MMM resurgence requires trustworthy models to be successful

As interest in marketing mix modelling grows, the industry must focus on trustworthy models that empower better marketing decisions, writes Monks VP of measurement APAC, Brett Camilleri.

The resurgence of marketing mix modelling is no accident. As marketers grapple with fragmented media channels, data siloes, and the collapse of third-party cookies, the pressure to prove ROI has never been higher.

Add increasing scrutiny from finance teams and the demand to do more with less, and it’s clear why marketing mix modelling (MMM) is experiencing a renaissance.

Fueling it is the launch of Google’s open-source platform Meridian, which has reignited industry interest and encouraged brands to explore in-house MMM capabilities.

While the barrier to entry for MMM is lower, the need for robust, reliable measurement remains critical. As adoption grows, so does the risk of poorly built models, which may be undermined by inaccurate data, flawed processes, or lack of expertise.

Inexperienced practitioners can misattribute channel effectiveness, overlook critical business context, and surface flawed insights that lead to poor decisions. This can undermine the credibility of the models and erode organisational trust in MMM altogether.

Key ingredients

So, what does it take to build trustworthy marketing mix models that deliver actionable insights and drive better business outcomes?

It starts with the right people. MMM thrives on collaboration, bringing together project leaders who align model design with business needs and manage senior stakeholders, experienced econometricians who build robust models and translate technical outputs into clear, accessible language, and domain experts who provide context and develop actionable, business-friendly insights. Without this blend of expertise, even the most advanced models can fall short.

The second critical ingredient is data. Trustworthy models rely on high-quality, validated data inputs. From media spend and impressions to pricing, promotions, and macroeconomic factors, every data point must be accurate, complete, and properly structured.

If the goal is to update models regularly, automating the data pipeline is essential. It is what transforms a one-off analysis into a scalable, decision-making engine.

A transparent modelling approach is essential to building confidence in the results. A technical lead must be able to clearly explain the methodology, underlying assumptions, and rationale for variable selection.

This clarity helps demystify the model and encourages meaningful engagement from the business. That means validating the outputs not only through statistical tests but, more importantly, against commercial reality.

From insight to impact

The final, and arguably most important, step is translating sophisticated modelling output into clear, practical decisions.

This means delivering results in business language, grounded in context, answering critical marketing questions, and supported by real-world budget optimisation scenarios that demonstrate the value of adjusting your media mix.

When teams understand what the model is telling them and how to act on it, MMM moves from insight to impact. Workshops, planning sessions, and ongoing support embed the model into decision-making and turn outputs into better business outcomes.

Trustworthy MMM unlocks smarter decisions, stronger ROI, and sustained growth when built with the right people, robust data, transparency, and a focus on delivering actionable, insight-driven recommendations.

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