license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
language:
- en
tags:
- subscription
- invoices
- synthetic-data
- mindweave
- billing
- recurring-revenue
- saas
- cohorts
- stripe
- test-data
- mrr
- churn
- saas-metrics
- revops
- payments
pretty_name: Subscription Billing (Synthetic) (Free Sample)
size_categories:
- 1K<n<10K
configs:
- config_name: customers
data_files: data/customers.csv
default: true
- config_name: invoices
data_files: data/invoices.csv
- config_name: payments
data_files: data/payments.csv
- config_name: subscriptions
data_files: data/subscriptions.csv
Subscription Billing (Synthetic) (Free Sample)
This is a free sample with 4,800 rows. The full dataset has 52,483 rows across 6 tables.
SaaS subscription billing dataset covering customer acquisition, trial conversion, invoicing, payments, plan changes, and churn over a two-year growth period. Includes monthly recurring revenue expansion, mixed billing cadences, dunning outcomes, and cohort-based retention patterns that match realistic B2B SaaS finance and RevOps workflows.
The dataset encodes a pricing change in month 10 that temporarily increases churn, while trial-to-paid conversion is held at 22 percent. Useful for MRR analytics, churn modeling, payments reporting, subscription lifecycle automation, dashboard demos, and finance data engineering tests.
Sample tables
| Table | Sample Rows |
|---|---|
| customers | 500 |
| invoices | 2,000 |
| payments | 1,800 |
| subscriptions | 500 |
| Total | 4,800 |
Full dataset
The complete dataset includes all tables with full row counts:
| Table | Full Rows |
|---|---|
| churns | 170 |
| customers | 5,000 |
| invoices | 21,250 |
| payments | 20,493 |
| plan_changes | 570 |
| subscriptions | 5,000 |
| Total | 52,483 |
Formats included: CSV, Parquet, SQLite
Get the full dataset on Gumroad
About
Generated by Mindweave Technologies -- realistic synthetic datasets for developers, QA teams, and data engineers.
Every dataset features:
- Enforced foreign key relationships across all tables
- Realistic statistical distributions (not uniform random)
- Temporal patterns (seasonal, time-of-day, day-of-week)
- Injected anomalies for ML training and anomaly detection
- Deterministic generation (same seed = same output)
Browse all datasets: https://mindweavetech.gumroad.com