نوع مقاله : مقاله پژوهشی
عنوان مقاله English
نویسندگان English
Customer churn is one of the fundamental and critical challenges in the e-commerce space that has a direct and widespread impact on the profitability, growth, and long-term sustainability of organizations. The main goal of this research is to provide a comprehensive and efficient model for predicting and preventing customer churn in the e-commerce context by utilizing modern business intelligence technologies and advanced machine learning algorithms. In this direction, the Support Vector Machine (SVM) algorithm was selected as a powerful tool for analyzing customer data and predicting their behavior. For this purpose, first, real and valid data was collected from customers of an active e-commerce platform. Then, data preprocessing steps including cleaning, normalization, and selection of influential features were carefully performed to optimize the quality of data for training the model. The SVM model was trained based on this processed data and then evaluated using standard evaluation criteria including Accuracy, Recall, and the F1 composite criterion. The results showed that the model has a good ability to identify customers at risk of churn with acceptable accuracy and can be used as an effective tool in predicting customer behavior. Next, in order to examine the managerial and organizational dimensions of the issue, hypotheses related to the factors affecting customer churn were developed and analyzed using valid statistical tests. The findings indicate that the business intelligence approach based on machine learning plays a prominent role not only in predicting churn but also in improving strategic decision-making processes and customer retention. This research, while promoting scientific knowledge in the field of customer churn management, provides practical and effective solutions to improve the organization's interactions with customers and increase their satisfaction and loyalty. Finally, the proposed model can help organizations to develop and implement targeted and efficient policies and strategies to reduce churn and strengthen customer loyalty by more accurately recognizing customer behavior patterns.
کلیدواژهها English
