A Critical Examination of Different Models for Customer Churn Prediction using Data Mining
Seema1, Gaurav Gupta2
1Seema, Research Scholar, Department of Computer Science and Engineering, Punjabi University Patiala (Punjab), India.
2Gaurav Gupta, Assistant Professor, Department of Computer Science and Engineering, Punjabi University Patiala (Punjab), India.
Manuscript received on 28 September 2019 | Revised Manuscript received on 10 November 2019 | Manuscript Published on 22 November 2019 | PP: 850-854 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F11640986S319/19©BEIESP | DOI: 10.35940/ijeat.F1164.0986S319
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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: Due to competition between online retailers, the need for providing improved customer service has grown rapidly. In addition to reduction in sales due to loss of customers, more investments are needed to be done to attract new customers. Companies now are working continuously to improve their perceived quality by way of giving timely and quality service to their customers. Customer churn has become one of the primary challenges that many firms are facing nowadays. Several churn prediction models and techniques are proposed previously in literature to predict customer churn in areas such as finance, telecom, banking etc. Researchers are also working on customer churn prediction in e-commerce using data mining and machine learning techniques. In this paper, a comprehensive review of various models to predict customer churn in e-commerce data mining and machine learning techniques has been presented. A critical review of recent research papers in the field of customer churn prediction in e-commerce using data mining has been done. Thereafter, important inferences and research gaps after studying the literature are presented. Finally, the research significance and concluding remarks are described in the end.
Keywords: CRM, E-Commerce, Dataset, Pre-Processing, Data Mining, Customer Churn Prediction, Model Building, Machine Learning.
Scope of the Article: Data Mining