Business Context
MMM can produce strong analytical signals, but leaders still need practical guidance on where to shift budget and how to act across channels. Without a decision layer, the distance between model output and business action stays too large.
That gap slows planning cycles and reduces the commercial value of advanced analytics.
What I Changed
I led development of an AI decision layer that sat on top of MMM outputs and translated those signals into advisory guidance for channel and budget allocation.
The design emphasized:
- clear interpretation of model output for non-technical decision makers
- recommendation framing around actionable allocation choices
- alignment with leadership planning cadence and governance
- explicit accountability for final human decision ownership
How the Team Executed
The team integrated modeling outputs, decision logic, and executive-facing communication into one operating flow. Instead of sending raw model signals downstream, the system produced structured guidance that leadership could evaluate and act on quickly.
This improved consistency in how analytical evidence informed spend decisions.
Outcome
The organization gained a stronger bridge between MMM analysis and business allocation decisions. Marketing leadership could move from interpreting raw output to evaluating clear options, increasing decision speed and confidence in planning conversations.