By Quick answer: What product metrics should a product manager track?
- Acquisition — installs, sign-ups, CAC.
- Activation — share reaching first value; time to value.
- Engagement and retention — DAU/MAU, cohort retention, churn.
- Revenue — conversion, ARPU, LTV, LTV:CAC.
- Quality — crash rate, latency, NPS/CSAT; plus guardrails.
Key takeaways
- Pick metrics that reflect user value, not vanity.
- A metric tree connects daily work to the north star.
- Always pair a target metric with guardrails.
- Segment everything — averages hide the story.
- Experiments establish cause; dashboards only show correlation.
Every product team has a dashboard. Fewer have a clear idea of which numbers matter, how they connect, and which ones lie. This reference covers the metrics every PM should know cold — definitions, formulas, and the traps — organised around the user lifecycle that Dave McClure's AARRR framework popularised2.
Acquisition
| Metric | Formula | Watch out for |
|---|---|---|
| Installs / sign-ups | Count per period | Vanity without retention |
| Cost per install (CPI) | Ad spend ÷ installs | Cheap installs that churn |
| Customer acquisition cost (CAC) | Acquisition spend ÷ new customers | Include all costs, not just ads |
Activation
| Metric | Formula | Watch out for |
|---|---|---|
| Activation rate | Users reaching the "aha" action ÷ new users | Define the action carefully |
| Time to value | Median time from sign-up to first value | Long tails hide behind averages |
Engagement and retention
| Metric | Formula | Watch out for |
|---|---|---|
| DAU / WAU / MAU | Unique active users per day / week / month | Define "active" as a meaningful action |
| Stickiness | DAU ÷ MAU | Compare within category |
| Day-N retention | Active on day N ÷ cohort size | Classic vs rolling definitions |
| Churn rate | Users (or revenue) lost ÷ users at start | Separate voluntary and involuntary churn |
Revenue
| Metric | Formula | Watch out for |
|---|---|---|
| Conversion to paid | Paying users ÷ eligible users | Trial vs direct purchase |
| ARPU | Revenue ÷ active users | Mix changes distort it |
| LTV | ARPU × average lifetime (or margin-based) | Optimistic lifetimes |
| LTV : CAC | LTV ÷ CAC | Payback period matters too |
| MRR / ARR | Monthly / annual recurring revenue | Exclude one-off revenue |
Quality
| Metric | What it tells you |
|---|---|
| Crash-free sessions / users | Stability |
| Latency (p50, p95) | Speed users actually feel |
| NPS / CSAT | Perceived satisfaction |
| Support tickets per 1,000 users | Friction and confusion |
Tie it together with a metric tree
North star
The metric that best captures value delivered to customers — such as nights booked for a travel marketplace1.
Input metrics
The levers that drive it — new users activated, frequency of use, conversion at key steps.
Team metrics
Each team owns the inputs it can move.
Guardrails
Metrics that must not worsen — uninstalls, complaints, latency.
Rules for using metrics well
- Prefer rates and cohorts over cumulative totals
- Segment by platform, channel, region, user type
- Pair every target with guardrails
- Use experiments to establish cause — Kohavi and colleagues' guide is the standard reference3
- Write metric definitions down so everyone measures the same thing
For context on retention numbers, see app retention benchmarks.
Key terms, defined
KPI — a key performance indicator. Cohort — users grouped by start date or source. Stickiness — DAU divided by MAU. LTV — lifetime value. CAC — customer acquisition cost. Guardrail — a metric protected while optimising another.
Frequently asked questions
What are the most important product metrics?
It depends on the product, but most teams track activation, retention and a north star metric that reflects customer value, plus revenue metrics for monetised products and quality metrics such as crash rate.
What is a good DAU/MAU ratio?
It measures stickiness — the share of monthly users active on a given day. Messaging and social apps are often far higher than utilities. Compare within your category and track your own trend.
What is the difference between a metric and a KPI?
A metric is any measurement. A KPI is a metric chosen as a key indicator of success for a goal. Teams track many metrics but focus on a few KPIs.
What are vanity metrics?
Metrics that look impressive but don't reflect value or guide decisions — total downloads, cumulative sign-ups, page views without context. Prefer rates, cohorts and outcomes.
What is a metric tree?
A diagram breaking a top-level metric (such as a north star) into the input metrics that drive it, so each team knows which levers they influence.
What are guardrail metrics?
Metrics that must not get worse while you optimise another — for example, uninstalls or support tickets when increasing notification frequency.
Sources
- Amplitude — The North Star Playbook
- Dave McClure — Startup metrics for pirates: AARRR (2007)
- Kohavi, Tang & Xu — Trustworthy Online Controlled Experiments
- Checked October 23, 2026. Figures that change — fees, salaries, prices — are reviewed every six months; if something is out of date, tell us.
Where to go from here
- North star metric examples — the top of your metric tree
- App retention benchmarks — context for your retention numbers
- Career guidesHow to get a growth marketing job in 2027: full-funnel thinking, channel economics, experiments and retention — from the hiring sideHow to get a growth marketing job in 2027: funnel metrics, CAC and LTV, channel tests, lifecycle and retention, measurement, pay in India and a plan.
- Templates & toolsOKR examples for product teams: 15 objectives with key results, plus how to write your ownOKR examples for product teams — activation, retention, monetisation, quality and discovery — with measurable key results, a template and common mistakes.
- Career guidesUnit economics and business finance skills for 2027: contribution margin, CAC and LTV, payback, cohorts, break-even, burn and runway, and using unit economics to make product, pricing and growth decisionsUnit economics for 2027 jobs: contribution margin, CAC, LTV, payback, cohorts, break-even, burn and runway, and decisions on pricing and growth.
- Career guidesUser onboarding and retention skills employers want in 2027: activation and aha moments, onboarding patterns, habit design, churn reduction and win-backUser onboarding and retention skills employers want in 2027: activation, aha moments, onboarding patterns, habit design, cohorts, churn reduction and win-back.
- Product managementProduct manager resume: examples, bullet formulas and a template that gets interviewsWrite a product manager resume that gets interviews: an outcome-first bullet formula, before-and-after examples, a one-page template and tips.
- Career guidesCustomer retention and churn prevention skills employers want in 2027: churn metrics, early warning signals, root causes, save plays, win-back and ethical cancellationChurn prevention skills employers want in 2027: churn metrics, early warning signals, root-cause analysis, save plays, win-back and ethical cancellation.
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