Tactical growth hacking playbook — rapid experimentation, growth loops, viral mechanics, referral flywheels, PLG hacks, content-led growth loops, community-driven growth, and low-cost acquisition tactics for B2B SaaS. Based on Sean Ellis, Brian Balfour, Andrew Chen, and Reforge frameworks. Use when designing growth experiments, building growth loops, or finding low-cost acquisition channels. Triggers on: "growth hacking", "growth loops", "viral mechanics", "growth experiments", "referral flywheel".
Tactical growth hacking playbook — rapid experimentation, growth loops, viral mechanics, referral flywheels, PLG hacks, content-led growth loops, community-driven growth, and low-cost acquisition tactics for B2B SaaS. Based on Sean Ellis, Brian Balfour, Andrew Chen, and Reforge frameworks. Use when designing growth experiments, building growth loops, or finding low-cost acquisition channels. Triggers on: "growth hacking", "growth loops", "viral mechanics", "growth experiments", "referral flywheel".
license
MIT
compatibility
Claude Code, Jesse, Codex, Hermes, Windsurf, OpenCode, Gemini CLI, Copilot, Zed, VS Code, Goose
Growth hacking isn't "one weird trick to 10x your users." It's a systematic
approach to finding and scaling the highest-leverage growth levers through
rapid experimentation. The mistake: copying tactics without understanding
the underlying growth model. A tactic that works for a consumer social app
(viral invite loop) will fail for enterprise SaaS (sales motion). This skill
covers growth loops, viral mechanics, referral flywheels, PLG hacks, and the
experimentation framework to find what works for YOUR product.
Funnels are linear: acquire → activate → retain → revenue → refer. Loops
are circular: the OUTPUT of one cycle becomes the INPUT of the next. Growth
loops compound. Funnels don't. Every great growth company has a dominant
growth loop.
Sean Ellis — Hacking Growth Process
Analyze: Data. Find the biggest opportunity.
Ideate: Brainstorm experiments. Score with ICE (Impact, Confidence, Ease).
Prioritize: Run highest-ICE experiments first.
Test: Run the experiment. Measure result.
Scale: Kill what failed. Double down on what worked.
Andrew Chen — The Cold Start Problem
Networked products have a "cold start" problem: they're worthless until enough
users join. The solution: build an "atomic network" — the smallest possible
network where the product provides value. Tinder: one college campus. Slack:
one team. Uber: one city.
Growth Models (Pick Your Dominant Loop)
Model
How It Works
Best For
Example
Content Loop
Content → traffic → signups → more content
Content-driven SaaS, SEO-heavy
HubSpot, Ahrefs
Viral Loop
User invites → new user → more invites
Consumer, social, collaboration tools
Slack, Notion, Loom
Sales Loop
Outbound → demos → customers → revenue → more SDRs
B2B SaaS, enterprise
Salesforce, Gong
Product-Led Loop
Free product → usage → upgrade → more usage
Developer tools, freemium
Miro, Figma, Notion
Paid Loop
Ads → signups → revenue → reinvest in ads
High-margin, high-LTV
Any profitable paid channel
Partner Loop
Partner → referral → customer → better partner program
Marketplace, integration-heavy
Shopify, Stripe
Community Loop
Community → engagement → product adoption → community growth
Competitor comparison pages: "X vs Y" pages for every competitor.
These rank quickly (buyers search for them) and convert at 5-10%.
Template galleries: Free templates (Notion, Figma, Google Sheets)
that include your branding and product links. Notion's template gallery
drives 5M+ monthly visits.
"Best X tools" listicles: Get listed on every "best [category] tools"
roundup. Reach out to authors. Offer unique data or quotes.
Free tools directory listings: Get listed on Product Hunt, G2,
Capterra, GetApp, AlternativeTo, SaaSWorthy, and niche directories.
Product-Led Growth Tactics
Sidecar free tool: Build a free micro-tool that solves one small
problem for your ICP — and naturally leads to your paid product.
Example: HubSpot's Website Grader → CRM. Ahrefs' free backlink checker
→ paid suite.
Collaboration as acquisition: When a user shares a file/link/page
with a non-user, that non-user sees your product. Loom, Notion, Figma,
and Miro all grew this way.
Watermark / "Powered by" link: Free plan includes your branding.
Typeform, Hotjar, and Calendly all used this to drive B2B growth.
Invite-to-unlock: "Invite 3 teammates to unlock [feature/premium
trial]." Dropbox's famous referral program gave 500MB per invite.
Public-by-default: User-generated content is public and indexed.
Canva designs, Notion pages, Substack posts — all discoverable.
Viral & Referral Tactics
Double-sided referral rewards: "You get $10, they get $10."
PayPal, Dropbox, and Uber all used this. Works best when the reward
is core product value, not cash.
Referral leaderboard: Public leaderboard of top referrers with
prizes. Harry's razors got 100K emails pre-launch via referral contest.
Waitlist referral priority: "Jump the line by referring friends."
Robinhood's waitlist hit 1M users before launch. Position in line
improved with each referral.
Milestone unlocks: "Unlock [premium template/tool] when 3 friends
sign up." Lower friction than "refer for cash."
Social share for bonus: "Share on LinkedIn for [bonus features/
extended trial]." Low-cost, high-reach.
Community & Distribution Tactics
Community as moat: Build a community (Slack, Discord, WhatsApp)
where your ICP hangs out. Figma's community, Notion's ambassador
program, Webflow's forum — all drive product adoption.
Integration marketplaces: List in your partners' marketplaces.
Every Slack app, every Notion integration, every HubSpot integration
is a distribution channel.
Newsletter swaps: Trade newsletter recommendations with 5
complementary newsletters. Each swap = 50-500 new subscribers.
Launch aggregators: Launch on Product Hunt, Hacker News, BetaList,
Uneed, SaaSHub. Each launch = 500-5,000 new users.
Low-Cost Paid Tactics
LinkedIn "Boosted" posts: Boost organic posts that already perform
well. 5-10x cheaper than LinkedIn Ads proper. $100/post can reach
10-20K ICP.
Retargeting: 2% of visitors convert on first visit. Retargeting
captures the other 98%. $5-10/day on retargeting can 2x conversion.
Newsletter sponsorships: Sponsor 3 newsletters reaching your ICP.
$200-1,000 per sponsorship. 1-5% clickthrough. Track with UTM.
Reddit Ads: Target specific subreddits where your ICP hangs out.
r/SaaS, r/startups, r/Entrepreneur. $5 CPM. High intent but very
anti-marketing — be genuine.
Affiliate/referral for customers: Existing customers refer you
for 10-15% of first-year revenue. Lower CAC than paid ads. Higher
close rates (warm intro > cold).
The Growth Experimentation Process
1. Identify Your North Star Metric
One metric that captures the core value your product delivers:
If Activation is 15% (industry avg 20-30%): biggest lever.
If Retention is 95%: not the lever. Move on.
Focus on ONE lever at a time.
4. Run Experiments (ICE Framework)
EXPERIMENT NAME: [descriptive]
HYPOTHESIS: We believe [change] will increase [metric] by [X%] because [reason].
ICE SCORE:
- Impact (1-10): [how big if it works?]
- Confidence (1-10): [how sure are we?]
- Ease (1-10): [how easy to implement?]
TOTAL: [I × C × E = score. Sort descending. Run highest first.]
RESULT: [metric before → after]
LEARNING: [what did we learn?]
DECISION: [scale / iterate / kill]
5. Scale What Works, Kill What Doesn't
Result
Decision
Beat target by 20%+
Scale. Invest more resources.
Hit target ±20%
Iterate. Try a variant.
Missed by 20%+
Kill. Document learning. Move on.
Broke something
Kill immediately. Post-mortem.
Output Format
GROWTH EXPERIMENT PLAN — [Company]
NORTH STAR METRIC: [metric]
CURRENT: [value]. TARGET: [value]
GROWTH MODEL:
[Equation showing how North Star is built]
BIGGEST LEVER: [variable] — currently at X%. Industry benchmark: Y%.
THIS WEEK'S EXPERIMENTS:
| # | Experiment | ICE | Owner | Due | Result |
|---|---|---|---|---|
| 1 | [name] | 270 | [name] | [date] | — |
| 2 | [name] | 210 | [name] | [date] | — |
| 3 | [name] | 160 | [name] | [date] | — |
SCALED (last week's winners):
- [Experiment]: increased [metric] by X%. Scaling by [action].
KILLED (last week's losers):
- [Experiment]: no significant impact. Learnings: [insight].
Implementation Checklist
North Star Metric defined (ONE metric, not a dashboard)
Growth model mapped (equation linking inputs to North Star)
Biggest lever identified (data-driven, not gut feel)
Experiments scored with ICE before running
Every experiment has a hypothesis (not "let's try X and see")
Results documented: metric before → after, learning, decision
Kill decisions are fast (underperforming experiments die within 1 week)
Scale decisions are data-backed (statistical significance, not randomness)
Quality Check
Before delivering, verify:
Output matches the user's stated request
Named frameworks or sources are reflected in the recommendation
The deliverable is specific enough for an agent to execute
Any assumptions, risks, or dependencies are explicit
No unsupported claims, invented facts, or private/internal references are included
Common Pitfalls
Tactic copying without model understanding. "Dropbox did a referral
program. We should too!" But you're an enterprise SaaS company where
referrals come from relationships, not viral loops. Fix: Build your
growth model first. THEN pick tactics that fit your model.
Running too many experiments. 10 simultaneous experiments = can't
isolate what worked. Fix: 2-3 experiments per week max. One variable
changed per experiment. Everything else held constant.
Over-optimizing the wrong metric. Growing signups 50% while
activation stays at 10% = growing a leaky bucket. Fix: Find the
bottleneck. Fix activation BEFORE scaling acquisition.
Declaring victory too early. "Our experiment increased conversion
by 15%!" With n=20. Statistical noise. Fix: Minimum 100 conversions
per variant before calling a winner. Use a significance calculator.
Scaling unscalable tactics. "We got 100 users from manually DMing
people on LinkedIn." Great. Now automate it or find a scalable channel.
Don't do 1,000 manual DMs. Fix: Scalable > manual. Find channels that
compound.
Killing too slow. "Let's give it another week." 3 weeks later: same
result. 3 months of momentum lost. Fix: Kill threshold: if confidence
interval suggests it won't hit target, kill immediately. Move on.
Execution Artifacts
references/framework-notes.md — Named frameworks and reference tables
templates/output-template.md — Deliverable shell for agent output
