1. Pyle, D. (2003) ‘Business Modeling and Data Mining’, Morgan Kaufman, San Francisco, CA.
2. Interestingly, this addition of finding customers that look like the ones who responded, but specifically the way they looked prior to responding, has to do with a confounding effect, too. This prevents that cause and effect might get confounded. If one erroneously tries to find customers that look like the ones who have acquired the product (the way the appear after they responded) one can get disappointing results. Variables that are a consequence of product uptake and are used for prediction have been labelled ‘leakers’ (Berry and Linoff), or ‘anachronistic variables’ (Pyle). In such a case, confounding might consist of inadvertently interpreting the effect of taking the product as a cause (predictor) of the propensity to acquire the product.
3. The ratio between these percentages is generally referred to as the lift of the model, a universal measure across algorithms to indicate the accuracy of the model.
4. Mayer, U. and Sarkissian, A. (2003) ‘Experimental design for solicitation campaigns’, in Proceedings of KDD-03, AAAI Press, Menlo Park, CA, pp. 717–722.
5. Calculating the target effect using the order recommended by the author is most convenient. Any other process would require some weighting calculation at evaluation time. Also, this procedure allows for straightforward comparison of random response rates over time as the campaign is rerun, regardless of changing targeting depth.