Marketing Mix Modeling

Marketing Mix Modeling (MMM) is a statistical method that measures the causal impact of each marketing channel on your sales, without individual tracking or cookies. Specifically, from aggregated data (media spend, sales by week and geographic area, context variables), a Bayesian regression model (here Google Meridian) decomposes each variation of your KPIs between your media channels, seasonality, promotions and organic baseline demand. It models the delayed effect of campaigns (a TV ad doesn't convert on the same day) and diminishing returns (beyond a certain budget, marginal impact decreases). Result: ROI per channel, response curves, and a factual basis for optimizing your mix, with zero cookie dependency.

Because digital attribution is reaching its structural limits, and open source frameworks like Google Meridian have made MMM accessible. MMM has existed since the 1960s, but was eclipsed for 20 years by click-based attribution. Three phenomena are putting it back at center stage: (1) the end of third-party cookies and strengthening of consent reduce attribution coverage, (2) top-of-funnel channels (Meta, YouTube, TV) generate effects that clicks don't capture, (3) open source frameworks (Google Meridian released in 2025, Meta Robyn) and cloud have divided implementation costs by 10. What was reserved for very large advertisers is now within reach for mid-market companies.

These are three complementary tools operating at different time scales. Attribution (GA4, data-driven) steers daily operations. It feeds Smart Bidding and real-time performance tracking. Its limitation: it depends on tracking, doesn't see delayed effects, and misses channels without clicks. MMM takes a broader view. It measures the causal contribution of each channel over the medium-long term, including what attribution can't see. Incremental tests (geo-experiments) create a controlled shock: cutting a channel in a geographic area for a few weeks and observing the impact. It's the gold standard for causal validation. Results calibrate the MMM and improve its accuracy. Attribution for daily operations, MMM for strategy, incremental tests for proof.

Google Meridian is the most advanced open source MMM framework on the market, developed by Google's research teams. We adopted it because it's transparent, auditable and aligns with our philosophy: no magic, just rigor. It natively integrates Reach & Frequency data (YouTube), geographic granularity (geo-level model), and Google Query Volumes as an industry demand signal. Its Bayesian approach allows injecting business knowledge via priors, which makes the difference between a theoretical model and a reliable decision tool. As a Google Partner, we master integration with the Google ecosystem (Ads, BigQuery, YouTube R&F).

A first usable model is typically delivered in 6 to 10 weeks. The timeline breaks down as follows: 2-3 weeks for data collection, cleaning and structuring (a critical, often underestimated phase); 2-3 weeks for modeling, calibration and diagnostics; 1-2 weeks for result analysis and recommendations. The limiting factor is almost always data: availability of historical data (ideally 2 years minimum on a weekly basis), quality of ad platform exports, CRM access. Our pipeline expertise (BigQuery, Airbyte, Dataform) significantly accelerates this phase.

MMM starts providing relevant answers when you invest more than 100K per year in media, spread across multiple channels. The determining criterion is not so much volume as mix complexity. As soon as you invest across multiple channels simultaneously, including top-of-funnel (Social, YouTube, Display), and have doubts about attribution, MMM delivers value. What also matters: channel diversity, available historical data (2 years weekly minimum), and ideally geographic granularity. Five years ago, the entry ticket was much higher. Open source frameworks and AI have changed the game.

No. sGTM and MMM are complementary, they address different problems. sGTM improves collection quality: it partially bypasses blockers, extends cookie duration, feeds Meta CAPI and Google Enhanced Conversions. It's an excellent technical lever for improving attribution and smart bidding reliability. But sGTM doesn't change the fundamental limitations of attribution: it doesn't measure non-clicked campaigns, doesn't model delayed effects, and remains dependent on consent. MMM operates at a different level, on aggregated data, without tags or cookies. However, a well-configured sGTM produces cleaner attribution data, and this cleaner data can serve as more reliable priors for MMM. The two reinforce each other.

Results are measured across three concrete axes. Decision clarity: you know which channel truly generates value, with confidence intervals. Not a single self-declared number from a platform, but an honest Bayesian distribution. Budget optimization: our clients see on average a 20 to 50% improvement in their media mix efficiency after reallocation. Concretely, more results for the same investment, or the same results with a reduced budget. Confidence to scale: channels like Meta Ads awareness, YouTube or Demand Gen, which seem unprofitable in last-click attribution, often reveal a much higher incremental ROI than classic dashboards suggest. MMM gives you the confidence needed to accelerate on these channels.