AARRR Pirate Metrics
A complete AARRR (Acquisition, Activation, Retention, Revenue, Referral) metrics framework. Maps your funnel, identifies the leakiest stage, and defines the one metric that matters most at your current growth stage. Free to copy, download, and use. No signup required.
# AARRR Pirate Metrics **Product:** [Name] **PM:** [Name] **Period:** [Week / Month / Quarter] **Date:** [Date] --- ## Overview The AARRR framework (coined by Dave McClure) maps the five stages every user passes through. The goal is not to optimise all five simultaneously — it is to find the leakiest stage and fix it first. | Stage | Question | Your primary metric | Current rate | Target | |---|---|---|---|---| | **Acquisition** | How do users find you? | | % | % | | **Activation** | Do users experience value on the first visit? | | % | % | | **Retention** | Do users come back? | | % | % | | **Revenue** | Do users pay? | | % | % | | **Referral** | Do users tell others? | | % | % | **Leakiest stage (lowest conversion rate):** [Stage] **Focus for this quarter:** [Stage and specific metric] --- ## 1. Acquisition *How users discover and arrive at your product.* | Channel | Visitors / month | % of total | CAC (₹) | Quality score (1–5) | |---|---|---|---|---| | Organic search (SEO) | | % | ₹ | | | Direct / brand | | % | ₹ | | | Paid (Google, Meta) | | % | ₹ | | | Referral / word of mouth | | % | ₹ | | | Social (LinkedIn, X, YouTube) | | % | ₹ | | | Product-led (free tools) | | % | ₹ | | | Other | | % | ₹ | | | **Total** | | 100% | ₹ | | **Best-performing acquisition channel:** [Channel — highest quality score per ₹ spent] **Acquisition metric to move:** [e.g. Organic signups from SEO — currently X, target Y] --- ## 2. Activation *The moment a new user first experiences the core value of your product — the "aha moment".* **Aha moment definition:** [The single action that strongly correlates with long-term retention. e.g. "User generates their first PRD within 7 days of signup"] **Activation funnel:** | Step | Users | Drop-off | Cumulative conversion | |---|---|---|---| | Signup | | — | 100% | | [Step 2 — e.g. Created first project] | | % drop | % | | [Step 3 — e.g. Uploaded first file] | | % drop | % | | [Step 4 — e.g. Viewed insights] | | % drop | % | | **Aha moment reached** | | % drop | **%** | **Activation rate:** [%] of signups reach the aha moment within [N] days. **Industry benchmark:** Consumer SaaS 25–40%; B2B SaaS 40–60%. **Biggest drop-off step:** [Step — and hypothesis for why] **One activation experiment to run:** [e.g. Add onboarding checklist, reduce steps to aha moment] --- ## 3. Retention *Whether users return and continue getting value.* **Retention by cohort (% of users active N days after signup):** | Cohort | Day 1 | Day 7 | Day 14 | Day 30 | Day 90 | |---|---|---|---|---|---| | [Month 1] | % | % | % | % | % | | [Month 2] | % | % | % | % | % | | [Month 3] | % | % | % | % | % | **Day-30 retention target:** [%] **Retention curve:** [ ] Still declining at Day 30 (no habit formed) [ ] Flattening at Day 30 (habit forming) [ ] Flat after Day 14 (strong habit) **Churn rate (monthly, paid users):** [%] **Involuntary churn (payment failures):** [%] **Voluntary churn (cancellations):** [%] **Top churn reason (from exit surveys):** [Reason] **One retention experiment to run:** [e.g. In-app nudge at Day 5 inactivity, onboarding email sequence] --- ## 4. Revenue *Whether users pay and how much.* | Metric | Value | MoM change | |---|---|---| | MRR | ₹ | % | | ARR (MRR × 12) | ₹ | % | | ARPU (blended) | ₹ | % | | Free → paid conversion rate | % | % | | Average revenue per paying user | ₹ | % | | Expansion MRR (upgrades) | ₹ | % | | Contraction MRR (downgrades) | ₹ | % | | Churned MRR | ₹ | % | | Net New MRR | ₹ | % | **Net Revenue Retention (NRR):** ``` NRR = (Starting MRR + Expansion MRR - Contraction MRR - Churned MRR) / Starting MRR × 100 NRR = (₹___ + ₹___ - ₹___ - ₹___) / ₹___ × 100 = ___% ``` Target: > 100% (growth without new acquisition). --- ## 5. Referral *Whether users bring others.* | Metric | Value | |---|---| | Viral coefficient (K-factor) | | | % of new signups from referral | % | | NPS score | | | % of users who have shared / referred | % | | Referral programme conversion rate (if applicable) | % | **K-factor formula:** ``` K = (invitations sent per user) × (conversion rate of invitations) K > 1 = viral growth K < 1 = supplemental channel only ``` **Current K-factor:** [Value] **NPS promoter segments:** [What types of users give 9–10 NPS scores?] --- ## 6. The one metric that matters (OMTM) At your current stage, one metric matters more than all others. Optimising the wrong metric at the wrong stage wastes resources. | Stage | Focus | OMTM | |---|---|---| | Pre-product/market fit | Activation | % reaching aha moment within 7 days | | Post-PMF, pre-scale | Retention | Day-30 retention rate | | Scaling | Acquisition + Revenue | CAC payback period | | Optimising | Revenue | Net Revenue Retention | **Your current stage:** [Stage] **Your OMTM:** [Metric] **Current value:** [Value] **Target:** [Value] by [Date] **Owner:** [Name]
How to use this AARRR Metrics template
Find the leakiest stage before optimising anything
The most common mistake is optimising acquisition (more signups!) when the real problem is activation (users sign up and immediately churn). Calculate the conversion rate at each stage. The stage with the lowest rate — or the biggest absolute user drop — is where to focus. More acquisition traffic poured into a broken activation funnel just burns CAC faster.
Define your aha moment with data, not intuition
Look at your retained users (those still active at Day 30) and your churned users, and find the action that distinguishes them in the first 7 days. That is your aha moment. PMs often define the aha moment as 'they complete onboarding' when the data shows it's actually 'they invite a teammate' or 'they complete the first output'. Run the analysis before assuming.
Separate voluntary and involuntary churn in India
Indian SaaS products have higher involuntary churn (payment failures via UPI/card) than global equivalents. Mixing them gives you a misleading picture. Involuntary churn is an ops problem — fix it with retry logic and grace periods. Voluntary churn is a product problem — fix it with activation and retention improvements. Track both separately from day one.
Pick one OMTM per quarter and resist moving it
Teams that chase all five AARRR metrics simultaneously make shallow progress on all of them. Pick the one metric that, if improved, would have the highest downstream impact on business outcomes. Assign it an owner, a target, and a date. Review weekly. Don't change it mid-quarter unless the business fundamentally changes.
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Frequently asked questions
In what order should we fix the AARRR funnel?
Always fix from the bottom up: retention before acquisition. If you increase acquisition on a leaky retention funnel, you're filling a bucket with a hole in it. Fix retention first (users who stay create word-of-mouth and expansion revenue), then activation (so new users reach the retained state faster), then acquisition (to scale what's working). Revenue and referral typically improve automatically when retention improves.
What's a good Day-30 retention rate for SaaS?
For B2B SaaS: 40–60% Day-30 retention is healthy at early stage; 60–80% is strong. For consumer apps: 20–30% is good; 40%+ is exceptional. The more important signal is the shape of the retention curve — a curve that flattens out (stops declining) indicates a retained core of habitual users, even if the absolute number is low. A curve that keeps declining to near zero at Day 90 means no habit has formed.
How do we calculate NPS and what's a good score?
NPS = % Promoters (score 9–10) minus % Detractors (score 0–6). Passives (7–8) are excluded. For SaaS products: NPS above 30 is good; above 50 is excellent; above 70 is world-class. More important than the absolute score is the trend (is it improving?) and the qualitative feedback from detractors — that tells you what to fix.
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