We’ve been using a lightweight MMM + incrementality approach to guide monthly cut/scale decisions. Nothing over engineered, just enough to get directional ROI and saturation curves we can trust.
How we do it:
Start with a simple MMM (Python/statsmodels or Robyn) with adstock + saturation to model diminishing returns for search, paid social, and webinars.
Layer geo/account-level tests where possible to validate lift. Synthetic control has actually been more reliable for us than classic holdouts when sample sizes are small.
Use the marginal ROI curve to define cut/scale thresholds. Eg: if marginal ROI < blended CAC target, we cut; if marginal ROI is still above target, we scale until saturation.
What we’ve seen:
Paid search saturates fast (~60–70% of its optimal point);
LinkedIn retargeting tends to stay efficient longer;
Content syndication is the most volatile unless paired with intent filters.
Happy to share the setup or sample notebooks if useful always keen to compare real-world MMM/incrementality workflows.
