Investigating the role of product features in preventing customer churn, by using survival analysis and choice modeling: The case of financial services
The enhancement of existing relationships is of pivotal importance to companies, since attracting new customers is known to be more expensive. Therefore, as part of their customer relationship management (CRM) strategy, many researchers have been analyzing why customers decide to switch. However, despite its practical relevance, few studies have investigated how companies can react to defection prone customers by offering the right set of products. Additionally, within the current customer attention hype, one tends to overlook the nature of different products when investigating customer defection. In this research, we study the defection of the savings and investment (SI) customers of a large Belgian financial service provider. We created different SI churn behavior categories by introducing two dimensions: (i) duration of the products (fixed term versus infinity) and (ii) capital/revenue risks involved. Considering these product features, we first gain explorative insight in the timing of the churn event by means of KaplanMeier estimates. Secondly, we elaborate on the most alarming group of customers that emerged from the former explorative analysis. A hazard model is built to detect the most convenient product categories to cross-sell in order to reduce their churn likelihood. Complementary, a multinomial probit model is estimated to explore the customers' preferences with respect to the product features involved and to test whether these correspond with the findings of the survival analysis. The results of our study indicate that customer retention cannot be understood by solely relying on customer characteristics. In sum, it might be true that not all customers are created equal, but neither are all products.
survival analysis customer relationship management multinomial probit data mining cross-sell analysis savings and investment features retention behavior