Abstract: Customers are one of the most valuable assets within an organization. Customer churn, the phenomenon of losing existing customers to competitors, has a significant impact on business. The predictive customer churn models provide the insights needed to pre-emptively address potential issues, enhance customer satisfaction, and develop effective retention strategies. These machine learning-based churn prediction models accurately categorize customers into two groups: those likely to remain loyal and those at risk of leaving. Given the complicated relationship among characteristics that impact customer turnover, it is essential to analyze the reasons behind high churn rates. Moreover, evaluating the effectiveness of various predictive models through hyperparameter optimization and cross-validation can help organizations fine-tune their approaches to forecasting churn and ultimately bolster their retention efforts. It is found in the present study that the boosted versions of the models are clearly superior to the ordinary (non-boosted) counterparts. By utilizing these advanced techniques, companies can effectively lower churn rates while fostering a more loyal customer base.

Keywords: Customer churn, Customer acquisition, Churn prediction model, hyperparameter optimization, cross-validation

Pages: 398-411

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