The Project is to analyze the rate of churn of bank customers and its visualization charts created using Power BI.
The bank aims to understand and analyze customer churn rates to improve retention strategies and enhance customer satisfaction. By utilizing bank data encompassing various attributes such as active membership status, age, balance, country, credit score, customer ID, estimated salary, lost customers, product number, and tenure, the bank seeks to identify patterns and factors contributing to churn. Specifically, the bank desires to examine churn status by gender, total customer count, total churn rate, and churn rate concerning credit score.
Churn rate is like a "leaving rate" for customers. It shows the percentage of customers who stop using a company's product or service over a certain period.
1.Gather bank data including active membership status, age, balance, country, credit score, customer ID, estimated salary, lost customers, product number, and tenure.
2.Perform data cleaning to handle missing values, outliers, and inconsistencies.
Conduct exploratory data analysis to understand the distribution and relationships between variables. Explore the distribution of churn status by gender. Analyze the distribution of credit scores among churned and retained customers.
Utilize visualizations such as bar plots, pie charts etc., to represent:
1.Churn status by gender: Compare the count of churned and retained customers by gender using a bar plot or pie chart.
2.Total customer count: Display the total count of customers using a bar plot or pie chart.
3.Total churn rate: Calculate and visualize the overall churn rate as a percentage.
4.Churn rate with credit score: Group customers by credit score ranges and visualize churn rates within each group using a bar plot or line chart.
1.Clear understanding of churn status by gender and overall customer count.
2.Insightful visualizations depicting churn rates and their correlation with credit scores.
3.Identification of key factors contributing to churn.