Predicting customer churn based on changes in their behavior patterns
نویسندگان
چکیده
Customer retention is one of the most important tasks a business, and it extremely to allocate resources according potential profitability customer. Most often problem predicting customer churn solved based on RFM (Recency, Frequency, Monetary) model. This paper proposes way extend model with estimates probability changes in behavior. Based an analysis data relating 33 918 clients large Russian retailer for 2019–2020, shown that there are recurring patterns change their behavior over single year. Information about these used calculate necessary estimates. Incorporating into predictive logistic regression increases prediction accuracy by more than 10% metrics AUC geometric mean. It also this approach has limitations related disruption behavioral external shocks, such as lockdown due COVID-19 pandemic April 2020. The identify making possible forecast degradation ability
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ژورنال
عنوان ژورنال: Business informatics
سال: 2023
ISSN: ['2587-8158', '2587-814X']
DOI: https://doi.org/10.17323/2587-814x.2023.1.7.17