Customer Attrition and Retention
Likhith D1, Vamsi A2, Rajashree Shettar3
1Likhith D*, Bachelor of Engineering, Computer Science, Rashtreeya Vidyalaya College of Engineering, Bengaluru.
2Vamsi A , Bachelor of Engineering, Computer Science, Rashtreeya Vidyalaya College of Engineering, Bengaluru.
3Dr. Rajashree Shettar , Professor, Dept. of Computer Science, RV College of Engineering, Bengaluru.
Manuscript received on April 20, 2020. | Revised Manuscript received on May 01, 2020. | Manuscript published on May 10, 2020. | PP: 925-928 | Volume-9 Issue-7, May 2020. | Retrieval Number: G5730059720/2020©BEIESP | DOI: 10.35940/ijitee.G5730.059720
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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: In a competitive environment, organizations and firms are susceptible to customer attrition. Customer attrition and customer retention terms are widely spoken about. Customer retention which is quite the opposite of attrition is important for the company’s sustainability in today’s market. Many studies have come up with an attempt to find factors that influence customer retention. Firms have long desired to know who might end their relationship with them. Similarly, companies try to find how many existing customers did not return to purchase. Customer attrition is a problem that deals with clients and customers who are attrited from a particular brand or firm. In simple terminology it deals with the loss of profit associated with companies. This paper deals with the different ways to overcome the increasing attrition rate among customers. It also includes the implementation of customer segmentation using RFM model and K-means clustering. It also includes the prediction of customer retention using logistic regression.
Keywords: LVM (Lifetime Value Model), attrition, churn
Scope of the Article: e-Commerce