Agent context
Recurly models
4 SQL models that turn the raw Recurly tables into the reports people ask about. Deploy them in Weld and your agent queries the definitions behind every number.
How the set is built
- 4Raw tables
- 4Staging
- 6Core
- 4Analytics
Staging cleans each raw table once, core holds the logic, and analytics is the table dashboards and agents read.
- Billing by monthWhat was invoiced, credited, collected and still owed, by month. The cash-side companion to the MRR models, and the closest match to Recurly's invoice export totalled by month.grain: month x currency · 1 raw table
- MRR by customer monthMRR per customer per month, and the movement that took it there: new, expansion, contraction, churn or reactivation.grain: month x customer (Recurly account) x currency · 3 raw tables
- MRR movementsThe MRR bridge by month: opening MRR, new, expansion, contraction, churn, reactivation, closing MRR, plus customer counts. The movements Recurly's subscription analytics show, rebuilt from invoiced line items.grain: month x currency · 3 raw tables
- Subscription churnEvery subscription that has ended, why it ended, and how much MRR it was carrying just before. Splits churn into customers who chose to leave and customers lost to failed payments, which the MRR bridge cannot tell apart.grain: one row per expired subscription · 4 raw tables
- Weld or dbt
- The same SQL in both dialects. Sync the folder with GitHub Sync, or copy the dbt models into your project.
- Check before you rely on it
- Reconcile a month against Recurly's own reports first. Each model's notes say what the synced data can and can't support.
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