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, andCASEexpressions - 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
users
| CSVDescription:
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
| PDFDescription:
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
| CSVDescription:
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
| CSVDescription:
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
| CSVDescription:
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
| CSVDescription:
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
| CSVDescription:
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
$1.99 $1.60 19% OFF
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