Between 60% and 75% of marketers say today’s advanced measurement approaches fall short on rigor, timeliness, trust, or efficiency (IAB, State of Data 2026).
But CMOs aren’t asking for a better Bayesian engine. They’re asking for a faster, more trustworthy answer to “what should I do with my budget on Monday morning?”
Today we’re launching Self-Serve in GrowthOS, an end-to-end agentic onboarding experience that removes the cost, time, and technical barriers to enterprise-grade commercial mix modelling. This means that any marketing team can now turn their media spend, pricing, and sales data into actionable decisions in under 24 hours, without any agency involvement required.
The false trade-off in MMM
For a long time, marketing mix modelling (MMM) was considered the measurement standard proving marketing effectiveness. But for most of the mid-market and growth stage businesses, the barriers to entry were simply too high.
As the internal pressure and demand for the CMO role increases, MMM is no longer equipped to sufficiently arm decision-makers with the full commercial context to own the revenue story. Measuring media spend alone does not account for factors like pricing, promo, distribution, or external market signals.
Marketers have been conditioned into seeing model access, quality, and fairness as necessary trade-offs.
Access. Legacy MMM is priced for the largest enterprise brands in the world, often costing seven-figure engagement contracts with agencies. First answers take months to arrive. Refreshes take another six to eight weeks. By the time the data lands, you’re two budget cycles past the decision it was meant to inform. A model that tells you paid social drove 18% of revenue but can’t account for the 20% price increase you ran alongside it isn’t a growth model. It’s an attribution shortcut.
Quality. When rigorous MMM is expensive and slow, the market produces shortcuts. Faster, cheaper solutions return answers in a fraction of the time. But a fast answer that’s wrong is more dangerous than no answer at all. Unstable coefficients, stripped-out granularity, and skipped validation mean results swing wildly between refreshes, and real-world forecast errors can result in millions in misallocated capital — eroding trust, credibility, and confidence in the numbers decision-makers rely on.
Fairness. MMM was supposed to solve the conflict-of-interest problem that broke last-click attribution. Now the same problem is showing up in out-of-the-box MMM solutions, with a different label. When the media platform runs the model that measures its own performance, the channels it owns get favoured. And when objectivity is treated as optional, marketers are left questioning whether the numbers serve their interests or the platform’s. If they’re sceptical of the figures, how can they expect their CFO to buy in?
Six of the ten biggest marketers in Australia run on Mutinex because they’ve refused to accept these as the cost of doing business.
How Self-Serve in GrowthOS works
Legacy MMM still relies on agencies and data science teams to implement, run, and refresh the models. Self-Serve in GrowthOS replaces that layer with agents that do the work autonomously, in hours instead of weeks.
1. Upload your data. Bring in your paid media spend, sales, and pricing data through a guided onboarding flow. Our agents check it against our data contract, standardise formats, align it to a weekly time series, and flag any gaps or mismatches before anything runs. (For now, Self-Serve in GrowthOS supports single-node deployments only.)
2. Run the model, sit back, and relax. Triggering a run starts the onboarding pipeline. Depending on your data volume, the run may take several hours. As a reference, Hershey’s successfully completed a full model build with us in roughly four hours.
3. See your insights in GrowthOS. Ask MAITE, our conversational AI layer, any question about your data and get actionable answers — with the numbers to back them up at your next leadership meeting.
For the first time, powerful commercial mix models are in the hands of the people actually making the budget decisions.