A multicriterion segmentation approach based on CLV components

Abstract

Most segmentation analyses use descriptive variables to group customers into homogenous segments in order to propose appropriate marketing actions and to optimize firms resources allocation. However, descriptive variables are usually fixed in time and lack actionability and responsiveness power. Some studies suggested that value based segmentation is the most significant from the standpoint of marketing activities. The customer lifetime value (CLV) metric, which aims to predict the future value of each customer, is often recommended as an interesting feature to segment customers. However, segmentation based on the two CLV components, number of transactions and lifetime, helps to better explain the customer behavior and to propose more effective marketing actions. In this work, we propose a Multicriterion segmentation approach based both on descriptive variables and on CLV components. The Multicriterion problem is solved using genetic algorithms by generating a set of Pareto-optimal solutions. The empirical analysis shows the ability of the proposed approach to characterize customer segments and to propose appropriate marketing actions.