Marketing Mix Modelling for a world without cookies
If you work in marketing, it is very likely that in recent years you have felt the ground moving beneath your feet. The measurement infrastructure we built during the last decade—based on third-party cookies, user IDs, and granular tracking—has transformed radically. Between privacy policies like Apple’s ATT and the definitive elimination of cookies, the old reliability of Multi-Touch Attribution (MTA) has vanished.
But not everything is bad news. In fact, in 2026 we are witnessing something fascinating: the scientific renaissance of a technique that many considered “old school.” We are talking about Marketing Mix Modeling (MMM).
Today I want to explain why returning to this statistical technique, born in the 60s but now supercharged by Artificial Intelligence, is probably the most innovative and critical decision you can make for your data analysis strategy.
The collapse of deterministic measurement
To understand why MMM is the new standard, we must first accept what has failed. Deterministic attribution (MTA) depended on being able to follow a specific user through different touchpoints (clicks) until conversion.
However, in the current landscape of 2026, we face two gigantic problems:
- Signal Loss: We can no longer see the user’s full path. There are black “holes” in the data due to privacy restrictions.
- The “Walled Gardens”: Big platforms protect their data, making it impossible to have a unified view based on user IDs.
Trying to use MTA today is like trying to assemble a puzzle with half the pieces missing. You can guess the image, but you will never be sure of the result. This is where modern MMM comes into play, not to track individuals, but to understand massive correlations.
Modern Marketing Mix Modeling: Agile, Continuous, and Granular
It is possible that when reading “Marketing Mix Modeling,” six-month consulting projects, high costs, and static data telling you what happened a year ago come to mind. Forget that image. The MMM of 2026 has nothing to do with its predecessors.
Thanks to Artificial Intelligence and data flow automation, the new MMM is agile and continuous. Open-source tools like Google Meridian and advanced SaaS solutions have democratized access, allowing CMOs and marketing directors to obtain insights almost in real-time.
The great strategic advantage is that MMM is privacy-resilient. It doesn’t need to know who “John Doe” is or what website he visited yesterday. Instead, it analyzes aggregated statistical correlations between your media investment and your business results. It is a macro view that respects the user and, at the same time, gives you the truth about your investment performance.
The Triangulation of Truth: MMM, DDA, and Geo-lift-tests
Now, at Luce IT we always advocate for technological honesty: no method is perfect on its own. MMM is fantastic for the holistic view, but perhaps it won’t tell you which exact keyword worked best yesterday at 10 AM.
That is why the winning strategy for this year is what we call the “Triangulation of Truth”. It involves combining three methodologies to cover all angles:
- MMM (Marketing Mix Modeling): For the global strategic and budgetary view. It allows you to measure what digital attribution always ignored: the impact of connected TV, out-of-home advertising (OOH), your brand’s “halo” effect, and even uncontrollable external factors like the economy or the weather.
- Data-Driven Attribution (DDA): For short-term tactical optimization in digital channels where we still retain signal.
- Incrementality Tests (Geo-lift tests): The secret ingredient. These are scientific experiments (like turning off advertising in one region and leaving it on in another) to calibrate and validate that what your model says is real.
From Correlation to Real Causality
Interest in MMM has skyrocketed 300% by 2025, and the reason is purely business. Adopting this model allows you to move from assigning budget based on correlation (last click takes the credit) to causality.
Think of it this way: if you retarget a user who had already decided to buy from you, the classic attribution model will tell you that ad generated the sale. MMM, calibrated with incrementality tests, will tell you the truth: that sale would have happened anyway. This level of insight allows you to move budget from tactics that only “harvest” demand to tactics that actually generate new demand.
MMM as a Strategic Decision
The transition to modern MMM is not just a technical update for your Data team; it is a strategic business decision. In a fragmented and privacy-conscious world, returning to advanced statistics powered by AI is the only way to regain full visibility of your media mix.
The technology is already here, it is more accessible than ever, and it allows for a transparency that was previously unthinkable. The question is not whether you should adopt it, but how long you can afford to continue operating without it.
At Luce IT, we know that precise measurement is the key to success. We help you implement advanced models with our Attribution Model and ensure the reliability and quality of your base information with our Data Platform. Do you want to make the leap to future measurement with data you can trust? Contact us.
Frequently Asked Questions about Marketing Mix Modeling
What is the main difference between Marketing Mix Modeling (MMM) and Multi-Touch Attribution (MTA)?
The fundamental difference lies in the data approach. While MTA tries to track the individual journey of each user (which is difficult today due to privacy and cookie loss), MMM uses statistical analysis of aggregated data to correlate media investment with sales results, without relying on personal identifiers.
How to measure the ROI of offline and online channels together
Thanks to the “renaissance” of MMM and the emergence of open source tools like Google Meridian and SaaS solutions, this technology has been democratized. It no longer requires million-dollar investments in consulting, allowing medium-sized companies to access sophisticated and agile measurement models.
How does MMM help measure the return of offline channels like TV or out-of-home advertising?
Unlike digital attribution, which is often “blind” to what happens outside the internet, MMM ingests data from all channels (online and offline) and external factors (economy, weather). This allows mathematically calculating how a TV campaign or a billboard contributes to total sales, offering a holistic view of ROI.
How to measure the ROI of offline and online channels together?
The only methodology capable of unifying both worlds reliably is modern MMM. Unlike digital tools that only “see” clicks, MMM ingests investment data from all your channels (TV, Radio, Outdoor, Social Media, Search Engines) and crosses them with your sales results and external factors (such as the economy or weather). This allows mathematically calculating the incremental contribution of each channel to global ROI, eliminating information silos and allowing you to compare the profitability of an ad on Instagram with a billboard under the same mathematical standard.
Incrementality tests (such as Geo-lift tests) are experiments that isolate a variable to see its real impact. They are vital for the “Triangulation of Truth” because they serve to calibrate and validate MMM predictions, ensuring that the model correctly distinguishes between causality (the ad generated the sale) and correlation.
