Marketing Mix Modelling with Bayesian Regression

Which advertisements have been effective?

2.0 Typical data for MMM

3.0 Challenges of the regression approach

The above figure illustrates how three linear regression models with the same quality of fit, measured by r-square, gives a conflicting recommendation to increase search spend more than $300 (an extrapolation from the observed range). The code that produced the above figure is here.

4.0 Generating a simulated dataset for MMM

4.1 Feature engineering

4.2 Collinearity and multi collinearity checks

The above correlation matrix suggests that we should remove either revenue.lag1y or revenue.lag1w. Out of the two, we keep revenue.lag1y to keep the seasonality-related input variable. Next, we check for multicollinearity.
Using VIF=10 as a threshold, we remove search_p3m from the rest of the analysis.

5.0 Variable selection techniques: Lasso vs. Bayesian Adaptive Sampling

5.1 Variables selection results

List of variables selected by each of the 3 Bayesian Regression Models, and lastly, by Lasso.

5.2 Leave-One-Out-Cross-Validation results

Performance of Bayesian Regression Methods is comparable to Lasso.

6.0 Future exercise: Injecting non-reference priors

7.0 Conclusion

Acknowledgments & references

PhD Candidate — NLP; Founder of SpectData.com

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