A survey of personalized treatment models for pricing strategies in insurance

We consider a model for price calculations based on three components: a fair premium; price loadings reflecting general expenses and solvency requirements; and profit. The first two components are typically evaluated on a yearly basis, while the third is viewed from a longer perspective. When considering the value of customers over a period of several years, and examining policy renewals and cross-selling in relation to price adjustments, many insurers may prefer to reduce their short-term benefits so as to focus on their most profitable customers and the long-term value. We show how models of personalized treatment learning can be used to select the policy holders that should be targeted in a company’s marketing strategies. An empirical application of the causal conditional inference tree method illustrates how best to implement a personalized cross-sell marketing campaign in this framework. •Models of personalized treatment learning are useful for pricing and marketing.•A summary of those models is presented for estimating changes in the customer value.•Insurers can adjust price to reduce short-term benefits.•Insurers can focus on most profitable customers and do efficient cross-selling.•The causal conditional inference tree method improves cross-selling rates.