Marketing Mix Modeling has been around for decades, but its role is evolving fast.
As data privacy rules tighten and attribution becomes harder to trust, MMM is gaining fresh relevance as a strategic tool for understanding what’s really driving performance. To make it work, marketers need more than just a model. They need alignment, accessibility, and strong data foundations.
Jonathan Sweeney is the Senior Director of Data at Gain Theory, a marketing effectiveness consultancy that’s been pioneering MMM since the 1970s. In this episode of The Undiscovered Metric, he joins host Mark Debenham to explore how MMM is being applied today, how MMM complements attribution, and what marketers need to do behind the scenes to get real insight.
Read on for key insights from the episode or watch the full episode below.
Why is MMM back on the radar in today’s data landscape?
MMM isn’t new, but it’s become more relevant in a changing landscape. As marketers try to connect fragmented datasets and fill the gaps left by cookies and consent frameworks, many are revisiting MMM as a way to measure performance across digital and offline channels without relying on user-level tracking.
Jonathan points to the shift in the digital landscape as the key driver for MMM’s resurgence. “Digital has brought huge amounts of new data, huge amounts of structures that just didn't exist prior to that,” he explains. “The methodologies behind MMM are built to get better insight into the detail of digital, which is much more varied and much wider spread than in other areas of marketing.”
This complexity has also exposed the limits of multi-touch attribution (MTA), which once promised marketers a clear, real-time view of the customer journey. “It’s not insurmountable,” Jonathan says, “but especially for the people that don't have the level of investment to work in data clean rooms, it’s become almost unattainable.”
MMM isn’t a magic bullet
As MMM returns to the spotlight, it's important to see it as part of a broader measurement toolkit. It’s not meant to replace other models.
“MMM isn’t a silver bullet,” Jonathan says. “It’s a really important tool as part of a best-in-class strategy.” Marketers shouldn't expect it to solve everything, but they can expect MMM to provide a clear, strategic view of how channels are contributing, especially when more granular attribution falls short.
What matters is how it’s used. MMM works best when it’s aligned with campaign goals, other analytics methods, and wider business objectives.
Use MMM to model trade-offs, not just outcomes
One of MMM’s biggest advantages is its ability to simulate trade-offs. It’s especially helpful when marketing teams need to justify decisions or balance conflicting priorities. Jonathan points out that the model is only as strong as the data behind it.
With the right inputs, MMM becomes a practical way to preview the impact of different strategies. “If I want to try and increase my brand awareness,” Jonathan explains, “then there may be some potential negative impacts on my short-term sales.” When those trade-offs are made clear in advance, marketers can communicate them confidently and avoid surprises.
This kind of modelling also makes it easier to secure future investment. “When it comes down to reviewing the activity,” Jonathan says, “you can say, well, yes, we did say this was likely going to have an impact.”
Why your measurement plan must align with the business strategy
MMM is most useful when it’s built to reflect the priorities of the business, not just the marketing team. Jonathan says this misalignment is more common than you’d think.
“There can often be a mismatch between the KPI that you’re measuring and what you're actually trying to achieve as a business,” he explains. For instance, a model built to optimize short-term sales won’t be helpful if the organisation is focused on long-term growth.
Closing this gap takes more than model tweaks. It requires a shift in how teams collaborate. “The real piece is communication,” Jonathan says. “Trying to bring in a data-informed culture within the whole organization.”
Sharing MMM insights across the business boosts alignment and impact.
That includes making the analysis accessible beyond the marketing team. “The outputs of the econometric analysis, the MMM—whichever analysis you're doing—should be available to everyone to review.” When stakeholders across finance, strategy, and leadership are engaged, marketing becomes more visible and better aligned to shared goals.
Don’t ignore competitive spend — it shapes your results
When asked what marketers should be paying more attention to in order to get ahead of the competition, Jonathan names something many teams ignore: competitive spend.
MMM relies on context. If you only model your own activity, you risk missing the influence of what others are doing. “How much have they spent in marketing? Where are they spending it?” Jonathan asks. “That is incredibly important because how competitive you need to be in the marketplace can really impact the value you get returned from your marketing.”
He adds that competitive gaps can be rare and valuable. “You'll often see pullbacks in spending, and those can be huge opportunities to be the only voice in a vertical or a space to really get home your message.”
Start with progress, not perfection
Jonathan’s final advice is simple. Don’t try to make MMM perfect before you start. Build something useful, then improve it over time.
“The idea of having everything perfect first time, I would say, is a way to ensure you get no results,” he says. “And I always say some results are better than no results.”
That means focusing on what matters most, identifying the data you actually need, being aware of its limitations, and being prepared to evolve the model as the business changes. Starting small also helps avoid the budget burn that comes with overly complex initiatives.
Jonathan shares one example where a company tried to model every variable in a single MTA project. The alignment issues were so great that nothing ever shipped. “They would not accept insight that didn’t have every single possible impact,” he says. “It was really frustrating that we never actually got to a final point and achieved it.”
That experience helped define how Gain Theory works today. “We can be more truthful in that kind of situation,” Jonathan says. “The value is not the solution. It's the insight that comes from it. The ‘what next?’ — that is the value.”
Final thoughts
Marketing Mix Modeling is only as valuable as the strategy and the data behind it. As Jonathan Sweeney shows, its real power comes from aligning with business goals and using the right data. Get that right, and MMM becomes a decision-making tool, not just a measurement method.
