An interactive Power BI dashboard project that analyzes customer churn behavior using a structured Excel dataset. Built to surface key churn drivers and retention trends that help businesses act before customers leave.
Customer churn is one of the most critical KPIs any business tracks. This project digs into churn patterns across dimensions like geography, age, gender, credit score, and credit card status — giving decision-makers a clear visual picture of which customer segments are most at risk.
| Tool | Purpose |
|---|---|
| Power BI | Data modeling and dashboard development |
| Microsoft Excel | Dataset storage and preparation |
📎 Dashboard 1 Screenshot · Dashboard 2 Screenshot · Dataset File
The Excel dataset is modeled in a star schema with one fact table and several dimension tables.
| Table | Columns |
|---|---|
Bank_Churn (Fact) |
CustomerID, Age, Balance, CreditScore, Bank DOJ, Exited, EstimatedSalary, GenderID, CreditID, ExitID, GeographyID, ActiveID |
CustomerInfo |
CustomerID, Surname |
ActiveCustomer |
ActiveID, ActiveCategory |
ExitCustomer |
ExitID, ExitCategory |
CreditCard |
CreditID, Category |
Geography |
GeographyID, GeographyLocation |
Gender |
GenderID, GenderCategory |
Date Master |
Date, Month, Month Order, Year |
(Entity-relationship diagram showing dimension and fact table relationships)
- What is the overall churn rate and retention rate?
- How does churn trend across years and months?
- Which gender has a higher exit rate?
- Do credit card holders churn less than non-holders?
- Which credit score categories are most prone to churn?
- How does churn differ across France, Germany, and Spain?
- Which age group has the highest retention?
High-level summary of churn metrics, breakdowns by geography, gender, and credit card status.
Time-series view of churn patterns alongside retention indicators by age and credit score.
- Churn Rate: ~20% of customers exited
- Retained Customers: 7,963 out of 10,000
- Credit Cards: Non-holders account for a disproportionately large share of churn
- Geography: France has the highest exit volume; Germany and Spain retain customers better
- Age: Churn peaks in the 30–45 age bracket
- Gender: Female customers exited at a slightly higher rate than males
- Credit Score: "Fair" and "Poor" score bands show the highest churn rates
This project equips business teams to:
- Identify high-risk customer segments across multiple dimensions
- Take targeted retention actions before churn occurs
- Align offers and services with the profiles most likely to stay
- Support data-driven decisions with always-on visual reporting
├── Dataset/
│ └── Bank_Churn_Dataset.xlsx
├── Dashboard/
│ ├── CustomerChurnAnalysis.pbix
│ ├── Dashboard_1.png
│ └── Dashboard_2.png
├── Schema/
│ └── ERD_Diagram.png
└── README.md
- Clone or download this repository
- Open
CustomerChurnAnalysis.pbixin Power BI Desktop - If prompted, reconnect the data source to the Excel file in
/Dataset/ - Explore the dashboards and apply slicers to filter by region, gender, year, and more