Why this matters
The AARRR framework (Acquisition, Activation, Retention, Revenue, Referral) helps Product Analysts connect user behavior to growth. You will use it to diagnose funnel drop-offs, set actionable KPIs, and prioritize experiments that move business outcomes.
- Prioritize: Decide which stage to focus on next quarter.
- Diagnose: Find the biggest leak in the user journey with data.
- Measure: Define consistent metrics and targets for each stage.
- Communicate: Present clear narrative from event data to business impact.
Concept explained simply
AARRR is a growth funnel that tracks a user’s journey:
- Acquisition: People discovering and visiting/signing up.
- Activation: Users reach first value (aha moment).
- Retention: Users return and keep getting value.
- Revenue: Users pay or generate monetizable value.
- Referral: Users invite others and amplify growth.
Mental model: The leaky bucket
Think of growth as filling a bucket. Pouring more water (Acquisition) is wasteful if there are holes (Activation/Retention drop-offs). Plug the largest leak first. Only then add more water.
Core metrics by stage
- Acquisition: New visitors, sign-ups, traffic by channel, CTR, CAC (cost per acquired user).
- Activation: Activation rate (users who reach first value / new sign-ups), time-to-activation, onboarding completion rate.
- Retention: D1/D7/D30 retention, WAU/MAU, churn rate, cohort retention curves.
- Revenue: Conversion to paid, ARPU/ARPPU, LTV, MRR/ARR, payback period.
- Referral: Invite rate, k-factor, referral conversion rate.
Choosing the activation event
Pick an event strongly predictive of long-term retention. Examples: 1) Task app: creating 3 tasks and completing 1 within 24h. 2) Music app: playing 10 tracks and following 1 artist. 3) B2B tool: connecting data source and viewing first dashboard.
Worked examples
Example 1: Funnel diagnosis
Weekly data for a mobile app:
- Landing visitors: 100,000
- Sign-ups: 15,000
- Activation (completed onboarding): 6,000
- D7 retained: 2,100
- Paid conversions: 900
Stage conversion rates:
- Visitor → Sign-up: 15%
- Sign-up → Activation: 40%
- Activation → D7 retained: 35%
- D7 retained → Paid: 42.9%
Biggest leak: Activation → D7 retention (35%). Prioritize retention experiments (e.g., habit-forming nudges, improved value reminders).
Example 2: Activation definition and KPI
Product: Collaborative doc tool. Hypothesis: Users who invite a teammate and receive at least one comment in first 48h are more likely to retain.
- Activation event: invite_sent AND comment_received within 48h.
- Metric: Activation rate = activated_users / new_signups.
- Baseline: 22%, Target: 28% in 6 weeks (~27% relative lift).
Example 3: Revenue and LTV
Subscription app:
- Monthly price: $10
- Monthly churn: 8%
- ARPPU uplift from add-ons: $2/month
Approx LTV (simplified): (10 + 2) / 0.08 = $150. If CAC is $45, payback is ~3.75 months. You can scale acquisition if cash flow allows and retention holds.
How to apply AARRR step-by-step
- Define your North Star metric and business goal.
- Map your user journey and critical events per AARRR stage.
- Establish baseline metrics for each stage with last 4–8 weeks of stable data.
- Find the largest, most fixable leak (impact x ease).
- Form hypotheses tied to user problems, not vanity metrics.
- Design experiments with clear success criteria and sample size estimates.
- Instrument events and QA your tracking with test users.
- Run, analyze, share insights, and decide: ship, iterate, or roll back.
Success criteria template
- Primary metric: Stage conversion (e.g., Sign-up → Activation).
- Guardrails: Retention, revenue per user, error rates.
- Minimum detectable effect: e.g., +10% relative lift.
- Duration: cover full decision window (e.g., 14 days for D7 retention).
Data requirements and instrumentation tips
- Tracking plan with event names, properties, and stage mapping.
- Consistent user identity across devices (anonymous_id to user_id merge).
- Timestamp everything with timezone clarity; store user locale when relevant.
- Define attribution windows (e.g., 7-day click) and keep them consistent.
- Filter internal traffic and bots; maintain a QA segment.
- Version your activation definitions so historical reports remain comparable.
Who this is for & Prerequisites
- Who: Product Analysts, Growth Analysts, early-stage founders wearing analytics hats.
- Prerequisites: Basic SQL, comfort with event data, understanding of funnels and cohorts.
Learning path
- Learn AARRR definitions and pick your product’s activation event.
- Build a baseline funnel and one retention cohort chart.
- Choose one stage to improve and define a metric target.
- Run a small experiment and measure impact end-to-end.
Common mistakes & self-checks
- Counting everyone instead of new users for activation. Self-check: Is denominator only NEW sign-ups?
- Changing definitions mid-period. Self-check: Did you version the metric and annotate dashboards?
- Optimizing one stage while hurting another. Self-check: Review guardrail metrics in every analysis.
- Short test windows that miss retention effects. Self-check: Is your duration long enough to observe the chosen metric?
- Attribution inconsistencies across channels. Self-check: Are windows and hierarchies aligned?
Practical projects
- Build a live AARRR dashboard with funnel, cohort, and ARPU widgets.
- Define and justify your activation event; present a 1-page rationale.
- Run an onboarding experiment and produce a pre/post AARRR readout.
Exercises
Do these in order. They mirror the interactive tasks below and in the Exercises section.
- Exercise 1: Compute stage conversion rates and identify the biggest leak. Propose one experiment to fix it.
- Exercise 2: Define a SQL-friendly activation metric for a product of your choice and suggest a KPI target.
- Exercise 3: Estimate LTV and acceptable CAC from churn and ARPU; state testable assumptions.
Self-check checklist
- Each stage rate uses the correct denominator.
- Activation definition is behavior-based and time-bound.
- Retention metric uses a clear window (D7, W4, M3).
- Revenue calc states price, churn, and any uplift.
- Assumptions are explicit and reasonable.
Mini challenge
In one paragraph, argue whether your next growth priority should be Activation or Retention. Use one metric and one user insight to justify.
Next steps
- Pick one stage to own for the next 4 weeks; define a target and a single primary experiment.
- Create an AARRR weekly review ritual with the team: 15 minutes, same metrics, same definitions.
Quick test
Everyone can take the test for free. Log in to save your progress and track improvement over time.