How to design a future-proof MMM to support an advanced marketing operation

Analytics
Measure
MMM
Attribution
Decision support
Decision making
Author

Kristian Dyhr Toft

Published

November 18, 2025

Illustration from Freepik

Designing a strong marketing measurement setup is a complex matter today, with a lot of decisions to be taken. Data and technology allow for a lot of possibilities, and choosing the right approach itself can be a huge topic of discussion. The field of marketing measurement has evolved enormously in the past years, with Marketing Mix Modelling (MMM) still being one of the cornerstones for many organizations. MMM has a lot of benefits when it comes to implementation and works well as part of the ecosystem in marketing decision making. Design of an MMM is however not a “one-size-fits-all” exercise, as you will have to make a number of choices to reach a well-functioning setup. In this blogpost we discuss a couple of topics that should be considered when entering into a design phase of an MMM solution.

With what frequency should the models and results be updated?

Any person in a marketing department involved in campaigns and media buying will typically be extremely impatient in relation to getting fresh insights into the current performance. Who wouldn’t like to measure the effect of the campaign that started yesterday? An MMM setup should definitely be able to deliver insights with a high frequency, but there are a few things to consider in relation to this topic:

Even though it sounds old-school, it is not recommended to start your MMM journey by setting up automated data flows from day one. It is much more valuable to start out by building robust models, understanding your business drivers fully and identifying the right and relevant data sources for your setup. That’s where you should start.

Once the initial models have been developed and are fully aligned, you should establish a system based on automated dataflows and instant model updates. This will save a lot of time in the modelling processes and ensure fast delivery of fresh results.

Automating the data flows will give you the possibility for very frequent (even daily) updates of results, which to some extent would be a dream scenario. Even though this is tempting, you have to think about whether your organization is able to react to results that are updated on a daily basis. Do you believe that the high frequency in the outputs will give information that is valuable, or would you be better off with automated reporting by week or by month? In many cases the latter actually turns out to work best when it comes to implementation of an MMM.

What KPIs should we use to measure campaign success?

The amount of potential KPIs to measure is enormous. Choosing the right ones for your organization is a fundamental part of the design.

An important rule of thumb is that you want to choose KPIs that are as closely linked as possible to the overall business performance. It can be tempting to track your campaign performance on metrics like clicks, engagement scores and view-through rates, but they don’t really tell anything about the real business outcome of your marketing efforts. A strong measurement setup should be based on real business KPIs such as revenue, transactions, in-take of new customers or similar.

It is better to start out with high-level KPIs for your business and plan for increased granularity as next step. By focusing on solid model building for high-level KPIs in the initial phase, you will reduce complexity and secure a quick and successful initial implementation of your MMM. Following that, you should go for a setup with an ambitious granularity level in the KPIs, which will enable you to deliver detailed insights into your marketing effects across sales channels, regions, by product, by segment etc. Once you reach this point in the implementation, the MMM setup will successfully obtain even broader relevance within your organization and among your main stakeholders.

Conclusion?

There are a number of things to look out for and consider when going into a design phase of an MMM. In this blogpost we have touched a couple of the most essential ones. The main point is to start “simple and focused” and then go from there to “automated and granular”. By following this approach, you are well underway to ensure a successful and future-proof MMM development for your organization!

If you want to learn more about how we help businesses and organizations obtain new levels of growth through data driven decision-making, don’t hesitate to reach out.