Open Access
Article
SciPap-1478
User Churn Model in E-Commerce Retail
by
Martin Fridrich 1,* 
and Petr Dostál 2
1 Faculty of Business and Management, Department of Informatics, Brno University of Technology, Kolejní 2906/4, Brno 61200, Czechia
2 Faculty of Business and Management, Department of Informatics, Brno University of Technology, Kolejní 2906/4, Brno 61200, Czechia
* Authors to whom correspondence should be addressed.
Abstract: In e-commerce retail, maintaining a healthy customer base through retention management is necessary. Churn prediction efforts support the goal of retention and rely upon dependent and independent characteristics. Unfortunately, there does not appear to be a consensus regarding a user churn model. Thus, our goal is to propose a model based on a traditional and new set of attributes and explore its properties using auxiliary evaluation. Individual variable importance is assessed using the best performing modeling pipelines and a permutation procedure. In addition, we estimate the effects on the performance and quality of a feature set using an original technique based on importance ranking and information retrieval. The performance benchmark reveals satisfying pipelines utilizing LR, SVM-RBF, and GBM learners. The solutions rely profoundly on traditional recency and frequency aspects of user behavior. Interestingly, SVM-RBF and GBM exploit the potential of more subtle elements describing user preferences or date-time behavioural patterns. The collected evidence may also aid business decision-making associated with churn prediction efforts, e.g., retention campaign design.
Keywords: Retail, Machine Learning, Customer Relationship Management, User Model, Churn Prediction, Electronic Commerce, Feature Importance, Feature Set Importance
JEL classification: C60 - General, M31 - Marketing
Received: 7 February 2022 / Revised: 2 March 2022 / Accepted: 7 March 2022 / Published: 5 April 2022
