Using control groups to target on predicted lift: Building and assessing uplift model

Various authors have independently proposed modelling the difference between
the behaviour of a treated and a control population and using this as the basis for targeting direct marketing activity. We call such models Uplift Models. This
paper reviews the motivation for such an approach and compares the various
methodologies put forward. We present results from using uplift modelling in
three real-world examples. We also introduce quality measures appropriate to
assessing the performance of uplift models, for both binary outcomes (purchase, attrition, click, default) and continuous outcomes (spend, response size or value lost). Finally, we discuss some of the challenges faced when building uplift models and suggest some key challenges for future research.