Behavioral attributes and financial churn prediction

• Published in: EPJ data science Vol. 7; no. 1; pp. 41 - 18
• Main Authors: Kaya, Erdem, Dong, Xiaowen, Suhara, Yoshihiko, Balcisoy, Selim, Bozkaya, Burcin, Pentland, Alex “Sandy”
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 19.10.2018; Springer Nature B.V; SpringerOpen
• Subjects: Behavior; Churn prediction; Complexity; Computer Appl. in Social and Behavioral Sciences; Computer Science; Credit card data; Customer behavior; Customer satisfaction; Customer services; Data mining; Data science; Data-driven Science; Decision making; Decisions; Demographics; Demography; Entropy; Investigations; Modeling and Theory Building; Prediction models; Predictions; Profits; Regular Article; Social sciences; Spatio-temporal patterns; Credit card data; Customer behavior; Spatio-temporal patterns; Churn prediction
• ISSN: 2193-1127; 2193-1127
• DOI: 10.1140/epjds/s13688-018-0165-5

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.