Behavioral attributes and financial churn prediction
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13688_2018_Article_165.pdf
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Author(s)
Kaya, Erdem
Balcisoy, Selim
Bozkaya, Burcin
Pentland, Alex Paul
Dong, Xiaowen
Suhara, Yoshihiko
Date Issued
October 2018
Journal
EPJ Data Science
Publisher
Springer Berlin Heidelberg
Citation
Kaya, Erdem, et al. “Behavioral Attributes and Financial Churn Prediction.” EPJ Data Science, vol. 7, no. 1, Dec. 2018. © 2018 The Authors
Version
Final published version
Abstract
Customer retention is crucial in a variety of businesses as acquiring new customers is often more costly than keeping the current ones. As a consequence, churn prediction has attracted great attention from both the business and academic worlds. Traditional efforts in the financial domain mainly focus on domain specific variables such as product ownership or service usage aggregation, however, without considering dynamic behavioral patterns of customers’ financial transactions. In this paper, we attempt to fill in this gap by investigating the spatio-temporal patterns and entropy of choices underlying the customers’ financial decisions, and their relations to customer churning activities. Inspired by previous works in the emerging field of computational social science, we built a prediction model based on spatio-temporal and choice behavioral traits using individual transaction records. Our results show that proposed dynamic behavioral models could predict churn decisions significantly better than traditionally considered factors such as demographic-based features, and that this effect remains consistent across multiple data sets and various churn definitions. We further study the relative importance of the various behavioral features in churn prediction, and how the predictive power varies across different demographic groups. More generally, the proposed features can also be applied to churn prediction in other domains where spatio-temporal behavioral data are available. Keywords: Churn prediction, Customer behavior, Spatio-temporal patterns, Credit card data
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1140/epjds/s13688-018-0165-5
