Exploring Listener and Subscription Data in Music Streaming with SQL Analytics
Group Category: Use Case
Product Category: Database Design & Development
Sub Category: PostgreSQL
Maximize subscription revenue and discover user value using advanced SQL insights.
Business Overview:
Revenue Optimization and Subscription Analytics Using SQL equips you with practical tools to analyze user subscriptions, payments, churn risk, and overall customer value on a music streaming platform. Using realistic, relational data, you’ll write queries that reveal the behavior behind upgrades, payment preferences, and subscription lifecycles. This product is ideal for analysts and data professionals working on monetization, pricing, and retention strategies.
Product Highlights:
- In-depth PDF guide with 5 advanced SQL use cases for monetization and subscription metrics
- Realistic 25,000-row dataset with linked tables for users, payments, subscriptions, and listening history
- Use cases reflect business scenarios like price sensitivity, churn indicators, and ARPU segmentation
- Built for professionals in revenue analytics, digital products, and customer lifecycle management
- A strong portfolio piece for demonstrating SQL proficiency in SaaS, streaming, and subscription-based business models
Learning Outcomes:
By solving these use cases, you'll gain practical experience in:
- Writing complex SQL queries using JOINs, window functions, GROUP BY, HAVING, and CASE expressions
- Segmenting users by listening behavior and subscription tier to evaluate upgrade likelihood
- Calculating ARPU and churn risk by analyzing payment history and activity trends
- Applying time-based analytics to measure retention and lifecycle value
- Building monetization dashboards with KPIs like LTV, conversion rates, and payment effectiveness
- Deriving strategic pricing and engagement insights to support growth and retention
user_listening_history
| CSVDescription:
Tracks individual song listens by users over time, forming the backbone of engagement and behavioral analysis.
- Contains user IDs, song IDs, and timestamps of listening events
- Enables calculations of WAU, MAU, retention curves, and session-based metrics
- Useful for identifying content popularity and high-engagement user cohorts
- Complements songs and subscriptions datasets to tie behavior to monetization
subscriptions
| CSVDescription:
Logs user subscription history across different tiers and durations, supporting lifecycle tracking and retention metrics.
- Contains subscription IDs, types (e.g., free, premium), and start/end dates
- Enables churn risk scoring, upgrade/downgrade pattern analysis, and user segmentation
- Useful for modeling lifecycle value and tracking conversion events
- Works in conjunction with users and payments datasets for monetization tracking
payments
| CSVDescription:
Captures detailed payment records from users across subscription plans and time periods, enabling financial tracking and monetization analytics.
- Includes user IDs, amounts paid, payment methods, and subscription references
- Critical for calculating ARPU, churn indicators, and subscription profitability
- Supports revenue trend analysis and payment behavior segmentation
- Complements subscriptions and users datasets for full monetization analysis
users
| CSVDescription:
Stores user profile data including identifiers, email, and subscription types, forming the master reference for customer segmentation.
- Contains basic personal information and subscription tier details
- Supports user-level filtering, demographic slicing, and cohort tracking
- Essential for mapping all other tables like payments, playlists, and engagement data
- Foundational dataset for retention, acquisition, and personalization modeling
$2.00 $1.25 37% OFF
About this Dataset:
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