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Nasr Esfahani, Davood, Mirsaeidi, Seyed Masoud. (1403). Predicting the Attraction and Retention of Customers in Sports Pools in Isfahan City using a Decision tree: Presenting a Data Mining-Based Model. , (), -. doi: 10.22108/asml.2025.143993.1060
Davood Nasr Esfahani; Seyed Masoud Mirsaeidi. "Predicting the Attraction and Retention of Customers in Sports Pools in Isfahan City using a Decision tree: Presenting a Data Mining-Based Model". , , , 1403, -. doi: 10.22108/asml.2025.143993.1060
Nasr Esfahani, Davood, Mirsaeidi, Seyed Masoud. (1403). 'Predicting the Attraction and Retention of Customers in Sports Pools in Isfahan City using a Decision tree: Presenting a Data Mining-Based Model', , (), pp. -. doi: 10.22108/asml.2025.143993.1060
Nasr Esfahani, Davood, Mirsaeidi, Seyed Masoud. Predicting the Attraction and Retention of Customers in Sports Pools in Isfahan City using a Decision tree: Presenting a Data Mining-Based Model. , 1403; (): -. doi: 10.22108/asml.2025.143993.1060

Predicting the Attraction and Retention of Customers in Sports Pools in Isfahan City using a Decision tree: Presenting a Data Mining-Based Model

Archives in Sport Management and Leadership
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 08 اسفند 1403
نوع مقاله: Original
شناسه دیجیتال (DOI): 10.22108/asml.2025.143993.1060
نویسندگان
Davood Nasr Esfahani* 1؛ Seyed Masoud Mirsaeidi2
1Assistant Professor, Department of Physical Education and Sports Sciences, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran.
2MA in Sports Management, Department of Physical Education and Sports Sciences, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran.
چکیده
The purpose of this research is collective learning techniques and a combination of classification algorithms in data mining, evaluated the performance of these algorithms to predict whether customers of Isfahan sports pools would drop over a 6-year period. The collected data pertained to 54,000 customers and covered 22 primary characteristics. To evaluate the features, two algorithms—Grasshopper Optimization Algorithm (GOA) and Simulated Annealing (SA)—were used for feature selection, while classification algorithms, specifically Decision Tree (DT) and K-Nearest Neighbors (KNN), were employed to classify and recognize customer behavior. The results indicated that the combined GOA-DT algorithm, with an accuracy of 90.91%, outperformed the SA-DT algorithm, which achieved an accuracy of 87.95%. Furthermore, the combined GOA-DT algorithm selected only 7 out of the 22 features that were effective in detecting customer churn, whereas the SA-DT algorithm led to the selection of 8 features. In the subsequent step, the KNN algorithm was used instead of the DT algorithm. The results demonstrated that the KNN algorithm, when combined with GOA and SA, ultimately reached an accuracy of 83.86% for the GOA algorithm, with the selection of 9 features. Based on these findings, it can be concluded that the combined GOA-DT algorithm exhibits the best performance, identifying 7 key characteristics: average monthly recharge of the usage account, number of ticket purchases, customer satisfaction level, discounts received, service quality, monthly services, and free training. Therefore, it is recommended that managers make decisions based on these identified characteristics to retain customers and estimate customer behavior using such methods.
کلیدواژه‌ها
Customer Behavior؛ Data Mining؛ Decision Tree؛ Isfahan؛ Sports Pools
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تعداد مشاهده مقاله: 35


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