Growth

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name: Growth description: Design and execute growth strategies with acquisition loops, activation, and retention systems. metadata: {"clawdbot":{"emoji":"📈","os":["linux","darwin","win32"]}}

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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