Revenue Optimization and Subscription Analytics Using SQL

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

1/7

users

| CSV

Description:
This dataset captures demographic and account metadata of users, forming the central reference for engagement and monetization analytics.

Contains user IDs, usernames, registration dates, and additional user attributes
Forms the join key for connecting listening behavior, subscription status, and payments
Essential for user profiling, lifecycle analysis, and segmentation
Supports personalized outreach, retention tracking, and growth analysis
Complements all content interaction and revenue datasets

Use Case Document

| PDF

Description:

This PDF outlines strategic SQL use cases designed to uncover revenue opportunities and subscription trends in a music streaming platform. It focuses on analyzing user behavior, payment methods, and pricing impact to support data-driven monetization strategies.

Includes practical use cases with objectives, SQL techniques, and business impact
Guides users through revenue segmentation, churn risk analysis, and price sensitivity tracking
Useful for improving retention, optimizing subscription models, and targeting high-value user segments
Supports business intelligence efforts with metrics like ARPU, LTV, and conversion behavior
Complements relational datasets spanning users, payments, subscriptions, and listening activity

payments

| CSV

Description:
This file contains detailed records of user payment transactions, forming the basis for revenue tracking and financial analytics.

Includes user IDs, payment amounts, timestamps, and payment methods
Enables analysis of payment trends, revenue breakdowns, and conversion behavior
Essential for calculating ARPU, revenue churn, and payment method performance
Supports subscription lifecycle insights and monetization optimization
Complements subscriptions and user datasets for revenue modeling

song_genres

| CSV

Description:
This dataset maps individual songs to genres, enriching content classification for filtering and recommendation systems.

Contains song IDs and genre IDs
Enables multi-genre tagging, diversity scoring, and genre affinity analysis
Essential for content discovery, personalization, and playlist generation
Supports content-based filtering in recommendation engines
Complements songs, user listening behavior, and playlist data

songs

| CSV

Description:
This file provides metadata on all songs within the platform, supporting track-level analytics and performance evaluation.

Includes song IDs, titles, durations, and artist/album references
Forms the core dataset for playback trends, content engagement, and curation
Essential for playlist creation, listening session analysis, and catalog navigation
Enables filtering by content type, duration, or popularity
Complements genres, likes, and listening history datasets

subscriptions

| CSV

Description:
This dataset tracks user subscription activity, capturing plan types, start/end dates, and free vs. paid tiers.

Contains user IDs, subscription start and end dates, plan names, and statuses
Supports cohort tracking, churn analysis, and pricing strategy evaluation
Essential for measuring subscription growth, retention, and upgrade behavior
Enables segmentation by plan type and tenure for targeted campaigns
Complements payments and user data for full revenue lifecycle modeling

user_listening_history

| CSV

Description:
This file logs user-song listening events with timestamps, supporting session-based analysis and behavioral segmentation.

Includes user IDs, song IDs, and timestamped listening activity
Enables recency/frequency analysis, content exposure tracking, and session modeling
Essential for personalization, content ranking, and churn prediction
Supports understanding of active users, trends, and listening depth
Complements subscriptions, users, and payment behavior datasets

Revenue Optimization and Subscription Analytics Using SQL

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