Predicting Customer Lifetime Value in Multi-Service Industries
AbstractCustomer lifetime value (CLV) is a key-metric within CRM. Although, a large number of marketing scientists and practitioners argue in favor of this metric, there are only a few studies that consider the predictive modeling of CLV. In this study we focus on the prediction of CLV in multi-service industries. In these industries customer behavior is rather complex, because customers can purchase more than one service, and these purchases are often not independent from each other. We compare the predictive performance of different models, which vary in complexity and realism. Our results show that for our application simple models assuming constant profits over time have the best predictive performance at the individual customer level. At the customer base level more complicated models have the best performance. At the aggregate level, forecasting errors are rather small, which emphasizes the usability of CLV predictions for customer base valuation purposes. This might especially be interesting for accountants and financial analysts.
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Bibliographic InfoPaper provided by Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam. in its series Research Paper with number ERS-2003-038-MKT.
Date of creation: 29 Apr 2003
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forecasting; value; customer relationship management; customer lifetime value; customer segmentation; database marketing; interactive marketing;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2003-12-07 (All new papers)
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- Rajagopal, 2006. "Fiscal Policy and Growth: The Case of the Spanish Regions," Economic Issues Journal Articles, Economic Issues, vol. 11(1), pages 9-24, March.
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