Data-Driven Business Intelligence for Operational Customer Churn Management

Dina K. Hassan^1,*, Ahmed K. Metawee²

¹Accounting Department, Faculty of Commerce, Kafr El Sheikh University, Egypt

²Accounting Department, Faculty of Commerce, Mansoura University, Egypt

Emails: dina.abdelsalam@com.kfs.edu.eg;  metawee68@mans.edu.eg

Abstract

In today’s data driven world businesses face a challenge in protecting customer strategies from operational churn. This paper explores the realm of data driven business intelligence with a focus on predicting and managing customer churn through analysis of analytics methods. Recognizing that customer attrition poses a threat to business sustainability, our research aims to harness the power of methods and discriminant analysis techniques. We examine Gradient Boosting Classifier, Ada Boost Classifier and Linear Discriminant Analysis to unravel patterns in customer behavior and predict churn likelihood. By utilizing a dataset that includes details about customer services account specifics and demographics we adopt an approach. Our comparative analysis of machine learning classifiers underscores their effectiveness in identifying patterns within the dataset. Importantly our findings emphasize the potential of machine learning as a strategy for managing churn.

Keywords: Business Intelligence; Operation Research; Customer Churn Management; Customer Segmentation.