What can Google's new Marketing Mix Modeling Meridian do?

Google has released "Meridian," an open-source solution for Marketing Mix Modeling (MMM). Google is taking the same step as Meta did in early 2021 with Robyn, only about four years later. Between 2021 and 2025, another provider of a promising open-source Marketing Mix Modeling solution has established itself in the market – PyMC with PyMC-Marketing.

But what are the differences between the three now-available open-source solutions, and what limitations exist?

We have been active as a Marketing Mix Modeling provider for over five years and continuously work with clients to increase the efficiency of their marketing activities.

We have taken a close look at the various open-source solutions for you and compared them with our self-developed MMM method, Scope.

Key Insights

1. Hierarchical Modeling

With Meridian from Google, it is now possible to use data from different geographic regions when training the model, even with an open-source solution. Training on granular data helps a model tremendously to determine the effect of media activities more accurately and reliably. This gives you a much more valid basis for making decisions to optimize your media activities, provided you have the necessary data from multiple regions.

2. Flexible Media Efficiency Over Time

Two of the open-source solutions assume by definition that the effect of a media variable is constant over time. On the one hand, this makes sense because the amount of data available in most MMM projects is only sufficient to determine the average effect of a media variable over the entire period under consideration with reasonable certainty. On the other hand, this does not correspond to reality, in which it is crucial for the media effect when and how media is played out in what form.

3. User Interface for Use Without Coding

Without coding knowledge, I cannot cope with any of the freely available open-source solutions. All three solutions are provided in the form of code that you can download and then execute to get the results.

Conclusion:

The ability to model hierarchically on regions is another big step forward for the capabilities of the available open-source solutions. The prerequisite for use remains coding knowledge or at least the ability to execute code and deal with error messages.

Furthermore, the release of Meridian by Google shows that Google has also recognized that the previously available digital attribution methods are no longer up-to-date and sufficient to evaluate the impact of marketing and media activities. The importance of Marketing Mix Modeling as the most important standard tool in the marketing measurement toolkit is underlined once again by this step.

How MMT can help you further?

The data available for Marketing Mix Modeling always remains the same, regardless of the solution used. We have developed a platform where you can easily use all existing open-source solutions without coding knowledge to train meaningful models, and we also provide our own, even more powerful method that eliminates some of the current limitations of the open-source solutions.

Without much effort, this allows you to take a comparative look at the results from the different solutions. Which model comes to which conclusions and why? This look at the different model results gives the user a deep understanding of how the instrument, Marketing Mix Modeling, and the effect of your media activities work. This deep understanding will enable you to make the right decisions for more media efficiency. Depending on your needs, you can either be the user of the platform yourself or leave the use to one of our MMM experts.