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In an environment where budgets are fragmented, cookies are disappearing and consumer attention is spread across dozens of platforms, Marketing Mix Modelling (MMM) is back on the table.
And this time, it doesn’t come alone: artificial intelligence is giving new life to a methodology that many thought was dead.
What is Marketing Mix Modelling (MMM) and why has it fallen into disuse?
Marketing Mix Modelling is a statistical technique that seeks to answer a crucial question: Which marketing channel really drives my sales?
It is based on analyzing historical data (investments, sales, seasonality, prices, promotions, etc.) to estimate the impact of each variable on business results.
For years, it was the foundation of analytical marketing… until digital marketing came along.
With the advent of individual tracking, cookies and performance marketing, many advertisers abandoned MMM for more immediate and seemingly accurate models, such as attribution modeling.
However, with the end of cookie-based tracking, stricter privacy policies and channel saturation, MMM is once again relevant.
Google highlights this in its “Top Digital Marketing Trends and Predictions for 2025” report as a key trend in the face of fragmented budgets and loss of direct traceability.
The new MMM doesn’t look like it did a decade ago. AI is transforming this approach in three essential dimensions:
In the past, building a marketing mix model could take weeks or months.
Today, machine learning algorithms make it possible to update and recalibrate models in near real-time, incorporating more variables (such as digital behavior, social sentiment, or CRM data) with greater accuracy.
Traditional MMM focused on “paid” or ATL channels (TV, radio, press).
Now, thanks to AI, online, offline and social data can be integrated, cross-referencing awareness, engagement, leads and sales metrics in a single model. This enables much smarter decisions about where and how much to invest.
AI-driven platforms generate automatic simulations:
“What would happen if I increase 10% my investment in Meta and reduce 5% in Google Ads?”
The model predicts results and suggests the optimal mix. It is no longer a matter of analyzing the past, but of anticipating the future with data-driven accuracy.
Let’s imagine a cosmetics brand with a presence in social media, e-commerce and physical retail.
Your marketing team faces a classic question, “Where should I invest more: influencers, search ads or social ads?”
Using an AI-driven MMM model, the system analyzes three years of historical data, pricing, promotions and consumer behavior.
The model reveals that:
With this information, the company can reallocate budget, project results and reduce advertising waste.
The renaissance of Marketing Mix Modelling is not nostalgia for the old school: it is the natural evolution of a market that demands more robust models, without relying on cookies, and powerful thanks to AI.
By 2026, teams that combine the analytical capabilities of MMM with the predictive agility of artificial intelligence will not only optimize their advertising spend, they will dominate evidence-based marketing strategy.