CODITECT Sensitivity-Driven Research Prompts
Overview
These research prompts are derived from the financial model sensitivity analysis. Each prompt targets a specific model driver that has high impact on outcomes.
CATEGORY A: CHURN REDUCTION (Highest ROI)
A.1 Onboarding Optimization Research
OBJECTIVE: Identify onboarding patterns that achieve <20% monthly churn for developer tools
CONTEXT:
- CODITECT model assumes 33% individual churn improving to 20%
- Each 5% churn reduction = ~$500K cumulative LTV improvement
- Must be self-serve (Individual tier CAC = $10)
RESEARCH TASKS:
1. Analyze best-in-class developer tool onboarding:
- Stripe: Time to first API call
- Vercel: Time to first deployment
- Linear: Time to first issue created
- Notion: Time to first shared page
2. Identify activation metrics that predict retention:
- What actions in Day 1 correlate with Month 3 retention?
- What is the "magic number" of actions?
- How do power users differ from churned users?
3. Research onboarding friction points:
- Where do users drop off?
- What causes abandonment?
- How do competitors reduce friction?
4. Study personalization techniques:
- Role-based onboarding paths
- Use case detection
- Progressive disclosure patterns
OUTPUT FORMAT:
- Onboarding benchmark matrix
- Activation metric recommendations
- Friction audit checklist
- Personalization framework
- A/B test hypotheses (prioritized)
A.2 Habit Formation Mechanics
OBJECTIVE: Research habit-forming patterns for daily developer tool engagement
CONTEXT:
- Daily active use is strongest retention predictor
- Competing for attention with existing IDE, terminal, browser
- Must become "default" behavior, not conscious choice
RESEARCH TASKS:
1. Analyze habit loop theory applied to dev tools:
- Cue → Routine → Reward framework
- Variable reward schedules
- Investment mechanics (stored value)
2. Research notification/engagement strategies:
- What triggers bring users back?
- Email vs. in-app vs. IDE notification effectiveness
- Frequency optimization (not annoying)
3. Study "switching cost" creation:
- Data accumulation (prompts, history, preferences)
- Workflow customization
- Team/social connections
4. Analyze streak/gamification patterns:
- GitHub contribution graphs
- Duolingo streaks
- What works for developers (not patronizing)
OUTPUT FORMAT:
- Habit loop design for CODITECT
- Engagement trigger playbook
- Switching cost feature roadmap
- Gamification principles (developer-appropriate)
A.3 Churn Prediction & Prevention
OBJECTIVE: Build a churn prediction and intervention framework
CONTEXT:
- Proactive intervention is 5x more effective than reactive
- Must identify at-risk users before they churn
- Intervention must be automated (scale constraints)
RESEARCH TASKS:
1. Research churn prediction models:
- What features predict developer tool churn?
- Lead time for prediction (days/weeks before churn)
- Model accuracy benchmarks
2. Analyze intervention strategies:
- What actions can prevent churn?
- Timing of intervention
- Personalization of messaging
3. Study win-back campaigns:
- Effectiveness rates
- Offer structures (discounts, features)
- Timing post-churn
4. Research NPS/CSAT correlation:
- Does satisfaction predict churn?
- What survey questions are predictive?
- Survey timing optimization
OUTPUT FORMAT:
- Churn prediction feature set
- Health score framework
- Intervention playbook by risk level
- Win-back campaign templates
CATEGORY B: GROWTH ACCELERATION
B.1 Viral Loop Design
OBJECTIVE: Design viral mechanics that achieve >1.0 viral coefficient
CONTEXT:
- Model assumes 250% M1-3 growth (viral/PLG-driven)
- Viral coefficient = invites sent × conversion rate
- Developer tools have natural collaboration triggers
RESEARCH TASKS:
1. Analyze successful dev tool viral loops:
- Slack: "Invite your team"
- Notion: "Share this page"
- Figma: "Collaborate in real-time"
2. Research developer-specific viral triggers:
- Code sharing/review
- Pair programming
- Knowledge sharing
3. Study referral program economics:
- Incentive structures that work
- Two-sided vs. one-sided rewards
- Credit vs. cash vs. features
4. Analyze social proof mechanics:
- "Used by X developers"
- GitHub stars/badges
- Community showcases
OUTPUT FORMAT:
- Viral loop design (primary + secondary)
- Referral program structure
- Social proof strategy
- Viral coefficient tracking framework
B.2 Content-Led Growth
OBJECTIVE: Design content strategy that drives organic acquisition
CONTEXT:
- Content is primary driver for Individual tier discovery
- Must compete for developer attention (crowded space)
- SEO + community + social distribution
RESEARCH TASKS:
1. Analyze dev tool content strategies:
- Vercel's Next.js documentation
- Stripe's guides and tutorials
- Supabase's content marketing
2. Research content types that convert:
- Tutorial vs. documentation
- Video vs. written
- Interactive vs. static
3. Study developer community building:
- Discord vs. Slack vs. forums
- Community-generated content
- Developer advocates/evangelists
4. Analyze SEO for developer tools:
- High-intent keywords
- Technical content optimization
- Stack Overflow/Reddit presence
OUTPUT FORMAT:
- Content calendar framework
- Content type prioritization
- Community launch playbook
- SEO keyword strategy
B.3 Product-Led Sales Motion
OBJECTIVE: Design Individual→Team→Enterprise upgrade path
CONTEXT:
- Model shows 80% Individual at M12, need Team/Enterprise growth
- Cannot afford high-touch sales for Team tier
- Enterprise requires sales assist but PLG-influenced
RESEARCH TASKS:
1. Analyze PLS (Product-Led Sales) patterns:
- Slack's bottom-up enterprise
- Figma's designer-to-org expansion
- Notion's team discovery
2. Research upgrade triggers:
- What in-product signals indicate Team readiness?
- How to surface "your team is using this"?
- Pricing psychology for upgrades
3. Study sales handoff mechanics:
- When does sales engage?
- What context do they need?
- CRM/product data integration
4. Analyze enterprise buying signals:
- Multiple users from same domain
- Feature usage patterns
- Support ticket themes
OUTPUT FORMAT:
- Upgrade trigger matrix
- PLS playbook
- Sales handoff criteria
- Enterprise signal detection
CATEGORY C: ENTERPRISE ACCELERATION
C.1 Compliance as Competitive Advantage
OBJECTIVE: Research compliance positioning for enterprise acceleration
CONTEXT:
- Enterprise tier has 13x LTV/CAC (highest)
- Compliance is CODITECT differentiator
- FDA/HIPAA/SOC2 are table stakes for target verticals
RESEARCH TASKS:
1. Analyze compliance-led sales strategies:
- Snyk's security-first positioning
- Drata/Vanta's compliance automation
- How does compliance accelerate deals?
2. Research compliance requirements by vertical:
- Healthcare: HIPAA, HITRUST
- Pharma: FDA 21 CFR Part 11, GxP
- Financial: SOC2, PCI-DSS, FINRA
3. Study compliance content marketing:
- Whitepapers that generate leads
- Compliance guides/checklists
- Webinar topics that convert
4. Analyze compliance partnerships:
- Consulting firm relationships
- System integrator partnerships
- Compliance platform integrations
OUTPUT FORMAT:
- Compliance positioning strategy
- Vertical-specific messaging
- Content/lead gen plan
- Partnership opportunities
C.2 Enterprise Feature Prioritization
OBJECTIVE: Identify enterprise features with highest deal impact
CONTEXT:
- Enterprise deals require specific capabilities
- Must prioritize limited engineering resources
- Some features are table stakes, others are differentiators
RESEARCH TASKS:
1. Research enterprise feature requirements:
- SSO/SAML (which providers?)
- SCIM provisioning
- Audit logging requirements
- Admin console capabilities
2. Analyze security questionnaire patterns:
- Most common questions
- Deal-blocking gaps
- Automation opportunities
3. Study enterprise deployment options:
- Cloud vs. on-premise demand
- Private cloud/VPC requirements
- Data residency requirements
4. Research enterprise pricing models:
- Flat fee vs. per-seat at scale
- Volume discounts
- Multi-year incentives
OUTPUT FORMAT:
- Enterprise feature priority matrix
- Security questionnaire gap analysis
- Deployment architecture options
- Pricing model recommendations
C.3 Healthcare Vertical Deep Dive
OBJECTIVE: Research healthcare vertical GTM strategy
CONTEXT:
- Healthcare is primary CODITECT target vertical
- HIPAA compliance is critical
- Long sales cycles but high retention
RESEARCH TASKS:
1. Map healthcare IT landscape:
- EHR vendors (Epic, Cerner, Meditech)
- Healthcare IT buying patterns
- Key decision makers (CIO, CISO, CMO)
2. Research healthcare developer ecosystem:
- FHIR/HL7 development
- Clinical decision support
- Healthcare AI applications
3. Study healthcare sales process:
- Typical sales cycle length
- Procurement requirements
- Pilot program structures
4. Analyze healthcare partnerships:
- EHR marketplace opportunities
- Healthcare IT consultants
- Health system innovation labs
OUTPUT FORMAT:
- Healthcare ICP definition
- Sales process playbook
- Partnership strategy
- Vertical-specific messaging
CATEGORY D: UNIT ECONOMICS OPTIMIZATION
D.1 CAC Efficiency Research
OBJECTIVE: Research strategies to maintain CAC at scale
CONTEXT:
- Individual CAC = $10 (must stay low for unit economics)
- Team CAC = $200
- Enterprise CAC = $1,000
- CAC inflation is major risk at scale
RESEARCH TASKS:
1. Analyze CAC trends in dev tools:
- Historical CAC inflation patterns
- Channel saturation effects
- Competition impact on CAC
2. Research efficient channels:
- Organic vs. paid mix
- Developer community channels
- Content marketing ROI
3. Study attribution models:
- Multi-touch attribution
- First-touch vs. last-touch
- Self-reported attribution
4. Analyze brand building impact:
- Brand awareness → lower CAC
- Developer reputation effects
- Community-driven acquisition
OUTPUT FORMAT:
- CAC benchmark by channel
- Channel efficiency matrix
- Attribution framework
- Brand building roadmap
D.2 Pricing Optimization Research
OBJECTIVE: Research pricing strategies that maximize revenue without hurting growth
CONTEXT:
- Current: Individual $15, Team $250, Enterprise $1,300
- Competitors: Copilot $10-19, Cursor $20
- Must balance growth vs. revenue
RESEARCH TASKS:
1. Analyze pricing research methodologies:
- Van Westendorp price sensitivity
- Conjoint analysis
- A/B testing approaches
2. Research value metric optimization:
- Per-seat vs. usage-based
- Outcome-based pricing potential
- Hybrid models
3. Study freemium optimization:
- Free tier feature selection
- Conversion rate benchmarks
- Usage limits psychology
4. Analyze price increase strategies:
- Grandfathering approaches
- Communication best practices
- Churn impact mitigation
OUTPUT FORMAT:
- Pricing research methodology
- Value metric analysis
- Freemium tier recommendation
- Price increase playbook
USAGE GUIDE
Priority Order (Based on Model Sensitivity)
| Rank | Prompt | Model Impact | Effort |
|---|---|---|---|
| 1 | A.1 Onboarding Optimization | Churn → LTV | Medium |
| 2 | B.1 Viral Loop Design | Growth rate | Medium |
| 3 | A.2 Habit Formation | Retention | High |
| 4 | C.1 Compliance Positioning | Enterprise mix | Low |
| 5 | B.3 Product-Led Sales | Team conversion | Medium |
| 6 | A.3 Churn Prediction | Retention | High |
| 7 | C.2 Enterprise Features | Deal velocity | Medium |
| 8 | B.2 Content-Led Growth | Organic CAC | High |
| 9 | D.1 CAC Efficiency | Unit economics | Medium |
| 10 | D.2 Pricing Optimization | Revenue | Low |
| 11 | C.3 Healthcare Vertical | Vertical depth | High |
Integration with Financial Model
After completing research:
- Update scenario parameters in
CODITECT_Sensitivity_Model.xlsx - Re-run weighted scenarios with new assumptions
- Document assumption changes in Assumptions sheet
- Update product roadmap based on findings
Document Version: 1.0
Created: February 5, 2026
Author: Hal Casteel with assistance from Claude 4.5
Derived from: CODITECT Sensitivity Model Analysis