The case for prescriptive analytics: a novel maximum profit measure for evaluating and comparing customer churn prediction and uplift models

Prescriptive analytics and uplift modeling are receiving more attention from the business analytics research community and from industry as an alternative and improved paradigm of predictive analytics that supports data-driven decision making. Although it has been shown in theory that prescriptive analytics improves decision-making more than predictive analytics, no empirical evidence has been presented in the literature on an elaborated application of both approaches that allows for a fair comparison of predictive and uplift modeling. Such a comparison is in fact prohibited by a lack of available evaluation measures that can be applied to predictive and uplift models.
Therefore, in this paper, we introduce a novel evaluation metric called the maximum profit uplift measure that allows one to assess the performance of an uplift model in terms of the maximum potential profit that can be achieved by adopting an uplift model. The measure is developed for evaluating customer churn uplift models and for extending the existing maximum profit measure for evaluating customer churn prediction models. Both measures are subsequently applied to a case study to assess and compare the performance of customer churn prediction and uplift models. We find that uplift modeling outperforms predictive modeling and allows one to enhance the profitability of retention campaigns. The empirical results indicate that prescriptive analytics are superior to predictive analytics in the development of customer retention campaigns.