A Stacking-Based Data Mining Solution to Customer Churn Prediction. Issue 2 (3rd April 2022)

• Record Type: Journal Article
• Authors: Shabankareh, Mohammad Javad; Shabankareh, Mohammad Ali; Nazarian, Alireza; Ranjbaran, Alireza; Seyyedamiri, Nader
• Is Part Of: Journal of relationship marketing. Volume 21:Issue 2(2022)
• Page Start: 124
• Page End: 147
• Publication Date: 2022-04-03
• Subjects: Stacking -- data mining -- customer churn -- CHAID decision tree -- customer relationship management -- predicting models; Relationship marketing -- Periodicals; Marketing -- Management -- Periodicals; Customer services -- Quality control -- Periodicals; 658.812
• DOI: 10.1080/15332667.2021.1889743
• Languages: English
• ISSNs: 1533-2667

Abstract

Abstract: In today's competitive world, organizations are in a constant struggle to retain their current customers while attracting new customers through various methods. Customer churn is a major challenge in different industries and companies. Despite their initial successful attempts at attracting customers, organizations soon face the fact that their current customers may turn away toward their rivals. By identifying churn candidates, organizations will be able to guarantee their future success by revising their customer relationship management policy. Analyzing the data of the telecommunications industries, this study provided an effective early-churn-detection solution using modern techniques by stacking data mining algorithms. Research findings indicate that integrating support vector machines (SVMs) with the chi-square automatic interaction detection (CHAID) decision tree can yield the best outcome. The results show the proper accuracy of the proposed churn prediction solution. In addition, stacking contributed to improved customer churn detection results.