Uplift Modeling Predicting incremental gains
Marketing strategies directed to random customers often generate huge costs and a weak response. Sometime, such campaigns tend to unnecessarily annoy customers and make them less likely to react to any communication. Uplift Modelling has turned out to be very successful technique in understanding difference between the behaviour of a treated and a control population and this has been used as the basis for targeted direct marketing activity. Uplift modelling has wide applications in customer relationship management for up sell, cross sell and retention modelling. It has also been applied to political election and personalized medicine. We tried to approach this problem with 2 different perspective, predictive response modelling and uplift modelling. For predictive response modelling and uplift modelling, we used 4 models: Logistic Regression (with and without bagging) three layered neural network along with decision tree to apply on a real world example. Three layered neural network demonstrated significant advantages of uplift modeling over traditional, response based targeting.