and budget decisions based on inventory and supply on a daily basis is not out of the ordinary for the fast-growing company – and they needed a reliable source of truth for these cross-departmental efforts. Enter Northbeam.
HexClad President Jason Panzer points out that “the level of analytical rigor in Northbeam’s models” gives him the confidence necessary to make high-level media buying decisions. Across the company, everyone has a “North Star” metric they consistently monitor to ensure they’re hitting their benchmarks. In HexClad’s weekly company meeting, most of the insights reported are pulled from Northbeam.
Around the World We Go!
Using Northbeam’s Breakdown Labels as well as the Cash Snapshot and Accrual Performance models, HexClad tackled 2022 with a new and improved omnichannel strategy. Year-over-year HexClad was able to increase revenue by 156% and reduce CAC -34%. The company focused on better understanding and improving their MER, increasing it from 3.9 in 2021 to 5.5x in 2022.
“We did a good job spending up to our efficiency goals all year long that by the time we hit Q4 we had juicy funnels full of people,” Rolain explained. “So, when we rolled out those offers, we just saw massive, massive growth.”
Today, HexClad is beginning to leverage more Northbeam functionalities in their strategy. For example the cookware brand is using customer cohorts from Northbeam’s LTV model to inspire product bundling and gather accurate insights from customers in newly-tapped international markets. With the Help of Northbeam, HexClad is poised to have another record-breaking year in 2023.
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Unlocking Growth With Dependable Data & Clear Customer Journeys: KITSCH
“Northbeam is my single source of truth and an analytics tool that every advanced marketer needs. I don’t even look at Facebook’s stats anymore and rely only on Northbeam to make tactical adjustments. Northbeam shows me metrics I didn’t even know to look for.“
–Carly London, Marketing Consultant, KITSCH
KITSCH is a Los Angeles-based women’s accessories brand built on positivity and hard work through supporting its community with elevated beauty products. Founded in 2010 by Cassandra Thurswell, the $87M brand started with a simple hair tie before expanding into beauty and personal care, including a popular silk sleep line. After iOS14 changed the digital privacy landscape, the brand grappled with identifying signals from noise across all channels, but found Facebook especially difficult to figure out. KITSCH realized they could no longer rely on third-party data and looked to find a first-party solution for the next phase of their growth journey.
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Wasted Spend from Incomplete Platform Data
KITSCH was used to making marketing allocation decisions based on in-platform data. However, after iOS14 reduced the efficacy of Facebook’s tracking, Carly London, KITSCH’s Marketing Consultant, noticed the ROAS and revenue metrics she was seeing weren’t matching with reality and struggled to understand the true performance of her campaigns.
“It’s so confusing to figure out what’s really going on with Facebook. I wasn’t able to get a complete and accurate picture of performance,” London said. “Sometimes Facebook would report positive ROAS on campaigns but I wouldn’t see results after we spent more.”
She realized the platforms were using limited modeling practices to extrapolate favorable results for their own products, even if underlying metrics like CTR or time spent on site indicated questionable traffic quality. Tired of wasting spend on campaigns that weren’t driving results, London began looking for an attribution software that doesn’t leverage third-party data or modeling that she could rely on for objective metrics.
The Northbeam Difference: First Party Data and World-Class Modeling
After onboarding, KITSCH was able to take advantage of Northbeam’s proprietary pixel to begin collecting first-party data, ending their reliance on in-platform reporting. Northbeam doesn’t use any third-party platform data in their attribution modeling, so KITSCH immediately noticed more plausible metrics that were in line with revenue performance. Northbeam also gave the team a sharper understanding of customer journeys by leveraging world-class Machine Learning models to accurately stitch users across sessions through a bespoke identity and device graph.
Armed with a clear picture of how campaigns were actually performing in Facebook, KITSCH began setting new benchmarks based on Northbeam’s attribution modeling. London used Northbeam’s Clicks Only model to compare ROAS across different campaigns to identify top-of-the-funnel strategies responsible for bottom-of-the-funnel conversions. Northbeam uncovered several hidden prospecting campaigns that Facebook had been underselling, so London began scaling spend to take advantage. With these insights, KITSCH was able to increase their Facebook ROAS +22% and revenue +60% while actually lowering CAC -17%.
With the top channel back on track and performing well, Yingying Kuang, Senior Director of Marketing, turned her attention towards optimizing KITSCH’s media mix to find new audiences. The team had been experimenting with Google Ads, but couldn’t figure out if spending on other platforms would have an incremental impact on their bottom line. Kuang used Northbeam’s Common Click Touchpoints feature to analyze platform interaction and discovered that Branded Search overlapped heavily with Facebook prospecting campaigns with an excellent conversion rate of about 50%. KITSCH now spends almost 20% of their media budget on Google with revenue up +88% and ROAS +70%.
Pursuing Omni-Channel Perfection
Northbeam gave KITSCH a much better grasp of marketing performance and a holistic understanding of which touchpoints were ultimately driving sales. Thanks to Northbeam’s first-party data and modeling, KITSCH was able to make effective allocation decisions driving +75% more revenue and improving ROAS +39% across all channels. KITSCH even saw an increase in spend efficiency with CAC decreasing 21%.
“The difference after Northbeam was night and day,” explained London. “We were stumbling around in the dark before, but Northbeam helped us cut through the fog and gave us the confidence to trust our metrics. We’re able to make smart and timely choices, and thanks to that we’re on the path to profitable growth.”
KITSCH continues to leverage Northbeam by using the platform’s ability to stitch users across multi-platform journeys to optimize their media mix. Due to the potential they saw with Google Branded Search, they are now testing out Bing and TikTok ads because they’re both discovery platforms. Although they only represent a small portion of total spend, KITSCH is already seeing promising results on both: Bing revenue increased +175% and ROAS +61% while TikTok ROAS also increased +64%, due to an efficiency gain with CAC down 35%.
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Creative Iteration and Optimization at Vessi Through Trustworthy First-Party Metrics
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"Northbeam's accurate attribution modeling is a game-changer for our creative feedback loop. Using insights that only Northbeam can give us, we can quickly test different hypotheses and iterate on our ad concepts, leading to better performance and ROAS."
–Shaun Hobbs, Sr. Performance Marketing Manager, Vessi
Founded in 2017, Vessi is a Vancouver-based footwear company that specializes in creating 100% waterproof sneakers for everyday adventures. 6 years and over a million pairs sold later, the brand has expanded into numerous products and markets to grow their customer base. After data privacy changes limited Vessi’s ability to track user data, the company realized it needed to cut through the noise by significantly boosting their creative optimization processes to resonate with and acquire customers. Faced with this daunting challenge, the team began to look for an attribution partner to help them profitably reach their growth goals.
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Flawed and Incomplete Data Leads to Ineffective Creative Strategy
Going into 2022, Vessi was grappling with several macro trends that severely limited their marketing effectiveness across all channels. Not only had privacy changes made it more difficult to decipher if their creative strategies and tactics were working, but also marketers were starting to shift away from highly-polished content towards UGC. Although UGC had several advantages like providing social proof and building trust through authenticity, the team needed an attribution partner they could rely on in order to iterate and adopt a hypothesis-testing approach.
“We badly needed marketing analytics that we could trust.” said Shaun. “We realized you couldn’t get away with sub-par creative anymore because targeting and tracking were so limited. We were trying to experiment with new ad concepts, but had no idea which ones were actually driving results. We often saw inflated stats on ads that promised performance, but didn’t result in conversions after scaling spend.”
Realizing they needed to adapt, Vessi decided to move away from the highly polished, curated content that had worked in the past, and towards user generated content that could be produced faster in response to consumer trends and cultural moments. In order to rapidly iterate on ads and improve performance, the team began looking for an attribution partner they could rely on to adopt this hypothesis-testing approach.
Performance Insights and Creative Optimization with Northbeam
Northbeam gave Vessi clarity on the true performance of their ads across all channels, as the team had always felt in-platform data wasn’t giving them reliable signals. After using Northbeam’s Clicks & Views model for several weeks and increasing spend on promising ads, they began to trust Northbeam more and more after being consistently rewarded with higher click-through rates and conversion rates. Confident they could trust their metrics, Vessi started leaning heavily into rapid creative iteration and experimentation.
“After Northbeam identified a winning piece of creative, we would read the comments and brainstorm on how we could make the ad just a bit better.” added Shaun. “We used Northbeam’s Motion integration to visualize and track how different variations performed, which helped to identify common themes that had potential. For example, we noticed Facebook campaigns featuring travel imagery and messaging outperformed many other concepts, so we doubled down and saw an 82% increase in revenue and 13% increase in ROAS.”
The team also used Northbeam to monitor “soft” metrics for specific objectives. For example, Vessi used New Visitor Rate to determine if prospecting campaigns were ultimately doing their job of driving new site traffic. This helped identify potential gems among strategies that weren’t hitting ROAS or revenue thresholds, but still showed promise and deserved a closer look for adjustments. This practice helped Vessi uncover new audiences like healthcare professionals and teachers who turned out to be big fans of the brand.
“We made sure to keep everything organized using Custom Labels to standardize our naming conventions, otherwise it could’ve gotten messy quickly.” explained Shaun. “That really made things much easier when we began experimenting with multiple hypotheses with many different audiences simultaneously. Our creative team really appreciated it while juggling all of our asks and tweaks.”
Becoming a Creative-Led Machine with Northbeam
With Northbeam, Vessi was able to get granular first-party data that dramatically boosted their creative efforts with timely and accurate information. This significantly improved their creative feedback loop by allowing for rapid iteration with their creative team to increase conversions. Overall, Vessi increased revenue +34% while boosting ROAS +8%. This was largely due to efficiency gains from better creative driving CTR +31% while finding more promising audiences (visits increased +41%) who were more likely to try Vessi (First-Time CAC -13%).
Northbeam also helped the brand identify video as their most promising format, so the team invested more effort into testing and creating more video concepts. As a result, revenue attributed to video ads increased +132%, new visits increased +96%, and Vessi improved their conversion rate +36% to take full advantage of these new customers. Vessi even managed to lower CAC -42% from video ads while improving ROAS +61%.
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Performance Benchmarks: Black Friday Cyber Monday 2023
Black Friday 2023 has come and gone. So how did it go, and how does your brand stack up?
Depends on who you ask.
Shopify self-reported that merchants drove a record high $9.3 billion in Black Friday Cyber Monday sales, a 24% increase year over year.
This number isn’t adjusted to account for all the new merchants Shopify has onboarded this year, so that volume increase could be driven by brands new to the platform. Therefore this isn’t the most useful benchmark.
Salesforce reports 5% online sales growth year over year across the BFCM season, with Friday and Saturday as the strongest days. Cyber Monday was the weakest day, with only 3% year over year growth. (Is Cyber Monday dead? I asked my mom if she did any Cyber Monday shopping, and she complained that I don’t call her enough. So the verdict is still out, but performance this year doesn’t look great.)
MasterCard’s Spendingpulse gave an assessment that most experts seem to agree with: ecommerce sales on Black Friday increased 8.5% year over year, while in-store sales only increased 1.1%.
The experts are suggesting most brands would see between a 1% and 8.5% year over year increase in revenue. But is it that simple? Let's look at the numbers from the "Cyber 5", the five days from thanksgiving to Cyber Monday.
“Cyber 5” Growth Benchmarks
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We looked at petabytes of ecommerce data and calculated percentile ranks of company performance based on year over year changes in revenue and first-time transactions (one of our favorite acquisition measures.) If your brand saw greater than a 3.7% increase in revenue and a 1.8% increase in first-time transactions across the Cyber 5, congratulations - you are at least in the 50th percentile of ecommerce businesses.
So, by these measures, if you did better than 5% increases in revenue year over year, you beat the worldwide average.
Don’t listen to “build in public” schmucks on X - use this chart to evaluate how you actually did this year, compared to everybody else.
Black Friday Cyber Monday Performance Benchmarks
Here’s charts showing ad performance metrics, comparing this year’s Cyber 5 versus last year’s.
Notable changes:
Facebook’s increasing share of budget is having brutal effects on clickthrough rate, cost per click, return on ad spend and customer acquisition costs. Performance marketers trust Facebook for conversions. Under intense pressure in a low-consumer-confidence environment, it’s no surprise budgets are consolidating to a trusted source of conversions.
What is surprising is the stunning almost 20% drop in clickthroughs. Although conversions technically increased, double-digit increases in CAC and double-digit reductions in ROAS probably nullify any conversion gains we saw on Facebook this year. No wonder TikTok is fighting for improved conversion tracking - we’re reaching critical Facebook mass.
TikTok budgets pulled back by 13% year over year. Although this is still one of our favorite growth platforms, the metrics don’t look good. 10% CPM increases, 12% CPCs, -12% conversion rate.
While this looks bad, I place blame on the advertisers themselves. Many still have no damn clue how to make a good TikTok ad, and these people are pulling down the averages with mountains of wasted spend.
Return on ad spend is down everywhere. This is just how it is. These ad platforms demand increasing profits from their advertising businesses. The profit motive drives these platforms to continually push up the prices of their placements. We will never see cheaper advertising than what we are experiencing at this moment. These data support that efficiency in your ad spend, via smart attribution and omnichannel experimentation, is the only way to win.
Industry performance during the Cyber 5.
Now the good stuff. Here’s two charts showing year over year changes, day by day, during the Cyber 5, broken down by industry, featuring blended revenue and first time transactions. Read ‘em and weep.
Some highlights:
Home & Garden took a dump. What happened you guys? I have a guess: growth of U.S. home ownership is tanking. No need to buy gardening gloves when you live on a seventh-floor walkup.
Beauty & Personal Care is slaying. These are typically the smartest Cyber 5 brands in the game. I bet Jones Road Beauty outperformed this by a significant amount. By these measures, most beauty brands using Northbeam likely ended up in the 75th percentile and above for growth.
It’s a good year for self improvement. Interestingly, both Health & Wellness and Sporting Goods & Fitness industries saw better first time transaction increases than they did blended revenue growth year over year. Is this driven by reduced prices? Lower AOV products? The new year will tell.
What about our white paper predictions on the Cyber 5?
Call it precognition, foresight, straight-up magic: were our assessments in the Q4 White Paper about this holiday season accurate?
Prediction: regardless of economic indicators, consumers are anxious about money and are likely to spend less than usual.
Result: false. Sure, we did spend more as a nation, but there’s some sketchy indicators. “Buy now, pay later” is up 47% year over year, and Cyber Monday is apparently irrelevant. We’re comfortable saying this weren’t as good as they could be.
Prediction: This will be the most fiscally expensive, creatively challenging, and competitively crowded performance advertising market we’ve ever seen.
Result: 100% true. Just look at those metrics above. Ads are more expensive or less efficient by double digits on like every channel.
Prediction: Cyber 5 sales will kick off the first week in November but consumer behavior won’t spike until around the 20th of November.
Result: true. I’ll do you one better: the data suggests that conversion rates spike on both Fridays in November prior to the holiday. Both 2022 and 2023 charts show as much. Plus, the upwards growth in conversion rate - our proxy for consumer buying interest - began to rise at the same pace after the 6th of November. This gives some weight to the “run sales early” argument, but the volume still pales in comparison to Black Friday itself.
Verdict: we were pretty damn close on most of our assessments. Next time we’ll be even more niche and focused in our predictions. I’m feeling lucky.
The Breakdown:
Here’s what you should know:
Year over year growth was not equal across all industries. The widely shared “8.5%” year over year growth number you see reported everywhere is an aggregate of many industries. Our reporting shows that growth is uneven - don’t measure your performance against numbers that aren’t relevant to your industry.
You should be studying what beauty and personal care brands did on Black Friday to attract repeat purchasers. Look at the year over year revenue growth numbers in the charts above, they’re killing it. These strategies often include smart early emailing campaigns and gift with purchase. Bonus data: Beauty and Personal Care saw their year over year blended revenue increase 39.7% in October. They’re doing something right.
Cyber Monday is losing power, so stop planning around it. Even looking at brands who did well during the Cyber 5, several of them saw their lowest year over year growth on Monday. As ALL shopping begins to pivot online, a day dedicated to it seems redundant. Also, it’s easier to apply discount codes and specific offers to online orders.
Thanksgiving is a decent day for sales, actually. We’re always so focused on Friday, but in several industries, Thanksgiving was the best day for sales. Maybe all your customers weren’t in food comas after all. Make Thanksgiving a part of your Cyber 5 media buy.
Black Friday shopping sucks and people care less about it - can you create excitement again. When was the last time you were legitimately excited to do holiday shopping? Long gone are the fistfights over Furbies we used to see in the early aughts. But are we right to point the finger at consumers? Black Friday discounts used to be steep enough to inspire violence. These days marketers are expecting 200% year over year gains on a 15% discount offered on a product bundle that’s pointless for customers but convenient for the 3PL. We’ve lost our Christmas spirit.
Next year’s Cyber 5 will be even worse. We’ll be in the immediate aftermath of the most consequential American election of a generation and the stifling ad spending that comes with it. Consumer confidence is set to drop, not improve. Not to mention CPMs continue to rise in price. There’s no relief coming, so don’t plan for it. Plan instead to be smart and efficient now.
Final comments
This isn’t the Cyber 5 we deserved, but it’s the one we got. Performance was divided across industry and creative lines. Brands who use proper attribution outperformed everybody else. Creative remains king - the brands with quality, authentic ads won while the “60% off!!!” screamers yelled into a void. This Cyber 5 defined what our algorithm-driven future will look like, so we hope you were paying attention.
Do Cost Per Clicks Matter?
A “cost per click” (or “CPC”) is a commonly-used metric in performance marketing. It stands for exactly that: the cost of a click on an ad campaign, ad set, or ad creative.
Intuitively, it feels like a good CPC means you’re running an efficient ad. Low cost per clicks means you’re getting people to click your ads at a relatively cheap price.
But is it that simple? Discussions about auction prices come up often on Linkedin and Twitter (X), usually without context.
Without context, any metric can be misconstrued. Whether or not CPCs matter depends entirely on the situation.
Let me show you a case study of a real situation of when high CPCs “mattered” and how we fixed it.
The Problem: $13 Cost Per New Visitor
Acceptable CPC ranges vary wildly depending on your product price, industry and business model. It’s almost impossible to say what a brand’s CPC should be without looking at the rest of their funnel first.
For example, you can look at “Cost Per New Visitor” (eCPNV), a derivative of CPC. This is the price you pay for a new visitor, obviously - but it’s an important measure for understanding how well your new customer acquisition is working.
If potential new customers aren’t clicking your ads, you aren’t going to get new customers. A high eCPNV means your ads aren’t bringing in new folks efficiently. That’s not what we want, right?
Let's look at an example. Below is a screenshot of real Northbeam data. For this brand in particular, a $13 eCPNV and $37 CPM are excessive.
This is a good indicator that this brand was spending too much on warm segments on Facebook Ads. These audiences already know a lot about this brand, which means we’re wasting spend pushing ads on them.
The Solution: Scaling Back & Diversifying Media Mix
This brand already had a large content library, so I moved some spend to YouTube. It made sense: I know video ad content is already working for this brand. Let’s take this content somewhere audiences are contextually primed for it.
Here, you can see in the first few days we are reaching much more people at a lower cost on YouTube compared to Facebook.
As you can see in the table, looking at Facebook and YouTube combined, our eCPNV is now 47% lower compared to the prior period.
Facebook eCPNV also dropped from scaling down spend. This means spreading our content across multiple channels resulted in more efficient results even in our primary selling channels like Facebook.
Despite the lower 1-day-click new customer acquisition costs (nCAC), we’re still hitting blended nCAC targets, so gaining the additional reach and cheaper email signup costs is a bonus win. That will have a downstream impact going into Q1 and beyond.
Plus these YouTube results are without any optimizations beyond an initial audience test, which suggests there’s an opportunity to unlock further performance.
Why reach and traffic volume matter
If you’re thinking about CPCs, eCPNVs, nCACs, or any number of other metric acronyms, you should always consider the core metrics they derive from. For this brand’s eCPNV problem, it is important to look at reach and volume of traffic.
Reach by itself doesn’t matter, or we’d all be manual bidding for clicks on the Google Display Network. You need to make sure there’s an acceptable level of purchase intent, too.
I use Northbeam to help me understand that intent by looking at some of the following metrics:
- % New Visits: ratio of new vs returning users based on data collected from Northbeam’s pixel. This is a great way of tracking site traffic in relation to your new customer acquisition strategies.
- eCPNV: Cost per new visitor, which is a more effective measure of reach than just looking at CPC. It’s more effective because it adds value to your reach: you know what you’re paying to bring new eyeballs (and their potential LTV) to your store.
- Cost per email signup: Existing customers and leads are already in your list. Email signups are a great measure of reach. It also marks them as at least somewhat interested - they wanted on your email list, after all. If you can acquire emails at a cheap price, those people are easily targeted using your email ad campaigns.
- New customer ROAS (NC ROAS) and new customer acquisition cost (nCAC): If you’re struggling to reach new people, these metrics will be low compared to returning customer ROAS and CAC. Northbeam gives you new, returning and blended.
There’s no one answer for growth
When trying to solve something like high CPCs, this was the easiest solution. Start with the low hanging fruit while you’re planning to solve for more complicated challenges.
Thinking about new channels is a great way to expand your options for problems solving. In this scenario, we were able to have a pretty significant impact on reach just by shifting some money around while planning our next batch of content to test on Facebook.
Scaling on Facebook Ads alone is more challenging than ever, and brands need to be able to analyze data from multiple channels to grow profitably.
I use Northbeam to solve the high CPCs problem (and many other) because the metrics provided in the platform give me accurate, actually actionable insights I can use.
So long story short: no matter your CPCs, Northbeam is a great tool for helping you understand that data deeper.
Want to chat more? My name is Zack Miller. I help 8-figure ecommerce brands grow with strategy & execution on paid social, Google and YouTube for a fixed-cost monthly retainer.
Here’s how to reach me:
https://twitter.com/growthzacks
https://www.linkedin.com/in/zack-miller/
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Northbeam's Definitive Guide to Q5 2023
Think the holiday season and peak shopping time is over after Christmas? Think again. You know we're all about arbitrage here at Northbeam.
If you’re a savvy advertiser, you’re probably already familiar with Q5 (the “hidden” quarter) and have been actively squeezing more performance "juice" out of the proverbial holiday promotional season lemon. In fact, both Meta and TikTok are vocal about encouraging more brands to increase spend in Q5 to capitalize on opportunities.
The good news is, even if you’ve never heard of Q5, it’s not too late to take advantage of this unique selling time. In this guide, we’ll give you our take on Q5, why you should care about it, and how you can make the best use of Q5 with your campaigns.
What is Q5?
Ask a couple of marketers what Q5 is and you’ll likely get slightly different responses. At Northbeam, our definition of Q5 is the period from late December to early-to-mid January. The fifth quarter is that hazy period between Christmas and the proper start of the New Year where the days blend together as people relax and unwind with family and loved ones. Most brands are replenishing BFCM investments and reducing campaign budgets across the board with (at best) only top performers active.
This is when most marketers take their vacations after the absolute gauntlet of Black Friday, Cyber Monday, and Christmas sales. While the competition is taking a break before Q1, this relatively quiet time in the year can help propel you past the competition if you’re well prepared.
Why is Q5 important?
It can seem counterintuitive to keep spending during “down” time, but there are several good reasons why brands should consider Q5 as part of the holiday season:
- Since most marketers will be slowing down after Christmas, there’s naturally going to be less competition in ad auctions.
This not only means fewer promotions and other advertisers to compete against, but also considerably lower ad costs vs. peak holiday CPMs. Adweek estimates that Meta CPMs were 28% lower during Q5 YoY, with ad fees in the last week of December at least 12% cheaper than previous weeks.
This is a great way to get the most bang for your buck, or get an even larger share of voice with the same amount of spend.
- Engagement on social media and digital devices remains high as people relax and spend time at home during the holidays.
Not only are people using their devices to disconnect more, but new devices that were gifted are also coming online. Usage peaks around Christmas and stays elevated all the way through early January for these reasons.
According to Snapchat, although engagement is highest on Christmas day, a surge of app downloads follow. 81% of TikTok users expect to spend the same amount of time or more on the app in Q5, and during the week after Christmas, TikTok views actually increased +24% vs the Q4 average. This means that even though ad costs are lower, customer engagement is in the same ballpark as more expensive weeks.
- Shopping intent also remains high after peak holiday sales are over.
Survey data shows that 92% of customers intend to keep shopping into Q5 as people exchange and return unwanted gifts for something they actually want. According to the National Retail Foundation, 70% of customers this year expect to shop in the week after Christmas, driven by continuing sales.
Gift cards are often used during this time to take advantage of ongoing sales, which explains why shopping behavior during Q5 shifts from gifting to more personal buying (which is better aligned with most brand messaging) as we approach New Years resolutions season.
In summary, not only is there less noise in the market from other ads and sales, but you’ll also enjoy high customer engagement and shopping intent at a healthy discount. Not a bad time to think about efficient customer acquisition.
Take advantage of Q5 with these strategies
Because of the dynamic of lower CPMs with high engagement, there’s an opportunity to get a discount on ads and unlock some incremental revenue at the same time. Let’s discuss a few scenarios that are perfect for Q5.
- People begin shifting from buying gifts to purchasing for themselves after Christmas and as we approach New Years.
We start thinking about resolutions and unsurprisingly “New Year New Me” brands generally see an uptick in conversions and performance at this time. If your brand is in the health & wellness, fitness, financial management, or other similar spaces tied to New Years Resolutions, you’re missing out by not taking advantage of cheaper ad fees at a time when people are actively looking for your products.
We’re advising our "New Year New Me" clients to double down and aggressively pursue revenue goals this Q5; you should, too.
- Seasonally relevant and consumable stock-up brands also do well during this time.
Winter clothing & accessories, ski & snowboard, beauty & skincare are just some examples of verticals that should seriously consider ramping up or at least extending spend through early-to-mid January. One of our best clients is a beauty brand powerhouse that has consistently made smart choices around their Q5 media mix and budget allocation.
In 2022, they actually spent 6% more in Q5 than the prior month, but efficiency metrics like ROAS (+2%) and CPA (-5%) were healthy due to high device usage and shopping intent. Best of all, revenue from Q5 was +8% higher vs. the traditional Q4 period which included Black Friday/Cyber Monday.
- Q5 is a great time to run awareness plays and retargeting campaigns because A/B testing will cost even less than usual.
Get a leg up on Q1 by using this period to start on your learning agenda to test creative, audiences, channels, copy, and so on. Ideally you’d want to have a large diversity of creative running not only to narrow down on themes that work, but also to start building up your pipeline for the rest of the year.
Brands can use this time to begin filling the top of their funnels for 2024 while their competition is asleep at the wheel, especially smaller brands who have the rare opportunity to outbid larger advertisers (those brands will largely have exhausted spend by this point and are in planning mode for next year). Be bold when others are fearful.
- Finally, Q5 is perfect for liquidating excess inventory or older SKUs.
We’d advise switching up the offer from your previous holiday promos and launching new creative to avoid customer fatigue. Make it clear this is a sale specifically for Q5 that will only run for a limited time to generate a sense of urgency.
How leading brands use Northbeam to tackle Q5
We surveyed our power customers on the ways they use Northbeam in Q5. Here are a few of our favorites:
- A big advantage of having infinite lookback windows is that customers acquired in Q5 will stay in your funnel even if their purchase intent comes later.
Unlike most other attribution solutions that have a limited lookback window (usually 7 days), Northbeam allows you to target all of the people in your pipeline regardless of when conversion actually happens. For example, let’s say your client is a swimsuit and summer essentials brand who traditionally has ignored Q5 in their media plan.
Using Northbeam, cohorts recruited or acquired during Q5 at a lower cost thanks to decreased CPMs can be integrated into the funnel once the high-selling season starts in spring and summer. With a shorter attribution window, those Q5 customers would get lost in the shuffle even if they were likely to convert down the line. Make sure you don’t miss out on revenue with shaky attribution modeling.
- Using the Customer LTV tab, you can track the average value of customers acquired during Q5 vs BFCM or any other time in the year. Let your own data tell you whether Q5 is a good time to increase spend or not.
- Although Q5 offers lower CPMs, this can easily be offset with worse efficiency or conversion rates. To carefully monitor and manage your ROI, you need a single source of truth that you can rely on to make budget allocation decisions.
What’s the good of doubling down on a campaign if your data is faulty and you weren’t investing in a winner to begin with?
Top brands including The Ridge, Hexclad, and Jones Road Beauty trust Northbeam’s expertise in attribution and customer journey analysis to capitalize and get even more value out of Q5 because they trust the accuracy and integrity of the data and modeling.
Bonus: Northbeam is a retention tool and value add for marketing agencies
With platform-reported data and GA4, Digital Marketing Agency partners often view Q5 as the season of churn as clients invest a majority of their available budget into BFCM and the holiday season before going dark. Northbeam can help agencies with retention efforts by building on the momentum from Q5 and applying learnings into their larger 2024 strategy.
We asked a few of our best partners on how they’re advising clients on Q5:
- Sol8: “Take advantage of the low CPMs on the Google TV network, optimizing for reach targeted to TB screens. At a time where there is a large amount of time off for families, you can leverage all the Google Ads audience targeting to multiply your viewership to multiple people in a household. The best part? It’s 81% cheaper than the CPMs on Hulu.”
- Blaze Digital: “Q5 is the time to push as ad costs plummet after the Christmas holiday. Really effective promotions tend to be 'end of year clear out sale.' This is also a big time to push the use of gift cards.”
- Embrace E-Commerce: "From an Amazon marketing perspective, leverage the Q5 period as a strategic opportunity that extends beyond low CPMs and CPCs. Its about comprehensive preparation and seizing a competitive advantage over other brands."
So should your brand or clients keep some cash on the sidelines for Q5 this year? If any of the above strategies or tactics resonate with you, reach out to us and we'll give you our take on whether or not it makes sense for your marketing team.
Featured photo by Karsten Winegeart on Unsplash
How Will Cookie Deprecation Affect Northbeam? An Explainer
I don’t know about you, but every year I resolve to cut sugar out of my diet only to find myself ordering gourmet cookies online by early February (at the latest). This year I’ll have no choice: Google recently confirmed their plan to completely deprecate third-party cookies by the second half of 2024 is on track.
Beginning in Q1, 1% of Chrome users will migrate to the new Privacy Sandbox ecosystem and have third-party cookies disabled. Google says this will give developers and advertisers enough time to learn the new environment and prepare for a cookieless future that will dramatically alter the internet landscape.
So fellow practitioners, what do we make of this? Is this the end of digital marketing as we know it, or the continuation of a trend in privacy concerns that Apple accelerated with the release of iOS14?
And more importantly: how is third-party cookie deprecation going to affect Northbeam data? Short answer: it won't.
What is "Cookie Deprecation?"
If this is the first time you’ve heard about Cookie Deprecation, let’s briefly discuss what cookies are and why they’re important for digital marketing.
Cookies 101: Cookies are small text files that help identify individual users on the web. There are cookies on every website you visit which are then stored on your computer’s web browser. They were first introduced in 1994 to allow people shopping online to store items in a virtual shopping cart; a functionality that we now take for granted as eCommerce marketers.
These are first-party cookies: cookies that belong to the owner of the website to help improve the user experience and collect anaytical information on customer behavior. Since they belong to the owner, these cookies are not at risk of being disabled by Google or any other browser provider.
Third-Party Cookies function the same as a FPC, but belong to someone else who mainly uses the cookie to track activity across the web for online advertising. These tracking (and problematic for privacy) cookies facilitate much of modern marketing by allowing external parties to create user profiles and target ads based on their individual behavior.
So why are they being banned? Shortly after their introduction, advertisers began using them to track user data including interests, demographics, location, often without the consent of everyday consumers. Shady practices such as “zombie” cookies that were difficult to delete eventually led to increased regulation such as the now ubiquitous consent pop-ups.
However, it was the ad tech giants who were the biggest culprits as evidenced by the Cambridge Analytica incident and subsequent repeated revelations of further violations.
In the mid- 2010s, data privacy gained more momentum as popular browsers including Apple’s Safari and Mozilla Firefox started taking these concerns more seriously and curbed the use of third-party cookies before eventually banning them altogether. Google’s move is notable however because 63% of global web traffic flows through Chrome.
Third-party cookies were already unreliable before, but the move to Privacy Sandbox will be their final death knell.
What are the implications?
Although we’ve been preparing for a cookieless future for a while, many marketers still rely on third-party cookies to understand consumer behavior such as purchases, interests, affinities, and browsing behavior. Insider reports that 78% of marketers still use cookies for ad buys, meaning most of us are in for a rude awakening very soon.
Let’s explore the effects of disabling third-party cookies on various platforms and strategies.
User tracking and targeting will be significantly hampered so any campaigns that rely on third-party cookies will see a drop in performance. Certain tactics such as behavioral targeting, retargeting (incl. cross-device tracking), and audience extension will all be dramatically less reliable once Chrome completely disables third-party cookies.
User profiles won’t be as detailed so targeting will lose some personalization, and the ability to build lookalike audiences with third-party data will no longer be possible. Frequency capping will also be affected due to the inability to accurately determine how many times an ad has been served to a specific profile. This only emphasizes the importance of leaning into a first-party data strategy as quickly as possible.
The big ad tech platforms rely on a mix of first and third-party cookies to target and serve ads. Post iOS14.5, marketers have already seen a drop in the efficacy of campaigns and precision in targeting, leading to fewer dollars being spent in social and digital. Facebook estimated a $10B loss in revenue in 2022 from Apple’s privacy changes; imagine how much more they stand to lose once Google phases out third-party cookies altogether.
Programmatic advertising may also see a drop in performance due to auctions relying heavily on targeting data. Expect these platforms to respond with more first-party products that rely on walled garden data (probably with a price premium) to keep your ad dollars in their ecosystem and circumvent some effects of cookie deprecation.
Measurement: Generally, multi-touch attribution is currently handled by a third-party cookie on a brand site associating a conversion event with an impression from third-party cookie on a publisher site. Without third-party cookies, marketers will need some sort of other identifier to attribute revenue to campaigns.
However there are already plenty of tools such as cohort analysis, segment analysis and media mix modeling (MMM) that can help you better understand performance without needing third-party cookies. In other environments such as CTV, cookies have never existed yet several analytical tools exist for attribution.
Probabilistic modeling and AI/ML are already speeding up this transition away from cookies; we think marketers will (as they always have) figure something out. Some even think attribution will significantly improve due to the high levels of bot activity in third-party data: a decline in fraudulent clicks and impressions should boost data integrity in general.
How will cookie deprecation affect Northbeam?
Short answer: it won't. We've run internal holdout tests where we've experimented with removing cookies from our modeling and 98% of our data was unaffected.
If you're using Northbeam for your ad attribution, cookie deprecation will not affect your reporting.
In our (humble) opinion, a cookieless future is another reason for marketers to move from targeting-led strategies to creative-led strategies. We’ve been preparing for the cancellation of third-party cookies since the beginning of Northbeam: many of our strategies post iOS14.5 were created to emphasize first-party data because we knew eventually that would be the only data we could rely on.
"Years ago, when we were starting Northbeam, we knew this was coming. Google's been telling us for years they were going to do away with cookies," said Austin Harrison, CEO and Co-founder of Northbeam. "So preparing for that future has been our mission. It's not just about cookies, it's about advertising more effectively in a more privacy-minded era. That's what we're about."
First and foremost, we don’t rely exclusively on third-party cookies in our attribution modeling. The core of Northbeam's MTA tracking solution uses a first-party cookie from your website, which is exempt from Google's third-party cookie deprecation plans.
We leverage only first-party data to resolve customer identities and expect to see minimal if any effect for our customers and clients because Chrome’s upcoming changes will not affect our pixel’s functionality. The first-party data we create has so many touchpoints that third-party cookies are just a tiny drop in the bucket of our overall attribution models.
Our collection and user stitching heuristics are designed for a cookieless future. It’s the reason why our customers (such as The Ridge) trusted us for their attribution needs: Northbeam is a future-proof solution in your marketing stack that will evolve to handle the changing environment.
That’s why we introduced MMM+ and are continuing to push out new features and updates. The Google announcement is mostly significant because of the scale of web traffic handled rather than the actual changes being implemented.
Although Northbeam brands won’t be affected by Google’s cookie ban, will yours? Reach out to us, we’re always happy to give you our take.
5 Reasons Why Northbeam is the Perfect Tool for Performance Marketing Agencies
Operating a Agency can be a Sisyphean task, to say the least.
From dealing with needy clients, validating ad performance, navigating privacy updates , and even just agreeing on attribution models— Agencies are punching far above their weight class when it comes to work load.
This reminds me of one of my favorite legends from Greek mythology. According to the Greek myth, Sisyphus is condemned to roll a rock up to the top of a mountain, only to have the rock roll back down to the bottom every time he reaches the top.
This is similar to the tasks agency operators face in 2024. Supporting a client for the long term can be an endless journey of blood, sweat, and tears.
We see Agencies make or break DTC brands all the time, the last thing they need to worry about is having accurate data. That’s where we come in.
Here's five reasons why Northbeam is the perfect tool to help your agency overcome the Sisyphean cycle.
Northbeam has infinite lookback windows and is unaffected by third-party cookie deprecation.
With the deprecation of third-party bookies in 2024 by Google and Yahoo, Northbeam becomes even more important to agencies and brands. We are 100% reliant on first party data, use DNS level tracking, and built our identity resolution in-house. This enables us to be unaffected by third party cookie changes and maintain full visibility on your customer’s journeys.
When advertising on paid social channels like Meta and TikTok, many agencies face the challenge of getting budget approval for top of funnel spend. Most DTC brand operators are still stuck in their ways with last click attribution are laggards when it comes to top of funnel spend… Makes sense as they see hardly any conversions on a last click basis in Shopify or GA4.
Northbeam provides an infinite attribution window and models built to emphasize intent from top of funnel. These both fuel your signal to optimize for new customer acquisition and ultimately scale your clients business. We see brands with flat growth consistently pivot to top of funnel strategies when onboarding with one of our expert Northbeam agencies.
Flexible payback and conversion lookback timeframes.
Understanding CAC payback periods are crucial for scaling DTC brands. In a world bloated with metrics around CTRs, CPMs, and even ROAS — when you boil it down to necessity the most important metric is CAC/LTV.
Northbeam measures "conversion lag," defined as how long your ad clicks continue to drive revenue. We see for high AOV brands that often times 30-50% of customers were in the funnel for more than 90 days before ultimately converting.
We enable you to look forward and forecast campaign performance down to the ad level, or in hindsight to set benchmarks. Both of these are dynamically calculated throughout the day to ensure you can be as competitive in the auction to achieve key KPIs.
We have brands spending $120 million annually with a CAC payback period of 12 months. With Northbeam they learned that all they need on Meta to be profitable is a .51 ROAS on a 1DC to achieve their lofty business targets of profitable growth.
Creativity is the alpha and the omega. Are you prepared?
I'll say it again: CREATIVE IS THE NEW TARGETING.
The two most common reasons for flat growth are low new customer acquistion acquisition and creative fatigue.
Northbeam’s Creative Analytics allows our agency partners to rapidly scale creative testing with new hooks, angles, and ad types. We also partner with best in class Creative tools like Motion to power creative testing workflows with first-party data.
Ultimately, Creative let’s you reach new audiences. Northbeam enriches your creative with powerful metrics like New Visit %, or even new vs returning data to make sure you're prospecting.
Alignment doesn't necessarily equal client retention.
Let’s face it. DTC operators are always going to be skeptical of the massive amount they are spending. Pair that with your agency retainer and you are bound to get a lot of heat from your clients on performance.
With Northbeam we enable you to turn your weekly/daily reporting into an integral part of your agency workflows. Save an unlimited amount of prepared views to make a CEO specific overview page, or paid social dashboard.
When a brand and agency are both optimizing on the same data, exciting growth happens. When both teams are using different data (cough cough… GA, platform data, AND MTA) then its bound to cause some problems in retaining clients
Customer journeys made easy and actionable.
Consumers have and always have bought products from where they spend their time. The problem is that today there is so many different places a consumer can buy from your brand.
From the numerous social platforms, TV, Google, Direct Mail, podcast, and good ol’ fashioned brick and mortar, consumers engage with brands in several different places before ultimately converting.
Northbeam highlights the customer journey at the order level, the customer level, across all customer paths, and even at the campaign level.
This means you can see every customer journey even for specific campaigns.
My favorite use case is understanding how Meta and Google interact with each other. Identifying where your brand search clicks are coming from is key to understand view through attribution, and continue to justify top of funnel spend.
Northbeam helps our agency partners retain their clients.
Our goal is to make sure that having accurate data is the last thing you have to worry about when operating your agency.
We take a hands on approach with our agency partners:
- Agency wide trainings
- Monthly Strategy calls
- Dedicated Slack Channels
- Industry insights + benchmarks
Our top goal is to make sure you are able to retain your clients by achieving KPIs with the most accurate attribution data.
How does Northbeam use AI?
Artificial intelligence (AI) is a force multiplier, allowing organizations to do more with less. Northbeam was founded with AI baked into its DNA; its team of in-house academics and AI experts built Northbeam to wield the power of machine learning, attribution modeling, and statistical analysis to help marketers achieve their most ambitious goals.
By using Northbeam as your marketing team's source of truth, you're not only using AI by extension - you're making AI the backbone of your marketing processes.
So, how exactly does Northbeam use AI?
What is artificial intelligence?
Let’s differentiate between different types of AI.
AI is an umbrella term that refers to the ability of a computer to think, act, and/or learn like a human. AI has many different applications, like machine learning (ML), generative AI, natural language processing (NLP), and large language models (LLMs) to name a few.
ML is a form of AI that learns over time and is able to use algorithms trained on data to create new models — even new AIs! — to perform a variety of complex tasks. It is the powerhouse of AI applications. Using a strong ML model is like putting a supercharged engine in your car, whereas generative AI is more akin to giving your car a fancy paint job.
Northbeam uses ML to analyze trillions of data points and come to conclusions about your performance with a superhuman degree of speed and accuracy. Its ML models are built to deliver next generation ad attribution and forecasting so you can make the best decisions possible.
Ad Attribution
Marketing attribution is the process of measuring and quantifying the individual impact of all of your campaigns on a desired outcome. If your desired outcome is a completed checkout, attribution helps you understand the effect of every activity a customer did before they finished their purchase. If your desired outcome is lead generation, proper attribution can show you which campaigns contributed to a lead ultimately converting on your website.
If you can get attribution right — if you can understand the exact impact of each touch on the buyer’s journey — then you can get your budgeting and spend right. The simple truth is that if you don’t know how your campaigns are performing and contributing to your bottom line, you can’t truly optimize your spend.
Northbeam’s proprietary ML models do the dirty work of attribution for you. They analyze first party data across thousands of parameters and assign a percentage attribution to each campaign or touch along a buyer’s journey.
Because Northbeam feeds its models with direct first party data, they are not susceptible to reporting bugs or changes in privacy settings.
“Our technology is very resilient to the current privacy landscape and we’ve built out offerings like MMM+ that are future-proof,” said Josh Rad, Principal Technical Product Manager at Northbeam.
“We don’t rely on third party cookies or tracking, which makes me confident in the quality, accuracy, and compliance of the data that comes into our system,” said Dan Huang, Chief Technology Officer at Northbeam.
Northbeam doesn’t look at activity in a vacuum. Its models combine data across platforms and channels to present a unified picture of your ad attribution so you can make informed spending decisions with the help of powerful AI.
“If you look at click-through data on other platforms, you see lots of purchases and only one touchpoint — you know in most cases the person did not simply type in the name of the website to make a purchase," Huang said.
"We use probabilistic machine learning models to predict and infer where that purchase actually came from based on each brand’s own historical data and performance."
“It’s a very customized model based on the brand’s true customer data,” Josh said. “Our machine learning is reducing the amount of traffic that other platforms or tools will say is direct, but actually isn’t.”
You heard them: Northbeam's machine learning fills in the naturally-occurring gaps you see in other ad attribution datasets.
Forecasting
Super-accurate ad attribution is already a boon, but what if you could forecast how different channels would continue to perform in the future? ML is especially suited for this type of task: intaking trillions of data points and using them to model or predict future impact.
“We can forecast your attribution windows based on your historical information,” Huang said. “A click today might generate revenue in the next thirty days, right? And that is valuable information if you want to know how your campaigns are performing now or will continue to perform in the future without actually having to wait thirty days to get that data.”
Northbeam’s ML can go beyond real-time to deliver dynamic forecasting at your fingertips. You can run simulations on the Northbeam platform and see how channels would perform at different spending levels. This lets you predict when diminishing returns may occur and optimize your ad spend for maximum ROI.
The best part is that Northbeam’s ML gets better over time: a hallmark of strong AI. As it learns your unique data and channel performance, its predictions and attributions become more fine-tuned, delivering more and more value with each use.
“No one can give you 100% ground truth. If someone tells you they can, they’re being misleading. We intentionally don’t train our model on your entire historical dataset because we want to validate and see if we can match the non-trained historical data. This tests the performance of our methodology, and our accuracy against ground truth,” Huang said. “This is called ‘backtesting.’”
But perhaps the actual best part is that you don’t need to be an AI expert like Dan or Josh to use Northbeam. Its platform’s backend is built on a foundation of industry-leading AI and its frontend is built with you in mind, so anyone on your team can get instant value with ease.
Get in touch to learn more about how ML can transform your marketing intelligence.
How To Not Get Fired: A 5-Step Guide for CMOs
Sky-high expectations combined with post-pandemic economic austerity measures and the ever-changing marketing landscape have led to the ousting of many a CMO.
The average tenure of a CMO in 2021 and 2022 was 40 months, the lowest it's been in a decade, and less than half the average tenure of a CEO, which is 85 months.
FastCompany summarizes the curse placed on the modern CMO:
“CMOs enter an organization that they perhaps don’t fully understand, and before they have the chance to find their footing, are tasked with meeting ambitious deadlines or targets. They are, in essence, set up to fail. And so, after a couple of years, they leave, only for the cycle to restart.”
You don't have to fall into this trap. Whether you're a CMO (or aspiring to be) there are ways to secure both your position and the success of your team.
At Northbeam, we work with hundreds of marketing leaders. We've seen what behaviors separate high-performing CMOs from those at-risk.
We spoke with Bryan Bumgardner, Director of Growth Marketing at Northbeam and Luca Taormina, Sr. Partner Manager at Northbeam to put together 5 tactical tips on how to do a radically excellent job as a CMO.
1. Understand the modern marketing landscape
The past twenty years have seen a veritable explosion of new marketing channels, strategies, and perspectives. An old playbook — even a two-year-old playbook — simply won’t work anymore. CMOs that bring the same strategy from one company to another are bound to come up against issues.
In November 2007, Facebook enabled brands to serve targeted ads to their potential customers, completely changing the marketing game and giving rise to true performance marketing.
In April 2021, Apple launched the iOS 14.5 update and drastically reduced the ability to track individual parameters, kneecapping an entire generation of growth marketers who were used to the "old way."
The landscape threatens to shift again as Google completely deprecates cookies this year. It’s difficult to keep up.
“We’re sitting in this really awkward time in which those media buyers-turned-CMOs that don’t have pre-2007 experience — suddenly the tool they were using is no longer as actionable as it once was,” said Luca.
Today’s CMO has to espouse a creative vision for the brand, while being a technologist skilled with data, while also having a strong sense of the business strategy. A unicorn, basically.
“We’re shifting towards this play with Apple and Google’s privacy updates where instead of being able to target people perfectly with technical implementations, we need to be both more creative-driven or algorithm-driven,” Bryan said.
"Plus you need to be more business-savvy than ever. Marketers are basically MBAs now."
“Talented CMOs truly understand this combination of how creative work can impact analytics and data and vice versa, and how to use both of them to your benefit,” Luca said.
“So it’s exciting; we’re filtering out the wannabes and the imitators.”
2. Embed yourself across teams
Why are COOs often called into take over for exiled CMOs? Because COOs have a bird’s-eye view of the entire business. They understand the product strategy. They understand the sales strategy. They understand the finances. And they know how marketing can fit into that equation in a complementary and holistic way.
Take note, CMOs: don’t stay in your lane. Get involved early and often in product and sales, customer success and finance, and any other teams that touch your company’s go-to-market flow.
A marketing department’s scope of control has never been broader. Consider this non-comprehensive list of marketing functions, and all the various departments at a company that could serve as potential stakeholders:
- PR
- Conferences
- Webinars
- Product marketing
- Sales enablement
- Partnerships
- Website management
- Newsletters and email campaigns
- Experiential marketing
- Community management
- Analytics and reporting
- Brand
- Content
- Communications
- Research
- SEO
- Digital strategy
- Social media
- Analyst relations
- Demand generation
CMOs are expected to either run these functions themselves, or manage people or agencies to do so. Community management requires a close understanding of the customer experience. Product marketing involves constant communication with engineering teams. Any paid channels necessitate an intimate relationship with finance’s priorities. The list goes on.
Only by having a clear picture of the entire company’s day-to-day and direction can you align your strategy fully with the overall vision — and get the information and resources you need to do your job!
Bonus: if you can manage some sway across departments, you can successfully avoid a situation where you are tasked with selling a sub-par product, or trying to grow the top of the funnel while the bottom is leaking severely. It’s a win-win, if you can swing it.
3. Become a master storyteller
Storytelling is what brings a brand to life and engenders life-long loyalty. But we’re not just talking about the stories we tell externally — CMOs have to become expert storytellers within their organization.
The truth is that most people who don’t do marketing have no idea what it is you do — not even (or especially not) your CEO or founder.
"There's a terrible refrain I've heard often after a senior marketing lead is ousted, " Bryan said. "People will say: 'What did that person even do here?' It's terrible to see a marketers true influence so misunderstood."
The vast majority (over 75%) of CEOs don’t have a background in marketing. Most come from operations, finance, or engineering. They don’t know the ins and outs of your role, how much budget you actually need, or what is a reasonable expectation or timeline for you to deliver on.
“Rarely do you see colleagues question any other discipline so openly,” said a CMO for Forbes. This results in a distinct and dangerous lack of trust.
Only 4% of CEOs say the CMO is the most trusted member of the leadership team — and only 32% trust them overall. That’s pretty dismal.
“I think this stems from a misunderstanding of marketing, a misunderstanding of what a CMO should be doing and the kind of results you can expect in 2024 and beyond,” Bryan said.
“In the event that a CMO is brought on when a founder doesn’t have a budget prepared, doesn’t have a staff ready, and they can’t speak to any of your questions particularly well, they’re not hiring a CMO: they’re hiring a co-founder.”
But often, they don’t recognize that they’re hiring a co-founder, an equal, and it’s your responsibility to make them understand that — your job may depend on it.
Storytelling can help build trust through affinity, persuasion, and pathos. Where data fails, storytelling wins, and vice versa. If you can’t make the case for your strategy through numbers, leverage your storytelling craft to educate the rest of your executive team about what you’re doing and why it matters.
"You aren't just storytelling for your customers, you're storytelling internally to your own team - make sure they understand what you're doing and why you're doing it," Bryan said.
4. Be ruthlessly realistic
“I think the absolute first conversation you need to have when you’re interviewing to be a DTC CMO is about budget,” Bryan said.
“Ask: how many people am I going to be permitted to hire? How many agencies can I hire? And do you agree that these people are required for us to succeed with the expectations that you’re setting for performance?”
“And if they don’t agree that you need an agency or two to handle your creative, your product marketing, whatever it may be, then you’re going to fail because they’re putting tire locks on your playbook before you’ve even started.”
CMOs are often met with a “just get it done” mentality from founders and CEOs. And while “just get it done” could be pulled off in other departments with sleepless nights and long-worked weekends, you can’t be expected to pull off miracles in the CMO role, where budget and resources are crucial — at least not with any regularity.
“The number one thing that is killing CMOs is the business shift into demanding ruthless profitability. Marketing is a huge line item on your P&L and one of the biggest variable costs. So when investors and boards are demanding profitability, they’re going to look at marketing first,” Bryan said.
Consider shifting your metrics towards harder numbers like cash, rather than engagement or marketing metrics that we know are relevant for growth but less understood by non-marketers.
“Profitability, cash in the bank, cash multipliers, and efficiency-focused metrics,” Bryan said. “Efficiency from day one is crucial. Don’t start with a massive budget; make your campaigns efficient from the start. Focus on capturing user data and building marketing around it.
"Look beyond traditional metrics and prioritize metrics that reflect your brand’s success and profitability. Brands are shifting their metrics constantly and what’s important today might not be tomorrow. Just try to focus on the things that matter and avoid falling into any unreasonable expectations.”
5. Innovate, innovate, innovate
Luca shared a quote from “The War of Art” by Steven Pressfield:
“The counterfeit innovator is wildly self-confident. The real one is scared to death.”
“I think that’s what a CMO should be: scared to death,” Luca said. “A CMO should not just be able to say ‘Hey, I hit a 5x return on ad spend this week’ and suddenly go around giving advice on how to succeed. A CMO really needs to understand that they’re dealing with a robust and changing audience.”
As a CMO, success has to be proven quarter after quarter, year after year. A successful campaign cannot be coasted on, but has to be replicated consistently, with continuous doses of ingenuity and innovation to keep it fresh.
“Your skills are your job security,” said Bryan. “Your playbook is your job security, it needs to constantly be evolving. You need to be able to put that into action and learn from it.”
Technology is your innovation superpower, too.
“Doing more with less is fundamentally a data problem. You need to have good data that explains the results of every little thing you do as a CMO. And it goes beyond having data to having accurate data that can tell the story you need to tell,” Bryan said.
“This is where Northbeam comes into play. You can draw a line from your activities to the revenue you’re generating, and take the guesswork out of attribution.”
If the first tip on this list is to have a thorough and up-to-date understanding of the ever-shifting marketing landscape, the last is to wield that knowledge as your superpower.
That knowledge will allow you to look ahead and innovate, to be creative and tactical and understand where the market, where your vertical, is going.
And here’s a bonus tip: enjoy the job. You can’t innovate without passion.
“You need to drink the tea,” said Luca. “You need to fall in love with the company and what it does. That company has to be the absolute savior.”
The implicit advice behind all of five of the tips above is to start with passion, and love for the art of marketing itself.
And those who care, use Northbeam.
Profitable CACs, Lifetime Value, and "Red Zones": How Dr. Squatch uses Northbeam
Founder and CEO Jack Haldrup started Dr. Squatch with a simple goal: create natural soap bars for men that didn’t have the synthetic ingredients found in most big brands. Jack suffered from a common skin condition and frequently shopped at farmer’s markets and health food stores to find soap that wouldn’t irritate his skin.
“But the average guy… isn’t going to hunt down these products and buy them from a farmer’s market,” Haldrup said, speaking to the San Diego Union Tribune. “And they’re also not shopping at health stores where these bars are widely sold.”
Jack began experimenting with making handmade soap and started selling bars out of his apartment. After getting rave reviews, Jack realized he had found a huge underserved market; men’s personal care was an afterthought compared to women’s products, and the space was long dominated by multi-national conglomerates such as P&G and Unilever.
It was ripe for disruption. Dr. Squatch combined this insight with savvy marketing that used tongue-in-cheek humor, most famously in their 2021 Super Bowl LV commercial, and the rest is history.
What started as a small batch DTC handmade soap store is now a $100M+ brand advertising across a dozen different channels. They’ve also expanded from DTC into retail: Dr. Squatch products fly off the shelves at Target, Walmart and other major retailers all over the nation.
Even with their massive success, the marketing team at Dr. Squatch still had a problem: they wanted to improve the performance of their sizable marketing spend. Before Northbeam, Dr. Squatch relied on flawed solutions like Google Analytics and in-platform reporting to make allocation decisions on their omnichannel ad spend – even though they knew data from those platforms were limited and biased. As the end of third-party cookies loomed ahead, the Dr. Squatch team decided it was the right time to upgrade their marketing attribution.
Dr. Squatch then turned to Northbeam as their top source of truth for marketing attribution.
We sat down with April Lonchar, Manager of Growth Strategy at Dr. Squatch, to discuss how they use Northbeam for everything from creative testing to performance benchmarking.
Northbeam's data as the centerpiece
“We’ve been with Northbeam since early 2023, so we’re probably a bit further along than most folks with embedding the platform into our ongoing routines,” April said.
“We actively advertise across most major channels so our media buyers rely heavily on Northbeam to gauge performance. They’re looking at metrics regularly in the Sales Page to decide whether to continue investing or not. Our Head of Creative also checks Northbeam frequently because that team is constantly experimenting with new concepts and making tweaks based on what we’re seeing. My job is to be the main conduit and basically translate what these Northbeam numbers mean for our teams.”
Because both paid and organic efforts flow into revenue from new customers, Dr. Squatch uses Northbeam as a single source of truth when making paid media decisions. The company relies on the platform to analyze the interaction between channels and identify root causes of growth.
“For example, when we saw a dip in new customers on our online storefronts, Northbeam helped us identify that organic acquisition was the primary driver. We just wouldn’t have been able to get that level of insight before Northbeam.”
Going further with Custom Metrics
Dr. Squatch started by selling bar soap but has since expanded into deodorant, lotion, hair care, and cologne. As the brand grew, the team shifted their focus from simply acquiring customers to evaluating the entire lifecycle, encouraging customers to purchase subscriptions and bundled products. These repeat buyers are valuable because they can increase customer lifetime value and allow for promotional pricing to get people in the door.
This strategy gives Dr. Squatch more flexibility with CAC targets and new customer offers because the brand is confident they can make up for it over the average customer’s lifetime by using lifecycle retargeting to expand across product categories.
April described how she was chatting with her Northbeam success manager about tracking subscription performance when they recommended trying Northbeam’s Custom Metrics. Instead of having to export Northbeam data into another spreadsheet, Custom Metrics allows April to create bespoke metrics to see how many customers were actually signing up for subscriptions.
“I created two new Custom Metrics in Northbeam: Take Rate and Upsell Rate,” April said. “Take Rate looks at the percentage of new customers who immediately convert to subscribers while Upsell Rate tracks the percentage of returning customers who then decide to sign up for a subscription.”
“Between the two, I’m able to quickly decipher which campaigns are the most successful at finding and converting subscribers.”
As part of her daily routine with Northbeam, April will sometimes give “red zone” ads another chance if they’ve shown promise at driving subscriptions. These campaigns get a longer leash than they otherwise would because the brand can stomach a higher CAC in exchange for the higher LTV gains. New channels also get judged on their ability to drive subscribers in addition to the team’s normal KPIs on efficiency and conversions.
Dr. Squatch uses two other Custom Metrics to help track subscription performance: Discount Rate and Return Rate. Like many popular DTC brands, Dr. Squatch uses discounts to entice customers in the door to try their products. Generally, customers love their soaps and other products so much that they stick around and buy more, but the team still wanted to track discount activity and have visibility into how many dollars were being spent to keep CAC at healthy levels.
April defines Discount Rate as the percentage of revenue that wouldn’t occur without promotions, and splits that out into new and returning customers. She’s able to look at each channel and decide if the discounts are worth the squeeze as an additional data point. April also layers Return Rate on top of that analysis, defined here as the amount of refunds coming from any given marketing channel.
“It’s important for us to offer a great customer experience, but getting too many returns can really eat into our margins,” April said. “I keep an eye on whether certain channels or cohorts are over-indexing on the amount of discounts and returns we would expect from an average campaign. If they’re already in the ‘red zone’ then it’s a pretty simple decision to cut those ads because returns can quickly inflate CAC beyond where we’d like to see it.”
Improving marketing ROI while staying future-proof
“I’m not really sure how I would do my job without Northbeam,” April said. “A big part of my role is allocating resources every week to all of our channels. Our finance team sets overall targets, and I’ll then tweak their recommendations based on my experience seeing the numbers every day. Our media buyers take it from there to divvy up that spend within their own platforms.”
“None of that would be possible without the trust we have in Northbeam’s data and modeling.”
Northbeam has become a key tool in Dr. Squatch’s tech stack, but more importantly, insights from the platform have proven invaluable in improving their commercial and topline performance.
In the first full year of using Northbeam, AOV improved +3% across the board – no small feat for a company of Dr. Squatch’s size – and the team has continued to build on that momentum by experimenting and innovating their marketing strategies; the company just closed an impressive BFCM and holiday season with revenue growing +31.8% and ROAS up +3.8% in Q4 ‘23 compared to the previous quarter.
Not only did Dr. Squatch effectively build and capture demand, they even managed to lower CAC -2.9% during a period with elevated CPMs and competition.
“We’re really excited to continue partnering with Northbeam as privacy changes - such as Google Chrome deprecating cookies - make our jobs just that much harder,” April said. “We initially picked Northbeam because we thought it would be a future-proof solution for our analytics needs and we look forward to pushing our capabilities with new features and releases.”
Serious Data For Serious Growth: How Northbeam Enables HexClad To Stay Efficient with Scale
Celebrity Chef Gordon Ramsay swears by HexClad for a reason.
This patented cookware is a hybrid of your favorite kitchen staples, combining the superpowers of stainless steel, cast iron, and nonstick into convenient and high-power cookware.
Founded in 2013, HexClad quickly grew its following with celebrities like Halle Berry, Oprah Winfrey, and Cameron Diaz singing its praises. In 2021, Gordon Ramsay invested in HexClad and became its lead global ambassador and spokesperson, leading to a record-breaking year for the company.
But maintaining meteoric growth year-over-year isn’t an easy task, which makes the subsequent years even more of a feat for the HexClad marketing team.
We sat down with Cameron Bush, Director of Growth Marketing at Hexclad to learn how Northbeam helps their marketing team not only get efficient but stay efficient.
Looking for a partner, not a provider
HexClad’s marketing team tried a lot of tools over the years to get the information they needed.
“It was a patchwork solution of Google Analytics, in-platform metrics, and static sheets that the marketing team was updating,” Cameron said. “And then we did trials with different marketing intelligence software; I often joke that most MTAs are just re-papered Google Analytics,” Cameron said.
Adding Northbeam to HexClad’s tech stack wasn’t just putting a new SaaS tool in the portfolio — it was a paradigm shift for the team.
“Switching to Northbeam was a pivotal moment in our switch to focusing on the top of the funnel because we finally had the confidence that we were able to measure it,” Cameron said. “The language of the team really matched the language we speak as far as what growth looks like, and we knew the data science rigor was there.”
Cameron found that finding the right MTA tool, as opposed to the heavy lift of building one yourself, is crucial to doing your best work. Having a strong partner in Northbeam is what sets a team like HexClad apart from the competition.
“Some vendors know a lot about what they know, whether that's math or data science or whatever it is, but they don’t know how to turn it into marketing a product and speak our language in that way. And that was something that Northbeam cracked the code on early on," Cameron said.
"We’ve tried all kinds of agencies, vendors, and softwares and Northbeam were the ones that really knew what we were doing as a growth marketing operation.”
The predictive modeling of Northbeam’s MMM+ tool is mirrored by the tactics of the Data Science team. Their deep well of expertise and hands-on approach provides additional insight into precisely how to leverage your data into more efficient spending, whether you know what to look for or not.
“Week to week, when I hop on a call with the team, they always have a new sheet that they’re walking me through and I think: I didn’t even know how to ask you for this, but you’ve already created it,” Cameron said.
“That kind of proactivity has been so helpful… Northbeam has been an integral building partner for the entire HexClad operation.”
Maturity brings a much harder battle
After two years in a row of 150% growth, HexClad remains bullish but realistic about the road ahead.
“Once we hit maturity and all the big changes we’ve made over the last few years panned out, we had to change gears and iterate and it became an exponential process of improvement,” Cameron said. “Once you hit that steady state it’s a much, much harder battle.”
“MMM has been a huge jumping off point. I wouldn’t know where to begin each month to decide what we spend on which days and why, and then set benchmarks for each of those in MTA. It’s all a huge puzzle that we’re putting together with a very lean team.”
“This is the year where a lot of those inklings and decisions and difficult marketing tests that have been pushed down the road are all coming to a head, and Northbeam has been a partner in that," Cameron said. "Trying to decide where to spend that next dollar is always our daily battle, and Northbeam is the only place we go to make that decision.”
Having a reliable system in place that accurately measures and predicts your spending is crucial when scaling year-over-year. Whether your goals are to maintain ambitious growth metrics like HexClad, or even when ramping your efforts down.
Cameron agrees: “We’ve tested hundreds and hundreds of pieces of creative over the last six months… and Northbeam has not steered us wrong yet.”
The metrics that make it happen
Let’s get into the weeds: which metrics does HexClad’s marketing team check daily?
“My obsessive metric is going to be ROAS, of course, but I love seeing first time versus returning customer ROAS. Not a lot of platforms give us that kind of visibility. We set a lot of our acquisition budgeting based on our first time customer orders, first time order value, and first time CAC, and then we can subtract the returning customer ROAS piece and credit retention for doing that,” Cameron said.
“That increase in returning customers year-over-year was directly measurable for us. Our email opt-in rates actually fell, but the conversion rates on those people that opted in increased massively because we were giving offers for second-time orders.”
“That’s something we’re looking to change now: we’re going to go back towards acquisition offers as well. Those flows, they just convert so well,” Cameron said.
“We watch our data rigorously every single day and we do huge creative testing exercises at the top of the funnel. A big part of measuring that is MMM. We wouldn’t have faithfully put 70% of our budget into Meta if we didn’t have some serious data to support that.”
“Once it proved itself week in and week out we were able to confidently scale and make projections that we genuinely believed in," Cameron said.
"And I think that’s not something that most growing direct-to-consumer businesses, particularly at this scale, are able to accomplish. Because it’s terrifying, you know, spending millions in a week.”
It’s unwise to continually increase your ad spend over time when you cannot effectively measure your return on investment. Northbeam provides the benchmarks you need to know if spending money is worth it; especially when your growth is at 150% and your ad spend is in the millions.
Like Cameron said: “you can’t spend those millions if you don’t absolutely know that you’re going to be able to pay for them at the end of the month.”
HexClad scaled their ad spend by 100% in 2022 and another 100% in 2023, improving their MER both times.
“We’ve done a lot of work with the Northbeam team to get us to this place, and it hasn’t failed yet, so we’re going to keep doing it.”
In an industry that is constantly evolving with new tools, legal privacy crackdowns, and SaaS snake oil salesmen coming out of the woodwork, it’s hard to know which tools will actually provide a significant value add.
“Moving to MMM+ is going to be massive for how we operate every day… all the product development that is going on to show us things like the half-life of a dollar is some of the coolest work I’ve ever seen. I didn’t know it could be done,” Cameron said.
A top-of-funnel beast
“This year we’re looking for 50-60% growth; we’re taking it easy,” Cameron said. “We’re looking at both Shopify and Amazon sales because that spillover is massive; if you chart paid media spend against Amazon revenue, it really grows when we start pushing the top of the funnel. And the majority of our revenue that’s coming out of Amazon is coming from direct search.”
People know HexClad — their addressable market has been, well, addressed. This is a position that any brand would love to be in.
“We’re in this new phase of realizing that we have a top-of-funnel beast and a ton of awareness that we’re really grateful to have because I don’t think a lot of brands are able to get to that well-measured top-of-funnel scale that actually ends up supporting the business,” Cameron said.
“We’ve been very, very lucky to have been profitable for the last three or four years, and to be able to use that profit to continue growing has been just incredible. I’ve never seen anything like it," Cameron said.
“We love being on the cutting edge of our industry, but also on the cutting edge with the software we use. Northbeam is helping us move into the direction we need to be going; it’s a marketing best practice bellwether.”
“If you would have asked me two years ago, I would’ve said the growth we’ve seen wouldn’t be possible… but it is, and it requires a really awesome partner. We’re grateful to the Northbeam team for that.”
The Future with YouTube Advertising
How to Navigate the Surge in Digital Ad Costs with YouTube Ads + Northbeam
Digital media is more expensive than ever. Meta’s rising Cost per 1,000 impressions (CPMs) alongside the skyrocketing cost per click (CPC) of Google Search, Shopping, and Performance Max (PMAX) are creating challenges for many advertisers.
Plus, since Meta is often positioned as an awareness and consideration channel as part of holistic marketing strategies, the rising costs are sending advertisers in search of cheaper “higher-funnel” media with similar (or better) performance.
Enter YouTube Ads.
YouTube advertising inventory can be purchased through Google Ads as “Video” ads. While Media buying teams are often intrigued about buying YouTube inventory, they rarely understand how to think about YouTube and measure its impact.
To be a better YouTube advertiser, you need to understand four things:
YouTube users rarely click on ads
Although you buy YouTube inventory with Google Ads, you can't think about YouTube through a traditional click-based lens like with Search and Shopping.
Clicks from YouTube tend to be a bonus side-effect of advertising, and shouldn’t be one of the primary key performance indicators (KPIs) considered when running YouTube campaigns.
The problem is that most digital advertising platforms are designed to measure media performance based on user click behavior. Without clicks, significant attribution is lost in campaign reporting.
Instead of clicks, your goal with YouTube is to generate the most engaged impressions and views for the lowest possible cost.
Since, when someone is on YouTube and sees an ad, they're likely already watching a video they chose to watch. The ad is interrupting their intended experience, which was to stay on YouTube and watch their content.
The ads you serve them need to be designed around this experience and the user's expectation. They probably have little desire to click on anything unless it's another YouTube video. So, expecting them to click on your ad will set yourself up to be disappointed and ultimately, unsuccessful.
When using Google Ads for reporting, Impressions, Views, View Through Rate (VTR), Cost per View (CPV) are some of the KPIs you should be referencing.
You can't rely solely on KPIs inside Google Ads
YouTube Ads are difficult to track within Google Ads using metrics like Conversions and ROAS. If you rely on these types of metrics to guide your media buying decisions, you’re probably leaving a ton of money on the table.
Combine Google Ads reporting with Northbeam.
Utilising Northbeam is a no-brainer. The long lookback windows alongside one-day view attribution and proprietary modelling allows advertisers to see and understand far more revenue attributed to YouTube than most other sources.
You also get a central source of truth for your Media Efficiency Ratio (MER) and Customer Acquisition Cost (CAC) metrics, since Northbeam uses your store’s revenue as an anchor to compare against all of your ad spend.
There’s a few other things to look at while scaling your YouTube campaigns.
▪ GA4: A free, useful tool for analyzing on-site behavioral reporting. Although you likely won’t get much in the way of revenue attribution for YouTube from GA4.
▪ Post-Purchase Surveys: As customers "How did you first hear about us?" directly on the order confirmation page, and list all of your marketing channels as options. This is a measure of customer-perspective marketing channel influence.
▪ Lift Measurement: Google surveys users on YouTube for statistically significant lift measurement. These can be expensive and should probably be your last choice.
▪ Search Lift Studies: With the help of your Google rep, you can identify measurable lift from Search, correlated to your YouTube Ads campaigns.
▪ Google Search Console: Branded search term data changes. If your YouTube campaigns are getting your brand to stick in peoples’ minds, they’ll inevitably go to Google and search for your brand.
▪Google Trends: Branded search term and category data changes (see real example below):
▪ Geo Study: One control location, one test location. Measure the difference in branded searches, traffic, and other signals that indicate users are being influenced by your YouTube Ads.
▪ MER/Customer Acquisition Cost (CAC): These should improve over time as more people, having become aware of your brand, start purchasing.
▪ Email/Short Messaging Service (SMS) Platform: Monitor opt ins. Typically, email and SMS opt ins will increase with more relevant traffic, which comes from YouTube mostly in the form of branded search.
Impression Frequency is a key component
Users probably won’t remember your brand or product if they see your ads only once or twice. It takes time for your message to stick inside people’s minds. If you're targeting the right audience with the right message, aim for an average impression frequency per user over a seven day period between four and six (measured within Google Ads).
That means you want every user to see your ads at least four to six times per week. User engagement tends to rise with higher frequencies, up to a point.
Try testing Ad Sequence campaigns for ensuring the same users are exposed to several different ads at a high frequency.
Creative quality needs to be high
Your ads won't perform if creatives aren't good enough. You can tweak the settings in Google Ads a million different ways, but your creatives are what make or break a campaign.
Here are some tips to getting YouTube Ad creatives right:
➡ Hook your viewers in the first three seconds
➡ Test running ads 30 seconds or longer
➡ Use audio and voice overs whenever possible
➡ Test different videos in different aspect ratios (square, vertical and horizontal)
➡ Use your brand prominently (logos, colours, messaging throughout)
➡ Use a call to action at the end of the video
➡ Use different videos to walk users through all steps of the customer journey:
- Brand feature video (introduce your brand to viewers in a memorable and engaging way)
- Product-focused video (features, benefits, product demo)
- Social proof video (customer UGC is great for this)
- Offer video (showcase your offer and why viewers should buy from you)
YouTube is not just another advertising channel; it's a strategic asset that, when used correctly, can significantly reduce costs while expanding reach. By focusing on engagement metrics, integrating advanced tools, and producing standout creatives, you can leverage YouTube to its fullest potential.
This Blog was written by contributing guest writer Craig Graham. Check him out below:
Craig Graham
CEO/Founder at Grayvault Consulting
https://www.grayvaultconsulting.com/
www.linkedin.com/in/craig-graham-grayvault
How Northbeam Gives The Kizik Team Answers to Questions They Didn’t Know They Had
Motion is magic, according to shoe brand Kizik. And we agree, especially if that motion looks like Kizik’s performance since it was founded: an upward curve reflecting a strong product and a powerful marketing engine.
Kizik’s comfortable and attractive shoes are unapologetically hand’s free; a novel innovation with multiple patents to its name and the growth to show for it.
Founded in 2017, Kizik entered a licensing partnership between Nike and its parent company, HandsFree Labs, in 2019, signaling the growing impact and popularity of slip-on sneakers as well as the unique value of its proprietary technology.
In 2021, Kizik started working with Northbeam to help capture data and better bring its revolutionary product to a direct-to-consumer audience.
We sat down with Jesse Semchuck, Kizik’s Head of Acquisition, and Brett Swensen, Kizik’s VP of Marketing to talk about how Northbeam supports Kizik to bring the magic of motion to a growing audience and make great marketing decisions along the way.
I didn’t know what I’d been missing
“I started using Northbeam in 2022 when I joined Kizik,” Jesse said. “And I didn’t really know what I’d been missing. At previous companies, we used last-click, Google Sheets, and Excel, but nothing that would show us the true lifetime value (LTV) like Northbeam does.”
But Brett was around during the transition to Northbeam. “We were maturing as a brand at the time and growing pretty rapidly and we started to expand our channels,” Brett said. “I remember meeting with the Northbeam team and laying out some of our struggles of growth and diversification and measurement. It was a challenging time for attribution and how to track success. Northbeam felt like a great solution to help us stitch our data points together and make better decisions as we grew.”
“It felt like the most sophisticated tool on the market. And as we talked through it and really understood the model and the science behind it, it made a lot of sense to take the leap,” Brett said.
Making the decision to adopt new software requires confidence in the data and abilities of the platform. It is a decision that impacts your whole team, so it’s important to think about software in the context of not only your current state but your goals for scale.
Brett agrees: “There wasn’t anything else that felt sophisticated enough, quite frankly, to meet our needs. We were one of the fastest growing direct-to-consumer brands out there and we needed support.”
“I can’t even count how many different brands I’ve recommended use Northbeam” Jesse said. “I think it’s a situation where you don’t know what you don’t know. And what has worked in the past might still seemingly continue to work, but you don’t know the insight that you’re missing until you can see it and use it. Now I just can’t envision a world where a brand would be ramping up direct-to-consumer spend and not using a tool like Northbeam.”
Northbeam provides answers to questions I didn’t know I had
“We’re in the business of selling shoes, but we also want to understand the customer journey throughout the sale,” Jesse said. “We don’t just want to sell them one pair. We want to sell them multiple pairs. We want them to become brand ambassadors. We want to engage with them long-term. And Northbeam helps us do that.”
Building brand loyalty is important for any DTC brand, and Northbeam is an integral partner in helping achieve a higher LTV. But how exactly does Northbeam help DTC brands achieve their goals?
“One of the metrics I love looking at is email sign up rate,” Brett said. “That’s a real leading indicator for success that maybe you wouldn’t otherwise look at. Maybe you’d shut off a campaign. But seeing that it can actually drive net new customers that show intent and maybe take a little longer to convert is powerful. That’s something people aren’t necessarily thinking about on their own.”
What metrics do you look at to see how customer intent plays out in the long run?
“I look at attribution model reports quite a bit,” Brett said. “We’re looking at clicks only as the first touchpoint, but we’ve also built some other views as well. We have a pretty big influencer program and it’s nice to be able to see breakouts for those different attribution models. So I’m constantly flipping between the different ones to determine what our spend mix should be going forward.”
“It’s great to get a pulse on the business week-over-week and see changes and trends,” Brett said. “Transitioning from the holidays to the new year is big for us, and it’s helpful to see year-over-year comps. The fact that we have years of data in there to look at how things have shifted over time has been a useful way to validate our decisions, especially amid the constant changes in the iOS landscape.”
“As we ramp up for the holidays, we spend a lot on prospecting and building audiences that look terrible in platform. Traditionally, other companies would shut that off. But we know by using Northbeam and the data we have that those usually back into sales come November and December.”
“It takes a lot of faith to run those campaigns when you’re banking on things paying off 30 or 60 days down the road. But having a tool that we can both look back at for historical context and look forward with to see how things play out eases some of that anxiety.”
Growing at the right pace is paramount. And access to intelligent data exactly when you need it is crucial to maintaining that pace.
“We don’t have a huge team,” Jesse said. “So when I’m looking at data, I’m looking for a quick answer to an important question. And oftentimes, I end up getting the answer to another three questions that I didn’t know I even had. This helps us move faster and helps us grow.”
Marketing supports a balanced ecosystem
Kizik has big marketing goals this year. Growth has been their focus over the last few years, but now sights are set on profitability and maximizing their existing traffic and audience.
“We’re looking at our retention efforts and being efficient with our spend more than we ever have before,” Brett said.
“We’re trying to find a balance now where we’re still pushing for growth and moving aggressively, but not moving so aggressively that we’re taking away from the bottom line. We’re looking at things like contribution margin that perhaps weren’t as big of a focus for us the previous few years. It’s a dance every day and every week to find that sweet spot.”
“In 2024, it’s a lot more about execution,” Jesse said. “For example, we want to be a little faster about our creative rotations and linking ads to inventory. Northbeam has tools that can help us with that.”
“We’re growing internationally; we’ve just signed with some distributors in two or three global locations,” Brett said. “And we’re opening five retail stores, plus wholesale. We’re becoming a lot more omnichannel as a brand and that requires deeper thinking and long-term planning, especially in the footwear space. We have a lot of operational excellence goals as a business.”
“A lot of what we do with digital advertising and what we track through Northbeam affects how people buy, it affects wholesale, it affects retail and Amazon. We have to continue to pound that drum that it’s an ecosystem that needs to stay in balance. Being able to tell that story is huge for the team.”
Staying on top of both trends and the competition is key to staying relevant in a space as saturated as footwear. And Kizik has been able to do so with the help of Northbeam.
“A lot of brands in our space and at our size do things in a similar way,” Jesse said. “I think we’re a little better at it, and that’s allowed us to grow. But I think to really separate from the pack, we have to execute at a very high level. That takes a lot of detail work and automated reporting, and Northbeam can help us do that.”
What is Media Mix Modeling (MMM)?
What is Media Mix Modeling (MMM)?
A History & Modern Application with Northbeam
In the ever-evolving landscape of marketing, the ability to measure the impact of various marketing activities is crucial. One of the most sophisticated and iconic approaches to measuring this impact is through Marketing Mix Modeling (MMM); which is more commonly referred to today as Media Mix Modeling. This method has proven its worth over decades, adapting to the changes in media consumption and technological advancements. This blog post dives into the history of MMM, explores its fundamental principles, and examines how modern companies like Northbeam are utilizing MMM to optimize marketing strategies in today's digital age.
The Origins of Marketing Mix Modeling
MMM has been a part of the marketing arsenal for more than 70 years. However, MMM began to gain significant traction in the 1960s when companies like Kraft Foods pioneered the use of this analytical method to launch products such as Jell-O. During this era, marketers had limited but rapidly-growing channels like television and magazines, and MMM allowed them to analyze the effectiveness of varying advertising levels across different regions and times of the year.
Fun Fact: Neil Borden, a famed professor of advertising at the Harvard Graduate School of Business Administration from the 1920’s to the 1960’s, popularized the term "marketing mix" in 1949 and laid the foundational theory underlying MMM. He, along with colleague James Culliton, came upon this term through describing marketing executives as “mixers of ingredients”—adjusting components like product, price, place, and promotion [the 4Ps] to meet customer demands. This concept evolved into MMM, which systematically quantifies the impact of these elements on sales and revenue.
How Does Media Mix Modeling Work?
At its core, MMM is an analytical approach that uses statistical methods to estimate the effectiveness of different marketing activities. By examining historical data, MMM isolates the impact of various marketing efforts on overall business performance. This model operates on two primary components:
Inputs
MMM considers a broad set of inputs, including:
- Marketing variables: Such as advertising spend, promotions, and product distributions.
- Environmental factors: Like economic conditions, seasonality, and competitive actions.
- Data quality: Ensuring the inputs are reliable and detailed enough to offer actionable insights.
Outputs
The outputs of an MMM are predictions and evaluations of key performance indicators (KPIs), typically focusing on metrics like sales volume or revenue. These outputs help marketers understand the return on investment (ROI) of different marketing strategies and guide future spending decisions.
Model Philosophy
MMM traditionally used linear regression models which assume that each variable has an independent and constant impact. However, modern MMM approaches often employ more sophisticated techniques, such as machine learning and AI, to capture the complex and dynamic interactions between variables.
Practical Applications of MMM
Media Mix Modeling can be applied to various strategic marketing activities:
1. Scorecards
These are retrospective analyses that help marketers understand what worked and what didn’t. They typically evaluate the ROI of past investments and are used to plan future marketing strategies.
2. Forecasting
MMM is used to simulate different marketing spend scenarios to predict their potential impacts on business outcomes. This helps in budget allocation and financial planning, answering questions like "What could be the impact if we increase digital spending?" This is where most marketers seeking “incrementality” would use MMM.
3. Optimization
Beyond forecasting, MMM helps in making real-time adjustments to marketing strategies, ensuring that budgets are allocated to the most effective channels. This involves understanding the law of diminishing returns and optimizing for long-term growth and profitability. This is where marketers will look at things like “cost curves”: visualizations of diminishing returns (and forecasted results) based on specific levels of spend.
MMM+ with Northbeam
Today, companies like Northbeam are revolutionizing MMM by integrating advanced technologies such as AI and machine learning. Northbeam’s MMM+ tool exemplifies modern MMM practices, offering features that are both innovative and practical:
- Real-Time Data Analysis: Unlike traditional MMM that often relied on outdated data, Northbeam’s tool analyzes weekly data for more timely insights.
- Demystifying Spend: Moving beyond the traditional understanding of where to place your dollars, the Northbeam MMM+ solution provides the ideal budget blend across all channels to maximize results for any and all KPIs you choose.
- Customizable Outputs: Northbeam’s MMM+ allows companies to focus on specific KPIs that align with their business goals, providing tailored budget recommendations based on these targets.
The Complementary Role of MTA
In addition to MMM, Northbeam recognizes the importance of Multi-Touch Attribution (MTA) in understanding the digital customer journey. While MMM provides a macro-level view of marketing effectiveness, MTA offers granular insights into the performance of individual campaigns and channels. Together, they enable a holistic view of marketing performance, guiding strategic decisions that optimize both online and offline channels.
Conclusion
Media Mix Modeling has come a long way from its early days with Kraft Foods and magazine catalogs. Its evolution has mirrored the complexity of the marketing world, growing from simple analytical tools to sophisticated models that incorporate real-time data and advanced statistical techniques. As companies like Northbeam continue to push the boundaries of what's possible with MMM, marketers are equipped better than ever to make informed decisions that drive business success in the digital age. Whether you are assessing the impact of historical marketing efforts or planning future strategies, MMM remains an indispensable tool in the marketer's toolkit.
The Best of Art and Science: Why Omnilux Uses Northbeam to Make Crucial Marketing Decisions
Omnilux is well-known today as a cult-favorite medical-grade LED light therapy for home use, but the company actually started in 2003 by selling medical devices directly to dermatologists and aestheticians for use in clinics around the world.
When Omnilux dove into the direct-to-consumer space, their customers changed, and so did their go-to-market challenges. Bringing this FDA-cleared, dermatologist-recommended therapy device to a new audience has been a process of learning and iteration since the beginning.
We spoke with Omnilux President Quinten Stanier and Chief Marketing Officer Layne Ergas about what the past years have looked like from a marketing perspective, and how they’ve been able to find success and unlock their target audience with Northbeam.
Holistic insights for confident decision making
“The direct-to-consumer space was a fairly new experience for us,” Quinten said. “To measure our marketing performance, we were using Google Analytics, in-platform data — Meta, Google, Pinterest, etc. — and Google Data Studio with several different API connections. It ultimately turned into a pretty unruly Frankenstein attribution tool that would often break. It was probably down 30% of the time. We were constantly fixing it and never really trusted the data that was coming in.”
Omnilux knew that it was doing well — it knew that its business was growing — but it couldn’t tie this performance back to specific marketing levers.
“We knew what was going in and what was coming out, but it was really about optimizing and understanding how we were scaling and if we were scaling as efficiently as possible.”
This desire for better data led them to search for a marketing intelligence platform that could give them the information they needed to feel secure in where they were spending each dollar of their marketing budget.
“We wanted to get a holistic view of performance and attribution and be able to see everything in one place so we could feel more confident in the data,” Layne said. “We evaluated other marketing intelligence platforms but Northbeam was the most fully integrated with the ad platforms that we were using, and it allowed us to be more flexible and customize reports. We have a fairly large international presence as well and Northbeam has enabled us to hone in on that side of our business.”
“Northbeam provides us with insights into our performance and shows us where we can improve, and the machine learning capabilities were better than the models that the competition was offering.”
“I also love the fact that Northbeam allows us to zoom in and zoom out down to the creative level, platform level, country level, and ad level — it gives us a lot of flexibility to better understand the customer journey.”
But access to data is one thing — how do you know what good performance looks like, and how do you get there?
“It’s a lot harder to hack media buying than it was maybe four or five years ago due to IOS changes,” Quinten said. “Understanding what’s a good thumb-stop rate, view-through rate, etcetera allows us to not just scale but inform future creative ideation and strategy down the road.”
True cross-functional value
But Northbeam hasn’t just facilitated the marketing team’s strategy, it’s also a go-to source of truth for other key team members across the organization.
“We have a lot of people in Northbeam pulling data,” Quinten said. “Whether it’s Layne, our CMO, or myself, the president. Our CFO looks there as well, and it allows for full functionality for the whole team.
“One of our goals has been to connect marketing and finance and have finance understand the marketing metrics that drive growth and profitability. Northbeam has been really helpful with that.”
Omnilux’s 2024 priorities
“One of our top goals this year is measuring performance by product type and product line so we can better understand how each product contributes to the overall business and where we can find scale,” Layne said. “Product mix evaluation is a really big focus for us this year, and we’re looking to get much more granular at the platform and geography level as well.”
“We’ve been working with Northbeam to get those views set up and they’ve been great at facilitating that project.”
“Another significant focus for us is investing in affiliate and influencer marketing. We engage with both traditional affiliates and PR-type affiliates. Northbeam has been instrumental in helping us understand how these distinct types of partnerships contribute to driving awareness and conversions.”
Girl Math or just simple ROI or both? You decide!
Omnilux has a broad range of channels across the customer journey funnel where it can reach its audience. “We’re kind of everywhere, so it’s really been about getting a better view of all the different platforms.”
“This year we’re doing a lot of landing page testing and investing more in personalized experiences for various ad campaigns and influencer partnerships,” said Layne. “Another area of focus for us is on subscriptions and retention. We have a range of skincare products where we offer subscriptions and we’re trying to better understand customer LTV by product and offer type.”
“Given the scale that we’ve observed over the last couple of years, the question we keep asking ourselves is: where is our next dollar best spent?” Quinten said. “And where are we hitting a point of diminishing returns? What is a ceiling and what is a glass ceiling?”
MMM+ blends art and science
With MMM+, Omnilux hopes to answer these questions and more.
“The MTA tool is great for understanding what’s happened,” Quinten said. “The MMM+ tool will help us better understand what’s going to happen and give us a range of outcomes to better run the business… it allows us to be smarter marketers and make actionable decisions with data in real-time.”
“We started advertising on TV in August and that has expanded our capability to spend on other channels because of the increased brand awareness,” Quinten said. “Just because something isn’t easily attributable with a click doesn’t mean it’s not producing positive outcomes for the business. We're leveraging MMM+ to better understand the relationship between TV spend and its ability to increase scale on other channels.”
The metrics that matter
Beyond MMM+ and MTA, the Omnilux marketing team makes sure to keep up with certain metrics on a regular basis to understand their marketing performance against goals.
“I look at spend and MER mainly,” Quinten said. “And if I know we are on budget and hitting our MER target, CAC and other metrics are typically strong.
“But it gets more granular as the responsibilities on various teams become more definitive. Our creative director, for example, is looking at thumb-stop rate and channel-specific data related to certain creative types.”
An unbiased outside perspective
Northbeam’s platform provides data, and our customer service provides insights into that data that a customer may not otherwise see.
“Our team is so constantly entrenched in the silo of Omnilux,” Quinten said. “Sometimes it's good to have an outside perspective that can look at our data in an unbiased way. They can make calls and ask questions like: why is this number this way? There are always valuable nuggets that challenge the way we think about the business and there’s great value in that.”
“We have access to our own data, but Northbeam has access to millions of dollars of spend every month across a ton of different categories. They have a good snapshot of the entire industry to an extent. That definitely helps when we have our conversations with them.”
“It’s just nice to have a team of people that we can check in with and ask for advice and recommendations,” Layne said. “The Northbeam team has been a huge help with getting our affiliate and influencer programs filtering in the way we want them to — that was a process that was really hard to wrap my brain around because of the various affiliate types we engage with and manage through a variety of platforms.”
“I feel like I can always reach out to Northbeam if I need advice or if I want to look at something a certain way but don’t know how to set up a view,” Layne said. “They are truly helpful with that.”
The 2024 Guide to Incrementality
Have you or a loved one recently been inundated with messages about incrementality? Can’t figure out if this is the latest fad or a legitimate evolution in analytics? In today’s fast-paced and data-driven marketing landscape, staying ahead of the curve is essential for success. In a sea of buzzwords and trends, incrementality has emerged as a contender with significant implications for marketing strategy and performance measurement. In this article, we’ll cover what exactly this purported “silver bullet” is, and what marketers need to know.
Section 1: What is incrementality?
Incrementality refers to the measure of the additional (or incremental) lift that a marketing initiative provides compared to baseline expectations. In other words, incrementality seeks to answer the question of “what additional value did my marketing activities contribute beyond what would have occurred naturally?”
Incrementality helps answer this question by comparing the performance of those who were exposed to marketing campaigns (treatment group) with a similar cohort that did not see those same campaigns (control group). By measuring the difference in outcomes between the two, we can determine the additional value generated by the campaign.
You’ll notice this is essentially describing the scientific method; like scientific experimentation, incrementality analysis begins with hypothesis testing, where marketers conduct randomized experiments to isolate the incremental impact of campaigns. This emphasis on empirical evidence is why incrementality is so revered: of all the popular analytical methodologies employed by marketers, incrementality is the only one that establishes a causal relationship as opposed to simply explaining the correlation between the independent and dependent variables.
These experiments can take many forms, but they all generally involve some way of randomly selecting users into a treatment or control group. A geographic experiment, for example, would segment the country into similar pockets and then randomly assign them to a treatment and control group. This is also called a holdout test since we’re withholding marketing exposure from a portion of the target audience.
Section 2: How is incrementality used?
Since we can rely on incrementality to establish a causal relationship, it’s often a great tool to validate results or justify marketing spend. Unlike other common marketing analytics tools that mostly conduct correlational analysis, this gives our data more credibility. The next time your finance department asks why they should keep spending dollars on a marketing initiative, you can simply refer to the results of your latest incrementality experiments with confidence. Without the marketing spend that you’re counting on, the company would be leaving money on the table. This really helps change the message from looking at marketing as an expense, and more as an investment.
Incrementality is commonly used for budget allocation purposes because we can run various types of randomized experiments on our top channels and campaigns to test if those marketing dollars are truly driving new revenue instead of cannibalizing existing sales. Not sure if Facebook Display is actually driving positive results for your business? Design an incrementality test and find out. Repeat the process for your top channels and let those results help you decide how to divvy up your marketing spend. Tactically, incrementality is also well-suited for channels that your team doesn’t have much experience with. Let’s say your team wants to spend more on a new channel, but you’re not sure if additional spend will generate the necessary ROI. If we run a pilot and conduct incrementality analysis on the results, we can extrapolate and forecast what a scale up would look like.
Section 3: What are some challenges of incrementality?
While incrementality testing is a powerful tool for measuring the true impact of marketing efforts, there are some challenges and limitations you should be aware of.
- Since incrementality involves experiments, a big challenge is ensuring the sample size is sufficiently large enough so that our results are statistically significant. Small sample sizes can lead to low statistical power, making it difficult to draw meaningful conclusions from the results.
- It’s impossible to remove all of the “noise” surrounding a marketing campaign so an omitted or hidden variable could dramatically skew your results. Did you hire more sales reps while the campaign was running? Did you increase more communication with the system so more sales occurred? When you randomly assigned treatment and control groups, were there hidden mechanisms that prevented true randomization such as inherent biases in the selection process? Remember that incrementality attempts to show causation, which means your chances of getting a bad read increase if there are important omitted variables. Incrementality also struggles to account for spillover effects (marketing efforts often impact individuals who were not directly exposed to the campaign, through word of mouth and other means) which are particularly important in digital marketing where these cross-channel interactions are more common.
- Incrementality is not a low maintenance, set it and forget it, type of tool. To get the most out of this technique requires a skilled marketing team that has a larger strategy and framework (in the form of a learning agenda or testing plan) that will guide what to test, when to test it, and most importantly, how to interpret those results and make actionable decisions off those insights.
- Note that incrementality experiments generally run for weeks not days, during which we cannot make changes to our campaigns because it will impact results. That means no swapping out of creative, A/B testing, or introducing any other variables while experiments are running. This is not ideal for larger channels because it requires us to disrupt our business as usual operations to accommodate a statistical technique.
Section 4: Incrementality vs. other popular analytics methods
Incrementality is often grouped together with other popular measurement methodologies including multi-touch attribution (MTA) and marketing/ media mix modeling (MMM). While all 3 can help marketers understand the effectiveness and impact of their efforts, they evolved for different reasons and thus are best suited for different things.
Before the digital advertising schism moved us away from traditional advertising channels like linear TV, MMM was the primary form of media measurement because it doesn’t rely on granular data. MMM excels at capturing older channels and the longer-term impact on revenue by leveraging historical data (both media and macroeconomic data) to predict future sales based on past behavior. However, MMM is very expensive to spin up and often takes months to deploy with a reporting lag after each quarter to update the model, so other methodologies sprang up to make up for these deficiencies.
MTA came next with the rise of digital media, along with detailed user tracking which gave marketers more granularity in terms of insights. By leveraging this richer user data to track customer journeys, MTA was far better adapted for the complex and cross-channel landscape and is the most bottoms-up approach as it collects data at the individual level and then aggregates this to create a model. However, with the rise of consumer privacy regulations, MTA may become less effective in the future if user-level tracking goes completely away (still looks to be sometime in the future given Google recently delayed cookie deprecation again) so marketers began to turn to incrementality experimentation.
You’ll notice that none of these methodologies are silver bullets; each have their own distinct advantages and disadvantages. The truth is that the best marketing teams leverage some combination (or all) of these 3 to guide their decision making because this can make up for individual weaknesses. Incrementality is simply the latest in a long line of measurement methods that marketers are using to try and gain an edge. Although the promise of finding causal relationships is tantalizing, the challenge of setting up experiments correctly keeps many companies from effectively using incrementality. If your audiences somehow overlap, your segment sizes are off, or a number of other possibilities somehow skew your results, you’re using incorrect data to make very impactful decisions (potentially for the worse).
If you want to learn more about the MTA, MMM and how they compare to incrementality, we wrote this awesome deep dive that goes into much more depth on what MTA and MMM are, and also provides a detailed comparison of the three. Click here to check that out.
The Marketer’s Guide to AI
You don’t need me to tell you that AI is a game changer. Just as the Industrial Revolution reshaped the way we worked in the 18th and 19th centuries, the emergence of artificial intelligence moves us into a new era. AI represents a paradigm shift in the way we interact with machines and process data, promising to upend industries, economies, and our daily lives. Rapid advancements in AI technology have already begun to change the way businesses operate, with automation and machine learning algorithms (more on these later) streamlining manual processes and augmenting our existing capabilities.
As marketers, AI offers us unparalleled opportunities for innovation. From personalized customer experiences and ultra-targeted advertising campaigns to predictive analytics and campaign automation, AI empowers us to unlock new channels for growth. In this guide, we’ll go over what artificial intelligence is, including the most crucial concepts you’ll need to know as a modern marketer in 2024 and beyond.
What is Artificial Intelligence?
Broadly defined, artificial intelligence (AI) is a field of computer science that focuses on creating systems and machines that can perform tasks and solve problems that would typically require human intelligence. Such tasks can range from recognizing patterns, making predictions, learning from experiences, and even understanding languages. At its core, AI aims to recreate synthetic, human-like intelligence in machines. This generally involves developing algorithms and models that enable computers to perceive their environment, reason about it, and make decisions accordingly. Digital assistants (Siri, Alexa), GPS guidance apps, self-driving vehicles, and generative AI tools (such as Open AI’s ChatGPT) are just a few examples of how artificial intelligence has changed the way we live and interact with machines in our daily lives.
Since AI aims to provide machines with human-like capabilities, it has the potential to help us save significant amounts of time by filling in for tasks that we’re not ideally suited for. Unlike humans, AI can sift and sort through large amounts of data to identify patterns that may have been missed by the human eye, automate otherwise tedious and manual tasks, and even mimic our language patterns (chatbots and LLMs). This has helped us tackle complex problems previously thought to be impossible due to the vast amounts of processing power required to solve them, such as using machine learning models to examine tissue samples and identify abnormalities at the cellular level, assisting pathologists in diagnosing diseases more quickly and accurately. In the financial sector, AI and ML algorithms have been invaluable in detecting fraudulent and criminal transactions by analyzing patterns and anomalies in large datasets to ensure the security of transactions. AI has even been used to identify suspicious betting patterns in the NBA, leading to a lifetime ban of a former player.
AI is generally split into two broad categories: weak AI and strong AI. Weak (or narrow) AI refers to AI that automates specific tasks. Although it can outperform humans, this is only true for the specific thing it was designed for such as recognizing patterns. Almost all of the AI systems that exist today are examples of weak AI, including marketing automation platforms, chatbots, and social media algorithms. Strong AI, or sometimes referred to as artificial general intelligence (AGI) is a type of AI which possesses human-like intelligence and adaptability, solving problems it’s never been trained for. When we see dystopian sci-fi films warning us of AI (Skynet anyone?), we often see strong AI that has pushed past human intelligence into a type of superintelligence. It’s important to note AGI is mostly hypothetical at this point, and it’s unclear whether we can ever create it because AGI would require machines to have consciousness and self-awareness, far beyond our current technological abilities.
Machine Learning: Continuous Improvement
The main approach to building AI systems is through machine learning: a subset of AI that focuses on developing techniques that enable machines to learn from large amounts of data by identifying relationships and patterns in the data. A ML algorithm uses various statistical techniques to “learn” to get progressively better at a task without having to be explicitly programmed for that task. The algorithm uses historical data as an input (such as previous marketing spend) to predict new output values (future expected sales revenue). ML uses a few different types of “learning” techniques including supervised learning (expected output for the input is known due to labeled data sets), unsupervised learning (expected outputs are unknown due to unlabeled data sets), and reinforcement learning (algorithms learn by interacting with an environment and receiving positive or negative feedback). As marketers, there are four types of machine learning methods that we should be aware of.
Neural Networks, the most popular way of conducting machine learning, are a series of algorithms that process data by mimicking the structure of the human brain. Neural networks consist of layers of interconnected nodes that analyze and pass information between each other by working together to decipher large datasets. They generally have an input layer which receives the raw data, followed by several layers which each apply some sort of data transformation before passing the result to the subsequent layer. After making its way through these processing layers, a final layer will produce the network’s output which could be a classification or some type of prediction. By adjusting the strength of connections between these nodes and layers, neural networks learn to recognize complex patterns within data, make predictions based on new inputs (that it has never seen before), and even learn from mistakes.
Deep learning refers to a type of multi-layered neural network that uses a large number of hidden layers (think dozens or even hundreds) that process input data to capture increasingly abstract features of that data to identify complex patterns. The “deep” in deep learning refers to the number of layers in the neural network - the more layers, the “deeper” the network. This makes deep neural networks well-suited for certain narrow AI tasks such as recognizing images, understanding human speech, and translating between different languages. As marketers, there are two key applications of deep learning and neural networks that you should know about:
Computer vision involves the automatic extraction, analysis, and interpretation of useful information from media formats such as images and videos. Deep learning has significantly advanced this field with the development of convolutional neural networks (CNNs), which are specifically designed for processing grid-like data by detecting spatial hierarchies and features like edges, textures, and objects. Computer vision can help digital marketers in several ways:
- Content Management: Computer vision can automatically tag creative assets, for instance categorizing by product lines, making it easier to find, store and retrieve visual assets related to specific products.
- Content Creation: GenAI (much more on this in the next section) can create realistic images from textual descriptions, convert sketches into photos, or even generate high-quality artwork. Instead of having to pay for expensive photo shoots, marketers can now generate or modify visual assets with the click of a button.
- Customer Engagement: New technologies such as visual search allows customers to search for products using images instead of text. Platforms such as Pinterest and Google Lens enable users to find similar items, thereby enhancing the shopping experience and driving incremental revenue. Augmented reality (AR) can also be used to allow customers to visualize products in different environments, which is particularly useful in certain segments such as fashion and home decor where an initial trial is crucial before making a purchase.
Natural Language Processing (NLP) focuses on the interaction between computers and human language, enabling machines to understand and even generate human language. Deep learning models, especially transformers (ChatGPT stands for Generative Pre-Trained Transformer), have pushed the boundaries of what is possible in NLP. Marketers already know that deep learning models like ChatGPT can create blog posts, product descriptions, social media copy, but here are a few other relevant use cases powered by NLP:
- Chatbots and Virtual assistants: AI-powered chatbots can handle customer queries in real-time, providing instant support and improving the customer experience without needing to hire a large human team. These systems use NLP to process, understand, and generate responses to customer inquiries, freeing up human agents for more complex tasks. A good example would be the various chatbots used by major airlines, or the pop-up chatbots found on SaaS and eCommerce websites.
- Sentiment Analysis: NLP can pick up on public sentiment and gauge how customers are responding to marketing activities. This includes analyzing customer reviews, social media posts, and other forms of feedback to figure out how to respond appropriately. This generally involves tracking the ratio of positive and negative sentiments so marketers can respond to customer concerns promptly.
GenAI: Fear for your job?
Let’s talk about the most hyped form of AI: Generative AI. GenAI is a broad label describing any type of AI that can produce text, images, video or audio clips by learning from training data and generating new, unique outputs that mimic the statistical properties of the training data. GenAI uses NLP and machine learning to create this new content, but it differs from other ML applications because its purpose is to produce wholly new things as opposed to simply recognizing and classifying data like other narrow AI (search algorithms for example). Two prominent types of generative models power most of the tools we use today:
- Generative Adversarial Networks (GANs): a class of models designed to generate synthetic data that closely mimics real data. Introduced in 2014, GANs consist of two main components: a generator neural network that generates new data samples, and a discriminator neural network that evaluates the authenticity of the generated data. The training process is “adversarial” because these two neural networks are asked to play each other in game-like scenarios until the discriminator can no longer reliably tell the difference between real data and synthetic data produced by the generator.
- Transformer-Based Models: Most people think of ChatGPT when they think of GenAI, which is a transformer-based model that uses an attention mechanism to weigh the importance of different words in a sentence. These models undergo a two-phase training process: in pre-training, the model is given a massive amount of text data to learn language patterns, grammar, and so on. During this phase, the model learns how to predict the next word in a sentence, given the words already preceding it for context. Afterwards, the model is fine-tuned on a smaller dataset to specialize in specific tasks such as text summarization, translation, or answering questions.
GenAI has the capability to significantly enhance marketing operations and strategies by allowing marketers to create personalized content at scale, improve customer engagement, streamline processes, amongst countless other use cases. Personalization and customization of content have become table stakes in an increasingly digital media landscape; generative AI offers us an avenue to quickly and easily produce large amounts of content to meet this demand for authentic experiences. Consider the immensely successful “Share a Coke” campaign: Coke found an ingenious way to create personalization for the masses, but it was a massive creative and logistical endeavor to design and produce hundreds of different cans. With GenAI, we can produce similar results without hiring multiple agencies and partners to pull it off. For example, Carvana used GenAI to generate 1 million+ unique videos that reminded customers of the day they met their Carvana vehicles. The marketing team realized that people often have special bonds with their cars, so they used machine learning models to collect basic data such as the car’s model, color, year, purchase data, and location - enriched with cultural events around that time and place to create bespoke “joyrides” to commemorate and celebrate that bond. The best part? It took less than half a workday to process and create all the videos. With GenAI, companies without the resources of Coca-Cola can pull off these types of campaigns within a reasonable amount of time and tighter budget constraints.
Brands and companies across different sectors have taken notice and are using GenAI to improve and increase customer engagement. Spotify is piloting AI-driven voice translation by providing additional languages for top podcasts, all in the podcaster’s voice. The technology leverage’s OpenAI’s voice generation to match the original speaker’s style, making for a more natural listening experience than traditional dubbing so Spotify is able to bridge language barriers and cater to international markets far more effectively than before. This is a step above the mundane chatbots and virtual assistants that have begun to dot the eCommerce landscape, but even these are seeing an evolution. Recent developments in multi-modal AI will further expand what AI chatbots can do to solve customer problems. Imagine a customer submitting a photo of a faulty product and the AI chatbot quickly and accurately providing a resolution based on previous customer service interactions fed into machine learning models. Furthermore, these bots are available 24/7 and can handle large numbers of inquiries simultaneously, offering immediate assistance which reduces wait times and improves customer satisfaction with your brand.
Arguably the most valuable aspect of GenAI for marketers is the potential to enhance creativity and the quality of our output while also reducing the cost of cognition to produce that work. This transformative technology spans across a wide variety of tasks: GenAI is already assisting marketers with crafting blog posts, analyzing consumer feedback, and designing new ad concepts for A/B tests. For creatives, the brainstorming process can be made much easier with GenAI by feeding a basic prompt to get a wide range of ideas. AI can suggest numerous variations on marketing activations, copy, taglines, all from a given theme or concept. Anyone who has experienced creative or writing blocks knows how invaluable it can be able to expand the pool of ideas quickly. Overcoming this “first-mile” problem of finally putting the proverbial pen to paper has caught the attention of major advertisers, as the CEO of WPP recently noted that GenAI can bring up to 10-20 times savings.
As marketers, we should be excited for the potential of GenAI to augment our own creative capabilities. Interestingly enough, a growing number of workers are instead concerned: a recent Forrester survey found that 36% of workers were worried about losing their jobs to AI and automation within the next 10 years. In our opinion, it’s still too early to worry about AI replacing us as knowledge workers. AI is still too prone to errors and generating false information, commonly referred to as “hallucinations.” You may have heard of the 2023 court case Mata v Avianca, in which attorneys submitted a brief researched using ChatGPT that unfortunately contained multiple fake extracts and case citations in a New York court. The lawyers were unaware that ChatGPT can hallucinate and failed to check that all the extracts and cases actually existed, and consequences were swift. The court dismissed the case, sanctioned the lawyers and fined their firm for acting in bad faith. Until GenAI can get to the point of becoming indistinguishable from real human output without errors, we’ll all unfortunately still have to answer emails and check Slack.
Final Takeaways
Whether we like it or not, AI will be top of mind for many marketing leaders for the next few years. With the ability to streamline processes, personalize customer experiences, and generative innovative content, AI offers marketers exciting opportunities for growth and efficiency. GenAI will become even more of a force multiplier in the future, so our best advice is to become comfortable and adept with these tools. Despite concerns about job security, the current capabilities of AI serve more as an optimizer rather than true replacement for human ingenuity and creativity. The next generation of marketers will be very skilled at partnering with machines; make sure you don’t get left behind. It’s crucial to stay informed about advancements and integrate these tools into your workflows and teams.
Media Mix Modeling (MMM): Build or Buy?
In the wide world of marketing analytics tools, Media Mix Modeling (MMM) stands out as particularly powerful. While multi-touch attribution (MTA) tools can go deep on the effect of individual touchpoints, MMM gives you thorough information about the performance of individual channels.
MMM goes beyond the day-to-day to give you a top-down look at performance so you can forecast, budget, and plan accordingly.
Read more: Why You Should Care About MTA, MMM, and Incrementality.
With more and more companies transitioning to MMM insights, the question arises: should you build a solution in house, or buy one off the shelf?
The Case for Building
Here are the three primary reasons you may want to build an MMM solution in-house:
- Customization
- Control
- Cost
Customization
The main benefit of building an MMM solution yourself is that you can decide exactly what it does and how it does it. You have full control over the scope and specs of this tool, and you can change that scope whenever you need to as your needs change.
Of course, this requires resources, but some companies may find that those additional resources are worth it in order to prioritize their own product needs over the potential needs of other customers with an off-the-shelf solution.
Existing solutions may not always align perfectly with your needs. If that’s the case, building an MMM solution may be right for you.
Control
In a similar vein, building your own solution gives you complete control — not only over the scope of the product, but also over your company’s (or customers’) data.
If you’re in a heavily-regulated industry like healthcare or financial services, you may have more peace-of-mind knowing that you can control your security protocols from start to finish.
Building an in-house solution offers you complete control over proprietary or sensitive information, so you don’t have to worry about potential security issues down the line.
Cost
Building an MMM tool is more expensive up front (more on that below), but if you expect your data needs to stay the same over the years, you may very well unlock cost savings in the long-term.
Once your MMM solution is developed, the cost of maintaining and updating it is significantly lower, and it may prove to be more affordable than an enterprise-grade solution.
This is especially true for large enterprises that deal with massive amounts of data and have the deep in-house expertise to manage a tool like MMM themselves!
The Case for Buying
There are four main reasons that you may consider buying an MMM solution off the shelf:
- Convenience
- Expertise
- Scalability
- Resources
Convenience
Let’s be real, it’s way more convenient to buy a ready-made solution than to build one in-house. Instead of kicking off an expensive, long-term process that involves project management and maintenance, you can do your research, take a few demos, make a decision, and be up and running in a matter of weeks — not months or years.
Buying a solution allows you to skip the planning, development, and testing time and get straight to the actionable insights.
This convenience is hard to beat, especially for rapidly-growing teams that need to focus all their attention on the task at hand: building their business.
Expertise
If you don’t have the in-house expertise to build an MMM product and/or interpret its outputs, buying a solution may be the best option for you. Most MMM solutions come with not just the product itself but a dedicated team to help you get started and answer any questions you have along the way.
Consider how long it would take (and how much it would cost) to hire or train the expertise needed to build, maintain, and make the most of an MMM product.
With a team like Northbeam, you get tailored recommendations, strategic insights, industry best practices, and more included in the cost of doing business.
Scalability
As your business grows, so will your data and analytics needs. The MMM model that worked for you last year may not work for you this year, or next. There is serious maintenance to in-house MMM, and that maintenance will involve necessary updates and upgrades to keep up with your growing business needs — and the changing marketing landscape.
What if privacy measures restrict your existing sources of data? What if new platforms arise that you need to incorporate?
Off-the-shelf solutions are built to scale with you, and come with a dedicated team that can continue to tailor the MMM product as needed. If you foresee your business growing and changing in the coming years, a ready-made solution may make more sense for you.
Resources
We’ve touched on resources already throughout this guide, but let’s break it down further.
Here are some questions to ask yourself to assess whether your team is adequately staffed to build an MMM tool in-house:
- When do you want to be up-and-running with an MMM solution?
- How many people (engineers, project managers, marketing experts, data analysts, etc.) would you need to dedicate to this project in the building stage?
- How many people (and how much of their time) would you need to dedicate to the maintenance of this product?
Consider doing some back-of-the-napkin math to put together a simple formula:
Cost of Building an MMM Tool In-House = Resources x Months x Salary Per Month
Here’s a simplified example:
Cost of Building an MMM Tool In-House = 1 Engineer and 1 Marketing Data Analyst x 6 Months x $12,500/month = $150,000
The above would be the initial cost to build an MMM tool — not the total cost. It doesn’t account for regular maintenance and updates. You may want to develop your own formula that takes into account the resources you would need to support this product.
Depending on your team and needs, it's likely that an off-the-shelf solution will prove more resource-effective.
Every Company is Different
Every company and use case is different. Depending on your particular situation, building an MMM tool may make the most sense. If that’s not the case, or if you want to talk it out with a professional, our team is here to help. Schedule a time to discover our MMM+ tool and how it can help scale your business to the next level.
How The Oodie Tracks the Metrics That Matter with Northbeam
The Oodie started with the wearable blanket. Now it's on a mission to make the most snuggly, cuddly, and softer-than-soft comfort wear in the world. It’s the top-rated wearable blanket in the world, but it’s also home to other cozy wearables, from sleepwear to beach outfits to pet accessories and beyond. .
In 2022, The Oodie brought on Northbeam as their marketing intelligence platform to help them grow in key markets and mature their marketing data.
We spoke to Sigrid Lundborg, The Oodie’s Head of Paid Media, about how her Northbeam usage has evolved and what value she gets out of having accurate, first-party data at her fingertips.
Why Northbeam?
There comes a time in every company’s life when they need better data to unlock better decision-making. That time for The Oodie was March 2022.
“We were looking for a partner with a more advanced attribution tool that leverages first-party data to provide deeper insights into the customer journey,” Sigrid said. “Given the rapidly-changing landscape, having a solution that is effective both now and in the future is extremely compelling.”
“Northbeam gives us that holistic view over our acquisition efforts. It helps us really understand what impact these channels are driving and the near-real-time data gives us the ability to pivot and scale fast and hard with data that we can trust.”
The metrics that matter
Sigrid spends a lot of time focusing on new versus returning customer metrics.
“Aside from metrics like CAC and ROAS, being able to fully analyze our percentage of new visits and what customers are being driven from which platforms from a first time or returning perspective has enabled us to evolve our strategy greatly.”
When she’s not doing incrementality testing, that is.
“As a paid team, we’re really looking to level up our sophistication in terms of performance metrics. So we’re digging into understanding the incremental value and what that means for what marketing is doing so we can maximize our efforts.”
“We’re currently conducting a lot of incrementality tests,” Sigrid said. “This helps us look at the campaign level to understand what’s really happening and what activities are actually driving incremental value. Is it a sale? Is it a new product launch? Or is it business as usual?”
The Oodie goes global
Breaking into a new market is tricky — Sigrid and her team would know. They manage The Oodie’s paid media around the world, from Australia to New Zealand all the way to Europe, the United Kingdom, The United States, and Canada.
“It’s quite a few markets, and they’re all at a different stage of their life cycle in terms of market penetration,” Sigrid said. “Some are quite mature and others have different brand awareness levels. We do need to tailor our approach somewhat and Northbeam has been a big help.”
“With Northbeam, we can have that comprehensive view across the customer journey and understand all the different touchpoints and channels and what’s happening in each, because what happens in one market doesn’t necessarily happen in another market. They’re all quite unique.”
“We test different channels and find that they do have different uptakes in different markets, probably because those platforms themselves have different penetration across different markets. Which is all quite interesting. Different products and designs also perform differently across geographies.”
Customer service you can rely on
Aside from trustworthy data she can use to unlock decision making, Sigrid cited customer service as one of Northbeam’s main selling points.
“The team has been phenomenal in their support and aligning with our goals to ensure that we’re using Northbeam to its full potential,” Sigrid said. “They’re very efficient at answering any questions that we have, and they’re always open to assisting with any data pulls or reports.”
“Northbeam has former media buyers supporting us, which makes it much easier to gain actionable insights and view the data in new and interesting ways. Their expertise helps us interpret metrics and trends, allowing us to make more informed decisions.”
“It feels like Northbeam is an extension of our team, in a way,” Sigrid said. “I feel really confident asking them any questions that we might have, or even just asking for their views or opinions on some of our reports or approaches. Having a team that is so knowledgeable in this space has been a massive benefit.”
What is Marketing ROI and How Can You Improve It?
Marketing return on investment (ROI) is a crucial metric to understand your marketing performance. This guide will cover what it is, how to calculate it, what to consider, and how to improve your ROI if it's not performing as you’d hoped.
Put simply, ROI measures the return generated from marketing activities relative to the amount of money spent. Calculating and understanding the ROI from all of your different marketing efforts can give you a great picture of how you’re performing — and help make sure you’re getting the best bang for your buck.
What is marketing ROI?
Marketing ROI is the profit or loss generated by different marketing activities. It is a critical component of understanding performance, and can help support profitability for your organization. It can enable you to make decisions about which campaigns to invest in and which to turn off, and support you as you make the case for budget for activities that really move the needle.
A high ROI indicates that a marketing campaign is generating a significant return compared to its cost, while a low or negative ROI suggests that it may be time to go back to the drawing board.
By calculating and analyzing your ROI, your team can more accurately allocate budget, measure performance, optimize strategy, and communicate its performance and needs with stakeholders.
How to calculate marketing ROI
You can calculate marketing ROI with a straightforward formula:
Marketing ROI = Profit from marketing / marketing costs
So if your LinkedIn ad campaign cost $1,000 and it generated $2,500 in revenue on your website, your ROI would be calculated as:
Marketing ROI = 2,500 / 1,000
Marketing ROI = 2.5
Some people prefer to express marketing ROI as a percentage. The formula would be the same except you would multiply the output by 100 and your marketing ROI would equal 250%.
A good marketing ROI varies depending on the industry, product, and type of campaign. You should work with your team to set a benchmark for ROI over time and try to improve it as you progress. For reference, a marketing ROI of 5:1 (meaning you earn $5 for every $1 you spend) is considered a solid rule of thumb.
What to consider when calculating ROI
It’s important to note that ROI changes depending on your time frame. Perhaps your LinkedIn ad generated $2,500 in its first day, but on a two-week timeframe it generated closer to $7,500. Your 14-day ROI would then be 7.5x your initial spend — that’s great!
ROI can also vary depending on how you define marketing costs. You should include direct spend in the denominator, but you can also include things like software and tool costs, personnel salaries, and more. This could be helpful if you are calculating ROI not on a campaign level but on a macro level, across all your campaigns for a given time period.
For example, If you wanted to know the ROI of everything you did last quarter or last year, you may decide that including salaries and tools will give you the most accurate picture.
Finally, attribution can be tricky with ROI. How do you know if someone clicked on your LinkedIn ad after they saw your Google ad first? How do you know which campaign to attribute marketing revenue to?
To complicate the picture, different platforms may take sole ownership of conversions when, in reality, a confluence of factors, impressions, and touches ultimately leads to a purchase. Tools like multi-touch attribution (MTA) can help with attribution questions like this.
When calculating your marketing ROI, it’s important to keep in mind timeframe, attribution issues, and any relevant marketing costs to get the clearest picture of performance.
How to improve your marketing ROI
If your marketing ROI isn’t where you want it to be, there are a number of interventions that can help you get back on track.
1. First, start by setting clear objectives. Your marketing ROI may be below the goal ratio of 5:1, but perhaps it is standard for your industry, product, or channel. Get a holistic picture of performance and set objectives based on your historicals — not numbers you read online. Use your current ROI to set a reasonable and attainable goal for the near future.
2. Consider using advanced analytics and marketing intelligence tools to track and analyze campaign performance so your ROI isn’t skewed by platform analytics or incomplete attribution data.
3. Focus on high-ROI channels — identify the channels that consistently deliver high ROI for your marketing efforts and allocate more budget to those channels.
4. Test and optimize your lower-performing channels to see if you can improve your ROI through small, consistent, and creative iterations.
5. Try to improve your customer targeting and refine your target audience. Effective customer targeting reduces wasted spend and increases the likelihood that leads will convert to customers as a result of your marketing efforts. Strong data and marketing intelligence platforms can help you refine your targeting.
6. Optimize your conversion paths. Leads are typically taken to a page when they click on your ads, so ensure that page is optimized for conversions. A seamless user experience and clear call-to-action (CTA) can significantly improve the success of your marketing campaigns — and your ROI.
How to think about ROI
Marketing ROI is a vital metric for any strong marketing team. It enables you to fully understand the impact of each marketing dollar and make informed decisions about spend. Use the definitions, formulas, considerations, and strategies for ROI improvement in this guide to help you get a firm grasp on ROI concepts so you can use them to your advantage and improve your overall marketing effectiveness.
In a rapidly-evolving marketing landscape, staying focused on the efficiency and success of your investments is key to maximizing returns and improving your performance.
What is Marketing Intelligence Software?
Northbeam has been called everything in the book: a marketing analytics platform, an MTA tool, an attribution platform, an ecommerce analytics platform, a data tool, and about a million other phrases. While none of these titles are untrue, we prefer the term “marketing intelligence” to capture our breadth of features and functionalities
As the marketing landscape shifts and grows, a new need arises for platforms that will synthesize multiple data points and sources together to derive not just information but intelligence: actionable insights. These platforms are catching on with sophisticated marketers that are tired of ever-expanding spreadsheets and platform analytics.
But what exactly is marketing intelligence?
Marketing intelligence defined
The phrase “marketing intelligence” refers to a robust and comprehensive system that gathers, processes, and analyzes all the data relevant to your marketing activities. These platforms then use that data to provide intelligence to help you make informed decisions and optimize your marketing strategy.
The key components of a marketing intelligence platform are as follows:
- Data collection and integration
- Analytics and reporting
- Attribution modeling
- Real-time monitoring
- Customer insights
- Optimization recommendations
Data collection and integration
Marketing intelligence platforms should ideally be able to integrate with all of your data sources so you can view information on all of your campaigns in one place. They aggregate data across social media, email campaigns, ads, website analytics, e-commerce platforms, and beyond to provide you with a unified picture of performance and a holistic view of your marketing activity.
Analytics and reporting
Marketing intelligence platforms provide tools to not just ingest this data but view it in a productive and actionable way. Proper analytics and reporting help you understand the effectiveness of different channels, campaigns, and strategies so you can adjust accordingly in real-time.
The most sophisticated platforms will also offer a layer of customizability so you can see the data you want to see how you want to see it, and create unique reports for different teams and teammates across your organization depending on their needs.
Attribution modeling
Marketing intelligence platforms should offer powerful attribution modeling beyond first- and last-touch. Tools like multi-touch attribution (MTA), media mix modeling (MMM), incrementality, and others contribute to the full picture of attribution so you can understand what is driving successful outcomes, and just as importantly, what isn’t.
Real-time monitoring
Customer preferences and behavior shift so quickly that it’s critical to get the data you need now, not next month. The best marketing intelligence platforms can process and display data on a near-real-time basis, making sure that you’re always up to speed on progress and performance so you can make the best decisions in the moment, when it matters.
Customer insights
Tailored customer insights help you understand the who, what, when, where, how, and why of your customer behavior. Detailed analytics can show you where your customers are coming from and every touch they make along their way to a purchase. They’ll let you break down performance by new and returning customers, and about a million other ways too, depending on what you want to know.
Optimization recommendations
Look for a marketing intelligence platform that doesn’t just ingest and surface data, but shows you exactly what to do with it. These platforms should have a seamless user experience and an intuitive way of surfacing insights that makes sure that the right action is the clearest path forward. Whether that’s with in-platform recommendations, an expert account manager at your side, or both, you should feel supported in making the most of your data for powerful decision-making. The most mature platforms will help you do that.
Learn more about marketing intelligence
While many platforms exist today that will help you do more with your marketing data, not all of them can be accurately classified as marketing intelligence platforms. While the category is new, the functionality is anything but: marketing intelligence aims to provide you with the best data so you can make the best decisions with ease.
2025 Marketing Trends to Watch Out For: Part 2
2025 will be here before you know it, so we’re covering the key marketing trends you need to know about to stay ahead of the competition. If you haven’t checked out part 1, we covered several seismic changes affecting the marketing landscape. From the rise of LinkedIn as the new hot platform to the potential banning of TikTok and the continued evolution of X (formerly known as Twitter), the next year is shaping up to be a very interesting one indeed. That’s not even mentioning how GenAI has completely upended the industry (and the larger world) and will continue to do so. In part 2, we’ll talk about the momentum of CTV in an increasingly cord-cutting world, new search methodologies, nano-influencers, and the growing awareness around data privacy.
Continuous Growth of CTV
In our modern digital world, Connected TV (ads shown on internet-connected TVs including smart TVs, external devices such as Amazon Fire sticks, gaming consoles, and even traditional TV offshoots like YouTube TV and Hulu with Live TV) has emerged as one of the most effective channels available to marketers. Even though the major social media platforms get most of the shine in digital marketing, CTV is one of the fastest-growing major ad channels in the US: projected to reach roughly $30 billion in 2024, a 22.4% growth from the previous year. That’s almost a third of all TV ad spending as cord-cutting becomes even more mainstream, especially among the younger Gen Z demographic. Linear TV viewership among Gen Z declines by a couple of million people a year, falling below 40 million viewers in 2024. On the other hand, there are 55 million Gen Z CTV viewers and counting. In fact, CTV ad spend is growing faster than the rate at which linear TV ad spend is declining. At some point towards the end of this decade, CTV will likely surpass linear TV altogether. Linear TV is still king for major live events such as the Super Bowl, Olympics, World Cup, etc., but even most of these are now available via CTV (albeit fractured across disparate streamers). This is driven by the fact that people in other generations are also choosing to spend more time watching CTV with their media consumption habits. In 2024, US adults spent 123 minutes per day watching CTV which lagged only mobile devices (235 minutes per day) in usage. CTV ad spend is forecasted to grow double digits through the end of 2027 and already accounts for 10% of ad dollars spent on digital formats.
Although CTV has “TV” in its name, CTV acts more like other digital channels such as social media rather than linear TV. CTV allows for precise segmentation and targeting based on interests, demographics, and viewing habits. Streamers have leveraged partnerships with retail media networks and other data brokers to get more accurate reporting and better attribution. CTV lets advertisers analyze trends in real time to aid in campaign optimization just like other performance channels. This is a distinct advantage over linear because we can track a consumer’s journey from first impression to purchase. Note that because streamers often exist as their own distinct walled gardens, advanced techniques such as multi-touch attribution are often needed to track a campaign’s performance across different networks. Last but not least, advertisers have a selection of innovative ad formats including shoppable ads that are more aimed at the bottom of the funnel rather than awareness. These also include formats not available in other channels such as overlays and less intrusive ads.
There are several players in CTV to keep an eye on, but Hulu is the biggest one in the US with $3.8 billion in projected ad revenues in 2024. The company was a first mover in building out an ad tier and live TV offering, so Hulu beat a lot of competitors to the punch when pitching for ad dollars. YouTube comes in second with $3.3 billion and has been aggressively building out its CTV infrastructure to grow that side of its ad business. Amazon has a few CTV products, including Fire Sticks, Fire TVs, Prime Video, and Freevee but is still very much a newer player that’s learning the ropes. Disney+ was able to leverage some of Hulu’s expertise as its corporate owner, but along with Netflix and Max, are still trying to find their footing in the ad-supported game. Notably, Apple TV+ doesn’t yet have a CTV offering, but this may change as the company invests more in digital advertising capabilities.
Newer Forms of Search
It’s no longer enough to have an SEO strategy in place for traditional search; in recent years voice and social search have emerged as popular alternatives to typing keywords into a search query to get answers. According to PwC, 65% of 25-49-year-olds speak to their voice-enabled devices at least once per day. Although voice-enabled search has been around for some time, recent breakthroughs and the wide adoption of smart speakers have accelerated this trend. In 2023, there were an estimated 200 million plus smart speakers in the U.S. Most smartphones, smart TVs, and an increasing number of connected devices feature voice search. In addition to the long runway of potential users, voice search is often faster and more convenient than typing into a search bar. Voice search is a critical opportunity to capture more organic search traffic, especially among this tech-savvy cohort of users who rely on smartphones and smart speakers to look for products and services. It’s important to note that while the foundational SEO strategies remain the same, voice search queries tend to be more conversational and closer to how we speak in real life. A text search might be phrased as “tacos near me” whereas a voice search might be something like “Where can I get the best tacos near me?” Voice search tends to shift focus toward the intent of the searcher as opposed to simply emphasizing keywords. Search engines also reward websites that optimize for voice searches, so investing in this channel can have positive effects on overall SEO and your site’s ranking.
Speaking of new forms of search, younger generations are turning to social media platforms for discovery as opposed to traditional search engines. Instagram is the top search engine for Gen Z with 67% of surveyed users claiming it as their first choice. TikTok was a close second with 62% and Google came in third with 61%. However, these numbers include YouTube, Google Maps, and Google Images which contribute a healthy amount of search traffic to Google, meaning Google Search is now a distant third for Gen Z. If we were to include Gen Alpha, these numbers would probably skew even heavier towards the social platforms. Although we don’t have official numbers, Google disclosed in 2022 that roughly 40% of youth use TikTok or Instagram to search for things like lunch spots rather than Google. Let’s be clear, we’re not saying this is the death of search engines like Google and Bing, but social media platforms are rising in the ranks as search tools across all demographic groups according to SOCI. This isn’t surprising since social platforms rely on algorithms to serve relevant content to users, so they’re naturally set up to be good search engines. Beyond personalization, social platforms tend to have more UGC and authentic content as opposed to Google results served to you by PPC ads. We expect to see this trend continue as younger generations (and even older ones) who grew up with social media increasingly rely on it for search.
Nano-Influencers: Small but Mighty
We’re not exactly sure when influencer marketing truly began, but the practice of companies using prominent figures and celebrities to endorse their products can be traced back to the late 19th century. Once social media gained traction in the early 2010s, the modern influencer was born on platforms such as Facebook, Instagram, YouTube, and now TikTok. Today, they’ve become an integral part of our digital marketing ecosystem because brands want to leverage the communities and audiences those influencers have cultivated. We can loosely define influencers into a few categories:
- Mega-influencers: influencers with over 1M followers, usually made up of celebrities, athletes, actors, rising stars, and other prominent public figures.
- Macro-influencers usually have between 100K and 1M followers
- Micro-influencers then are between 10K and 100K followers
- Nano-influencers have the smallest audience, with somewhere between 1K and 10K followers.
Nano-influencers are a somewhat new addition to the influencer marketing world as their audiences have traditionally been seen as too small to make a difference. Many of us operate under the false assumption that the biggest names will be the most effective at promoting brands, but if we think about our own day-to-day purchase decisions, we’re often influenced by much smaller niche voices who are either part of our community or an expert that we found through research. These figures, with their smaller followings, are often seen as more trustworthy and relatable than the larger influencers who aren’t as approachable to the average consumer. This is because nano-influencers tend to be ordinary digital creators who have a knack for social media and have amassed a small audience along the way. Their followers consider them almost as peers because nano-influencers are highly authentic, presenting honest views of their lives as opposed to the high production value and glossy content from bigger influencers. The biggest benefit of working with nano-influencers is that they have a much higher engagement level than the other categories of influencers. This is because they have more time to respond and interact with their audience and develop meaningful connections. Nano-influencers generally inhabit a specific niche and have a deep understanding of their followers’ interests and preferences. This means they’re very skilled at creating highly specialized content that resonates deeply with their followers. This combination of high engagement rates and loyal audiences who trust the nano-influencer's recommendations translates into higher conversion rates when they endorse a product or service. Last, but not least, nano-influencers tend to be at the very beginning of their journey so they will be much more cost-effective to work with than bigger creators. Thanks to this lower cost, it’s much easier to get positive ROI on your nano-influencer campaigns without even taking into account the higher conversion rates. Partnering with these creators while they’re still up-and-coming allows for the brand and influencer to grow together and organically, even if they may require more coaching and mentoring upfront to get quality content. According to an internal survey conducted by influencer marketing agency Aspire, 70% of brands are already working with smaller creators. We expect to see this trend continue into the holiday season and beyond.
Data Privacy
Five years ago, data privacy was generally considered an afterthought; something your legal team dealt with that wasn’t on the forefront of our minds. Today, that’s changed dramatically as data privacy has become a priority for regulators, consumers, and businesses. Privacy laws such as the EU’s GDPR and California’s CCPA regulations spurred a chain reaction of other countries and US states passing their own laws. Examples include Brazil’s General Law for Data Protection, Egypt’s Law No. 151, and Canada’s Digital Charter Implementation Act. As of May 2024, 17 US states have passed data privacy regulations, with several more expected to join them. It’s clear the legislative pace will only pick up, but we’ve also seen regulators take a stronger stance on enforcement actions. In 2023, companies were fined over 2 billion euros for violations of the GDPR (more than the past 3 years combined). Large tech companies including Meta, TikTok, and X (formerly Twitter) have been fined over $3 billion since the passing of GDPR. In the US, the FTC is currently investigating several companies over their use of sensitive data, including geo-location, health, and children’s privacy. One of the most notable cases involves Kochava, a geolocation data broker that the FTC alleges has been selling data that can be used to trace the movements of individuals to and from sensitive locations such as health clinics, shelters, addiction recovery centers, etc. Depending on the outcome of this case, the use of geolocation data may be severely restricted going forward for targeting purposes.
Another regulatory area to keep an eye on is the development of AI policy by governments worldwide. After ChatGPT exploded in popularity, governments became wary about the need for large amounts of training data to train AI models which could infringe on data privacy. Italy temporarily banned ChatGPT in March 2023 because OpenAI was using personal data to train the model without asking for consent (including children’s data because there weren’t age verification tools implemented at that point). OpenAI did create a form for European users to opt out of their personal data being used to train the model and created an age verification tool, but this did little to assuage the EU’s concerns about data privacy. The European Data Protection Board created a ChatGPT task force to develop suitable AI regulations, with several other countries including the US, UK, and China drafting their own guidelines.
In late 2023, Google claimed that its plan to completely deprecate third-party cookies was on track for the second half of 2024. However, in April 2024 the company announced that they would be further delaying the sunset of third-party cookies until some time in 2025 (although the exact timing is yet to be determined). Why? The company acknowledged that regulators (especially the UK’s Competition and Markets Authority) and advertisers still had key concerns about the implications of removing cookies. The CMA is concerned that transitioning to the Privacy Sandbox will only strengthen Google’s position at the expense of its competitors. At the same time, agencies and marketers can rest a little easier knowing they have more time to figure out a plan to replace third-party cookies in their strategies. We won’t be holding our breath on when Google will actually phase out cookies, but expect the company to figure out a way to launch Privacy Sandbox with the blessing of regulators. Google still expects to complete this in 2025, but would anyone be surprised at all if they kept delaying it for a few more years? Only time will tell!
Accounting Modes 101: What Marketers Need to Know
Today’s top marketers aren’t just marketers: they’re data analysts, product insiders, customer champions, growth strategists, and more. Marketers have to hold an overview of the entire organization in their mind in order to best position their company’s products and services for success. They have to be savvy with numbers and comfortable getting down with accounting principles to make the best of their budget.
Understanding the different accounting modes — the different ways that ROI can be calculated and accounted for — makes a significant difference in how you assess the performance of your marketing campaigns.
In this guide, we’ll cover basic accounting modes and discuss the two most common ones on the Northbeam platform and beyond: Cash Snapshot and Accrual Performance.
What are accounting modes?
In this context, accounting modes refer to the different ways in which conversions and revenue can be credited within an analytics platform. Northbeam uses the Cash Snapshot and Accrual Performance modes to mirror traditional accounting methods used in corporate finance: cash basis and accrual accounting.
At a high level, accounting modes can shape how you interpret key metrics like revenue, return on ad spend (ROAS), and media efficiency ratio (MER).
Cash Snapshot mode credits all revenue and conversions to the date when a given transaction actually takes place. This mode is particularly useful for measuring immediate cash flow. It helps you keep track of what is coming in on a daily basis. For example, if a customer makes a purchase on your website tomorrow, all the associated revenue would be attributed to that date.
Accrual Performance mode, on the other hand, credits revenue and conversions to the dates when relevant marketing touchpoints occur. This allows marketers to see a more accurate reflection of how different channels and campaigns contribute to conversions over time. If a customer interacted with your Meta ad yesterday, clicked on your email today, and made a purchase tomorrow, revenue would be distributed across all of these individual dates and touchpoints.
Read More: What Makes Northbeam’s Data Different?
When should you use each mode?
Cash Snapshot mode
Cash Snapshot mode is more commonly used because it aligns with the way that businesses typically track their finances — based on when cash is received. For example, if you’re reporting on MER, Cash Snapshot mode can provide a clear view of the ratio between total revenue and total spend on a daily basis. This method is straightforward and easy to understand, making it a go-to for many marketers.
However, the simplicity of Cash Snapshot mode can sometimes lead to oversimplified or misleading interpretations of marketing performance. For example, if you launch an ad campaign that leads to significant engagement but no immediate purchases, Cash Snapshot mode might make the campaign seem like a failure, even if it leads to more purchases down the line.
Accrual Performance mode
Accrual Performance mode offers a more nuanced understanding of how your marketing efforts are contributing to conversions. By attributing revenue to the individual dates when marketing touchpoints occur, this mode gives you a clearer picture of which channels and campaigns are truly effective and generating ROI. This can be particularly valuable for marketers focused on scaling paid media and calculating ROAS. It can also be useful for products or services with long sales cycles where individual touchpoints need to be accounted for to get the full picture.
On the other hand, Accrual Performance mode could be over-complicated for straightforward sales cycles or simple marketing strategies. If you don’t have a lot of resources, or aren’t running a lot of campaigns, you may choose to keep things as simple as possible.
The benefits of choosing the right accounting mode
Take the time to think through your options and choose the right accounting mode so you can make the best decisions for your business. If you’re in doubt, try running both and looking at how the results compare. Northbeam lets users choose their preferred accounting mode, or toggle between the two for comparison.
Choosing the right accounting mode helps you:
- Assess the true impact of your marketing efforts
- Improve your budget allocation
- Manage your daily finances
- Align your marketing strategy with your business goals
- And more.
When in doubt, chat with an expert. Northbeam’s dedicated advisors are happy to talk you through which accounting mode is best for your unique situation.
Data-Driven Marketing Is the Future — Here’s Why It Matters
The future may be uncertain, but one thing is not: we have more data at our fingertips than ever before, and this is only going to become more true over time. And in this data-rich environment, how we do marketing has fundamentally changed.
Sorry, Mad Men lovers: the days when marketing decisions were based entirely on intuitive assumptions or creative instincts is fading. While creativity matters more now than ever before due to the mass proliferation of AI-generated campaigns, decisions in today’s competitive marketing environment have to be backed by cold, hard data.
This is reflected by the rise of data-driven marketing. Within this strategy, Creative remains a large portion of the picture, but decisions must be driven first and foremost by data. That being said, not all data is created equally. In this blog post, we’ll delve into what data-driven marketing is, why not all data makes the cut, and the benefits of adopting a data-driven approach to your marketing strategy.
What is data-driven marketing?
At its core, data-driven marketing is the approach of leveraging data to make marketing decisions and measure success. Sounds simple, right? But data-driven marketing goes beyond spreadsheets and platform data.
What we’re talking about is the distinction between setting a strategy and then using data to measure results, and looking at data at each step of the process. With a data-driven approach, you would:
- Use data to establish a complete understanding of customer behavior, preferences, and pain points
- Use data to derive a strategy based on customer data points
- Use data to validate and refine your initial data-backed hypothesis
- Use data to measure and communicate results
- Repeat
A data-driven approach might take advantage of tools like customer segmentation, personalized campaigns, predictive analytics, A/B testing, and more.
The maxim that numbers don't lie has never been more true. When marketing strategy is based on data rather than theories, you’re more likely to set yourself and your team up for success with each and every dollar.
And rather than eliminate room for experimentation, having a data-backed strategy lets you experiment within a more controlled environment, giving you a better shot at achieving your goals.
Not all data makes the cut
But what happens if you base your data-driven marketing strategy on inaccurate or misleading data? This is so often the case, especially if we’re talking about platform analytics.
Nearly all platforms that sell ads have their own analytics suites that aim to let you see how your ads are performing. While these analytics suites are easy to read and access, they don’t provide the best data. They don’t create space for multi-touch attribution, and have a hard time attributing credit to other platforms or campaigns. In this way, they fail to represent the complicated nature of the buyer’s journey.
TL;DR: Platform data is interesting, but it’s not good enough to inform important decisions.
We need to ask ourselves: how hard is my data working for me versus how hard am I working for it?
Many marketers keep complicated spreadsheets of different data points, collated in one place to support their decision making. But this type of reporting, no matter how skillful, will be subject to very human error and bias issues.
This maxim also holds true: you just don’t know what you don’t know. What important data points are right outside of your scope of view? Are you looking too broadly? Should you be going deep on a campaign level?
In-depth analytics takes precious time, and granularity is crucial today in an age where marketing is ever-present on infinite platforms and in every area of a person’s life. Each touch matters, and it’s hard to account for that in a manually-updated spreadsheet.
When it comes to big data, machine learning has changed the game. Marketing intelligence platforms that use machine learning to turn billions of data points into digestible and actionable insights will help you wield data-driven marketing strategies in a way that really moves the needle and takes as much guesswork out of the equation as possible.
The benefits of data-driven marketing
Embracing a data-driven marketing approach can bring numerous benefits to your organization. Here are some of the most compelling reasons to adopt this approach:
- Optimized marketing spend - data-driven marketing helps you make every dollar cent count. With data backing each and every decision, you can more efficiently spend your budget on the most effective channels and strategies to reduce wasted spend and maximize ROI.
- Better decision-making - data is a marketer’s best friend. It provides a solid foundation to make informed decisions for impact. With strong data points, marketers can rely on concrete evidence rather than assumptions and educated guesses, leading to more effective and strategic planning.
- Improved targeting and personalization - data unlocks the ability for marketers to segment their audience more effectively than ever before and deliver highly-personalized messages that resonate individual customers. This tailored approach can lead to higher engagement and conversion rates.
- Enhanced customer insights - by analyzing millions of data points of customer behavior, marketers can gain a deeper understanding of their target audience’s preferences, behaviors, and underlying pain points. This knowledge can enable you to create more relevant and compelling marketing campaigns.
- Real-time adjustments - data-driven strategies are made possible by near-real-time data points so marketers can quickly identify what’s working and what’s not. This allows for agile adjustments and ensures that all your marketing efforts are always driving towards the best results
Why data-driven marketing matters
Data-driven marketing is a fundamental shift that allows today and tomorrow’s top marketers to make smarter decisions, create more effective campaigns, and achieve better results with the resources at hand. While marketers have always used data, the difference at play is akin to a paradigm shift: we have access to more data than ever before, and powerful AI tools to help us really take advantage of it to drive informed action. Marketers who don’t use this data to its fullest potential risk falling behind the competition — or just a lot of wasted spend.
By adopting sophisticated data-driven marketing platforms that leverage powerful artificial intelligence, marketers can overcome the challenges associated with data quality, availability, and reliability to make better decisions that inform better outcomes.
In a world where every click, interaction, and transaction generates data, the ability to analyze and act on this information in a seamless and immediate way will be the real differentiator that sets marketers apart.
BFCM 2024 Predictions: How Did We Do?
As the dust settles and the credit cards cool from this year’s Black Friday and Cyber Monday (BFCM) weekend, it's time to reflect on how our early 2024 bets stacked up against the actual trends shaping the season.
Earlier this year, Bryan Bumgardner, Northbeam’s Director of Growth Marketing, and Brayden Cruz, Senior Media Strategist, shared their 2024 BFCM predictions. In this blog post, we’ll break down what we got right, what we got wrong, and what we’re looking forward to next year.
Our 2024 BFCM predictions
Sky-high online sales
With ecommerce growing exponentially, 2024 was poised to outpace the $38 billion spent during Cyber Week in 2023. A greater focus on mobile optimization and seamless online experiences was expected to drive this growth, particularly with more consumers avoiding in-store crowds.
Buy Now, Pay Later (BNPL) gains momentum
Following a 17% year-over-year increase in BNPL adoption in 2023, we anticipated even higher reliance on installment payments in 2024. Younger demographics, especially Gen Z, have embraced BNPL as a way to manage holiday spending without straining budgets.
Personalization is the name of the game
This year, we predicted that brands would increasingly rely on AI-driven tools to deliver customized recommendations and targeted campaigns. This trend was predicted to be pivotal for converting the growing number of online browsers into buyers.
Supply chain stabilization
Unlike prior years marked by global disruptions set off by the political landscape, COVID-19, and wars, 2024 predicted a smoother supply chain, allowing retailers to manage inventory and deliver products more efficiently.
How the predictions played out
Sky-high online sales
This year’s Black Friday and Cyber Monday online spending confirmed our prediction of robust growth. On Black Friday, shoppers spent a record-breaking $10.8 billion online. This record was broken only a few days later on Cyber Monday with a whopping $13.3 billion in sales, making it the biggest online shopping day of all time.
Shoppers worldwide will spend an estimated $240 billion online during the holiday season, marking an 8.4% increase from 2023. In addition to Friday and Monday, Cyber Week as a whole accounts for over 25% of global holiday spending, emphasizing its position as the most significant contributor to annual retail performance.
About 75% of shoppers planned to shop online on BFCM, up 7% from last year. Notably, mobile shopping continued its dominance. Over 75% of shoppers have between 1-5 shopping apps on their mobile phones, and 50% of Gen Z and Millennials shop exclusively on their phones.
Buy Now, Pay Later (BNPL) gains momentum
BNPL adoption surged as anticipated, particularly among Gen Z and Millennial shoppers: 39% of Millennials planned to use BNPL during BFCM, followed by 38% of Gen Z shoppers.
By offering flexible installment options, retailers captured budget-conscious consumers navigating high inflation and financial pressures. This year, BNPL drove $18.5 billion in online spend during BFCM alone, an 11% increase from last year.
Personalization is the name of the game
Personalization proved to be a major differentiator during this year’s BFCM, particularly with the growing role of AI.
Generative AI tools were employed by 40% of shoppers to discover deals, specific items, or personalized recommendations, demonstrating how advanced technology is reshaping the shopping landscape.
Last year, AI influenced over 17% of all holiday orders in November and December — and there’s no reason to believe this trend hasn’t increased in 2024. Today, 70% of consumers use generative AI tools like ChatGPT to enhance their shopping experience. This includes finding the best deals, finding specific items online quickly, and getting brand recommendations.
Supply chain stabilization
Unlike previous years marred by supply chain disruptions, 2024 benefited from relative stability. Inventory shortages were minimal thanks to better forecasting, technologies like RFID, and smoother logistics, and retailers leveraged this stability to provide a more seamless shopping experience, ensuring consumers had access to popular products without significant delays.
But while political instability and an international pandemic can impact supply chains, there are other issues to consider, such as logistic worker strikes — like the U.S. port strike in September — or cyber attacks. Nearly 200,000 customers were affected by supply chain cyber attacks worldwide in 2024. As technologies improve on all sides, retailers and companies need to bring intense focus to cyber security to maintain supply chain stability over time.
Overperform with Northbeam
Our team took a look at the year-over-year data for brands that have been with us for multiple BFCM periods, and the results speak for themselves: Northbeam usage is correlated with significant performance growth over BFCM and the holiday period over time.
With Northbeam at their disposal, our clients know the impact of every dollar, and they’re more empowered to put money where it counts with full confidence.
Client A: Supplement company sees explosive ROAS
As an example, let’s look at “Client A,” a supplement company (anonymized to protect their strategy). In 2023, they spent $2.2 million on ads across the November shopping period, and brought in $5.1 million in revenue. That’s a 2.3x ROAS — not bad, but not stellar.
In 2024, they only spent $260,000 on ads — a 90% decrease — and made $8.2 million in revenue. That’s a 31.5x ROAS. How did they do it?
In 2023, Client A spent significant dollars across a wide portfolio of Facebook, Google, Snapchat, and Amazon ads. With Northbeam data in hand, they were able to hone in on the channels and campaigns that made the biggest impact and delivered the most bang for their buck. The result? Based on ad efficiency calculated by Northbeam data, Client A decided to shift a significant amount of advertising dollars into Amazon ads in 2024.
In 2023, Client A spent $38,000 on Amazon ads. In 2024, they upped that spend to $143,500 — nearly a 4x difference — and reduced spend across other platforms accordingly.
The result? Their Amazon ads had an outsized impact on revenue. Northbeam gave Client A the intelligence to approach the BFCM period with a data-backed strategy — and it paid off by the millions.
Client B: Beauty company sees big growth led by new channels
Let’s look at another example in the form of “Client B,” a beauty company. In 2023, they spent $213,000 across channels for the holiday shopping period and brought in $340,000 in revenue. In 2024, they spent $346,000 and made $1.15 million in revenue.
A spend increase of 60% resulted in nearly 4x the revenue!
How did they do it? With Northbeam’s data to enable their decision-making, Client B had the confidence to explore new channels and expand spend in strategic ways. While they maintained even spending on Meta and Google, they added in TikTok and boosted their Amazon spend, knowing that they’d be able to track the impact of each dollar.
Client B’s informed experimentation paid off with a record-breaking BFCM period!
What to watch for in 2025
As we look ahead, several strategies can help retailers succeed next year:
- Early campaigns remain a cornerstone of success. Retailers should aim to launch their outreach at least three weeks before BFCM to capture attention before competitors flood the market.
- With mobile devices accounting for most e-commerce traffic, retailers must invest in mobile-first designs, faster load times, and user-friendly checkout experiences.
- Rising consumer skepticism about Black Friday deals underscores the need for transparency in promotions: over 57% of shoppers questioned the legitimacy of sales and deals.
- Offering flexible payment methods, including BNPL, is not just a value-add but a necessity for capturing budget-conscious shoppers.
The 2024 BFCM season highlighted how innovation and preparation are key to thriving during the busiest shopping period of the year. While early predictions largely held true, the evolving consumer landscape emphasizes the need for agility, transparency, and strategic investments in personalization and technology.
Will these same predictions and outcomes hold true for the rest of the 2024 holiday shopping period? Let’s see what the end of 2024 has in store.
Northbeam: the premiere growth tool
Looking at the nearly $1 billion in revenue and $200 million in ad spend across Northbeam clients this year, here is how performance stacked up against data from last year’s BFCM:
The data above was derived from performance tracked across all Northbeam customers, across all industries, who had data inclusive of both 2023 and 2024 time ranges. Data represents the total across the five days of BFCM, comparing 2023 and 2024 year-over-year. All data is cash, on a 1-day click attribution window.
As we reflect on the 2024 BFCM season, it’s clear that data-backed decision-making is a cornerstone of success for retailers.
Northbeam’s MMM+ delivers unparalleled insights into how various marketing channels drive growth and impact revenue. Unlike traditional MMM, which often feels like a black box, Northbeam’s approach combines granular attribution data with intuitive, actionable reporting. This ensures that even complex multi-channel strategies can be demystified and fine-tuned with confidence.
The result? Brands can better allocate resources, maximize ROI, and make informed decisions across their entire marketing mix — even in the most competitive periods like BFCM.
AppLovin Performance: Key Insights from Northbeam Users
The Opportunity
With AppLovin creating buzz in the DTC advertising space, many are asking:
- Is AppLovin performance truly worth the social media hype?
- Is this channel a worthwhile test or a distraction for scaling brands?
Although AppLovin campaigns for eCommerce have been live for only 1–2 months for most Northbeam customers (and over the peak BFCM period), many marketers are eager to understand its performance through a larger, data-driven lens beyond anecdotal social media buzz. Based on early data, AppLovin shows promise in both activating and reactivating users to drive a high ROAS and new customer percentage:
- Activating New Users: AppLovin’s video ads—integrated into mobile games—capture the attention of users who may have seen your product elsewhere but remain unconvinced. These full-screen, partially unskippable ads (up to 60 seconds) are touted to effectively transition users from passive awareness to meaningful engagement.
- Reactivating Past Customers: AppLovin also performs well at re-engaging past customers. With Northbeam’s tracking, which allows an infinite lookback window, we’ve identified cases where users with 100+ touchpoints made purchases just minutes after viewing an AppLovin ad. This suggests a unique ability to reignite action among lapsed customers.
With Northbeam’s advanced tracking capabilities and deterministic data approach, we can provide an unbiased look at how AppLovin performs for real brands in real campaigns.
Adoption Stats
Early adoption rates suggest that AppLovin has gained rapid traction as a viable channel for DTC brands:
- 75% of Northbeam’s top 100 spending DTC brands are active on AppLovin.
- Over $10M in AppLovin spend has been tracked through Northbeam, with several brands exceeding $1M in spend over the past two months.
Performance Overview
The following data was sourced from Northbeam clients who spent advertising dollars on AppLovin between October 1st and November 30th. All figures are based on Northbeam’s clicks-only attribution model (Multi Touch Attribution), reflecting a clear and conservative view of performance.
Share of Wallet
- While Meta still leads with a 63% share of wallet across Northbeam's customer base, AppLovin accounts for 5.71% of total budgets among brands live on the platform—outpacing other channels like TikTok, Snap, YouTube, and Pinterest.
- In November, AppLovin captured 10%+ of ad spend for 31 brands, with one brand allocating as much as 34% of their budget to AppLovin.
- Brands spending on AppLovin grew their overall media budgets an average of 125% YoY, with AppLovin playing a key role in this expansion.
ROAS (Return on Ad Spend)
- AppLovin’s average ROAS is 45% higher than Meta and 74% higher than secondary platforms like TikTok, Snap, YouTube, and Pinterest. The platform’s click-driven nature contributes significantly to these strong performance metrics
- While 1-day efficiency is high on AppLovin, other channels tend to have a larger lift in ROAS when using longer attribution windows. This suggests that most of AppLovin’s impact is driven on the day of the conversion, while platforms like Snapchat, Youtube, and Tiktok can take 7-14 days to achieve comparable ROAS. Not necessarily a bad thing, but important to keep a close eye on as this can vary from brand to brand.
- With increased platform saturation, we expect efficiency to decrease over time. However, for now, AppLovin is performing well for Northbeam users testing this channel. It is important to note that there is still limited understanding of the long term impact of AppLovin due to the channel being so new. Brands leveraging Northbeam will be able to measure the channel holistically across historical benchmarks on other platforms to get a better understanding of long term value.
New Customer % and New Visit %
- AppLovin delivers 39% higher nROAS than Meta and 100%+ higher than secondary platforms.
- Strong ability to drive new customer acquisitions; with 85% of purchases coming from 1st time customers. This is key as click data is conservative, so this is especially validating that performance isn’t fluff and is actually driving acquisition.
- However, AppLovin’s New Visit Rate of 55% is lower than Meta (65%) and secondary platforms (~70%). This indicates the platform may currently target narrower, more familiar audiences. Despite this, it appears to convert first-time customers at a more efficient rate than other platforms.
- Customer acquisition is a key metric for most DTC brands, but it’s crucial to identify which channels are most efficient at converting first-time customers vs channels that are better at driving highly qualified new visits to your website.
- Understanding the complexities of your business is essential for gaining clarity on your customer journey and the time it takes for a customer to make a purchase after their first exposure to your brand. Brands with complex products or higher AOV, such as those in luxury, electronics, or furniture, typically experience longer consideration periods. This underscores the importance of balancing both short-term and long-term impacts in your decision-making process.
CPMs
- On average, AppLovin’s CPMs are 3.95% lower than Meta’s, but more than 20% higher than secondary platforms. Given AppLovin’s currently smaller share of wallet, it’s likely that as brands continue to scale CPMs are expected to be higher than Meta.
- While CPMs are a decent gauge for traffic efficiency, higher CPMs are commendable for higher quality audience and performance.
- As AppLovin spend and adoption increase in the DTC space in 2025, it will be interesting to observe how auction prices are impacted and if they can remain as competitive as Meta at scale
Why It’s Working
AppLovin’s new eCommerce offering shows promise as an alternative marketing channel for DTC brands seeking to diversify beyond Meta. Its ability to activate new users and reactivate past customers fills a crucial gap for brands aiming to scale.
AppLovin is currently highly competitive in terms of auction costs, with a large volume of clicks on the platform driving new customer conversions. This has contributed to strong overall performance to date and something to keep a close eye on with increased spend volume.
Northbeam users have quickly scaled on AppLovin by leveraging real-time performance insights powered by our first-party data and machine learning. With Northbeam’s accrual accounting methodology, brands can set 1-day profitability benchmarks aligned with their long-term objectives. This approach has allowed brands to assess the immediate impact of AppLovin in comparison to other channels, while continuing to get a deeper understanding of its full impact over time.
Customer Sentiment
Although AppLovin is seeing strong performance within Northbeam, our customers have had various opinions on the channel thus far:
- “AppLovin seems to be outperforming most channels, but will this last?”
- “We’re cautiously optimistic but impressed so far. The results are undeniable.”
- “AppLovin ROAS looks impressive, but I’m worried it’s just retargeting users.”
Concluding Thoughts from Northbeam
Northbeam’s Head of Media Strategy, Ryan Kovach, reflects on AppLovin’s role in reshaping channel diversification for DTC brands:
- Optimistic but Measured: Early results show AppLovin’s potential, with strong ROAS and nROAS positioning it as a channel worth considering for expanding media budgets.
- Demand Generation Excellence: AppLovin offers an intriguing combination of scale and performance, with early signs of capturing 10%+ of overall ad spend for several brands.
- Areas for Improvement: Metrics like New Visit Rate raise questions about audience diversity and potential future saturation, but its performance on ROAS and customer reactivation make these concerns secondary.
Guidance for Marketers
- Leverage Northbeam’s data quality for precise tracking:some text
- Use Northbeam to track both clicks and view-throughs on AppLovin, providing deterministic measurement of how the channel interacts with others, across devices, and throughout the customer journey.
- Monitor key performance metrics like CPMs, CAC, ROAS, and nROAS to assess scalability.
- Consider AppLovin for both demand acquisition and customer re-engagement campaigns.
- Set profitability benchmarks tailored to AppLovin with Northbeam:some text
- Establish CAC payback period targets to align with your brand’s goals and optimize campaigns down to the creative level.
- Use New Visit % and New Customer % to validate audience quality.
The Road Ahead
AppLovin’s managed-service model is currently limited to brands spending $20K+/day on Meta, but its self-serve platform launch in 2025 promises broader accessibility. Stay tuned for more analysis- Northbeam will continue refining insights with additional data pulls and case studies in early 2025, including:
- Channel Overlap (Touchpoint Data)
- First-Touch Attribution Comparisons
- Analysis of Brands Allocating Higher Spend Shares
- View-Through Measurement with Enhanced Clicks + Views
As DTC brands increasingly adopt AppLovin, its focus on activating and reactivating users positions it as a promising channel for 2025 and beyond.
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