User Churn Model in E-Commerce Retail

User Churn Model in E-Commerce Retail

Článek WoS

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.

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.

User Model; Churn Prediction; Customer Relationship Management; Electronic Commerce; Retail; Machine Learning; Feature Importance; Feature Set Importance

User Model; Churn Prediction; Customer Relationship Management; Electronic Commerce; Retail; Machine Learning; Feature Importance; Feature Set Importance

FRIDRICH, M.; DOSTÁL, P.

2022

05.04.2022

Univ Pardubice, Fac Economics Adm

Pardubice

1804-8048

Scientific Papers of the University of Pardubice-Series D-Faculty of Economics and Administration

30

1

Česká republika

1

12

12

https://editorial.upce.cz/1804-8048/30/1/1478

http://hdl.handle.net/11012/204120

SciPap-01478