Abstract
The customer lifetime value (CLV) metric aims to predict the importance level of each customer, offering to companies the ability to group them into homogenous segments, to propose appropriate marketing actions and to optimize resource allocation. CLV is widely suggested as a new base to segment customers. The Pareto/NBD and the BG/NBD are the most relevant CLV models, assuming that the number of transactions performed by customers follows a Poisson distribution. The BG/GCP has the particularity to model the number of transactions using the Conway–Maxwell–Poisson (CMP) distribution which is a generalization of the Poisson distribution providing additional flexibility when modeling discrete data. In this paper we propose to compare segmentation performance of the BG/GCP compared to the Pareto/NBD and the BG/NBD models, and to select the most efficient one. This performance is evaluated using three different clustering methods namely K-means, Fuzzy C-means and EM Clustering. Using two simulated datasets, presenting respectively an over and an under dispersion from Poisson distribution, the empirical analysis shows that the BG/GCP model based on CMP flexibility offers the best segmentation performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dasgupta, A., & Raftery, A. E. (1998). Detecting features in spatial point processes with clutter via model-based clustering. Journal of the American Statistical Association, 93(441), 294–302.
Fader, P. S., Hardie, B. G., & Lee, K. L. (2005). “Counting your customers” the easy way: An alternative to the Pareto/NBD model. Marketing Science, 24(2), 275–284.
Jerath, K., Fader, P. S., & Hardie, B. G. (2011). New perspectives on customer “death” using a generalization of the Pareto/NBD model. Marketing Science, 30(5), 866–880.
Kumar, V., Shah, D., & Venkatesan, R. (2006). Managing retailer profitability-one customer at a time! Journal of Retailing, 82(4), 277–294.
Mzoughia, M. B., & Limam, M. (2014). An improved BG/NBD approach for modeling purchasing behavior using COM-Poisson distribution. International Journal of Modeling and Optimization, 4(2), 141–145.
Schmittlein, D. C., Morrison, D. G., & Colombo, R. (1987). Counting your customers: Who-are they and what will they do next? Management Science, 33(1), 1–24.
Van Rijsbergen, C. J. (1979). Information retrieval. London: Butterworths.
Venkatesan, R., & Kumar, V. (2004). A customer lifetime value framework for customer selection and resource allocation strategy. Journal of marketing, 68(4), 106–125.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Mzoughia, M.B., Limam, M. (2016). CLV Model Selection for Segmentation Perspective. In: Bilgin, M., Danis, H., Demir, E., Can, U. (eds) Business Challenges in the Changing Economic Landscape - Vol. 2. Eurasian Studies in Business and Economics, vol 2/2. Springer, Cham. https://doi.org/10.1007/978-3-319-22593-7_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-22593-7_18
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22592-0
Online ISBN: 978-3-319-22593-7
eBook Packages: Economics and FinanceEconomics and Finance (R0)