Bank Customer Churn Analysis in Power BI
Completion Date:
Project Overview
Dive into the dynamic domain of bank customer churn analysis, where decoding the numerous factors shaping customer exits serves as the gateway to unlocking strategic pathways towards strengthened retention and elevated satisfaction levels.
Bank customer churn analysis involves the examination of factors contributing to customers leaving a bank services. By scrutinizing various aspects such as satisfaction scores, complaints, tenure, product usage, and demographic information, banks can identify patterns and predict potential churn risks. Analyzing churn data enables banks to implement targeted retention strategies, enhance customer satisfaction, and ultimately reduce churn rates. Through the utilization of advanced analytics techniques and data-driven insights, banks can proactively address customer needs, improve service quality, and foster long-term relationships with their customers.
Role:
Problem Statement
The Bank Customer Churn Dataset serves the critical purpose of customer retention to ensure the long-term sustainability of the business. The primary goal of the dataset is to facilitate the prediction of customer churn, thereby enabling the bank to proactively address customer attrition. It includes essential customer-related attributes such as Customer ID, Credit Score, Geography, Gender, Age, Tenure, and Account Balance. The dataset serves as a valuable resource to customer behavior, preferences, and patterns, aiding in the development of effective strategies for customer retention and engagement.
By analyzing these key attributes, the bank aims to proactively identify potential churn indicators and implement targeted initiatives to enhance customer satisfaction and loyalty.
Executive Summary
Data Dictionary
Key Takeaways
The churn analysis of bank customers conducted in this project has provided valuable insights into the factors influencing customer retention and satisfaction. By leveraging Power BI visualization capabilities and data manipulation techniques, key trends and patterns in customer behavior were identified, including demographic distributions, product usage patterns, and financial profile impacts.
Next Steps
