article
This study designs a customer churn prediction model for Hartlief Shop and Bistro (HSB) to enhance profits and customer relationship management. Using a quantitative approach, it analyzes historical data (2017–2023) through Decision Tree, Naïve Bayes, and Support Vector Machine models. The dataset contains 4,972,788 records across 22 variables. Key steps include data collection, preprocessing, model development, and evaluation. The study segments customers based on purchasing behavior and predicts future churn trends. Findings highlight the value of accurate churn prediction in driving profits, with model evaluations revealing the Decision Tree as the most optimized. Recommendations include refining data preprocessing and exploring advanced machine learning techniques to improve future predictions and retention strategies.
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DOI: 10.1109/etncc63262.2024.10767454
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