Exploring the Relationship Between Visit Frequency and Customer Retention in Study Cafes Using AI-Based Predictive Modeling
This study empirically investigates actual customer repurchase behavior by analyzing behavioral log and payment data collected from study cafe users in an attendance-based learning service environment. Unlike prior studies that primarily relied on survey-based measures, this study integrates attendance records, payment data, and explainable artificial intelligence (XAI) techniques to provide a data-driven understanding of customer retention behavior. It compares the predictive performance of traditional regression models with machine learning approaches. Specifically, logistic regression, random forest, XGBoost, and neural networks were employed, with SHAP analysis applied as an XAI technique. The results indicate that visit frequency has a significant positive effect, supporting H1, while stay-duration variables show only limited and inconsistent effects, providing partial support for H2 and H3. Total payment in May negatively affects subsequent repurchase, suggesting possible saturation or substitution effects, thereby supporting H4. Age demonstrates a negative effect (supporting H5), and regional differences are captured by the XGBoost model (supporting H6). In terms of predictive performance, machine learning models outperformed logistic regression, with XGBoost achieving the strongest overall results among the evaluated models (ROC-AUC = 0.676; PR-AUC = 0.642). Overall, this study contributes to the literature by presenting empirical evidence based on behavioral data and by highlighting the practical interpretability of integrating XAI techniques. From a managerial perspective, the findings provide actionable insights for designing customer retention strategies based on visit frequency, spending behavior, and regional characteristics, thereby supporting AI-driven decision-making in attendance-based service environments.