📊 User Funnel & KPI Performance Analysis

Analyzed 4,715 user activity records to construct end-to-end product conversion funnels and calculate key performance metrics across 5 stages using SQL.

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Project Overview

Detail	Info
Dataset	user_kpi_data.xlsx
Records	4,715 events
Users	1,800 unique users
Period	January – March 2024
Products	10 (P1 – P10)
Funnel Stages	5 (Visit → Signup → Add to Cart → Checkout → Purchase)

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📈 Key Findings

🔻 Funnel Drop-off

Stage	Users	Drop-off
Visit	1,800	—
Signup	1,441	↓ 19.9%
Add to Cart	1,134	↓ 21.3%
Checkout	828	↓ 27.0% ⚠️ Highest
Purchase	630	↓ 23.9%

Key Insight: Biggest drop-off at Add to Cart → Checkout (27.0%) — primary area for optimization.

🎯 Conversion Rates

Step	Rate
Visit → Signup	80.1%
Signup → Add to Cart	78.7% ✅ Best
Add to Cart → Checkout	73.0% ⚠️ Lowest
Checkout → Purchase	76.1%
Overall	35.0%

Top Products

P1 (498) · P2 (482) · P3 (476) · P4 (473) · P5 (470)

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🗃️ SQL Scripts — 8 Sections

1. Database & Table Setup — schema creation
2. Data Exploration — record counts, date range, stage distribution
3. Funnel Analysis — users per stage, drop-off %, conversion rates
4. KPI Metrics — session duration, page views, returning users
5. Product Performance — interactions and conversion per product
6. Drop-off Insights — by device, region, and stage
7. Time-Based Analysis — daily, weekly, monthly trends
8. KPI Summary — single query with all key metrics

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📁 Repository Structure

User-Funnel-KPI-Analysis/
│
├── 📄 README.md
├── 📊 user_kpi_data.xlsx              ← Raw dataset (4,715 records)
├── 🗃️ kpi_analysis_queries.sql        ← All SQL scripts (8 sections)
└── 🌐 kpi_dashboard.html              ← Interactive HTML dashboard

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Author

Gauri Sharan