MMM Was Never Asked/ Built to Decide — Now Business Needs It To: How Agile, Granular Large Causal Models Make That Possible

MMM was built to explain what already happened — never to decide what happens next. That was fine when it stayed an analytical tool. But business has changed what it’s asking of it: it now wants MMM to drive the decision itself, continuously, as conditions shift — not produce a report someone interprets once a quarter. CIMM’s own paper, “Models as Masters?” (July 2026), names exactly this shift: MMM has quietly moved from analyzing historical performance into infrastructure now expected to carry real decision-making authority. The paper asks, in effect, whether the model is actually ready for a job it was never built for. Most of the industry’s answer so far has been to make the same model faster or better governed — which doesn’t change what job it was built to do.

This session argues MMM can’t be handed that authority as-is, for two structural reasons no amount of governance or faster refresh cycles fixes. First, MMM outputs a channel-level coefficient — business decides at platform, audience, market, week — so someone still has to translate the model’s output into a real action, which means the model isn’t deciding anything, it’s still describing, one step removed. Second, MMM is fit on a quarter or a year of history and re-estimated on a schedule, so by the time it’s deployed, the world it learned from has already moved — a model that only updates when someone reruns it can’t be trusted to decide on an ongoing basis.

What makes it possible: Large Causal Models built for granular, continuous decisioning — closing both gaps at once, so the model can act at the resolution the business actually operates in, and keep deciding as new outcomes arrive, not on a fixed re-estimation cycle.

Key Takeaways

• Why MMM was built to describe, not decide — and why business now wants that authority anyway, per CIMM’s own “Models as Masters?” framing of the shift
• The two structural gaps — actionable granularity and continuous learning — that keep MMM from earning the decision-making role it’s being asked to fill, and why faster refresh cycles or better governance don’t close either one
• What granular decisioning actually looks like: model output at the platform, audience, and market level a business can act on immediately — not a channel coefficient someone still has to interpret
• Why agile, continuous learning — not scheduled re-estimation — is what lets a model be trusted with real-time, ongoing decisions as market conditions shift
• How Large Causal Models make that possible — turning a descriptive measurement tool into a system built to run the business, not just report on it