Growth Hacking

Growth hacking is a discipline that combines product, data, and marketing to find the most efficient levers for sustainable user and revenue growth. Unlike traditional marketing, it is rooted in rapid experimentation, quantitative measurement, and closed-loop feedback between product behavior and acquisition channels.

The best growth practitioners treat retention as the foundation, activation as the multiplier, and virality as the compounding force. Hacks without retention are just churn machines. This skill gives an agent the frameworks, vocabulary, and tactical playbooks to design experiments, build growth systems, and reason about compounding growth.

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When to use this skill

Trigger this skill when the user:

• Wants to design or audit a growth loop or viral loop
• Needs to build or improve a referral program
• Asks about optimizing an activation funnel or improving time-to-value
• Wants to reduce churn or improve retention using cohort analysis
• Asks about AARRR metrics, pirate metrics, or north star metric selection
• Needs to run growth experiments and prioritize them (ICE, PIE scoring)
• Is implementing product-led growth (PLG) or a freemium model
• Wants to find the "aha moment" and engineer onboarding toward it

Do NOT trigger this skill for:

• Pure paid advertising campaign execution (creative, ad spend optimization) - use a performance marketing skill instead
• Brand strategy and positioning work disconnected from product or funnel metrics

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Key principles

1. Measure everything - Every growth decision must be anchored to data. Define metrics before running experiments. If you can't measure it, you can't improve it. Instrument events, track cohorts, and baseline before changing anything.

2. One metric that matters (OMTM) - Focus each growth phase on a single north star metric that best predicts long-term value. Optimizing many metrics at once diffuses effort and obscures causality.

3. Experiment velocity wins - Teams that run more experiments per week consistently outperform those that run fewer but "bigger" experiments. Lower the cost of an experiment, raise the volume. Most experiments fail - that's fine, fail fast.

4. Retention is the foundation - Acquiring users into a leaky bucket is burning money. Fix retention first. A product with 40% Day-30 retention can grow efficiently; one with 5% cannot be saved by acquisition spend.

5. Sustainable growth over hacks - Short-term hacks (spam, dark patterns, manufactured virality) destroy trust and churn users. Build growth systems that deliver genuine value at each step so growth compounds rather than collapses.

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Core concepts

AARRR pirate metrics

Dave McClure's framework maps the full user lifecycle into five measurable stages:

Stage	Question	Example metric
Acquisition	How do users find you?	CAC, channel attribution, organic vs paid split
Activation	Do users have a great first experience?	Day-1 activation rate, aha moment conversion
Retention	Do users come back?	Day-7/30/90 retention, churn rate, DAU/MAU
Referral	Do users tell others?	Viral coefficient (K), NPS, referral invite rate
Revenue	Do you make money?	MRR, LTV, LTV:CAC ratio, expansion revenue

Always diagnose which stage is broken before prescribing a fix. See references/growth-frameworks.md for the full AARRR diagnostic template.

Growth loops vs funnels

A funnel is linear and one-way: Acquire -> Activate -> Retain -> Monetize. Every user enters at the top and exits somewhere below. Funnels are necessary but not sufficient for compounding growth.

A growth loop is circular: the output of one cycle becomes the input of the next. Examples:

• Viral loop: User invites friend -> friend signs up -> friend invites more friends
• Content loop: User creates content -> content ranks in search -> new users find it -> create more content
• Sales-assisted loop: Lead signs up -> sales converts -> expansion revenue funds more sales

Loops compound; funnels don't. Design for loops. See references/growth-frameworks.md for loop templates.

Viral coefficient (K-factor)

K = invites_sent_per_user * conversion_rate_of_invite

• K > 1: viral growth (each user brings more than one new user)
• K = 0.5-1: strong word of mouth, supplements other channels
• K < 0.3: product is not meaningfully viral; focus elsewhere

Improving K requires either increasing invites sent (motivation) or increasing invite conversion (landing page, offer, trust).

Cohort analysis

Group users by the time period they first performed a key action (signup, first purchase, etc.) and track their behavior over subsequent periods. Cohort analysis isolates the effect of product changes from the noise of a changing user mix.

Key cohort views:

• Retention curve: % of cohort active at Day N - flat curve = good retention
• Revenue cohort: cumulative LTV by cohort - improving means product is getting better
• Activation cohort: % that hit aha moment within Day 1, 3, 7

North star metric

A single metric that best captures the value your product delivers to users AND correlates with long-term business health. It aligns the entire company on what matters.

Company	North Star Metric
Slack	Messages sent per active team
Airbnb	Nights booked
Spotify	Time spent listening
HubSpot	Weekly active teams using 5+ features

A good north star is: measurable, leads revenue, reflects user value, actionable by the team. See references/growth-frameworks.md for the selection template.

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Common tasks

Design a growth loop

1. Map the current user journey end-to-end
2. Identify the "output" of one user's experience that could become an "input" for another user (shared content, invites, referrals, SEO-indexed pages)
3. Name the loop type: viral, content, paid, sales-assisted, or product-embedded
4. Define the loop's single conversion rate to optimize (e.g., invite acceptance rate)
5. Instrument every step, establish a baseline, then run experiments on the weakest link

Example - viral loop for a doc tool: Create doc -> Share with external collaborator -> Collaborator views -> Prompted to sign up -> Signs up and creates their own doc -> Loop restarts

Build a referral program

A referral program amplifies natural word-of-mouth with structured incentives.

Design checklist:

• Define the trigger: when is the user most likely to refer? (post-aha moment, post-purchase)
• Choose reward structure: double-sided (sender + receiver both win) outperforms one-sided
• Set reward type: cash, credits, upgrade, or social recognition
• Make sharing frictionless: pre-written message, one-click send, email + link options
• Confirm referral loop is closed: referred user's experience must deliver the same aha moment that motivated the invite
• Track: referral invite rate, referral conversion rate, K-factor, referred-user LTV vs organic LTV

Reward tiers by product type:

• B2C consumer app: credits or cash (Uber, Airbnb model)
• B2B SaaS: seat upgrades, feature unlocks, or billing credits
• Marketplace: transaction credits valid on next purchase

Optimize activation funnel

Activation is the bridge between acquisition and retention. A user is "activated" when they experience the core value of the product for the first time (the aha moment).

Optimization process:

1. Define your aha moment concretely (e.g., "creates first project with one collaborator")
2. Map every step from signup to aha moment
3. Measure drop-off at each step
4. Prioritize the step with the largest absolute drop-off (not percentage)
5. Run A/B tests: reduce friction (fewer fields, social login), add guidance (tooltips, progress bars), or add incentives (template library, example data)

Common activation levers:

• Reduce time-to-value: pre-populate sample data so users see value before entering their own
• Remove setup friction: defer configuration until after first value is delivered
• Personalize onboarding: route users to different paths based on role or use case
• Add social proof at friction points: show "2,000 teams set this up in 3 minutes"

Improve retention with cohort analysis

1. Pull cohort retention curves segmented by: acquisition channel, onboarding path, company size, or feature adoption
2. Identify which cohort has the flattest retention curve (best retention)
3. Find the behavioral difference between high-retention and low-retention cohorts (which features did they use? how fast did they reach aha moment?)
4. Build that behavior into the default onboarding path for all new users
5. Re-run cohorts 4-8 weeks later to confirm improvement

Retention benchmarks by product type:

Product	Good Day-30 Retention
Consumer social	25-40%
B2B SaaS	40-70%
E-commerce	10-25%
Mobile game	10-20%

Run growth experiments (ICE framework)

Score each experiment on three dimensions (1-10 each):

• Impact: How much will this move the target metric if it works?
• Confidence: How sure are you it will work, based on data or analogues?
• Ease: How fast and cheap is it to run this experiment?

ICE Score = (Impact + Confidence + Ease) / 3

Run the highest-scoring experiments first. Document hypothesis, metric, baseline, result, and learning for every experiment regardless of outcome. See references/growth-frameworks.md for the full ICE scoring template.

Design onboarding for the aha moment

The job of onboarding is to get users to the aha moment as fast as possible.

Onboarding design principles:

• Delay account setup (email verification, profile completion) until after first value
• Use empty state screens to show what the product looks like when it's working, not a blank canvas
• Guide the user through exactly one action that delivers immediate value
• End the first session with a "save your progress" hook that creates a reason to return

Aha moment discovery process:

1. Pull data on users who churned in week 1 vs users who retained to week 4
2. Find the feature/action that correlates most strongly with retention
3. Find the time-to-that-action for retained users (e.g., "within 3 days")
4. Make that action the explicit goal of onboarding

Implement product-led growth (PLG)

PLG makes the product itself the primary driver of acquisition, activation, and expansion.

PLG motion types:

• Freemium: Free tier acquires users; paid tier converts power users
• Free trial: Full access for a limited time; urgency converts
• Usage-based: Pay as you grow; low friction entry, aligned incentives

PLG implementation checklist:

• Identify the natural sharing or collaboration moments in the product
• Build a free tier that delivers genuine value (not a crippled demo)
• Define upgrade triggers: usage limits, collaboration features, or admin controls
• Instrument product qualified leads (PQLs): users showing intent signals (hitting limits, inviting many teammates, high usage frequency)
• Build sales-assist motion that surfaces PQLs to the sales team in real time

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Anti-patterns

Anti-pattern	Why it fails	What to do instead
Optimizing acquisition before fixing retention	You fill a leaky bucket - CAC rises, LTV falls	Achieve 30% Day-30 retention before scaling acquisition spend
Vanity metric focus	Total signups, downloads, or followers don't predict revenue or retention	Pick a north star metric that reflects active value delivery
Running too many experiments at once	Interactions between experiments contaminate results	Run one experiment per user surface at a time; isolate variables
Copying competitor tactics without understanding context	A tactic that works for Dropbox at scale fails for a 500-user startup	Understand why a tactic works before adopting it; validate with your own data
Dark patterns for short-term conversion	Fake urgency, hidden unsubscribe, forced virality - all damage trust and LTV	Every growth mechanic should deliver value to the user, not just extract it
Skipping cohort segmentation	Aggregate retention curves hide the signal in the noise	Always segment cohorts by acquisition source, onboarding path, and key feature adoption

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Gotchas

1. Optimizing activation before you understand what the aha moment actually is - Teams often build onboarding flows toward the wrong milestone. "Completed profile" or "uploaded first file" feels like activation, but if it doesn't correlate with Day-30 retention, you've optimized the wrong funnel step. Always validate the aha moment against retention cohort data before optimizing toward it.

2. Viral K-factor calculations ignore invite fatigue cycles - K-factor measured in week 1 post-launch will overestimate steady-state virality because early adopters are your most enthusiastic inviters. Measure K-factor across 90-day cohorts, not just the launch burst, to get a realistic picture of your viral loop's durability.

3. A/B test contamination from multiple simultaneous experiments - Running two experiments on the same user surface at the same time (e.g., two onboarding copy tests) means users may see combinations of variants, making it impossible to attribute results to a single change. One experiment per user surface, enforce isolation in your experimentation platform.

4. Referral programs that reward too early produce fraudulent referrals - Triggering referral rewards at signup (rather than at activation or first payment) creates an arbitrage opportunity where users refer fake accounts for the reward. Tie rewards to the same activation milestone that predicts real retention.

5. Freemium free tier that's too good prevents upgrades - If the free tier covers all core use cases, users have no natural reason to upgrade. The free tier must deliver genuine value at a scope that naturally hits a ceiling for power users - time, seats, usage volume, or collaboration features are common upgrade triggers. Define this ceiling before launching freemium, not after watching conversion rates disappoint.

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References

For detailed templates and frameworks, load the relevant file from references/:

• references/growth-frameworks.md - AARRR diagnostic template, ICE scoring sheet, north star selection guide, growth loop templates, viral coefficient calculator

Only load a references file if the current task requires deep detail on that topic.