User Engagement Analytics Dashboard Using SQL
Group Category: 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
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
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
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
Expert Service
Similar Products
Product Performance Dataset
Topics: SQL, PostgreSQL, Retail Performance
Basic Professional Data Analysis
Topics: SQL, PostgreSQL, Data Quality Analysis
Restaurant Performance & Menu Optimization
Topics: SQL, PostgreSQL, Data Analytics
Similar Services
Finding the best experts for you...
No Services Yet
Expert services for this product will appear here once available.
Top User Reviews
Loading reviews...
Be the first to review this product!
Please try refreshing the page.