An improved customer lifetime value model based on Markov chain

Firms are increasingly looking to provide a satisfactory prediction of customer lifetime value CLV, a determining metric to target future profitable customers and to optimize marketing resources. One of the major challenges associated with the measurement of CLV is the choice of the appropriate model for predicting customer value because of the large number of models proposed in the literature. Earlier models to forecast CLV are relatively unsuccessful, whereas simple models often provide results which are equivalent or even better than sophisticated ones. To predict CLV, Rust et al. 2011 proposed a framework model that performs better than simple managerial heuristic models, but its implementation excludes cases where customer's profit is negative and does not handle lost-for-good situations. In this paper, we propose a modified model that handles both negative and positive profits based on Markov chain model MCM, hence offering a greater flexibility by covering always-a-share and lost-for-good situations. The proposed model is compared with the Pareto/Negative Binomial Distribution Pareto/NBD, the Beta Geometric/Negative Binomial Distribution BG/NBD, the MCM, and the Rust et al. 2011 models. Based on customer credit card transactions provided by the North African retail bank, an empirical study shows that the proposed model has better forecasting performance than competing models. Copyright © 2014 John Wiley & Sons, Ltd.