The team needed a planning system that joined marketing spend, demand signals, and business constraints into a model executives could actually use when defending budget.
Channel teams had different views of incrementality, which made budget conversations political instead of analytical.
MMM outputs were useful to data science but too static for leadership conversations that changed every planning cycle.
Forecasts had to handle campaign launches, seasonal shifts, channel saturation, and uncertainty without pretending the model knew more than it did.
Meridian modeling core
We used a Meridian-style MMM workflow to estimate channel contribution, saturation, lag effects, and uncertainty in a structure the analytics team could maintain.
Scenario optimizer
A proprietary optimizer translated model outputs into budget allocations across search, social, retail media, and brand channels while preserving planning constraints.
Executive explanation layer
The AI layer explained which channels were constrained, where spend was saturated, and how the plan changed under alternate demand or budget assumptions.
- Reconciled channel, spend, calendar, and demand data into a single modeling table with explicit assumptions.
- Built a scenario interface around the model so executives could compare plans rather than read a static deck.
- Added drift monitoring and assumption review so the model stayed useful after the first planning cycle.
Budget planning shifted from channel-by-channel justification to portfolio-level tradeoff analysis.
The analytics team could defend recommendations with model evidence, uncertainty, and business constraints in one place.
The planning layer became a foundation for later demand forecasting and campaign simulation work.
MMM is valuable when it leaves the notebook. The win is the operating system around the model: scenario generation, constraints, explanation, and recurring review.