🕹️ PlayerPulse Analytics

End-to-End Product Analytics Pipeline for Mobile Game Retention

🌟 Project Overview

This repository showcases a complete Data Analytics lifecycle for a mobile gaming studio. I engineered a synthetic dataset of 176,000+ player events, architected a MySQL database to handle telemetry, and developed a high-fidelity Power BI dashboard to identify critical churn points and optimize marketing ROI.

🧠 The Business Challenge

A hypothetical mobile game studio observed high initial installs but stagnating long-term revenue. I performed a deep-dive analysis to:

1. Identify Onboarding Friction: Pinpoint exactly where players drop off during their first 24 hours.
2. Evaluate Channel Quality: Compare the LTV potential of Organic traffic vs. Paid Ads (TikTok, Google, Facebook).

🛠️ Technical Implementation

• Data Engineering (Python): Developed data.py to simulate realistic telemetry streams with behavioral skews (e.g., higher churn in specific ad cohorts).
• SQL Architecture (MySQL): * Designed Database Views (vw_retention_data) for high-performance BI reporting.
  • Utilized Conditional Aggregations and Date Offsets to calculate rolling cohort retention.
• Visualization (Power BI): Designed a studio-standard interactive dashboard using a dark-mode aesthetic to provide executives with a "pulse" on player health.

💡 Key Insights & Actionable Recommendations

1. The "Level 5" Core Loop Friction

• Discovery: While 85% of players complete the tutorial, only 34.8% reach Level 5.
• Recommendation: Flatten the difficulty curve between Levels 3 and 5. Implement "Early Win" rewards to bridge the gap and increase the conversion to the Monetization stage.

2. Organic vs. Paid Quality Gap

• Discovery: Organic players exhibit a 44.14% Day-1 Retention, significantly outperforming TikTok (29.59%) and Facebook (29.28%).
• Recommendation: Reallocate 15% of the Paid UA budget into App Store Optimization (ASO) to leverage higher-quality organic growth.

🚀 How to Run

1. Run data.py to generate the dataset.
2. Import the CSV into MySQL and run the SQL scripts to build the analytical views.
3. Open playerpulse_dashboard.pbix (or view dashboard.png) to explore the insights.

📂 Repository Structure

• dashboard.png: High-resolution view of the interactive BI tool.
• data.py: Python engine for telemetry data modeling.
• db_setup.py: Script for initializing the database schema.
• funnel_analysis.sql: Query logic for onboarding stage conversion.
• playerpulse_dashboard.pbix: The raw Power BI dashboard project file.
• retention_analysis.sql: SQL View architecture for cohort analysis.
• data/: Folder containing the generated player_telemetry.csv dataset.

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Author: Tanya Jain
Aspiring Business Analyst