By R. Larsson, Advertising Week
As pressure mounts for marketers to prove what truly moves the needle, a new frontier is emerging inside Marketing Mix Models: creative effectiveness. With performance gaps widening and media signals no longer telling the full story, creative quality has become the variable advertisers can’t afford to ignore. In this Q&A, Andy Marrs, Global Client Partner at Vidmob, unpacks why creative is rapidly becoming the most powerful lever in modern measurement, how leading teams are integrating it into MMMs, and what this shift means for accuracy, accountability, and the future of marketing science.
Q: Marketers are under greater pressure to prove what works. What is driving the surge in advertisers wanting to measure creative effectiveness in Marketing Mix Models (MMMs)?
Our research with Meta, Kantar, Ekimetrics, Objective Platform, Analytic Edge, and Ebiquity shows that there is a clear shift across the market. Many advertisers have reached a stage where media signals alone cannot explain performance outcomes, and the gaps are becoming harder to ignore. Creative quality influences outcomes in a way that affects both long-term brand impact and short-term sales. As Richard Woodward from Ebiquity states, “Of all the levers that you can pull to improve ROI, creative is the one that can have the biggest impact.”
This is why demand for creative insight is rising so quickly. Matt Andrew at Ekimetrics captured it well when he said, “Creative used to only become a part of the conversation for more mature advertisers. Now, we see advertisers looking for creative measurement to be a part of the first stages of their measurement program.”
The other factor is the acceleration of AI. Teams can now quantify creative at scale, which was difficult to achieve before. Once that capability became real, interest in modelling creative quality in MMMs surged.
Q: When advertisers integrate creative into their MMMs, which approaches are helping them get meaningful clarity?
The goal for most teams is to understand why performance changes, not simply whether it did. Creative data offers that layer of explanation. Modelers are using structured creative quality scores, AI-derived visual and structural attributes in advanced MMM frameworks, as well as running calibration with incremental lift experiments to get there. Meta strongly encourages this kind of depth. Alfonso Calatrava from Meta explains that “MMM has shifted from a high-level strategic tool to a more tactical decision-making framework”.
Leading MMM practitioners are evolving their methods too. Objective Platform is using creative inputs within Bayesian frameworks. Analytic Edge is exploring causal inference. Kantar is combining creative asset analysis with performance data. The common foundation is consistent, scalable creative data, which allows these models to capture a variable that was previously missing.
Q: What benefits are advertisers seeing once creative data is part of the model?
Key benefits include improved model accuracy and more actionable results. When creative quality is included, attribution sharpens and predictions become more reliable. Some teams see dramatic differences in outcomes when creative quality shifts. Arno Witte at Objective Platform described this clearly: “High-quality creatives drove up to 36% stronger brand impact and 28% higher sales compared to average creatives.” Insights like this reshape how advertisers think about their top levers.
There is also the strategic benefit. Creative data helps teams understand which elements drive results across channels and formats. It creates a shared understanding between creative, strategy, and media functions. That shared view matters because it supports stronger planning, clearer decision-making, and more confident investments in future work.
Integrating creative data sounds promising, but also complex. What challenges do advertisers face when they try to do this?
The first challenge is defining creative in a structured way. Creativity is subjective, and teams need a practical framework that avoids oversimplification. Alfonso Calatrava at Meta pointed out that “creativity has a much more subjective component and is much more complex to categorize in terms of quality.” The second challenge is the sheer volume of assets. Digital environments produce rapid creative turnover, which makes manual processes impossible. Henrik Busch at Kantar noted that “the primary challenge is the sheer volume and complexity of creative data.”
Model capacity is another factor. MMMs can only accommodate a limited set of inputs. Every variable must add value, so creative signals need to be consistent and scalable. This is why many teams rely on continuous streams of structured creative data. It gives them the clarity they need without overwhelming the model.
Q: As creative becomes measurable in deeper ways, how do you see this shaping the future of MMMs?
A more integrated measurement ecosystem is starting to form. Creative and media performance are being evaluated together, supported by AI that uncovers patterns quickly and helps teams refine assets with much greater ease. Analysts expect more personalised models and more consistent cross channel insights as creative signals mature. Some expect future environments to reveal new layers of response. Richard Woodward at Ebiquity expressed this well when he said, “Creative success isn’t just about numbers. It’s about connecting with people, and the evolution in MMM measurement helps us get closer to understanding how we do that.” That human element will stay central.
The opportunity ahead lies in unifying these perspectives. When creative intelligence and marketing science operate inside one framework, the industry gains a clearer view of what is truly driving growth. That is where the next gains in effectiveness will come from.
