A Stacking-Based Data Mining Solution to Customer Churn Prediction

Mohammadjavad Shabankareh · Mohammad Ali Shabankareh · Alireza Nazarian · Alireza Ranjbaran · Nader Seyyedamiri

19 citations0/0 files storedJournal of Relationship Marketing

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

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.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 19 citations exist upstream.

References

1998Forecasting with artificial neural networks:2008Application of data mining techniques in customer relationship management: A literature review and classification 2004The effects of customer satisfaction and switching barrier on customer loyalty in Korean mobile telecommunication services 2019Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series 2002Data mining techniques for customer relationship management 2011New insights into churn prediction in the telecommunication sector: A profit driven data mining approach 2006Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques 2004Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.