User Churn Model in E-Commerce Retail
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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.
KeywordsUser Model, Churn Prediction, Customer Relationship Management, Electronic Commerce, Retail, Machine Learning, Feature Importance, Feature Set Importance
Document typePeer reviewed
Document versionFinal PDF
SourceScientific Papers of the University of Pardubice, Series D. 2022, vol. 30, issue 1, p. 1-12.
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