AI SaaS Metrics Dashboard from CSV

The reason SaaS metrics keep getting recomputed differently every quarter is that the source data is rarely shaped the way the metric needs it. Stripe gives you invoices; MRR is a recurring concept that has to be derived. A subscription that started mid-month should contribute a prorated amount to that month and the full amount the next; a discount should reduce MRR but not net cash collected; a trial conversion should be net-new MRR even though no payment cleared the day before. Sreniq applies the standard SaaS conventions for each of these — and shows its work in the chart caption — so you can reconcile against your own model instead of guessing what the dashboard did.

Cohort retention is where the time-to-insight gain is biggest. Built by hand, a cohort heatmap is an afternoon of pivot-table wrangling: bucket customers by signup month, count who is still paying N months later, divide. Sreniq does it in one shot from a customer-month table. The same goes for the secondary cohort cuts that actually drive product decisions — retention by plan tier, by acquisition channel, by initial deal size — which are usually skipped because nobody has time to redo the pivot for each segment.

Once the dashboard exists, the natural rhythm is monthly: refresh the file, look at the new MRR breakdown (new vs expansion vs churn vs contraction), check the latest cohort row, and read the AI-written summary to see if any number moved more than expected. The full page makes a clean appendix for board updates, and the public link can be shared with investors without granting Stripe access.