Flywheel Growth Model Designer
Marketing StrategyintermediateClaude 3.5 Sonnet or GPT-4o. Claude excels at systems thinking and connecting operational constraints to strategic design. GPT-4o is stronger at rapid iteration if you're refining multiple flywheel scenarios. Both handle the complexity of mapping friction points to AI solutions without oversimplifying.
When to Use This Prompt
Use this when you need to move beyond AI pilots and design a sustainable growth system that compounds. It's ideal when your team is drowning in operational debt, tools are siloed, and you need to show CFO-level ROI by connecting faster execution to pipeline impact.
The Prompt
You are a growth strategy consultant helping a marketing leader design a sustainable flywheel that compounds revenue over time.
## Context
I'm building a growth flywheel for [COMPANY/PRODUCT] that moves beyond one-off campaigns to create a self-reinforcing cycle. The goal is to identify where AI can reduce operational friction and accelerate the loop.
## Current State
- Primary customer segment: [TARGET AUDIENCE]
- Current revenue model: [REVENUE MODEL]
- Main friction points in customer journey: [DESCRIBE 2-3 KEY BOTTLENECKS]
- Team size/resources: [TEAM COMPOSITION]
- Existing tools/systems: [LIST KEY PLATFORMS]
## The Flywheel Framework
Design a 4-5 stage flywheel where:
1. Each stage has a clear input and output
2. Success in one stage fuels the next (compounding effect)
3. Operational debt is identified and AI solutions are mapped to reduce friction
4. Revenue impact is traceable at each stage
## Deliverable
Provide:
- **Flywheel stages** (name, description, key metrics)
- **Friction audit** (where time leaks, where rework happens)
- **AI leverage points** (specific AI applications that reduce operational debt, not just speed up tasks)
- **Proof points** (how to measure lift before scaling)
- **90-day implementation roadmap** (which stage to optimize first for fastest ROI)
## Key Constraint
Focus on ONE high-friction workflow where revenue is at stake. Avoid tool-first thinking—design the system first, then identify tools. Show how this compounds over 6-12 months.
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Tips for Best Results
- 1.Start with the revenue-critical stage, not the easiest one. Identify where time leaks directly impact pipeline or retention, then work backward to design the full loop.
- 2.Separate 'faster' from 'better.' AI that just speeds up existing work hits the same bottlenecks. Focus on AI that eliminates handoffs, approvals, or rework entirely.
- 3.Use real metrics, not vanity metrics. 'Faster content creation' is output. 'Content that converts 15% more leads' is outcome. Connect every stage to revenue.
- 4.Test the first stage before designing the full flywheel. Prove lift in one high-friction workflow (30-60 days), then expand. This builds credibility and uncovers system dependencies.
Example Output
## Flywheel: SaaS Product-Led Growth Model
**Stage 1: Activation (Free Trial → Onboarding)**
- Input: Signups from product hunt, organic search
- Output: Users completing core workflow in first 7 days
- Key metric: 7-day activation rate
**Stage 2: Expansion (Usage → Upgrade)**
- Input: Active users hitting usage limits
- Output: Conversion to paid tier
- Key metric: Expansion revenue per user
**Stage 3: Advocacy (Happy Customers → Referrals)**
- Input: Customers reaching ROI milestone
- Output: Net promoter referrals, case studies
- Key metric: Referral-sourced ARR
**Stage 4: Retention (Engagement → Renewal)**
- Input: Ongoing product usage and feature adoption
- Output: Renewal + expansion at contract renewal
- Key metric: Net revenue retention
## Friction Audit
**Biggest leak:** Onboarding takes 3 weeks manual setup. Sales team spends 40% of time on post-sale enablement instead of new deals.
## AI Leverage Points
- **Personalized onboarding flows** (AI generates custom setup guides based on user role/industry)
- **Automated expansion triggers** (AI monitors usage patterns, flags upgrade-ready accounts for sales outreach)
- **Case study generation** (AI synthesizes customer data into advocacy assets)
## Proof Point (30 days)
Reduce onboarding time from 3 weeks to 5 days using AI-guided setup. Measure: activation rate lift + sales time freed for new pipeline.
## 90-Day Roadmap
1. **Weeks 1-4:** Automate onboarding (highest friction, fastest ROI)
2. **Weeks 5-8:** Deploy expansion triggers (feeds sales pipeline)
3. **Weeks 9-12:** Build advocacy layer (compounds referral growth)
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Courses, workshops, frameworks, daily intelligence, and 6 proprietary tools — built for marketing leaders adopting AI.
Trusted by 10,000+ Directors and CMOs.