Subscription analytics

Subscription analytics is a system of metrics and reports for a recurring business: recurring revenue (MRR/ARR), customer and revenue churn, lifetime value (LTV), acquisition cost (CAC) and their ratio, conversions (trial→paid etc.), cohort retention analysis, expansion/contraction. The goal is to see the real state and dynamics of the subscription business and make data-driven decisions. Honestly about 'data and definitions', this is key: analytics is as reliable as the data is clean and the metric definitions correct. Garbage or gaps in data give nice but wrong reports (garbage in — garbage out). Plus many metrics are calculated differently: churn can be measured by customers or by revenue, over different periods, with different treatment of pauses and downgrades; LTV depends on assumptions. The same 'metric' can mean different things to different people. We honestly fix definitions and verify data rather than give a number without context. Honestly about 'measure ≠ improve': analytics shows WHAT is happening (e.g. high churn in month 2) but improves nothing by itself. The value is in ACTIONS on the data: find the cause and fix it. A dashboard that is looked at but not acted on is useless. Honestly about actionable vs vanity metrics: it is important to measure what can be influenced and what leads to decisions, not 'nice' numbers for a report (vanity metrics). We focus on actionable ones. Honestly about the effect: it gives a clear picture and a basis for decisions, helps notice problems and growth points, but it is not a growth guarantee — actions on the data decide. Honestly about access: billing/subscription data, correct event tagging are needed. An important boundary: this is analytics (picture+decisions); billing (the data source) — 1028; pricing experiments — 1031; dashboards (visualization) — 1044. Picture this: instead of 'blind decisions by gut feel' — an honest picture of MRR/churn/LTV with actions on it. The base price starts from 45,000 ₽ (depends on metrics and integrations).

• Subscription decisions are made blindly, without metrics.
• The real dynamics of MRR, churn and LTV are unclear.
• Metrics are calculated differently, the numbers contradict each other.
• Dashboards exist but are not acted on.

• Setting up key subscription metrics (MRR/ARR, churn, LTV, conversions, cohorts)
• Fixing clear metric definitions (churn/LTV are calculated differently)
• Verifying data quality and completeness (against garbage in)
• A focus on actionable metrics (not vanity ones)
• Honest boundaries (data/definitions; measure ≠ improve; no growth guarantee)
• A link with billing (1028), experiments (1031), dashboards (1044)
• Tying metrics to decisions and actions
• Handover and review with you

• A clear honest picture of MRR, churn, LTV, cohorts
• Consistent metric definitions (no contradictions)
• Actionable metrics leading to decisions
• Honest boundaries (clean data needed; measure ≠ improve; no guarantee)

• We determine the needed metrics and their correct definitions
• We verify data, set up calculation and reports
• We tie them to decisions/actions, honestly set boundaries with you

• Can analytics metrics be fully trusted?

  Only with clean data and clear definitions, honestly: analytics is as reliable as the data is clean (garbage in — garbage in reports). Plus many metrics are calculated differently: churn by customers or revenue, over different periods; LTV depends on assumptions. The same 'metric' can mean different things to different people. We fix definitions and verify data rather than give a number without context as an absolute.

• Set up analytics — and the business will grow?

  The dashboard itself — no, honestly: analytics shows WHAT is happening (e.g. high churn in month 2) but improves nothing by itself. Growth comes from ACTION on the data — find the cause and fix it. A dashboard that is looked at but not acted on is useless. We tie metrics to decisions, but honestly: to measure is not to improve, and analytics does not guarantee growth.

• The more metrics on the dashboard, the better?

  No, honestly: it is important to measure actionable metrics — what can be influenced and what leads to decisions — not to bury the dashboard in 'nice' vanity numbers for a report. An excess of metrics scatters attention and masks the main thing. We focus on a few actionable indicators with clear definitions, not the maximum of charts for impression.