Prediction of Customer Churn on e-Retailing 
M Jaeyalakshmi1, S Gnanavel2, K S Guhapriya3, S Harshini Phriyaa4, K Kavya Sree5
1Jaeyalakshmi M, Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India.
2Dr. S Gnanavel, Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India.
3Guhapriya K S, Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India.
4Harshini Phriyaa S, Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India.
5Kavya Sree K, Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India.
Manuscript received on February 28, 2020. | Revised Manuscript received on March 22, 2020. | Manuscript published on March 30, 2020. | PP: 5541-5545 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9550038620/2020©BEIESP | DOI: 10.35940/ijrte.F9550.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The technology has always been an instigating factor in progress for human civilization which resulted in driving the customer services to a greater need. The enrichment of technology has amplified and embellished the customer interaction among various business to consumer sectors. These technological upgrading have a huge impact on the retail industry which is an ever-growing market with key competitors around the world. In a consortium of multiple competitors in the same business, the re-engagement of disinterested customers is essential rather than winning a new customer. The sustenance of a customer can be figure out by Churn Prediction. Churn prediction is a new promising method in customer relationship management to analyze customer retention in subscription-based business. It is the activity of identifying customer with a high probability to discontinue the company based on analyzing their past data and behavior. It looks at what kind of customer data are typically used, do some analysis of the features chosen, and initiate a churn prediction model. Thus, churn prediction is a valuable approach in identifying and profiling the customers at risk.
Keywords: Data Mining, Machine Learning, Churn Prediction, Customer Retention
Scope of the Article: Data Mining.