Content Performance and Artist Analytics Using SQL
Group: Use Case
Product Category: Database Design & Development
Sub Category: PostgreSQL
No Solution Available
You will have to implement it on your own.
About this Product
Measure music performance and artist growth with real data and advanced SQL techniques.
Business Overview:
Content Performance and Artist Analytics Using SQL enables you to explore key music content metrics across songs, albums, and artists. You’ll use structured data to detect viral songs, track artist popularity, evaluate album completion rates, and measure how different listener groups engage with content. Perfect for data professionals in media or streaming, this product gives a clear path from SQL query to content strategy.
Product Highlights:
- Detailed PDF guide featuring 5 real-world SQL use cases covering song, album, and artist performance
- Based on a 25,000-row dataset reflecting listening patterns, artist metadata, and user behavior
- Combines content analytics with user segmentation, completion rate evaluation, and skip rate analysis
- Ideal for analysts in music tech, content operations, or artist relations looking to extract actionable insights
- Builds a strong portfolio project focused on media analytics, artist reporting, and data-driven engagement tracking
Learning Outcomes:
By solving these use cases, you'll gain practical experience in:
- Writing complex SQL queries using
JOINs,CTEs,GROUP BY,HAVING, andwindow functions - Identifying fast-rising content using lag-based growth analysis and viral detection
- Measuring album completion rates with conditional logic and percentage tracking
- Analyzing user skip behavior using session-based comparisons and time thresholds
Resources
Description:
This dataset logs timestamped user listening activity, forming the basis for behavioral segmentation and playback analytics.
- Contains user IDs, song IDs, and play timestamps
- Enables session reconstruction, frequency analysis, and peak usage detection
- Essential for understanding engagement depth, time-of-day trends, and loyalty metrics
- Supports churn prediction, playlist optimization, and personalized recommendations
- Complements songs, users, and artist datasets for listener journey modeling
Topics
Skills
Languages
English
Level
Expert
Business Domain
Media and Entertainment
Project Mentors
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