Marketing Mix Modeling
Find out which marketing channels are actually driving sales — not just spending. We break down the real contribution of each channel and show you what happens to revenue when you shift budget, so you can spend smarter next period.
Dataset
300 Records
Advertising_Data channel and product sold data
1. Analysis Name
Marketing Mix Modeling (MMM)
We show you which channels are genuinely moving the needle and estimate what happens when you reallocate spend — so your budget decisions are based on measured contribution, not attribution guesswork.
2. Problem Context
What you'll be able to decide
Where should next quarter's incremental marketing budget go? Which channels are pulling their weight and which are coasting on others? MMM gives you a model-backed answer — controlling for every channel at once — so the reallocation conversation is grounded in data.
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.
Channel ROI Ranking
Model
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R2 (Holdout)
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5. Conclusion
Recommended media mix direction
Why this is the best answer
Bayesian Uncertainty (if selected)