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Customer Segmentation and Churn Pattern Analytics in European Banking
Subject area: Management and Commerce · Area of research: Finance
DOI: https://doi.org/10.64388/IREV9I12-1718879
Abstract
Customer churn is a significant challenge in the banking industry because retaining existing customers is substantially more cost-effective than acquiring new ones. This study analyzes customer churn patterns using segmentation-driven analytics on a European banking dataset containing 10,000 customers from France, Spain, and Germany. The objective is to identify customer segments with elevated churn risk, evaluate demographic and financial factors associated with churn, and provide actionable recommendations for improving customer retention. The analysis focuses on geography, age groups, credit score bands, tenure categories, account balances, customer activity, and product ownership. Findings indicate that customer engagement, geography, age, and financial profile significantly influence churn behavior. The study concludes with strategic recommendations for targeted retention programs and customer relationship management initiatives.
How to cite this paper
@article{1718879,
author = {Pavan Arvind Giri},
title = {Customer Segmentation and Churn Pattern Analytics in European Banking},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {12},
pages = {2450-2453},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1718879.pdf},
abstract = {Customer churn is a significant challenge in the banking industry because retaining existing customers is substantially more cost-effective than acquiring new ones. This study analyzes customer churn patterns using segmentation-driven analytics on a European banking dataset containing 10,000 customers from France, Spain, and Germany. The objective is to identify customer segments with elevated churn risk, evaluate demographic and financial factors associated with churn, and provide actionable recommendations for improving customer retention. The analysis focuses on geography, age groups, credit score bands, tenure categories, account balances, customer activity, and product ownership. Findings indicate that customer engagement, geography, age, and financial profile significantly influence churn behavior. The study concludes with strategic recommendations for targeted retention programs and customer relationship management initiatives.},
month = {June},
doi = {https://doi.org/10.64388/IREV9I12-1718879}
}