Marketing Mix Modeling Retakes its Role in Media Optimization

Thoughts on our presentation at the ARF Attribution and Analytics Accelerator 2022 Conference, November 14-17th

Nov 01, 2022

Mark Garratt, Partner and Co-Founder at in4mation insights

We are at an interesting inflection point in the world of advertising media, for sure. Since the days of media being dominated by broadcast advertising (remember those voices shouting that “TV is KING!” in the early days of digital? How quaint that sounds today!) through to the moment in time when the act of buying media was disrupted by programmatic and all manner of tech enabled deployment, the most common descriptor we heard of the media landscape was that it was becoming “fragmented”.

From where we sit, we have seen factors for building the optimal media plan as increasingly complicated and opaque. This has always made it challenging for brands to make informed decisions on how and to whom to allocate your media budget to get the best return.

It is also important to note that for a while the gold standard for managing media spend decisions, marketing mix modeling (MMM), seemed to take a back seat to the elusive but tantalizing promises of multi-touch attribution alone as the holy grail for measuring media effectiveness. Despite perhaps not being in vogue, we stuck to MMM because of what we knew in our gut was the better solution overall IF the challenge was complexity and multi-dimensional influence.

We knew there were no short cuts. So we have spent over 20 years building, refining and applying our core competency for our clients in a technical approach to MMM that leverages the power of robust computational modeling tools (for exactness it is called Hierarchical Bayesian analytics, but that is a subject for another post). It has been proven to deliver better, more granular and flexible results to measure and optimize media spend for optimal return.

It was therefore gratifying to us when the ARF dedicated an entire track in their upcoming Attribution Accelerator conference to the topic: The Renaissance in Marketing Mix Modeling: Meeting New Expectations.

We will be presenting there with our client from OLLY, specifically on how the right kind of MMM fueled by a robust Bayesian approach can solve for the most complex combination of factors, and fragmented data.

Why a renaissance, you might ask, and why are there new expectations?

If anything, the fragmentation in the media industry has accelerated exponentially since the days when the sword fights over media effectiveness were confined to the battle over TV versus the nascent category loosely defined as Digital. Today’s media plan is distributed across hundreds if not thousands of potential touchpoints that involve choices across channels that didn’t exist 5 years ago: TikTok, Instagram, Podcasts, DOOH, and now the exploding sector that is referred to as Retail Media.

MTA as a standalone as originally envisioned has yet to really deliver on its promise, and its future reality is made all the more challenging with the pending demise of cookies and increasingly technical and regulatory focus on privacy. With the right integration into MMM, (delivered as quickly and cost effectively as we have proved is possible with our own solutions) has the potential in the future to be game changing, but we need both to be functioning optimally in a world of much more complex expectations. We are developing just such an integration.

The “perfect storm” that is also brewing, of course, is the added specter of brands reducing marketing spend in the face of inflationary factors leading to an economic downturn.

Increasingly the C-Suite is challenging all business teams to do more with less, and marketing is not immune to this directive.

What we at in4mation insights are pursuing with our clients is the objective of “doing the best with less”, specifically within their media budgets that were being stretched thin even before the drumbeat of reduced marketing spend! How do we do this? By providing robust evidence-based MMM analytics that reveal the optimal contributions of all media channels no matter how fragmented and complex their interactions might be.

Is our MMM approach new? We would be more likely to say that it is a product of years of investment, innovation, and validation to make media return on investment more predictable and attainable no matter the complexity of the factors. If that constitutes a renaissance, we are happy to guide our clients out of the Dark Ages!

Contact us at to talk about how our solutions can help you apply the best of MMM techniques to the increasingly complicated world of media spend management.