Subscription analytics for MRR movements, cohorts, churn, and retention metrics.
ChartMogul is subscription analytics software that connects to billing systems like Stripe, Chargebee, and Recurly and normalizes their data into standard SaaS metrics: MRR and its movements (new, expansion, contraction, churn, reactivation), cohort retention, LTV, and net revenue retention. Founders, finance, and RevOps teams use it as the shared source of truth for recurring revenue reporting, segmented by plan, geography, or custom attributes. In the revenue stack it sits on top of billing as the analytics layer — it computes and explains the numbers rather than producing invoices.
Which of the capability map's modules ChartMogul covers — each links to the module's own page, with every tool that supports it.
| Module | Phase | Depth | Note |
|---|---|---|---|
| Grow Revenue | |||
| Revenue Waterfall / Cohort Analytics | Retention & Insights | Core | MRR movement waterfalls and cohort retention from normalized billing data |
| Net Revenue Retention Analytics | Expansion Channels | Core | |
| Contraction & Downgrade Analytics | Retention & Insights | Supported | contraction surfaced as a first-class MRR movement type |
ChartMogul's value is opinionated normalization: it turns messy billing events from multiple systems into consistently defined MRR movements, so metric debates end at the definition rather than the spreadsheet. Compared to building the same reporting in a BI tool, it ships the SaaS metric logic — cohorts, movements, NRR — pre-built and billing-aware.
You can, but normalizing billing events into correct MRR movements — prorations, plan changes, refunds, multi-currency — is deceptively hard to get right and keep right. ChartMogul ships that logic maintained for each billing source, which is most of the work.
It normalizes whatever the billing system invoices, so usage revenue appears in the metrics, but volatile consumption makes MRR a blunt lens. Heavily usage-based businesses typically complement it with consumption analytics on their own data.