Mining for the truly responsive customers and prospects using true-lift modeling: Comparison of new and existing methods

True-lift modeling, also known as uplift modeling, combines predictive modeling and experimental method to enable marketers to identify the characteristics of ‘true’ treatment responders separately from the characteristics of ‘baseline’ or control responders (that is, those who would have responded anyway). By concentrating truly ‘persuadable’ treatment targets in the top deciles, true-lift models achieve the same (or more) amount of response with fewer treatments (and lower treatment costs). The identified characteristics of the ‘persuadable’ population can then guide the hypotheses of future experiments and pinpoint the most responsive recipients for the treatment in future. This article explains the concept of true-lift modeling in detail, reviews existing methods, contrasts with the traditional approach, proposes new methods that can be implemented with most standard software, and recommends metrics for model assessment and comparison in true-lift modeling. Several new and existing methods are applied to three data sets from the financial services, online merchandise and retail industries. Built on the findings from our study and prior experience, we recommend some guidelines on usage of true-lift modeling methods.