Marketing Mix Modeling
Question: which media channels drive sales most efficiently and where should incremental budget go? Data: channel spend and sales outcomes from the Advertising dataset. Method: choose between traditional linear regression and Bayesian ridge MMM, then inspect contribution share, fit quality, and uplift scenarios.
Dataset
300 Records
Advertising_Data channel and product sold data
1. Analysis Name
Marketing Mix Modeling (MMM)
This page estimates channel effectiveness, compares deterministic and Bayesian modeling assumptions, and summarizes the channel contribution story in business terms.
2. Problem Context
What problem this page answers
MMM helps with budget planning. Instead of relying only on raw spend or single-channel lift, it estimates marginal channel relationships while controlling for other channels in the same model.
3. Observed Data
Observed and modeled sales behavior
The model-fit chart compares observed sales with model predictions across all records, while the table summarizes channel coefficients and contribution share.
Channel Summary
4. Workflow
How the mix recommendation is built
Channel coefficients quantify directional impact, contribution share contextualizes portfolio weight, and scenario analysis estimates incremental gain from controlled budget lifts.
Estimate Model
Fit either traditional OLS or Bayesian ridge to channel and sales data.
Assess Fit
Validate out-of-sample quality with R2, RMSE, and MAE on holdout records.
Simulate Budget Shift
Estimate expected incremental sales from +10% channel spend changes.
Model
-
R2 (Holdout)
-
5. Conclusion
Recommended media mix direction
Why this is the best answer
Bayesian Uncertainty (if selected)