Bayesian semiparametric customer base segmentation of mobile phone users based on longitudinal traffic data

• Published in: Applied stochastic models in business and industry Vol. 31; no. 5; pp. 721 - 731
• Main Author: Scarpa, Bruno
• Format: Journal Article
• Language: English
• Published: Blackwell Publishing Ltd 01.09.2015
• Subjects: Algorithms; Bayesian nonparametrics; Contamination; customer profiling; Dirichlet problem; functional clustering; functional data analysis; Mathematical models; Segmentation; statistical methods for customer relationship management; Telecommunications; telephone traffic; Traffic engineering; Traffic flow
• ISSN: 1524-1904; 1526-4025
• DOI: 10.1002/asmb.2085

Customer segmentation is one of the most important purposes of customer base analysis for telecommunication companies. Because companies accumulate very large amounts of data on customer behavior, segmentation is typically achieved by profiling and clustering traffic behavior jointly with demographic data and contracts characteristics. Unfortunately, most algorithms and models used for segmentation do not take into account the longitudinal characteristics of data. In particular, in telecommunication traffic analysis, the importance of decreasing patterns of traffic in customers' lives is well known, and it is relevant to aggregate all clients with such a pattern, while other unknown clusters may be of interest for the marketing manager. Our approach to address this problem is based on specifying the distribution of functions as a mixture of a parametric hierarchical model describing the decreasing pattern segment and a nonparametric contamination that allows unanticipated curve shapes in subjects' traffic. The parametric component is chosen based on prior knowledge, while the contamination is characterized as a functional Dirichlet process. Copyright © 2014 John Wiley & Sons, Ltd.