growth

North Star Metric (Define First)

Pick ONE metric that: - Reflects core value delivered to customer - Leads revenue (not lags) - Entire team can influence

Examples by business type: - Marketplace: transactions completed - SaaS: weekly active users or actions - Media: time spent or content consumed - E-commerce: purchase frequency

All other metrics ladder up to this.

AARRR Funnel (Measure Each)

Define specific metrics for each stage: 1. Acquisition: How users find you → visits, signups 2. Activation: First value moment → completed onboarding, first action 3. Retention: Coming back → DAU/MAU, return rate by cohort 4. Revenue: Paying you → conversion rate, ARPU, LTV 5. Referral: Bringing others → viral coefficient, referral rate

Find the weakest stage—that's your focus.

Growth Loops (Build These)

Identify which loop fits your product:

Viral loop: User → invites friends → friends become users - Measure: viral coefficient (invites × conversion rate) - Needs: sharing valuable to user, not just company

Content loop: Create content → SEO/social → users → some create content - Measure: content created per user, traffic per content - Needs: user-generated content or team-generated

Paid loop: Revenue → reinvest in ads → users → revenue - Measure: CAC vs LTV, payback period - Needs: unit economics that work (LTV > 3× CAC)

Sales loop: Sales → customers → case studies/referrals → leads - Measure: pipeline velocity, referral rate - Needs: sales team, high ACV

Activation Checklist

Define the "aha moment"—when user gets value: - [ ] What specific action indicates user "got it"? - [ ] How long should it take? (First session? First week?) - [ ] What % of signups reach it currently? - [ ] What steps are required before it?

Remove every obstacle between signup and aha moment. Measure time-to-value and optimize ruthlessly.

Retention Analysis

Cohort retention curves reveal truth: - Flatten = habit formed, product has value - Decline to zero = product problem, not growth problem - Early drop = activation problem

Actions: - Plot weekly/monthly retention by signup cohort - Find what retained users did that churned didn't - Make that action part of onboarding

Channel Selection

Score potential channels: | Channel | CAC estimate | Volume potential | Speed to test | |---------|--------------|------------------|---------------|

Prioritize: low CAC + high volume + fast to test first.

Channel categories: - Paid: Meta, Google, TikTok, influencers - Organic: SEO, content, social, community - Product: referral, virality, integrations - Sales: outbound, partnerships

Test 2-3 max simultaneously. Kill losers fast.

Experiment Framework

For each experiment, document: - Hypothesis: "If we [change], then [metric] will [impact] because [reason]" - Metric: specific number you're moving - Sample size: how many users needed for significance - Duration: how long to run

Prioritize with ICE: - Impact (1-10): how much will it move the metric? - Confidence (1-10): how sure are you it will work? - Ease (1-10): how fast/cheap to implement?

Run highest ICE scores first.

Quick Wins Checklist

Common high-impact, low-effort fixes: - [ ] Reduce signup form fields to minimum - [ ] Add social proof to landing page - [ ] Implement abandoned cart/onboarding emails - [ ] Add referral program if none exists - [ ] Fix the slowest page load - [ ] Add exit intent offer - [ ] Personalize onboarding by use case

Referral Program Design

Components: - Incentive: what giver and receiver get - Mechanic: how sharing works (link, code, invite) - Trigger: when to prompt (after value, not before) - Tracking: attribution for rewards

Test: Is the incentive good enough to overcome sharing friction? Double-sided incentives (both get value) outperform one-sided.

Metrics Dashboard

Track weekly at minimum: - North Star metric - Funnel conversion by stage - Retention by weekly cohort - CAC and LTV (if spending on acquisition) - Active experiments and results

Segment by: acquisition source, user type, geography.

Common Traps

• Optimizing acquisition when retention is broken—pouring water into leaky bucket
• Too many experiments running—can't tell what worked
• Vanity metrics (signups, pageviews) vs value metrics (activation, revenue)
• Copying competitor tactics without understanding their context
• Not running experiments long enough for statistical significance