User Engagement Analytics Dashboard Using SQL
Group: Use Case
|Product Category: Database Design & Development
|Sub Category: PostgreSQL
About this Product
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, andwindow functions - Using date logic and
INTERVALto 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
Resources
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
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