Engagement Intelligence through Behavioral Analysis in Music Streaming with SQL

Group Category: Use Case

Product Category: Database Design & Development

Sub Category: PostgreSQL

Uncover platform stickiness and consumption intent through SQL-powered behavioral signal analysis.

Business Overview:
Engagement Signals in Music Streaming helps streaming platforms understand how users interact with songs, playlists, albums, and content curation features. The dataset tracks passive vs. active behaviors (such as liking but not listening), album completions, playlist diversity, and weekly traffic trends. These signals allow businesses to build personalization algorithms, spot high-potential content, and refine UX to improve retention.

Product Highlights:

Detailed SQL scenarios for playlist diversity, album completion, and passive user identification
Includes 5 behaviorally rich use cases derived from realistic music app structures
Leverages key engagement signals to inform recommendation and content strategies
Ideal for analysts, product managers, and data scientists working on content experience
Schema involves user behavior, playlist activity, song and album metadata, and listening logs

Learning Outcomes:
By completing this product, you will:

Use SQL to measure engagement intensity and user interaction patterns
Learn to work with nested joins, CTEs, window functions, and day-based grouping
Evaluate diversity metrics in playlists and passive engagement indicators
Segment users based on content creation vs. consumption behaviors
Build analytical models that support retention and content discovery strategies

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albums

| CSV

Description:
This file catalogs albums in the music platform, enabling content grouping and performance tracking at the album level.

Contains album IDs, names, and associated artist references
Serves as a structural layer for organizing songs into collections
Essential for analyzing release patterns, artist output, and content lifecycle
Supports timeline reporting and content planning workflows
Complements songs and artists datasets for full catalog analysis

playlist_songs

| CSV

Description:
This file maps individual songs to user-created playlists, enabling analysis of playlist curation, popularity trends, and listening behaviors.

Contains playlist and song IDs, forming a many-to-many relationship
Essential for analyzing curated content and thematic playlists
Supports playlist recommendation engines and user preference modeling
Complements playlists and songs tables for full content discovery flows

songs

| CSV

Description:
This file contains metadata for all songs in the streaming catalog, including artist and album references, durations, and file paths.

Tracks include song titles, durations, and media URLs
Provides structural basis for streaming analytics and search indexing
Ideal for track-level engagement metrics and playback insights
Complements artists, albums, and genres datasets

songs_liked

| CSV

Description:
This file logs the songs users have explicitly liked, enabling preference analysis and feedback-based personalization.

Contains user IDs, song IDs, and timestamps
Useful for understanding content resonance and loyalty
Supports recommendation models and "liked playlist" generation
Complements users and songs for engagement tracking

playlists

| CSV

Description:
This file captures playlist-level data, including user ownership, titles, and optional descriptions.

Includes playlist IDs, user IDs, and textual metadata
Supports analysis of user-generated content and listening patterns
Enables measurement of playlist creation trends and personal curation behavior
Pairs with playlist_songs.csv and users.csv to explore engagement

user_listening_history

| CSV

Description:
This file tracks when each user listened to specific songs, forming the backbone of user activity and behavioral analysis.

Contains user IDs, song IDs, and listen timestamps
Enables session reconstruction, engagement scoring, and trend discovery
Ideal for churn detection, recommendation modeling, and A/B testing
Complements songs, users, and subscription data

users

| CSV

Description:
This file defines user profiles on the streaming platform, capturing key identifiers and personal attributes.

Contains user IDs, names, emails, and subscription types
Useful for segmentation, personalization, and churn analysis
Enables demographic profiling and listening pattern correlation
Complements usage history, payment records, and subscriptions

Engagement Intelligence through Behavioral Analysis in Music Streaming with SQL

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