A survey on customer churn prediction using machine learning and data mining techniques in e-commerce

Gopal, Priya; Mohdnawi, Nazri

Abstract

In recent era, major companies from e-commerce sector should focus on customer identification through churns in accordance to their business strategy as the market saturation increases. Several e-commerce professionals and industrialists highlighted /enlisted/specified that identifying churn customers whose subscription period is about to end or those likely to migrate services from pre-existing company to another is known/represented as customer attrition. In order to retain loyal customers, earlier prediction of client behavior plays a vital part in real-time marketing. In this survey work, the difficulties in the prediction of customer attrition in the motor insurance sector are represented along with various data mining techniques comprising deep learning and machine learning advancements. It also emphasizes on the churns within the customer management cycle with surroundings. An overview of the survey performed orderly provides construction of churn prediction model, various methods of prediction utilized and their application in the business sector.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)
• Start Date: Dec 8, 2021
• End Date: Dec 10, 2021
• Acceptance Date: Oct 31, 2021
• Online Publication Date: Mar 1, 2022
• Publication Date: Mar 1, 2022
• Deposit Date: Jan 8, 2025
• Peer Reviewed: Peer Reviewed
• Book Title: 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)
• ISBN: 9781665495530
• DOI: https://doi.org/10.1109/CSDE53843.2021.9718460
• Keywords: Customer churn; Customer Churn Prediction; Data Mining Methods; Predictive models and Performance metrics (key words)
• Public URL: https://uwe-repository.worktribe.com/output/13604429