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.
Attribution breaking down? Here's the one thing that actually fixes it (without arguing about UTMs or last-touch nonsense). Most teams I meet are stuck in the same loop: CAC looks fake, Meta + Google both claim 120% of revenue, and the board wants “efficiency” while every dashboard tells a different story. Incrementality modelling is the only way I’ve ever seen to cut through that fog. I just published a short breakdown on how I build a practical incrementality framework using synthetic control models no academic fluff, just something a growth or demand team can actually run and defend. If you're dealing with budget cuts, channel overlap, or proving paid lift beyond organic intent, this will help.Dropping a quick breakdown from a recent incrementality project for a mid-market SaaS team that kept reporting “channel growth” but wasn’t seeing any revenue uplift. We ran two things:
GEO Holdout Test (4 weeks)
Synthetic Control Modelling (8 weeks)
Headline result: Only 62% of the conversions that Google/Meta claimed were actually incremental. The rest? Cannibalization, retargeting bias, branded leakage, and last-click magic tricks. Key lifts:
+13% true incremental lift after adjusting for organic baseline
CAC dropped from $412 → $274
$180K/month in wasted spend eliminated
Branded search: 17% incremental
Meta retargeting: 22% incremental (yes, really)
Prospecting: 64% incremental
Once the dust settled, payback dropped from 9.1 → 5.8 months, and we shifted budget toward actual net-new creation (content, product activations, partnerships). Takeaway: If you haven’t run a proper holdout or synthetic model in the last 12 months, your ROAS is probably lying to you. If anyone’s working on measurement frameworks or rebuilding attribution models post-cookie death, happy to swap notes.
Interesting. Would love to know if there are any real world use cases who have adopted it and attributed to KPI metrics especially using casual inference.
Hi All, I'm Shiv I live in Bangalore, India Connect with me on LinkedIn - https://www.linkedin.com/in/shivhoysala/ For fun: I love EDM, cult follower of Pryda (eric prydz) My fav beach : Krabi 2. Visit my portfolio for building marketing engine (GTM, ABM, Product Marketing) https://rebrand.ly/Shiv-portfolio
