Why CPG Marketers Are Outgrowing Their MMM Stack

For many consumer packaged goods (CPG) marketing teams, the traditional marketing mix model (MMM) has become a strange kind of liability. It was supposed to be the source of truth on what's working. Instead, in many organizations, it has become the thing the team resents most: expensive to license, slow to update, and rigid in exactly the moments when the business needs it to flex. A new product launch, a sudden retail media shift, a competitor's price move: by the time the model catches up, in most cases the decision has generally already been made on gut feel.

This isn't a fringe complaint. Inside large CPG organizations, one of the more candid frustrations we hear relates to the cost and rigidity of off-the-shelf MMM tools. One team we’ve been talking to is now trying to build measurement capability in-house rather than keep paying for a system that doesn't move at the speed of the business. We think that instinct, to bring measurement closer to the people making decisions, is the right one. However, the problem is that building it from scratch, with a lean internal team, is slow, expensive, and hard to keep current as channels and data sources multiply.

The timing pressure only compounds the issue. Epsilon's 2026 benchmark study found that 67% of C-suite leaders describe their company's AI use as "extremely mature," compared with just 33% of the senior managers actually running campaigns day-to-day. That gap is both tangible and meaningful simply because it signals that the tools the senior leadership team is excited about are not the tools the people closest to the data are use with confidence. In potentially simpler terms, a marketing intelligence layer that only the data science team can operate isn't solving the problem, it's actually just relocating it.

Where Agentic Marketing Changes the Equation

Kana believes that agentic marketing done right should provide an operating layer that is built to learn, so that a marketer's judgment and decisions compound instead of getting rebuilt from scratch every time. You could say that Kana Marketing Intelligence is where that definition earns its keep. A traditional MMM engine is a static model: someone builds it, someone maintains it, and every meaningful change to the business (i.e. a new channel, a new SKU, or a shift in retail media mix) requires a rebuild or a costly re-engagement with the vendor. It doesn't get smarter on its own. It just grows stale.

Here's where two of Kana's principles do real work:

1. Our belief in “outcomes, not tools” means the measure of success isn't a dashboard full of coefficients, it's whether a marketer gets a decision-grade answer fast enough to act on it. 
2. Our advocacy for a “trusted stewardship” model matters a ton here: if a brand is going to move budget based on what a model says, it deserves to have full confidence that its own performance data is being used correctly and stays its own asset, rather than being spun into someone else's black box.

How We Recommend CPG Marketing Teams Begin Agentic AI Adoption

The practical shift is to stop treating marketing intelligence as a periodic modeling exercise and start treating it as a standing capability the team queries in the same way it would ask an analyst a question. Concretely, that means connecting Kana's Marketing Intelligence directly to the performance data already flowing through the stack, media, retail media networks, promotional and pricing data, without waiting for a quarterly refresh cycle. It means providing the capability for marketing and data teams to ask their plain-language questions about channel efficiency, incrementality, and budget reallocation directly within the application. And it means getting an answer that is grounded in the brand's own data in minutes rather than weeks. Last, but absolutely critically, it means setting up the system so every new question and every new data source makes the next answer better, instead of requiring a new modeling engagement each time the business changes.

The goal is not to replace the discipline of good measurement. The goal is to remove the multi-week lag between a question and an answer, so that measurement stops being a retrospective report and starts being a live input that informs hourly, daily, or weekly decisions.

Why Kana Is Built for This

Kana's Marketing Intelligence is built around a brand's actual data environment and actual journey, not a generic template that has to be bent to fit. Our system keeps learning as your business changes, so a new retail media partner or a shifting channel mix doesn't send the team back to a vendor for a rebuild.

For a CPG marketing or data leader who has watched an MMM investment go stale faster than the business moves, that combination, precision, speed, and a system that keeps pace, is the difference between a report and a working advantage. If that gap sounds familiar, we'd welcome the chance to show you Kana Marketing Intelligence running on your own data. Get in touch today.

Sources

• 2026 benchmark study: Marketing's AI inflection point (Epsilon, 2026)