GrowthMentor AI Prompt Library - English Version

You are a growth strategy expert with deep venture capital experience. 

Your mission - analyze companies and find:
• Unexpected patterns that analysts miss
• Hidden problems in growth strategies
• Specific opportunities to improve metrics
• Provocative but data-backed conclusions

Response style requirements:
- Specific numbers and percentages (not "significantly", but "3.2x")
- Actionable recommendations with timelines
- Comparisons with successful cases
- Predictions with clear reasoning
- Format suitable for viral social media content

Never use generic statements. Always provide:
- Specific percentages instead of "significant increase"
- Concrete company examples instead of "many companies"
- Timelines with dates instead of "soon" or "eventually"
- Dollar amounts and multiples where applicable
- Confidence levels for predictions

Analyze [Company Name] and identify their main strategic mistake.

Format your answer as:
• What they're doing now: [current approach]
• The problem: [specific mistake]
• Cost of mistake: [losses in % or $]
• What needs to change: [concrete solution]
• Expected impact: [improvement forecast]

Provide examples of similar companies that solved this problem.

What growth opportunities is [Company Name] missing?

Analyze:
1. Underutilized acquisition channels
2. Unoptimized funnel stages
3. Missed audience segments
4. Untapped viral mechanics potential

For each opportunity specify:
- Potential impact (in % growth)
- Implementation difficulty (1-10 scale)
- Timeline to results
- Success examples from other companies

Analyze [Company Name]'s unit economics and find the weak points.

Calculate and assess:
• CAC across channels (and trend)
• LTV by cohort (and retention curve)
• CAC payback period (vs. industry standard)
• LTV/CAC ratio (healthy = 3x+)
• Gross margin per user

Red flags to identify:
- Which metrics are in danger zone
- Where the model breaks at scale
- Hidden costs they might be ignoring
- Comparison with category leaders

Provide 3 specific actions to improve unit economics with expected ROI.

Compare growth strategies of [Company A] vs [Company B] vs [Company C].

Analyze:
• Unit economics of each (CAC, LTV, payback period)
• Go-to-market approaches and effectiveness
• Product-market fit indicators
• Scalability potential of each model

Predict:
- Who wins in 18 months and why
- What strategy each should steal from competitors
- Critical risks for each player
- Key inflection points to watch

Your prediction confidence level: [%]
Betting odds for each: [ratios]

Compare [Company Name] metrics against top performers in their category.

Find gaps in:
• Conversion rates by funnel stage
• Retention curves (D1, D7, D30, D90)
• Monetization metrics (ARPU, expansion revenue)
• Growth efficiency (LTV/CAC, magic number, payback)

For each gap specify:
- How far behind (in multiples/percentages)
- What leaders do differently
- Actionable steps to close gap
- Realistic timeline to reach benchmark
- Which gaps are most critical to address first

Analyze [Company Name]'s market positioning vs competitors.

Evaluate:
• Current positioning statement (what they claim)
• Actual differentiation (what data shows)
• Competitor positioning overlap
• White space opportunities

Find:
- Where positioning is weak/generic
- Unique angles they're missing
- How to reposition for better conversion
- Category leadership opportunities

Provide: specific repositioning recommendation with expected impact on CAC and conversion.

Predict [Company Name]'s trajectory over next 12-18 months.

Forecast:
• Revenue trajectory (+/- %)
• Key inflection points (when and why)
• Main growth bottlenecks
• Moments requiring pivots or strategic shifts

Base predictions on:
- Current metric trends
- Market dynamics in their segment
- Competitive pressure
- Historical patterns of similar companies

Risk factors that could break forecast: [list with probabilities]
Confidence level in forecast: [%]

Assess market timing for [Company Name] right now.

Analyze:
• Market readiness for their solution (scale 1-10)
• Competitive landscape density
• Technology adoption curve position
• Economic factors affecting their category

Determine:
- Ideal moment for aggressive scaling
- Windows of opportunity in next 6-12 months
- Threats from new entrants
- Market saturation timeline

Recommendation: accelerate or slow down? Why?
What signals to watch for timing shift?

What could kill [Company Name]? Analyze failure risks.

Identify top 5 threats:
1. [Threat] - Probability: [%] - Impact: [1-10]
2. [Threat] - Probability: [%] - Impact: [1-10]
etc.

For each threat:
- Early warning signals
- Prevention strategies
- Mitigation if it happens
- Similar company examples

Which threat is most likely and most dangerous?
What should they monitor obsessively?

Find common patterns among successful startups in [Industry/Category].

Analyze top 5 winners and identify:
• Common growth tactics in first 18 months
• Typical pivots before breakthrough
• Critical metrics for each stage
• Timing patterns for fundraising
• Go-to-market playbook similarities

Find anti-patterns:
• What doesn't work in this category
• Common failure points
• Misleading vanity metrics
• Costly mistakes to avoid

Applied to [Company Name]:
- Which patterns they're executing correctly
- Where they deviate from winning formula
- What needs correction
- Unique advantages they have

Assess viral potential of [Company Name] and suggest mechanics for organic growth.

Analyze:
• Current viral coefficient (estimate from available data)
• Natural user sharing motivations
• Network effects in the product
• Referral program opportunities

Propose 3 viral mechanics:
1. Low-hanging fruit (quick implementation, decent impact)
2. Medium complexity (moderate effort, high viral potential)
3. Advanced mechanism (complex but game-changing)

For each mechanism:
- Expected viral coefficient improvement
- Implementation complexity (1-10)
- Success examples from other companies
- Potential downsides or risks

Is [Company Name] ready for their next funding round?

Evaluate:
• Traction metrics vs. stage benchmarks
• Story strength (narrative + data)
• Team completeness
• Market timing and sentiment

Grade readiness (A-F) in:
- Product-market fit proof
- Growth trajectory
- Unit economics health
- Competitive positioning
- Team strength

Recommendation:
- Raise now / wait X months
- Optimal raise amount and valuation
- Which metrics to improve before raising
- Investor types to target

Create a 90-day action plan to accelerate [Company Name]'s growth.

Month 1 (Quick wins):
• 3 tactics for immediate impact
• Metrics to track progress
• Resource requirements
• Expected results

Month 2 (Foundation building):
• 2-3 strategic initiatives
• Process improvements
• Team/tool requirements
• Success criteria

Month 3 (Scale preparation):
• Scalability improvements
• Advanced growth experiments
• Next stage preparation
• Risk mitigation

For each tactic:
- Expected impact (% improvement)
- Success criteria
- Potential risks and mitigations
- Dependencies and prerequisites

How should [Company Name] reallocate resources for maximum growth impact?

Analyze current allocation (if known) and propose:

• Where to cut spending without hurting growth
• Where to redirect freed resources
• New investment priorities by ROI
• Team structure changes for growth

Budget reallocation recommendations:
- Marketing channel optimization (cut X, boost Y)
- Product development priorities
- Hiring plan adjustments
- Technology infrastructure needs

Expected outcome from optimization: [specific metrics and timeline]
Risk-adjusted ROI for each recommendation: [calculate]

Design 5 growth experiments for [Company Name] to run this quarter.

For each experiment:

**Experiment [N]: [Name]**
- Hypothesis: [what you're testing]
- Metric: [what you're measuring]
- Success criteria: [specific threshold]
- Sample size needed: [number]
- Duration: [weeks]
- Implementation cost: [$]
- Expected impact if successful: [%]
- Confidence level: [%]
- Risk level: [low/medium/high]

Prioritize experiments by:
1. Expected impact
2. Ease of implementation
3. Learning value

Give a provocative but data-backed opinion on [Company/Industry Trend].

"Unpopular opinion" format:
• What everyone thinks: [common belief]
• Why that's wrong: [your arguments]
• What's actually true: [alternative view]
• Evidence: [data, examples, logic]
• Why this matters: [implications]

Make the statement controversial enough for debate, but backed by solid reasoning.
Include at least 3 data points supporting your contrarian view.

Predict the future of [Industry] category based on current signals.

Make 3 predictions for 2-3 years out:
1. Bold prediction (30% probability)
   - What happens: [description]
   - Key drivers: [what causes this]
   - Winners/Losers: [who benefits/suffers]
   
2. Likely scenario (60% probability)
   - What happens: [description]
   - Key drivers: [what causes this]
   - Winners/Losers: [who benefits/suffers]
   
3. Conservative outcome (90% probability)
   - What happens: [description]
   - Key drivers: [what causes this]
   - Winners/Losers: [who benefits/suffers]

For each scenario:
- Timeline key milestones
- What companies should do to prepare
- Current signals supporting this scenario
- Black swan risks that could change everything

Analyze publicly available data about [Company Name] and find insights others are missing.

Sources to check:
• Job postings (hiring signals)
• Product changes (strategic pivots)
• Social media activity (market reception)
• Review sites (customer sentiment)
• Competitor moves (market dynamics)

Find and explain:
1. Hidden growth signals
2. Warning flags
3. Strategic shifts in progress
4. Market position changes

For each insight:
- Evidence supporting it
- Why it matters
- What it predicts about next 6 months

If you were CEO of [Company Name] for next 90 days, what would you do?

Priorities:
1. [Priority] - Why: [reasoning] - Impact: [expected result]
2. [Priority] - Why: [reasoning] - Impact: [expected result]
3. [Priority] - Why: [reasoning] - Impact: [expected result]

What you'd STOP doing:
• [Activity] - Because: [reasoning]

What you'd START doing:
• [Activity] - Because: [reasoning]

First week action items:
- Day 1: [action]
- Day 2: [action]
- Day 3: [action]

Expected outcome after 90 days: [specific metrics]

"Avoid generic phrases. Required:
- Specific percentages instead of 'significantly'
- Concrete examples instead of 'many companies'
- Timelines with dates instead of 'soon'
- Dollar amounts where possible
- Confidence levels for predictions
- Risk probabilities for scenarios"

"Don't be afraid of controversial conclusions if data supports them.
Goal - create content that makes people think and discuss.
Challenge conventional wisdom where data contradicts it.
Be provocative but always data-backed."

"Every insight must include:
- What to do specifically (actionable step)
- How to measure result (metric)
- When to expect effect (timeline)
- Effort required (1-10 scale)
- Expected ROI or impact"

"Go beyond surface-level analysis:
- Find second and third-order effects
- Identify non-obvious connections
- Challenge assumptions
- Consider alternative explanations
- Think in systems and feedback loops"

"This is too generic. Give more specific insights:
- Actual numbers and metrics
- Examples of specific companies
- Actionable recommendations with timeline
- Confidence levels for predictions"

"This analysis is too safe. Find more controversial angle:
- What is everyone doing wrong here?
- Which conventional wisdom is actually false?
- Where does market fundamentally misunderstand something?
- What unpopular opinion does data support?"

"Go deeper on [specific aspect]. Need:
- Root cause analysis
- Second and third-order effects
- Hidden connections with other trends
- Non-obvious implications
- What this means for [specific stakeholder]"

"Make this more compelling:
- Start with surprising hook
- Use narrative structure
- Include concrete examples
- Add human element
- End with clear takeaway"

"Compare [Company A] vs [Company B] unit economics and growth strategies.
Then predict: who wins in 18 months and why?
Include confidence level and key metrics to watch."

"Find main growth problem for [Company Name].
Design 3 experiments to solve it.
Create 90-day timeline for implementation.
Predict impact on key metrics."

"Find success patterns in [category] among unicorns.
Apply patterns to [Company Name].
Identify gaps and create action plan.
Predict outcome if executed well."