Dave-s-Portfolio

Fan Engagement and Attendance Case Study (SQL)

📌 Context

This project analyzes fan engagement and attendance data for sports analytics, using multi-step SQL analysis to uncover patterns in fan behavior, ticket sales, and engagement metrics.

🛠️ Techniques Used

• SQL: Data cleaning, filtering, aggregation, joins
• Visual storytelling: Step-by-step screenshots of the analysis workflow

📊 Process

1. Data Cleaning
   • Removed duplicate records
   • Standardized fan engagement categories
2. Filtering
   • Focused on active engagement periods
   • Segmented by fan demographics and attendance patterns
3. Aggregation
   • Calculated attendance metrics
   • Identified key engagement drivers

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📷 Visuals

What each step’s code does:

This analysis follows a 13-step workflow, each focusing on a specific aspect of the fan engagement analysis:

Step 1: Initial data exploration	Step 2: Data validation and quality checks	Step 3: Fan demographics analysis
Step 4: Attendance patterns by date	Step 5: Engagement metrics calculation	Step 6: Revenue analysis per fan segment
Step 7: Joining attendance with fan profiles	Step 8: Season ticket holder analysis	Step 9: Peak attendance identification
Step 10: Fan retention metrics	Step 11: Engagement trends over time	Step 12: Final aggregation and insights
Step 13: Summary metrics and conclusions

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📈 Results

• Attendance Patterns: Identified peak attendance periods and fan demographics driving engagement
• Revenue Insights: Calculated revenue per fan segment to optimize pricing strategies
• Engagement Trends: Revealed retention patterns and season ticket holder behavior

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🎯 Teaching Takeaway

This case study shows how multi-step SQL analysis with joins, filtering, and aggregation can reveal comprehensive insights into fan engagement and attendance patterns.

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🔗 Links

• SQL Script: grocery_sales_analysis.sql