Marketing Mix Modeling (MMM) Overview
Marketing Mix Modeling (MMM) Overview
Marketing Mix Modeling (MMM) is Attribution.ai's privacy-safe, top-down approach to estimating how much each channel actually contributes to revenue. While pixel-based attribution tells you which click happened last, MMM tells you which spend pattern caused incremental sales — even when those channels are not directly trackable (CTV, podcast, OOH, branded search, etc.).
When to use MMM
Use MMM when:
- You spend across three or more channels and need to compare them on the same axis.
- You suspect diminishing returns on a channel (Meta saturation, Google brand-search overlap).
- You want to stress-test budget shifts before committing real spend.
- You operate in a privacy-restricted environment (iOS 14, MAID-deprecated, B2B with no pixel coverage on the buyer journey).
MMM is complementary to attribution and incrementality, not a replacement. The Measurement Confidence Layer in Attribution.ai blends MTA, MMM, and lift tests into a single readiness signal so you know which to trust for any given decision.
What you'll find in Dashboard → MMM
- Models — list of trained models, with R², MAPE, and last-trained timestamps.
- Decomposition — channel-level revenue contribution stacked by week. Click any bar to see the underlying spend, lag, and saturation parameters.
- Response curves — diminishing-returns curve per channel. Find the inflection point beyond which $1 of additional spend returns less than $1 of revenue.
- Budget optimizer — drag spend between channels to see modeled revenue impact, with confidence bands. Save scenarios for finance review.
Data requirements
For a useful MMM, Attribution.ai needs roughly:
- 52+ weeks of daily revenue history (more is better — 104 weeks is the sweet spot).
- Daily spend by channel for every paid surface you want modeled (we pull this automatically from connected ad platforms).
- Promotional / seasonality calendar (we infer this from order patterns; you can override in Settings → MMM).
If you connected your store fewer than 52 weeks ago, MMM will run in directional mode with wider confidence bands. Numbers are still useful for ranking channels and spotting saturation, but absolute lift estimates should be re-validated once you have a full year.
Reading the results
A model is decision-ready when:
- R² ≥ 0.75 on hold-out weeks
- MAPE ≤ 15% on the most recent 4 weeks
- No single channel contributes >70% of explained variance (a sign of overfit / collinearity)
If your model fails any of these, the dashboard shows a directional badge and we recommend
running a lift test in Dashboard → Incrementality on your largest paid channel before
acting on the MMM output.
Frequently asked
How often is the model retrained? Every Sunday, automatically, on the most recent 104 weeks of data.
Can I exclude a channel?
Yes — Settings → MMM → Channel inclusion.
Why is my brand-search contribution so high?
Branded search captures intent that other channels created. Attribution.ai's MMM splits this
into a "demand pass-through" component when an incrementality test has been run on brand
search; otherwise it lumps the entire branded-search bucket together. Run a brand-search
holdout in Dashboard → Incrementality to disambiguate.