Dynamic customer lifetime value prediction using longitudinal data: An improved multiple kernel SVR approach

Customer lifetime value (CLV), as an important metric in customer relationship management (CRM), has attracted widespread attention over the last decade. Most CLV prediction models do not take into consideration the dynamics of the customer purchase behavior and changes of the marketing environment such as the adoption of different promotion policies. In this study, a framework for the dynamic CLV prediction using longitudinal data is presented. In the framework, both the dynamic customer purchase behavior and customized promotions are considered. An improved multiple kernel support vector regression (MK-SVR) approach is developed to predict the future CLV and select the best promotion using both the customer behavioral variables and controlled variable about multiple promotions. Computational experiments using two databases show that the MK-SVR exhibits good prediction performance and the usage of longitudinal data in the MK-SVR facilitate the dynamic prediction and promotion optimization.

► Longitudinal data are introduced into the dynamic CLV prediction model. ► An MK-SVR approach is proposed to model the controlled and independent variables. ► The controlled variable about multiple promotions is incorporated into MK-SVR. ► The dynamic customized promotion policy is determined by maximizing the CLV.