SaaS Subscription & Churn Analytics Dataset
- kaggle.com
Updated Jul 21, 2025
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EmailClick to copy linkLink copiedCiteRivalytics (2025). SaaS Subscription & Churn Analytics Dataset [Dataset]. https://www.kaggle.com/datasets/rivalytics/saas-subscription-and-churn-analytics-datasetCroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.Dataset updatedJul 21, 2025AuthorsRivalyticsLicenseMIT Licensehttps://opensource.org/licenses/MIT
License information was derived automaticallyDescriptionRavenStack is a fictional AI-powered collaboration platform used to simulate a real-world SaaS business. This simulated dataset was created using Python and ChatGPT specifically for people learning data analysis, business intelligence, or data science. It offers a realistic environment to practice SQL joins, cohort analysis, churn modeling, revenue tracking, and support analytics using a multi-table relational structure.
The dataset spans 5 CSV files:
accounts.csv – customer metadata
subscriptions.csv – subscription lifecycles and revenue
feature_usage.csv – daily product interaction logs
support_tickets.csv – support activity and satisfaction scores
churn_events.csv – churn dates, reasons, and refund behaviors
Users can explore trial-to-paid conversion, MRR trends, upgrade funnels, feature adoption, support patterns, churn drivers, and reactivation cycles. The dataset supports temporal and cohort analyses, and has built-in edge cases for testing real-world logic.
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Simulated data for SaaS user behavior analysis
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
RavenStack is a fictional AI-powered collaboration platform used to simulate a real-world SaaS business. This simulated dataset was created using Python and ChatGPT specifically for people learning data analysis, business intelligence, or data science. It offers a realistic environment to practice SQL joins, cohort analysis, churn modeling, revenue tracking, and support analytics using a multi-table relational structure.
The dataset spans 5 CSV files:
accounts.csv – customer metadata
subscriptions.csv – subscription lifecycles and revenue
feature_usage.csv – daily product interaction logs
support_tickets.csv – support activity and satisfaction scores
churn_events.csv – churn dates, reasons, and refund behaviors
Users can explore trial-to-paid conversion, MRR trends, upgrade funnels, feature adoption, support patterns, churn drivers, and reactivation cycles. The dataset supports temporal and cohort analyses, and has built-in edge cases for testing real-world logic.