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Payment Analytics & Reporting

Tracking and reporting on key financial metrics including MRR, churn rate, LTV, payment success rates, and revenue breakdowns to understand business health and inform decisions.

Also known as: revenue analytics, billing metrics, MRR tracking, payment reporting, revenue reporting

Description

Payment analytics and reporting provide visibility into the financial health of your business by tracking key metrics derived from your payment data. Core SaaS metrics include Monthly Recurring Revenue (MRR), Annual Recurring Revenue (ARR), MRR growth broken down by new, expansion, contraction, and churned components, customer Lifetime Value (LTV), Customer Acquisition Cost (CAC), LTV:CAC ratio, churn rate (both customer and revenue churn), and Average Revenue Per User (ARPU). These metrics should be computed from your own transaction data rather than relying solely on Stripe's Dashboard, as you often need to segment by dimensions Stripe doesn't know about (product tier, acquisition channel, customer cohort).

Building a revenue analytics pipeline typically involves syncing relevant Stripe data into your own database using webhooks or Stripe's Data Pipeline product. Key objects to track include Subscriptions (status, current price, quantity), Invoices (amount_paid, lines, period_start, period_end, discount), Charges (amount, refunded amount, dispute status), and Customers (created date, metadata). From this data, you compute MRR by summing the normalized monthly value of all active subscriptions, factoring in discounts, multi-quantity subscriptions, and annual plans (divide by 12).

Reporting should be available at both aggregate and granular levels. Executive dashboards show MRR trend, churn rate, and LTV over time. Finance reports provide recognized revenue, deferred revenue, refunds, and disputes for accounting purposes. Operational reports track payment success rates by payment method and region, average payment processing time, retry success rates, and dunning funnel conversion. Consider feeding this data into a BI tool (Metabase, Looker, Mode) or building custom dashboards for different stakeholder audiences.

Prompt Snippet

Compute MRR by querying active subscriptions and normalizing to monthly amounts: sum(CASE WHEN interval = 'year' THEN amount / 12 ELSE amount END) with adjustments for discounts and multi-quantity line items. Break MRR movements into components by comparing subscription snapshots between periods: new_mrr (subscriptions created this month), expansion_mrr (upgrades), contraction_mrr (downgrades), churned_mrr (cancellations), reactivation_mrr (re-subscribed). Sync Stripe data via webhooks into a local subscriptions_history table with (subscription_id, status, mrr_amount, changed_at) for point-in-time queries. Build a /admin/metrics API endpoint that returns { mrr, arr, churn_rate, ltv, arpu, payment_success_rate } computed from your ledger tables, cached with a 1-hour TTL.

Tags

analyticsmrrrevenuereportingmetricsbusiness-intelligence