📊 UPI Transaction Funnel Analysis

🔹 Overview

An end-to-end Product Analyst project analyzing UPI transaction funnels (init → amount_entered → PIN → bank_auth → completed).
Covers SQL, Python, and Tableau, simulating real product analytics work.

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🔹 Problem Statement

Understand user drop-offs in a UPI transaction journey:

• Where do most users drop off?
• How do banks and devices compare?
• What errors cause failures?
• What is the average time-to-complete?

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🔹 Tools Used

• SQL (DBeaver + SQLite) → Funnel queries
• Python (Pandas, Matplotlib, Jupyter) → Time & Error analysis
• Tableau Public → Interactive dashboard

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🔹 Analysis Performed

1. SQL

   • Global funnel counts
   • Per-bank and per-device funnels

2. Python

   • Average transaction time (init → completed)
   • Error breakdown by bank & device

3. Tableau

   • Funnel dashboard with conversion %
   • Segmented funnels (bank/device)
   • Error & time visualizations

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🔹 Key Insights

• Biggest drop-off: Completed stage (87% after BankAuth)
• SBI has longer completion times (~4.2s vs HDFC ~2.9s)
• Top error: NETWORK_TIMEOUT (35% of SBI failures)
• Overall funnel completion: 79%

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🔹 Deliverables

• SQL scripts (/sql/)
• Python notebooks (/python/)
• Tableau dashboard (/tableau/)
• Data samples (/data/)

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🔹 Live Dashboard

Tableau Public Link (insert your published link)

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🔹 Author

👤 Bhumesh Lalwani
Product Analyst | Skilled in SQL, Python, Tableau