This repository contains a Churn Analysis Dashboard built using Power BI to visualize customer churn trends in a banking dataset. The dashboard provides insights into customer demographics, activity, product usage, and key factors influencing churn rates.
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Total Customers Overview: Displays the total number of customers (10K) and churn rate (~15-20%).
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Customer Demographics:
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Gender Distribution
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Activity Status (Active vs. Inactive Customers)
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Credit Card Ownership
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Country-wise Distribution (France, Germany, Spain)
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Product-wise Distribution
Customers & Churn Rate by Age Group
Customers & Churn Rate by Credit Scores
Customers & Churn Rate by Account Balance
Demographics & Activity
Female customers (54.57%) slightly outnumber male customers (45.43%).
51.51% of customers are inactive, posing a potential churn risk.
70.55% own a credit card, while 29.45% do not.
France has the highest number of customers (50.14%).
Product 1 is the most used, while Product 3 has the lowest adoption.
Higher churn observed in older customers (51-60 age group).
Lower credit scores (≤ 400) correlate with higher churn rates.
Customers with low account balances (1K-10K) have the highest churn.
Engage inactive users through targeted promotions.
Improve customer retention for the 51+ age group and low-credit-score customers.
Encourage adoption of Product 3 to increase engagement.
Analyze high churn in lower account balance groups and offer personalized incentives.