Growth
Verified@ivangdavila
npx machina-cli add skill @ivangdavila/growth --openclawNorth 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:
- Acquisition: How users find you → visits, signups
- Activation: First value moment → completed onboarding, first action
- Retention: Coming back → DAU/MAU, return rate by cohort
- Revenue: Paying you → conversion rate, ARPU, LTV
- 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
Overview
Growth guides you to pick a North Star metric, map every stage of the AARRR funnel, and build repeatable growth loops. It focuses on aligning teams to acquire, activate, retain, monetize, and refer customers for scalable revenue.
How This Skill Works
Choose a metric that drives revenue and is controllable by the team. Define metrics for Acquisition, Activation, Retention, Revenue, and Referral, identify the weakest stage, and select a growth loop (viral, content, paid, or sales). Use an ICE-prioritized experimentation framework and activation/retention analysis to optimize time-to-value.
When to Use It
- Launching a new product and needing initial traction
- Scaling an existing product after achieving product-market fit
- Addressing high churn by improving activation and retention
- Optimizing paid acquisition while maintaining healthy unit economics
- Building a referral program to accelerate growth
Quick Start
- Step 1: Define your North Star metric and map Acquisition, Activation, Retention, Revenue, and Referral metrics
- Step 2: Select the Growth Loop that best fits your product (viral, content, paid, or sales) and design initial experiments
- Step 3: Run ICE-prioritized experiments, measure impact, and optimize activation and retention
Best Practices
- Define and align on a single North Star metric that ties to revenue
- Map the AARRR funnel and target the weakest stage
- Choose and validate Growth Loops (viral, content, paid, or sales) that fit your product
- Use ICE scoring to prioritize experiments and monitor KPI impact
- Leverage activation and retention analysis to accelerate time-to-value and remove bottlenecks
Example Use Cases
- A SaaS product tracks weekly active users and activation; improvements to onboarding boost first-value moments and retention
- A marketplace uses transactions completed as its North Star and activates a viral loop through inviting friends to join
- A content-driven product builds an SEO/social content loop that turns readers into creators and repeat visitors
- An e-commerce brand runs a profitable paid loop with CAC/LTV checks and a payback period target
- A product with a referral program prompts sharing after delivering value to balance growth with incentives
