growth-advisor

name: growth-advisor description: | Advises on growth strategy, user acquisition, retention, and scaling. Use when: discussing growth tactics, user acquisition, retention problems, platform strategies, understanding users, PLG, or scaling growth. Includes: Adjacent User Theory, Understand Work, Kindle Fire Strategy, Platform Cycles, Data Network Effects, PLG frameworks. Sources: Bangaly Kaba, Casey Winters, Brian Balfour, Elena Verna.

Growth Advisor Skill

Help users develop and execute growth strategies using proven frameworks.

When This Skill Activates

• "How do we grow?"
• "Growth is slowing"
• "User acquisition strategy"
• "Retention is dropping"
• "Should we use this platform/channel?"
• "Our metrics are flat"
• "How do we scale growth?"
• "PLG strategy"

Framework Selection Guide

Framework 1: Adjacent User Theory

Source: Bangaly Kaba - Lenny's Podcast Key Insight: Your current users are not your next users. Growth comes from understanding users just outside your current base.

What is an Adjacent User?

• Just outside your current user base
• Has the need your product serves
• Tried or considered your product
• Something prevented adoption/retention

Why Power Users Blind You

When you dogfood as a power user:

• Your history informs your experience
• Algorithms optimized for you
• Features are familiar
• You've learned workarounds

"If you create a new Gmail account and look at YouTube today, I guarantee it's a completely different and significantly worse experience."

Signs Your Adjacent User is Different

• Cohort retention curves declining
• Same product, worse metrics for new users
• New market metrics lag established markets

Adjacent User Process

Step 1: Map Adjacent User Segments

Step 2: Experience Product as Adjacent User

• Create a brand new account
• Use different device
• Don't use power-user knowledge
• Document every friction point

Step 3: Research Adjacent Users

• Interview churned/bounced users
• Survey people who didn't convert
• Do on-the-ground research in new markets

Step 4: Map the Gaps

Step 5: Prioritize Fixes

• Size of adjacent segment
• Severity of friction
• Feasibility of fix

Instagram Case Study

Problem: Users retained well initially, then left at 7-9 months. Discovery: Algorithm prioritized celebrities. Users followed celebs but had no friends. First post → no engagement → left. Fix: Prioritize friend connections in onboarding. Result: Retention doubled in 18 months.

Pitfalls to Avoid

• Optimizing only for power users
• Assuming adjacent users are similar
• One-time research (needs to be ongoing)
• Not experiencing product firsthand

Framework 2: Understand Work Framework

Source: Bangaly Kaba - Lenny's Podcast Key Insight: Most teams follow identify-justify-execute. Should be understand-identify-execute.

The Anti-Pattern

1. Someone says "We should build X"
2. Team pulls data to justify X
3. Months building X
4. X launches → metrics flat
5. No learning captured

The Right Pattern

1. Do understand work first
2. Insights reveal what matters
3. Identify opportunities from understanding
4. Execute with higher conviction

Calibrate the Ratio

Types of Understand Work

Engineering: Instrument logging, architecture review Data Science: Funnel analysis, cohort studies PM: User research synthesis, competitive analysis Design: UX audit, user journey mapping

Making Understand Work Explicit

Step 1: Put on Roadmap Understand work is explicit work items, not "background"

Step 2: Assign Owners Like any other project

Step 3: Run in Parallel

Each Sprint:
├── Execution Work (things we're confident about)
└── Understand Work (things we need to learn)

Step 4: Empower Push-Back When someone says "build X", team asks: "What do we understand about this?"

Expected Results

• Higher experiment win rates (60-70% vs typical 20-30%)
• Faster long-term velocity
• Better prioritization

Pitfalls to Avoid

• Treating understand work as optional
• All understand, no execute
• Not documenting learnings
• Same ratio forever (should shift)

Framework 3: Kindle Fire Strategy

Source: Casey Winters - Lenny's Podcast Key Insight: Use non-scalable "kindle" tactics to ignite scalable "fire" growth loops.

The Concept

• Kindle: Non-scalable tactics to start growth (partnerships, manual outreach, PR)
• Fire: Scalable loops that sustain growth (viral, SEO, paid)

Why Kindle Matters

• Scalable loops need initial fuel
• Early users seed network effects
• Content/data needed for algorithms
• Learning what resonates

Common Kindle Tactics

Common Fire Loops

Building Kindle → Fire

Step 1: Identify Target Fire Loop What scalable loop will ultimately drive growth?

Step 2: Work Backward to Kindle What non-scalable actions would ignite that loop?

Step 3: Execute Kindle Intensively Go deep, not broad. Manual, unscalable effort.

Step 4: Measure Fire Ignition Is the loop starting to work on its own?

Step 5: Shift Resources to Fire As loop takes hold, move from kindle to fire

Example: Pinterest

Kindle: Invited craft bloggers manually, seeded content Fire: SEO (pins rank), viral (sharing), recommendation loop

Pitfalls to Avoid

• Trying to scale before loop works
• Skipping kindle (loops need fuel)
• Wrong kindle for the fire you want
• Staying in kindle mode too long

Framework 4: Distribution Platform Cycle

Source: Brian Balfour - Lenny's Podcast Key Insight: Every platform goes through cycles from open to closed. The window to build moat is limited.

The Four Steps

Step 0: Conditions for Opening

• Platform saturated easy growth
• Needs new value creation
• Competitive pressure
• Technology shift

Step 1: Platform Opens

• New features/APIs available
• Early adopters get outsized reach
• Moats can be established quickly
• Window: typically 12-24 months

Step 2: Platform Matures

• Algorithm favors platform goals
• Organic reach declines
• Pay-to-play increases
• Competition increases

Step 3: Platform Closes/Resets

• Commoditized (compete on efficiency)
• Closed (diversify or exit)
• Reset (new cycle begins)

Moat Types During Opening

Platform Evaluation

Step 1: Assess Cycle Stage Where is this platform in its cycle?

Step 2: Evaluate Moat Potential Can you build something defensible?

Step 3: Timeline Check How long until window closes?

Step 4: Decision | Fit + Early + Moat Potential | → | Invest heavily | | Fit + Late + No Moat | → | Test but don't bet | | Low Fit | → | Skip |

Preparing for Maturation

• Capture first-party data (emails, etc.)
• Build owned channels
• Diversify acquisition mix
• Develop paid efficiency early

"The best time to diversify off a platform is when it's working best for you."

Pitfalls to Avoid

• Entering too late
• Over-investing in single platform
• Not building moat during opening
• Ignoring maturation signals

Framework 5: Data Network Effects

Source: Casey Winters - Lenny's Podcast Key Insight: Usage data can improve the product, creating a compounding advantage.

What Are Data Network Effects?

Product improves as you get more data from users:

• Better recommendations
• Smarter defaults
• Improved algorithms
• Personalization

Types of Data Advantages

1. Personalization (Individual)

• Your history improves your experience
• Example: Netflix recommendations

2. Collective Intelligence (Aggregate)

• All users' data improves everyone's experience
• Example: Waze traffic data

3. Training Data (ML)

• Data trains models that improve product
• Example: GPT trained on text

Building Data Network Effects

Step 1: Identify Data That Would Improve Product What data, if you had it, would make the product better?

Step 2: Design Collection How can you collect this through product usage?

Step 3: Build Feedback Loop Data → Improvement → More Usage → More Data

Step 4: Make Improvement Visible Users should notice the product getting better

Assessment Questions

• Does more usage generate valuable data?
• Does that data improve the product?
• Can competitors replicate this data?
• How long to accumulate meaningful advantage?

Pitfalls to Avoid

• Assuming all data is valuable
• Not closing the loop (collecting but not using)
• Ignoring privacy concerns
• Overstating the moat (data often reproducible)

Framework 6: PLG Fundamentals

Source: Elena Verna - Lenny's Podcast Key Insight: Product-led growth uses the product itself as the primary driver of acquisition, conversion, and expansion.

PLG Components

Acquisition: Users find and try product without sales Activation: Users experience value quickly Monetization: Users convert when ready Expansion: Usage grows within accounts

PLG vs. Sales-Led

PLG Metrics to Track

Common PLG Motions

Freemium: Free tier forever, pay for more Free Trial: Full product, limited time Reverse Trial: Start paid, downgrade if needed

PLG + Sales Hybrid

Not either/or. Many successful companies layer sales on PLG:

1. PLG for self-serve segment
2. Sales for enterprise deals
3. Product-qualified leads (PQLs) route to sales

Pitfalls to Avoid

• Forcing PLG where it doesn't fit
• No clear path to conversion
• Too much friction in trial
• Ignoring expansion revenue

How to Apply This Skill

1. Diagnose the growth situation

   • Slowing growth → Adjacent Users + Understand Work
   • New channel → Platform Cycle
   • Need sustainable loops → Kindle Fire
   • Data advantage → Data Network Effects
   • Self-serve motion → PLG

2. Walk through the relevant framework

3. Help build specific action plan

4. Recommend measurement approach

Related Skills

• /strategy-advisor - For strategic direction
• /goal-setter - For growth metrics
• /gtm-advisor - For go-to-market

Full SOPs (Deep Dives)

Core Growth Frameworks

• Adjacent User Theory
• Understand Work
• Kindle Fire
• Platform Cycle
• Data Network Effects
• Instagram Case Study

PLG (Product-Led Growth)

• PLG Implementation
• Free-to-Paid Conversion
• PLG Metrics & Benchmarks
• PLG Motion Evaluation
• PLG Team Structure JEUE
• Onboarding Activation
• Reverse Trial Freemium
• Word-of-Mouth Growth

Engagement & Retention

• Hierarchy of Engagement
• Network Effects Assessment
• Retention Improvement
• User Activation

Marketplaces

• Marketplace Hierarchy
• Marketplace Liquidity
• Two-Sided ROI
• Marketplace Expansion
• Gardener Framework

Consumer & Virality

• Consumer App Virality
• Consumer App Subscription

Growth Teams

• Growth Team Building
• Growth Team Structure
• Growth Experiment Prioritization
• Growth Leader Hiring

Enterprise & International

• Enterprise B2B Growth
• International Expansion
• International Product Expansion