accounts
9,371 rows
- idtext · key
- nametext
- domaintext
- lead_sourcetext
- created_attime
A hose dataset · vertical
Trials, seats, invoices, declined cards and the retries that save some of them. Every metric is derived from the rows, none is stored.
2,130,787 rows · last row moments ago
The receipt
The whole dataset grows from this one seed. Everyone who downloads it gets the same rows with the same ids, today and next month, so whatever you build on it keeps working after the data refreshes.
per day, last 90 days
Schema · 8 tables
9,371 rows
15,105 rows
6 rows
9,371 rows
14,047 rows
15,773 rows
20,101 rows
2,047,013 rows
What the data claims
Why this one
Every SaaS metrics guide explains MRR, net revenue retention and dunning with a chart, and none of them includes the table the chart came from. Quenchly is that table. It is a mid-market team-workflow product on per-seat plans, generated by hose as an operational billing schema: accounts, users, a price book, subscriptions with their change log, invoices, payment attempts, and raw product events.
Nothing is pre-aggregated. MRR is live subscriptions times seats times
the plan price. Churn splits into requested and payment-failure
cancels because the cancel event records the reason. Dunning
recovery is an invoice whose successful payment was not its first.
Daily active users come from app_events, the way they would from a
real analytics export.
The business has a history. Trials convert at about one in five and open a 14-day window. Churn front-loads into the first quarter of a subscription and settles near two percent a month. Usage falls in the weeks before a voluntary cancel, not after it. In its third year the company raises prices. The price book gains new rows and renewals migrate to them. Annual signups rush in during the month before. The same seed regenerates all of it, byte for byte.
The other datasets