User Engagement Analytics Dashboard Using SQL

Group Category: Use Case

Product Category: Database Design & Development

Sub Category: PostgreSQL

Track user activity, retention, and engagement with SQL-powered dashboards.

Business Overview:

User Engagement Analytics Dashboard Using SQL helps you explore and report on critical engagement KPIs in a music streaming platform. You’ll use SQL to identify the most active users, analyze retention over time, compare free vs premium usage, and classify users into lifecycle segments. This product is ideal for analysts and product teams who want to drive engagement, retention, and subscription growth through data insights.

Product Highlights:

Strategic PDF guide featuring 5 detailed use cases focused on user engagement and activity tracking
Leverages real-world user listening, subscription, and genre interaction data (25,000+ rows)
Includes advanced segmentation techniques to group users into New, Active, At-Risk, or Churned
Designed for data analysts, growth teams, and SQL learners working in digital platforms
A powerful portfolio piece demonstrating end-to-end engagement analytics using SQL

Learning Outcomes:

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

Writing complex SQL queries using JOINs, GROUP BY, HAVING, and window functions
Using date logic and INTERVAL to calculate retention windows and active timeframes
Segmenting users based on recent activity and building behavior-based user cohorts
Analyzing listening habits across genres and subscription types
Developing dashboards from SQL output that support engagement and reactivation strategies

1/5

Use Case Document

| PDF

Description:

This PDF presents a SQL-based approach to building a user engagement dashboard for a music streaming platform. It covers key behavioral analytics like listening frequency, retention patterns, subscription impact, and lifecycle segmentation.

Includes multiple use cases with business goals, SQL techniques, and expected outcomes
Helps analyze user activity trends, churn signals, and content engagement metrics
Useful for designing re-engagement campaigns and enhancing subscription value
Supports product teams and data analysts with actionable engagement KPIs
Complements structured datasets covering users, listening history, subscriptions, and content metadata

song_genres

| CSV

Description:
This dataset links songs to their respective genres, adding classification and context to each track in the platform.

Contains song and genre ID mappings
Supports multi-genre tagging and genre diversity evaluation
Essential for content discovery, filtering, and personalization engines
Enables trend tracking across genre types and user preferences
Complements song metadata, playlist creation, and listening analytics

user_listening_history

| CSV

Description:
This dataset logs timestamped listening events by users, offering deep insight into behavioral patterns and content engagement.

Contains user IDs, song IDs, and playback timestamps
Enables analysis of frequency, recency, and listening sessions
Essential for engagement scoring, churn prediction, and session behavior tracking
Supports segmentation of users based on usage intensity and time-of-day activity
Complements subscriptions, payments, and user profile data

songs

| CSV

Description:
This file provides essential metadata for each song available on the platform, forming the core of the music content layer.

Contains song IDs, titles, durations, and references to artists or albums
Enables playback analytics, popularity tracking, and content classification
Essential for powering recommendations, playlist logic, and engagement insights
Supports integration with user behavior and genre tagging datasets
Complements user interactions and monetization datasets

users

| CSV

Description:
This file holds key demographic and registration details of platform users, serving as a central entity in behavioral and subscription analysis.

Contains user IDs, usernames, and registration timestamps
Serves as a join key across listening activity, payment, and subscription datasets
Enables user segmentation, retention analysis, and cohort creation
Essential for personalized content delivery and lifecycle modeling
Complements all behavioral, financial, and content-related datasets

User Engagement Analytics Dashboard Using SQL

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