| 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
| Situation | Use This Framework |
|---|
| Growth slowing, cohorts degrading | Adjacent User Theory |
| Don't know why metrics move | Understand Work |
| Need scalable growth loops | Kindle Fire Strategy |
| Evaluating platform/channel | Distribution Platform Cycle |
| Using data as competitive advantage | Data Network Effects |
| Product-led growth strategy | PLG Frameworks |
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
| Dimension | Current User | Adjacent User |
|---|
| Sophistication | Expert | Beginner |
| Geography | US/EU | Emerging markets |
| Use case | Primary | Secondary |
| Device | Latest phone | Older phone |
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
| Current Need | Adjacent Need | Product Gap |
|---|
| Find friends who use app | Find anyone they know | Graph is celebrity-heavy |
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
- Someone says "We should build X"
- Team pulls data to justify X
- Months building X
- X launches → metrics flat
- No learning captured
The Right Pattern
- Do understand work first
- Insights reveal what matters
- Identify opportunities from understanding
- Execute with higher conviction
Calibrate the Ratio
| Team State | Understand | Execute |
|---|
| New to problem | 40-60% | 40-60% |
| Some understanding | 20-30% | 70-80% |
| Deep understanding | 10-20% | 80-90% |
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
| Tactic | Best For |
|---|
| Partnerships | B2B, platforms |
| PR/Press | Consumer launches |
| Manual outreach | Marketplaces |
| Events | Community products |
| Influencers | Consumer social |
| Content creation | SEO-driven products |
Common Fire Loops
| Loop | Characteristics |
|---|
| Viral/Referral | User invites drive growth |
| SEO | Content ranks, drives traffic |
| Paid acquisition | Profitable CAC/LTV |
| Network effects | Product improves with users |
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
| Moat | Description |
|---|
| Content | First valuable content |
| Audience | First-mover followers |
| Data | Proprietary insights |
| Relationships | Platform partnerships |
| Brand | Category ownership |
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 | Sales-Led |
|---|
| Self-serve trial | Demo required |
| Usage-based pricing | Contract negotiation |
| In-product conversion | Sales closes deal |
| User is buyer | Buyer ≠ user |
PLG Metrics to Track
| Stage | Metrics |
|---|
| Acquisition | Sign-ups, visitor-to-signup |
| Activation | Time-to-value, activation rate |
| Monetization | Free-to-paid, trial conversion |
| Expansion | Net revenue retention, seat expansion |
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:
- PLG for self-serve segment
- Sales for enterprise deals
- 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
-
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
-
Walk through the relevant framework
-
Help build specific action plan
-
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
PLG (Product-Led Growth)
Engagement & Retention
Marketplaces
Consumer & Virality
Growth Teams
Enterprise & International
