ADEC 7430 Boston College Machine Learning Model & Cost Effectiveness Worksheet
I’m working on a data analytics project and need support to help me study.
A charitable organization wishes to develop a machine learning model to improve the cost-effectiveness of their direct marketing campaigns to previous donors. According to their recent mailing records, the typical overall response rate is 10%. Out of those who respond (donate) to the mailing, the average donation is $14.50. Each mailing costs $2.00 to produce and send; the mailing includes a gift of personalized address labels and assortment of cards and envelopes. It is not cost-effective to mail everyone because the expected profit from each mailing is 14.50 x 0.10 – 2 = -$0.55. We would like to develop a classification model using data from the most recent campaign that can effectively captures likely donors so that the expected net profit is maximized. The entire dataset consists of 3984 training observations, 2018 validation observations, and 2007 test observations. Weighted sampling has been used, over-representing the responders so that the training and validation samples have approximately equal numbers of donors and non-donors. The response rate in the test sample has the more typical 10% response rate. We would also like to build a prediction model to predict expected gift amounts from donors – the data for this will consist of the records for donors only.