This article was based on the interview with Fredrik Skantze, CEO & Co-Founder at Funnel by Greg Kihlström, AI Adoption and Marketing keynote speaker for The Agile Brand with Greg Kihlström podcast. Listen to the original episode here:
For CMOs and other marketing leaders, the promise of a single source of truth has always felt just over the horizon. Yet, with each passing year, that horizon seems to be receding into the distance like a mirage. The challenge is no longer a lack of data; it’s a deluge. We are swimming, and occasionally drowning, in an ocean of metrics from a dizzying array of platforms. In 2014, when Funnel was founded, there were roughly 1,000 marketing technology products. Today, that number has ballooned to over 13,000, with 3,000 of them launching in the last year alone. This exponential growth in complexity means our traditional methods of measurement—the comfortable dashboards and familiar attribution models we’ve relied on—are becoming increasingly inadequate.
The tidy, deterministic world of marketing measurement, propped up for years by third-party cookies, is crumbling. In its place is a landscape defined by signal loss, AI-driven ad platforms that operate like black boxes, and a fundamental need to rethink how we connect spend to outcomes. This isn’t a time for panic, but it is a time for a sober, sophisticated reassessment of our toolkits and philosophies. It requires us to move beyond simplistic debates and embrace a more holistic, multi-pronged approach to understanding performance. It’s about building a measurement framework that is as resilient and adaptable as the market itself.
The End of “Either/Or”: Embracing Triangulation as the Gold Standard
For years, the measurement debate in many marketing departments has been framed as a choice: Media Mix Modeling (MMM) for a top-down, strategic view, or Multi-Touch Attribution (MTA) for a bottom-up, granular analysis. The reality is that this “either/or” framing is now obsolete. The deprecation of cookies has created significant gaps in user-level tracking, making a pure MTA approach less reliable. Simultaneously, the speed of digital marketing demands more real-time insight than traditional MMM can often provide. The path forward isn’t choosing one over the other; it’s about using them in concert, along with other methodologies like incrementality testing, to paint a complete picture.
This concept, what Frederik Skantze calls triangulation, is the new gold standard for sophisticated marketing teams. It acknowledges that no single algorithm has all the answers in an era of signal loss. By layering these different models, you can have them inform and validate one another, creating a far more accurate and resilient understanding of performance. This isn’t just about attributing conversions; it’s about genuinely modeling the business. As Skantze explains, this requires a deeper understanding of marketing’s true impact, including the baseline sales you’d achieve even without marketing, the lagging effect of ad spend (ad stock), and the diminishing returns of each channel as you increase investment.
“The world isn’t deterministic anymore. Cookies are essentially going away… so you really have a lack of signal and signal loss. And to make up for that, you need to do two things. You need to use all the data that’s available… and then you need to use all the algorithms available. Now, individually, these algorithms, because of signal loss, have challenges. But when you use them together and have them influence each other, they provide a very accurate picture… For most brands, if you actually stop marketing, you probably get 70% of the sales anyway, because you have a really strong brand. That’s called the baseline. So you need to model your baseline.”
For enterprise leaders, this is a critical strategic shift. It moves the conversation from “which channel gets the credit?” to “how can we model our marketing ecosystem to predict outcomes and optimize our budget allocation?” It’s a more complex undertaking, certainly, but it’s one that reflects the reality of the modern media landscape.
Beyond the Dashboard: The Rise of Conversational Analytics
The primary interface for data analysis hasn’t fundamentally changed in decades. We build dashboards, we create reports, and we use filters and drop-down menus to query our data. While effective to a point, this process is inherently static and reactive. It relies on the user knowing what question to ask and how to manipulate the interface to get the answer. Generative AI is poised to upend this paradigm completely, ushering in an era of “conversational analytics.”
Imagine being able to simply ask your data platform, “Which campaigns in the EMEA region had the highest incremental CPA last quarter for customers with an LTV over $500?” and receiving an instant, accurate answer with supporting visualizations. This is the promise of a chat-based interface for analytics. It democratizes data analysis, making it more accessible to a wider range of marketers, while also allowing seasoned analysts to iterate and explore hypotheses at a much faster pace. As Skantze points out, this is a difficult technical problem to solve, as the AI needs to deeply understand the context and structure of your specific marketing data. However, the potential to fundamentally change how we interact with and derive insights from our data is immense.
“Forever data analysis has been the same. You sort of look at the data and then, you know, you sort of create dashboards and use drop downs or SQL to sort of to create graphs… But now with generative AI, there is an opportunity and a new interface is emerging for how to interact with your data, which is sort of this chat interface and essentially conversational analytics where you can have a conversation with your data.”
This isn’t about replacing analysts; it’s about augmenting them. By removing the friction of traditional BI tools, marketers can spend less time pulling reports and more time on strategic thinking, interpretation, and action. For leaders, fostering an environment that is ready to adopt these new interfaces will be key to maintaining a competitive edge in data-driven decision-making.
The Data Feedback Loop: Re-educating the Ad Platforms
One of the most profound shifts in digital advertising is the rise of AI-driven campaign management tools like Google’s Performance Max and Meta’s Advantage+. These platforms have largely automated the tactical work of bidding and optimization, a job that once consumed countless hours for performance marketers. While these tools are incredibly powerful, they have a critical weakness: the same signal loss that affects our internal measurement also affects them. They can’t see what happens after the click with the same clarity they once did. They don’t know which conversions lead to high-margin sales, which customers have the highest lifetime value, or which result in low return rates.
This creates a new, crucial role for the advertiser: to complete the data feedback loop. It’s no longer enough to just pull data from the ad platforms for analysis. We must now push our enriched, first-party conversion data back to them. By telling Google and Meta which conversions we value most and how much we attribute to their efforts, we are essentially training their algorithms to find more of our ideal customers. This is a symbiotic relationship. Their AI needs our business intelligence to perform optimally. Skantze highlights that this is not a minor tweak; it’s a massive lever for performance improvement.
“Now, you know, on Google there is performance max, there’s meta advantage. It’s an AI black box. It does this better than any human, but it doesn’t have that signal. It doesn’t actually know what happens on your website… You need to in sort of near real time, go back and push that data into these ad platforms and say, here I had these conversions. This is how much I attribute them to you. And this is how much they’re worth to me. If you do that, you can improve your performance per platform by something like 20%.”
This is modern, data-driven marketing in its purest form. It requires a sophisticated data infrastructure capable of unifying ad spend data with backend business data, running advanced attribution, and then feeding those insights back to the platforms in near real-time. For enterprise marketing leaders, establishing this two-way data flow is no longer a “nice-to-have”; it is a competitive necessity.
The discipline of marketing measurement is undergoing a necessary and profound evolution. The comfortable certainties of the past are gone, replaced by a more complex but ultimately more realistic operating environment. The way forward is not to search for a single, magical algorithm but to build a robust, integrated ecosystem. This involves embracing the triangulation of multiple measurement models to create a resilient view of performance, preparing for a future where we converse with our data as easily as we do with a colleague, and understanding that our role now includes actively training the platform AIs that drive so much of our advertising.
This shift demands more from us as leaders. It requires us to be architects of sophisticated data systems, not just consumers of reports. It requires an investment in both technology and talent capable of navigating this new terrain. While the complexity can seem daunting, the opportunity is significant. The brands that master this new reality—that learn to triangulate truth from messy data, to feed the algorithms that feed them, and to ask deeper questions of their analytics—will be the ones that not only survive but thrive in the next era of marketing.
