Growth Analytics By Gregor Spielmann, Adasight

The 12 Growth Analytics Metrics Every Team Should Track

Most growth teams track too many metrics and understand too few. The result is a dashboard full of numbers that don't connect to decisions. This guide covers the 12 growth analytics metrics that actually drive decisions — what they measure, why they matter, and the benchmarks that separate good from great. For each metric, we include the formula, the questions it answers, and the most common mistakes in how teams interpret it.

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Acquisition metrics: measuring quality, not just volume

The three acquisition metrics that matter are: CAC (Customer Acquisition Cost) — total acquisition spend divided by new customers acquired in the same period, broken down by channel. Payback period — months to recover CAC from a given cohort, calculated as CAC divided by average monthly gross margin per customer. And acquisition channel mix — the % of new customers from each source (paid, organic, referral, direct). The common mistake is optimizing for signup volume rather than downstream retention and revenue. A channel with a 3% activation rate costs 3× more than one with a 9% rate, even if the raw CAC looks the same.

Activation metrics: finding and measuring the aha moment

Activation rate is the % of new users who reach the product's key value moment within a defined window (usually 7 days). Defining what that value moment is requires analysis — typically, you identify the behavior that most strongly predicts long-term retention. For a SaaS tool, it might be 'created first report' or 'invited a teammate'. For a marketplace, it might be 'completed first transaction'. Time to activate — the median hours between signup and first key action — is a leading indicator of activation rate and responds quickly to onboarding changes.

Retention metrics: the most honest signal of product-market fit

D1/D7/D30 retention: the % of users who return on day 1, day 7, and day 30 after their first session. These are the benchmark metrics for consumer apps. For B2B SaaS, use week-1/week-4/month-3 retention. The shape of the retention curve matters as much as the absolute number: a retention curve that flattens (rather than declining to zero) signals product-market fit. L28 (the % of users active on at least one of the past 28 days) is a more robust measure of engagement than DAU/MAU for many product types. Net revenue retention (NRR) — what % of revenue from last year's cohort is retained this year, including expansion — is the most important retention metric for subscription businesses.

Revenue metrics: connecting behavior to monetization

LTV:CAC ratio — lifetime value divided by CAC — is the master metric of growth economics. A ratio above 3:1 is generally considered healthy for SaaS; above 5:1 is excellent. Expansion MRR rate — the monthly growth in revenue from existing customers through upgrades and cross-sell — is a leading indicator of product stickiness and pricing model effectiveness. Growth accounting (new MRR + expansion MRR - churned MRR = net new MRR) is the clearest way to diagnose whether growth is coming from acquisition efficiency or retention leakage.

Growth metrics audit checklist

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Frequently asked questions

What is a good D30 retention rate?

D30 retention benchmarks vary significantly by product category. For consumer social apps, 20–25% is strong. For daily-use utility apps, 40%+ is achievable. For B2B SaaS, monthly retention is more relevant: 90–95% monthly user retention is good; 95%+ is excellent. The most important thing is tracking your own curve and whether it's improving, not benchmarking against different product types.

What is a good LTV:CAC ratio for a SaaS company?

The standard benchmark is 3:1 (LTV is 3× the cost to acquire the customer). At Series B+, leading SaaS companies typically target 5:1 or better. Below 1:1 means you're losing money on every customer before they churn. The calculation requires defining LTV correctly — typically as (average monthly gross margin per customer) × (average months retained) × (discount factor for time value).

How often should growth metrics be reviewed?

Weekly review of leading indicators (activation rate, early retention, week-over-week active user growth). Monthly review of lagging indicators (LTV:CAC, payback period, NRR, cohort retention at 3+ months). Quarterly review of the full growth model to re-examine strategic priorities. The goal is a rhythm where weekly data drives tactical decisions and monthly/quarterly data drives strategic ones.

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