aiskillstore/growth-strategy

Growth Strategy

Modern growth hacking: loops + product-led growth + disciplined experimentation, under privacy and deliverability constraints.

When to Use

• Designing growth strategy or GTM plans
• Planning experiments and A/B tests
• Optimizing activation, retention, or referral flows
• Building viral/referral loops
• Reviewing growth tactics for ethics/compliance

Core Principle

If a "hack" doesn't strengthen a loop or an input metric, it's noise.

1. Growth Model First

North Star Metric (NSM)

• Single metric aligning the whole org
• Plus input metrics (leading indicators you can move weekly)
• Avoid vanity metrics

Growth Loops > Funnels

• Loops: Closed systems where outputs feed inputs → compounding growth
• Funnels: Linear → diminishing returns

Common loops:

| Loop Type | Example | | --------- | ----------------------------------------------- | | Viral | User creates → shares → new users | | UGC/SEO | User creates content → indexed → new users find | | Paid | Revenue → reinvest in ads → more revenue | | Sales | Customer → case study → new leads |

Product-Led Growth (B2B/SaaS)

Product itself drives: Acquisition → Activation → Retention → Monetization

2. Instrumentation

Event Taxonomy

• Clean identity resolution: anonymous → user → account
• Cohort retention tracking
• Activation milestones defined

Incrementality

• Holdouts / geo splits when attribution is noisy
• Don't trust last-click blindly

Metric Categories

| Type | Examples | | ---------- | --------------------------------------- | | Core | NSM + input metrics | | Guardrails | Churn, spam rate, refunds, latency, NPS |

3. Experimentation Engine

Intake System

• Single queue + scoring (RICE/ICE)
• Weekly cadence

Test Definition (Required)

• [ ] Hypothesis
• [ ] Target segment
• [ ] Success metric
• [ ] Guardrail metrics
• [ ] Sample size rule
• [ ] Kill criteria

High-ROI Test Areas

• Onboarding steps
• Paywall copy
• Pricing/packaging
• Referral incentive
• Landing page variants
• Lifecycle messages

4. Lever-Specific Playbooks

Activation & Onboarding (Highest ROI)

• Reduce time-to-value
• Templates, importers, "one-click first win"
• Progressive disclosure (ask when needed, not upfront)
• Guided setup flows

Viral/Referral Loops

• Build shareable artifacts (reports, badges, embeds)
• "Invite teammates" as natural workflow
• Reward activated referrals, not just signups

Content + SEO

• Programmatic SEO: template + real value + strong linking
• Audit/prune thin pages (don't endlessly generate)
• Quality > quantity

Lifecycle (Email/Push)

Deliverability is gating factor:

• SPF/DKIM for all senders
• DMARC for bulk
• Keep complaint/spam rates low

Community-Led Growth

• Seed right early members
• Great "first experience"
• Connect to business outcomes (support deflection, referrals)

5. Privacy & Measurement Constraints

Expect

• Less reliable cross-site tracking
• Cookie-based attribution unstable
• Platform policy changes

Adapt

• First-party data focus
• Server-side signals
• Incrementality testing
• Design measurement that survives policy changes

6. AI in Growth

Good Uses

• Generate creative/landing page variants to test (humans review)
• Summarize qualitative feedback
• Cluster objections
• Speed up research

Avoid

• "AI content spam" at scale without quality control
• Backfires in SEO and brand

7. Hard Red Lines

If a tactic can't survive being in a postmortem or public doc, don't ship it.

Never:

• Spam (email/SMS)
• Fake reviews
• Scraping that violates ToS
• Dark patterns
• Deceptive pricing/consent

Output Format

When proposing growth initiatives:

## Initiative: [Name]
**Loop/Lever**: [Which growth loop or lever this strengthens]
**Hypothesis**: [If we do X, Y metric will improve by Z because...]
**Input Metric**: [What leading indicator we're moving]
**Guardrails**: [Metrics that must not regress]

### Implementation
[Concrete steps]

### Measurement
[How we'll know it worked]

### Kill Criteria
[When to stop if failing]

Quick Checklist

Before shipping any growth tactic:

• [ ] Does it strengthen a loop or input metric?
• [ ] Is the hypothesis testable?
• [ ] Are guardrails defined?
• [ ] Is it compliant with platform ToS?
• [ ] Would you put it in a public doc?
• [ ] Does it respect user privacy?
• [ ] Is deliverability accounted for (if email)?

See references/ for detailed playbooks.