Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/334768 
Erscheinungsjahr: 
2022
Quellenangabe: 
[Journal:] Scientific Papers of the University of Pardubice, Series D: Faculty of Economics and Administration [ISSN:] 1804-8048 [Volume:] 30 [Issue:] 1 [Article No.:] 1478 [Year:] 2022 [Pages:] 1-12
Verlag: 
University of Pardubice, Pardubice
Zusammenfassung: 
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.
Schlagwörter: 
User Model
Churn Prediction
Customer Relationship Management
Electronic Commerce
Retail
Machine Learning
Feature Importance
Feature Set Importance
JEL: 
C60
M31
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
cc-by Logo
Dokumentart: 
Article

Datei(en):
Datei
Größe
427.56 kB





Publikationen in EconStor sind urheberrechtlich geschützt.