Counting your customers: who are they and what will they do next?
Management Science
Expert Systems with Applications: An International Journal
The Journal of Machine Learning Research
A modified Pareto/NBD approach for predicting customer lifetime value
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Time-varying effects in the analysis of customer loyalty: A case study in insurance
Expert Systems with Applications: An International Journal
Segmentation of telecom customers based on customer value by decision tree model
Expert Systems with Applications: An International Journal
Improving customer retention in financial services using kinship network information
Expert Systems with Applications: An International Journal
International Journal of Business Information Systems
Hi-index | 12.06 |
The move towards a customer-centred approach to marketing, coupled with the increasing availability of customer transaction data, has led to an interest in understanding and estimating customer lifetime value (CLV). Several authors point out that, when evaluating customer profitability, profitable customers are rare compared to the unprofitable ones. In spite of this, most authors fail to recognize the implications of these skewed distributions on the performance of models they use. In this study, we propose analyzing CLV by means of quantile regression. In a financial services application, we show that this technique provides management more in-depth insights into the effects of the covariates that are missed with linear regression. Moreover, we show that in the common situation where interest is in a top-customer segment, quantile regression outperforms linear regression. The method also has the ability of constructing prediction intervals. Combining the CLV point estimate with the prediction intervals leads to a new segmentation scheme that is the first to account for uncertainty in the predictions. This segmentation is ideally suited for managing the portfolio of customers.