Decision support models for optimal personalized marketing interventions in insurance
In many important settings, subjects can show significant heterogeneity in response to a stimulus or "treatment" The notion that "one size may not fit all" is becoming increasingly recognized in a wide variety of fields, ranging from economics to medicine. This has drawn significant attention to personalize the choice of treatment, so it is optimal for each individual. An optimal personalized treatment is the one that maximizes the probability of a desirable outcome. We call the task of learning the optimal personalized treatment personalized treatment learning. From the statistical learning perspective, this problem imposes important challenges, primarily because the optimal treatment is unknown on a given training set. A number of statistical methods have been proposed recently to tackle this problem. The purpose of this paper is to show that causal conditional inference trees and its natural extension to causal conditional inference forests can provide a solution to the purpose of selecting the best targets for cross selling an insurance product. We will discuss its usefulness in insurance pricing and retention as well.