An end-to-end data analytics project that visualizes and tracks credit card transactions and customer data using Excel, MySQL, and Power BI.
This project demonstrates how raw financial and customer data can be transformed into meaningful insights through automation, SQL integration, and interactive dashboards.
- Automated Data Refresh from MySQL to Power BI
- Credit Card Transaction Dashboard with date-wise trends, category distribution, and transaction analysis
- Customer Dashboard with segmentation, credit limits, and behavioral insights
- Cleaned and structured data pipeline starting from Excel to SQL
- Insightful visualizations for decision-makers
- Microsoft Excel – Initial data cleaning and formatting
- MySQL – Data storage, transformation, and query handling
- Power BI – Visualization and dashboard development
- High Spending Categories
Insight: Categories such as Bills, Entertainment, and Fuel exhibit higher transaction volumes and amounts.
Action:
- Partner with top vendors in these categories for exclusive credit card offers or cashback.
- Create category-specific reward programs to incentivize usage.
- Low Usage Customers
Insight: A segment of customers (like those in the 20-30 and 60+ age group) either make very few transactions or remain inactive.
Action:
- Run reactivation campaigns for these users as limited-time bonuses or discounts.
- Offer EMI or balance transfer promotions to increase engagement.
- Customer Segmentation
Insight: Certain customer demographics (age groups, credit limits, city-wise data) show higher usage patterns.
Action:
- Tailor marketing campaigns for active segments.
- For low-performing segments, offer beginner-friendly cards or educational content to build trust.
- Credit Limit Utilization
Insight: Some users frequently utilize a high percentage of their credit limit.
Action:
- Target these customers with credit limit increase offers.
- Suggest premium cards with better limits and rewards.
- Transaction Frequency Trends
Insight: There are noticeable peaks during certain months (festive seasons, sales, etc.).
Action:
- Pre-plan marketing offers and cashback events just before these spikes.
- Use Power BI alerts to notify the sales team of upcoming patterns.
- Declining or No-Use Cards
Insight: A percentage of cards are not used after activation.
Action:
- Conduct a win-back campaign with "use within 7 days and get ₹500 cashback" type offers.
- Simplify onboarding and usage instructions for new users.
- Geographic Hotspots
Insight: Certain states (Texas, New York, California, etc) have higher credit card penetration and usage.
Action:
- Focus on underperforming regions through regional partnerships and offers.
- Increase visibility via targeted digital ads in low-usage areas.
- 'Credit Card Customer Report.pbix': Power BI dashboard visualizing customer demographics and credit card usage patterns.
- 'Credit Card Transaction Report.pbix': Power BI dashboard analyzing transaction trends, spending behavior, and card activity.
- 'Credit Card Financial Report.pdf': PDF report for dashboard preview.
- 'README.md': Documentation file explaining the project structure, data sources, and how to interpret the dashboards.
- 'credit_card.csv': Primary dataset with credit card attributes like card category, annual fees, interest earned, etc, updated till 24 December 2023.
- 'customer.csv': Master dataset containing customer details including ID, age, gender, income group, etc, updated till 24 December 2023.
- 'cc_add.csv': Extended credit card dataset, updated till 31 December 2023 to support future refreshes in Power BI.
- 'cust_add.csv': Extended customer address dataset continued till 31 December 2023, enabling seamless weekly updates in Power BI dashboards.