Behavioral Insights from User Data Using SQL

Group Category: Use Case

Product Category: Database Design & Development

Sub Category: PostgreSQL

Unlock user behavior insights and sharpen your SQL skills with real music engagement data.

Business Overview:

Behavioral Insights from User Data Using SQL helps you explore user engagement in a music streaming app. You’ll identify power users, uncover personal song preferences, monitor daily listening habits, and analyze playlist creation activity. This hands-on product is ideal for analysts, marketers, and SQL learners who want to use data to drive retention, personalization, and engagement strategies.

Product Highlights:

Focused PDF guide with 5 behavioral SQL use cases based on real user activity
Each scenario connects SQL logic with business outcomes like engagement tracking and personalization
Built on a structured dataset of 20,000+ rows covering users, songs, playlists, and listening history
Perfect for those interested in user retention, app analytics, or music-tech use cases
Strong project for portfolios showing behavioral insights and data-driven decision-making

Learning Outcomes:

By solving these use cases, you'll gain practical experience in:

Writing complex SQL queries using JOINs, GROUP BY, HAVING, and CTEs
Analyzing user-level activity data to understand behavior and listening habits
Using time-based SQL functions to study patterns like recent activity or daily trends
Applying window functions (ROW_NUMBER) to extract user-specific preferences
Segmenting users by activity and curating behavioral profiles for product or marketing decisions

1/5

artists

| CSV

Description:
This file maintains structured records of music artists, forming a core entity for content mapping and performance analysis.

Contains artist IDs and names
Acts as a reference point for linking songs, playlists, and listener interactions
Essential for tracking content output, popularity trends, and catalog depth
Enables segmentation by artist activity, collaborations, or genre focus
Supports artist-level analytics, discovery, and content personalization

songs

| CSV

Description:
This file holds detailed metadata for individual tracks, serving as the foundation of content-level performance analysis.

Contains song IDs, names, durations, and linked artist/album IDs
Supports consumption analysis, popularity trends, and catalog optimization
Essential for playlist integration, song-level reporting, and user interaction mapping
Enables filtering, recommendation, and genre tagging workflows
Complements listening history and user engagement datasets

user_listening_history

| CSV

Description:
This dataset logs user interactions with songs over time, providing the foundation for behavioral analytics and engagement scoring.

Contains user IDs, song IDs, and timestamped playback events
Enables frequency, recency, and session-based analysis
Essential for churn modeling, personalization, and retention metrics
Supports user segmentation, listening patterns, and content feedback
Complements user and content metadata for lifecycle analytics

playlists

| CSV

Description:
This dataset captures details of user and system-generated playlists, enabling playlist-level engagement and trend tracking.

Contains playlist IDs, titles, creation timestamps, and user references
Enables insights into curation habits, genre diversity, and playlist lifecycle
Essential for analyzing playlist popularity, creation trends, and content clustering
Supports recommendations, curator analysis, and playlist-driven discovery
Complements user and song datasets for end-to-end playlist analytics

users

| CSV

Description:
This file provides demographic and account-level data for platform users, forming the base for behavioral and cohort analysis.

Includes user IDs, registration details, and basic profile information
Serves as a join key for playlist activity, song interaction, and engagement tracking
Essential for segmenting users by activity level, registration date, and demographics
Supports user lifecycle modeling, onboarding analysis, and retention strategies
Complements listening history, playlists, and social data

Behavioral Insights from User Data Using SQL

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