Content Performance and Artist Analytics Using SQL
Group Category: Use Case
Product Category: Database Design & Development
Sub Category: PostgreSQL
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
artists
| CSVDescription:
This file maintains a directory of music artists, serving as a central reference for content ownership, popularity tracking, and catalog segmentation.
- Includes artist IDs and display names
- Supports mapping of songs, albums, and user interactions to creators
- Essential for artist-level reporting, trend analysis, and fanbase insights
- Enables filtering by artist contribution and content volume
- Complements genre, album, and engagement data for full creator profiling
Use Case Document
| PDFDescription:
This PDF outlines advanced SQL-based use cases for analyzing how music content and artists perform on a streaming platform. It focuses on extracting insights related to viral trends, artist growth, album completion, and user engagement across various listener segments.
- Includes defined use cases with objectives, business outcomes, and expected SQL results
- Guides users through complex queries involving window functions, CTEs, and multi-level aggregations
- Useful for identifying rising talent, high-skip tracks, and content that resonates across demographics
- Supports product, marketing, and analytics teams in optimizing music catalog monetization
- Complements relational datasets across users, artists, songs, and subscriptions for a full-spectrum analysis
albums
| CSVDescription:
This dataset contains structured information about music albums, acting as a grouping mechanism for related songs and artist content.
- Contains album IDs, names, and associated artist references
- Enables album-based tracking, release frequency analysis, and content planning
- Essential for understanding catalog structure and lifecycle stage of content
- Supports timeline visualizations and artist discography analysis
- Complements songs and artist metadata for complete content mapping
songs
| CSVDescription:
This dataset contains detailed metadata about individual tracks, forming the foundation of content-level analysis in the music platform.
- Contains song IDs, titles, durations, and references to artists and albums
- Enables consumption tracking, playback behavior, and content discovery
- Essential for building playlists, reporting engagement, and ranking songs
- Supports filtering, recommendation engines, and skip-rate detection
- Complements user listening and song preference data
songs_liked
| CSVDescription:
This file captures user interactions through song likes, providing explicit preference signals and user engagement trends.
- Contains user IDs and liked song IDs with timestamps
- Enables song popularity scoring and personal recommendation input
- Essential for feedback loops in content ranking and personalized curation
- Supports user preference modeling and emotional connection tracking
- Complements listening history and user profiling for recommendation engines
user_listening_history
| CSVDescription:
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
users
| CSVDescription:
This file maintains demographic and account-level details of users, serving as the foundation for segmentation and engagement analytics.
- Includes user IDs, registration dates, and location-related attributes
- Forms the join point for listening behavior, preferences, and playlist actions
- Essential for cohort analysis, retention modeling, and personalization logic
- Enables filtering by region, activity status, or lifecycle stage
- Complements all behavioral and content interaction datasets
$2.00 $1.49 25% OFF
About this Dataset:
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