Abstract—The concept of Customer Lifetime Value (CLV)
attempts to account for the anticipated future profitability of
each customer during his lifetime with the firm. In
non-contractual context, in which the firm does not observe
customer defection, the measurement of the CLV metric
presents the challenge of choosing the appropriate model that
provides satisfactory prediction of the future purchasing
behavior of customers. The most prevalent models in
non-contractual setting are the Pareto/NBD and the BG/NBD
which are based on statistical distributions and assume that the
number of transactions follows a Poisson distribution. However,
many applications have an empirical distribution that does not
fit a Poisson model. In this paper we propose an improved
BG/NBD approach for modeling purchasing behavior using
COM-Poisson Distribution, which is a generalization of the
Poisson distribution to a two-parameter distribution, offering
more flexibility and fitting better real world discrete data. An
empirical study based on customer credit card transactions
shows that the proposed model has better forecasting
performance than competing models.
Index Terms—Com-Poisson, prediction, modeling, customer
lifetime value.
Mohamed Limam is with Academic Affairs and Research at Dhofar
University, Oman and Laboratory of Operations Research Decision and
Control of Processes (LARODEC) at ISG of the University of Tunis, Tunisia
(e-mail: Mohamed.limam@isg.rnu.tn).
[PDF]
Cite: Mohamed Ben Mzoughia and Mohamed Limam, "An Improved BG/NBD Approach for Modeling
Purchasing Behavior Using COM-Poisson Distribution," International Journal of Modeling and Optimization vol. 4, no. 2, pp. 141-145, 2014.
