QN#17: An unequal triangle
In 2019, Google published its "Three Grand Challenges" document that set the foundation of today's accepted view that good measurement combines Marketing Mix Models (MMM), experiments, and attribution. From these developments to the more recent branding of “Modern Measurement,” the visual depictions are usually some kind of variation of these triangular and now “multi-bubble” charts.
From a “searching for the truth” standpoint, they make perfect sense. However, for practical business situations that demand decisive action, do the three vertices of the triangle carry the same weight?
They don’t. For most businesses, the triangle is fundamentally unequal. One side is longer, heavier, and far more critical than the others.
This is not to challenge the view that all three elements have a role to play, or that a well-designed experiment is the closest we can get to a perfect measure of “true incremental impact.” Our argument is based on a pragmatic reality: in terms of driving transformative business strategy, MMM is likely the most useful and impactful tool for companies.
The Foundation: Why Modeling and MMM Carries the Most Weight
While experiments provide causal truth on a small scale and attribution offers a high-frequency pulse on digital tactics, MMM is the only framework that provides a panoramic view of the entire business.
It’s Strategically Holistic. An MMM is a must-have for any organization spending regularly on more than two marketing channels for the last couple of years. Its purpose is to answer the C-suite's most pressing questions: "What is our optimal total budget?" and "How do we allocate it across our portfolio to maximize growth?". If your MMM is not serving as a strategic guide to budgeting and you spend regularly on more than two channels, you have a massive optimization journey ahead of you.
It’s Flexibly Comprehensive. Unlike the rigid structures of other methods, MMMs can be adapted to every business situation. The choice of variables, the modeling of brand effects, the inclusion of competitor actions, pricing, and economic factors—this flexibility allows it to mirror your business's unique reality.
It Fosters the Best Business Conversations. Because an MMM incorporates all business drivers, it forces a conversation that transcends the marketing department. It becomes a shared language between Marketing, Finance, and Operations, connecting marketing spend not just to sales, but to profit, market share, and long-term value.
Experiments, by contrast, are often related to very specific questions and are unlikely to deliver a transformational impact on their own. The same goes for attribution, which, by design, delivers optimizations within digital channels. As important as this is, it is not an analysis of the fundamental drivers of the business. Companies using post click attribution as their main strategic input are at least ignoring 75% of the real impact of marketing and the marketing channels they could be using to grow their business.
MMM aims to incorporate the business knowledge needed so that decision makers do not have to lean that much on their own, valuable but limited historical experience or biases and can incorporate those in the model so that in the end they can make better business decisions.
Deconstructing the Other Vertices
If MMM forms the strategic base, incrementality and attribution are the supporting—but unequal—sides. Understanding their distinct roles and limitations is crucial.
Incrementality: The Costly Pursuit of Causality
Incrementality setups are the undisputed gold standard for understanding causality. They answer the question: "Did my marketing action cause this business outcome?". However, this truth comes at a price. These experiments are costly, both in the technical expertise required to get them right and in the significant opportunity costs associated with having holdback groups that are not exposed to your marketing.
Geo-experiments are often hailed as a cross-platform solution, but best practices for their adoption are rare to find, and they are expensive and complex to implement correctly. Despite the cost, having a robust understanding of causality is non-negotiable when justifying significant shifts in marketing investment.
Attribution: A Misleading and Misunderstood Tool
"Attribution" is perhaps the most misleading vertex of the triangle. Do we mean a cross-channel Multi-Touch Attribution (MTA) model or the attribution reported within a specific advertising platform? The distinction is critical.
Multi-Touch Attribution (MTA) is often argued to be increasingly limited. In a world with less identifiers, its ability to stitch together a comprehensive user journey across platforms and devices is severely reduced to a limited post-click view of the world. The days of id-based attribution are gone and those trying to stitch marketing actions and business outcomes with personal ids cannot be for the purpose of understanding the value of these marketing actions. However, modeling has come into the rescue to multi-touch attribution and these models are embracing new methodologies to regain relevancy. Nevertheless, their scope remains narrow.
Platform Attribution is even more problematic when used for comparison. Trying to compare the ROAS from Meta with the ROAS from Google is an exercise in futility—it’s not apples and oranges; it’s apples and pears. Each platform operates within its own walled garden, measuring its logged-in users with its own proprietary methodology. These numbers are fundamentally different and impossible to compare directly. Their true purpose is to provide a consistent signal to feed their internal bidding algorithms, not to inform your cross-channel budget allocation.
Platform Incrementality
Similar thoughts come to mind with platform-reported incrementality lift studies, where ever-changing, black-box methodologies make cross-platform comparisons unreliable. These are, in turn, extremely valuable tools when you want to have a good grasp of what is the incremental impact of a campaign on different KPIs, with different setups or creatives. A must have in your toolbox which has to be taken with rigour and across different moments in time to have a consistent idea of incrementality using these tools.
So, What to Do in This Unequal Triangle?
The goal is not to discard the triangle but to use it wisely, acknowledging its inherent imbalance. This means establishing a clear operational hierarchy.
Lead with MMM for Strategic Decisions. Your MMM should be the primary tool for annual and quarterly budget setting. It provides the strategic "why" and "how much" for your marketing plan.
Use Incrementality to Validate and De-Risk. Deploy costly experiments surgically. Use them to validate the major directional shifts suggested by your MMM. If your model suggests cutting your largest channel's budget by 30%, a geo-experiment is the perfect tool to de-risk that decision before you commit.
Combine Them Pragmatically. The textbook approach to combining incrementality and MMM involves using experiment results as Bayesian priors. This is powerful but not the only option. A more pragmatic approach can work: if an experiment shows a channel has a much higher incremental ROAS than its attributed ROAS, you can work to manually adjust or re-attribute conversions from "Direct" or "Organic" channels in your reporting to better reflect this reality. It's a "naive" but often effective way to bridge the gap between models.
Leverage Attribution for Tactical Optimization. Accept platform attribution for what it is: a valuable, but siloed, tool for in-flight campaign optimization. Use it to improve performance within a channel whose budget has already been set by the MMM. Do not use it to decide whether to move money from one platform to another.
Beyond the Triangle: The Blind Spots of a Short-Term Focus
Perhaps the biggest danger of the measurement triangle is that even a perfectly executed framework can create strategic blindness. All three methods are inherently focused on measuring relatively short-term effects. By focusing only on this, you risk ignoring the fundamental laws of advertising that drive long-term, sustainable growth.
The triangle is often silent on:
The Power of Creativity: As we've discussed before, creative quality is a massive driver of incremental sales. A framework that only optimizes media spend without accounting for the quality of the message is missing the biggest piece of the puzzle.
The Value of Brand Building: The triangle is poor at capturing the slow, cumulative impact of brand awareness and mental availability. A relentless focus on short-term, attributable conversions can lead companies to underinvest in the brand-building activities that reduce price sensitivity and increase baseline sales over time. An MMM which is not accounting for long term impact through intermediate metrics is also limiting the view at that level and overestimating the short term impact of marketing.
The Necessity of Reach: The goal of reaching a relevant portion of your total target audience is a cornerstone of marketing effectiveness, championed by the likes of the Ehrenberg-Bass Institute. An obsession with narrowly targeted, high-ROAS segments can starve a brand of the broad reach it needs to grow.
Your Measurement Framework Is Your Competitive Advantage
In this complex landscape, it is tempting to wait for a single, perfect solution that you can simply implement to solve all your measurement problems. That solution does not exist today.
Your competitive advantage today lies in your ability to build and leverage a proper measurement framework that works for your organization and the way it makes decisions. This means having the ability to understand the impact of your main marketing activities, how they interact, and which levers to pull depending on the direction the business is heading. It means planning your budgets with a data-driven approach and optimizing campaigns for maximum impact.
This is not easy. It requires a concerted effort to put together the systems, processes, and—most importantly—the people to thrive in this complexity. The goal is not to find a single source of truth, but to master the art and science of combining these diverse, unequal inputs into a coherent strategy that drives sustainable growth. That is the game we are in.
Industry Updates
Google Trends API in Alpha
Google has launched an API for pulling Google Trends data, which is one of the most exciting dasets of consumer behaviour in the world. So far the data could only be accessed via the Google Trends tool and in some very selected collaborations. With this new API, access is broadening and it signals that likely it will be even easier in the future. However, it’s still an Alpha and we are unsure on how big it is. In the meantime, specially for modeling-related purposes, remember that Meridian alredy incorporates the ability to include Google searches. Link with more details.
Spanish Effies
It’s summer and this means that the 2025 Effie Jury in Spain (composition) is really busy! A total of 265 cases made it to this stage (long list details). We wish the jury a lot of energy and wisdom!
Why most brands fail at incrementality testing
Recast, a solution for MMMs has a very interesting YouTube channel where they are interviewing Andrew Covato on the topic of incrementality testing. Risk aversion and being afraid to fail stands out as one of the main reasons why incrementality testing is hard to adopt. Moving from attribution based decisions towards incrementality based decisions is hard and risky, easiest move is to stay.
Meta reports on incrementality
Last May, Meta published a new paper called “Building a suite of truth: A hybrid approach to measuring incrementality”. Another spin in the triangle with a broader focus on the experiments piece:
It includes many healthy recommendations on how you should approach measurement which are very well aligned with the ideas in this newsletter. The idea of having calibration and regular experimentation as core competences in your marketing team is a strong callout as opposite as having people making decisions based on experience and gut feeling.
In a more practical direction, Haus published “The Meta Report: Lessons from 640 Haus Incrementality Experiments”, showing strong ideas about the ability of the Meta Ads platform to deliver positive results on incrementality tests by having a full funnel strategy rather than focusin only on lower-funnel campaigns. The idea of the uniqueness of experiments and that testing is unique for each business is also included in the conclusions. This also speaks to the fact that experiment data is messy and that more automation does not mean more incrementality. Food for thought for planning your next campaign to deliver incremental results rather than just attributed conversions.
Google Analytics MCP server launch
The MCP server allows you to connect an LLM like Gemini to our Google Analytics property and talk to our data.
Take a look at it yourself and look at a glimpse of what it is possible with this MCP server in the video below:
Oldies but goldies
We have seen in LinkedIn a book recommendation where many reputable individuals on marketing measurement have left their reaction so we thought this could be the best Oldie but goldies for this summer.
The book is "Reality in Advertising", written by Rosser Reeves in 1961. In a world saturated with complex digital metrics, Reeves' rigorous, evidence-based approach is a powerful reminder that the fundamental challenges of marketing measurement have been with us for decades.
The use of the term "advertising laws", a concept that echoes the scientific, evidence-based principles later popularized by Byron Sharp in his 2010 book, "How Brands Grow". While Sharp's work is grounded in decades of research on buyer behavior, Reeves took a different path, focusing on what makes advertising itself effective. Many of his conclusions, drawn from analyzing packaged goods sixty-four years ago, are quite well aligned with modern effectiveness thinking:
Ad Penetration (Reach) Drives Performance: Reeves argued that the reach of an advertisement is a primary driver of its success. This is a direct precursor to the modern emphasis on maximizing broad reach to all category buyers, a cornerstone of the Ehrenberg-Bass school of thought.
Maximize Audience Size, Optimize Frequency: One of his key pieces of advice was to "Reach your audience less often…and make your audience as large as you can". This is a strikingly sophisticated take on media planning, anticipating the contemporary debate around effective frequency and the diminishing returns of bombarding a small audience segment.
Reeves advocated for a simple yet powerful measurement: compare the percentage of people who buy your product and can recall your advertising against the percentage who buy but are not familiar with the ad. The difference, which he termed "pull-over," serves as a direct measure of the ad's effectiveness.
Reeves was relentlessly focused on the link between ad exposure and actual purchase behavior and one of the main pilars for such link was the Unique Selling Proposition (USP) a concept on differentiation that became popular but is challenged today as in most mature markets, achieving a truly unique, sustainable product or service feature is extremely rare. Instead we should prioritize distinctiveness, where what matters more is being instantly recognizable and memorable through consistent and unique brand assets (logos, colors, slogans, jingles, etc.). The goal is not to be different, but to be thought of easily and often in a buying situation.
We would love to hear your thoughts on this. Probably it depends based on your industry and market maturity. While we try to be different, if you choose to read us for distinctiveness, that’s also fine for us. Just leave a comment.