PREDICT CUSTOMER CHURN BY USING ROUGH SET THEORY AND NEURAL NETWORK
Publish place: 9th International Industrial Engineering Conference
Publish Year: 1391
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
IIEC09_098
تاریخ نمایه سازی: 26 اسفند 1391
Abstract:
A major concern for modern enterprises is to promote customer value, loyalty and contribution through services which can help establishing long-term relationshipswith customers. Organizations have found that retaining existing customers is more valuable than attracting new customers. Therefore, preventing customer churn by customer retention to achieve maximum profit is a critical issue in customer relationship management. In order to effectivelymanage customer churn for companies, it is important to build a more effective and accurate customer churn prediction model. Data mining and statistical techniques can be used to construct prediction models. This paper aims to identify most appropriate models base on data mining techniques. In this paper, rough set theory has been used for feature selection. It aims to find the most effective features in order to reducecustomer loss. Then, neural networks are used in order to create the model. Finally, to evaluate performance of the model five measures (accuracy, precision, Recall, F-measure, Lift) were used. Results show that our proposed model provides acceptable performance in terms of evaluation measures.
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Authors
Razieh Qiasi
University of Qom
Zahra Roozbehani
University of Shahid Beheshti
Behrooz Minaei-Bidgoli
University of Science and Technology
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