Banking Analytics: Credit Card Launch & RAAST Switcher Strategy

This project was developed to simulate a real-world Tier-1 Bank scenario: launching a premium Credit Card in a saturated market. Instead of a generic marketing approach, I utilized Gap Analysis and Income Utilization metrics to identify a hidden segment of high-value customers—"The RAAST Switchers"—who were actively spending but bypassing credit cards in favor of instant bank transfers.

Key Achievements

• Identified 140M PKR Revenue Opportunity: Discovered "locked" revenue in RAAST transactions that could be captured by specific credit card features.
• Strategic Segmentation: Moved beyond basic demographics to behavioral profiling, pinpointing "Salaried IT Employees" in Hyderabad as the most profitable target.
• Behavioral Modeling: Developed the "Spend-to-Income Ratio" (43% Rule) to identify customers with the highest safe lending capacity.
• Actionable "Hit List": Filtered the data down to exactly 438 high-priority leads for the sales team to contact immediately.

Technologies Used

• Power BI for end-to-end dashboarding and storytelling
• Advanced DAX for calculating "Share of Wallet," "Ticket Size Gaps," and "Income Utilization"
• Power Query for data transformation and resolving circular dependency errors
• Star Schema Modeling to link Fact_Spends efficiently with Customer Demographics

Challenges Solved

• The "Saturation" Problem: The dataset indicated 100% of customers already owned a credit card. I had to pivot the strategy from "Acquisition" to "Share of Wallet" analysis to find value.
• Circular Dependency Errors: Solved complex sorting issues in the Date Dimension (Month Name vs. Month Number) using Power Query logic instead of Calculated Columns.
• Complex Gap Analysis: Created dynamic DAX measures to compare "Credit Card Ticket Size" vs "RAAST Ticket Size" to prove that credit users spend more per transaction.

Results

• Delivered a data-driven launch strategy recommending a "Lifestyle & Essentials" card.
• Proposed specific product features (e.g., 5% Cashback on Electronics) based on the discovery that target users were buying gadgets via bank transfer.
• Proved that focusing on Hyderabad (172M Spend) would yield higher ROI than larger cities like Karachi.