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Research Methodology

Overview

This document outlines the research methodology used in the GTM strategy analysis for ResearchFlow AI.

1. Market Sizing Methodology

TAM (Total Addressable Market)

Approach: Combined bottom-up and top-down validation

Bottom-up Calculation:

  • Identified 7 distinct knowledge worker segments
  • Sourced population data from UNESCO, World Bank, OECD
  • Summed segment populations
  • Result: ~78M users

Top-down Validation:

  • Started with global knowledge workers (1.2B from McKinsey)
  • Applied 25% filter (those doing research synthesis)
  • Result: 300M users

Final TAM: Average of both approaches = 189M users (used 300M conservatively)

Sources:

  • UNESCO Institute for Statistics (PhD students, researchers)
  • World Bank Development Indicators
  • OECD Science & Technology Indicators
  • McKinsey Global Institute

SAM (Serviceable Available Market)

Approach: Sequential filtering from TAM

Filters Applied:

  1. Language: 20% use English-language tools (global market)
  2. Digital adoption: 90% use digital productivity tools
  3. AI willingness: 70% willing to use AI-powered tools (Gartner 2025)
  4. Payment willingness: 60% willing to pay for productivity SaaS

Calculation: 300M × 0.20 × 0.90 × 0.70 × 0.60 = 22.7M users

Validation: Cross-referenced with Gartner's 50M AI productivity tool users estimate

Final SAM: 50M users (using industry estimate)

SOM (Serviceable Obtainable Market)

Approach: Beachhead market capture model

Beachhead Segment: Academic researchers & PhD students

  • Global population: 13M (UNESCO data)
  • English-speaking: 30% (4M)
  • Actively researching: 80% (3.1M)

Market Capture Funnel:

  1. Awareness: 5% via GTM campaigns = 156K aware
  2. Sign-up: 20% conversion = 31K sign-ups
  3. Activation: 40% create knowledge graph = 12.5K activated
  4. Market Share: Target 10% = 1.25K users

Alternative Calculation: 4% of SAM = 2M users

Final SOM: 2M users (12-month target, conservative)

2. Competitive Analysis Methodology

Competitor Selection

Criteria:

  • AI-powered productivity/note-taking tools
  • Launched in last 5 years OR added AI recently

  • 10K users OR significant funding

  • English-language product

7 Competitors Selected:

  1. Notion AI (horizontal platform)
  2. Mem.ai (AI-first notes)
  3. Reflect (networked notes)
  4. Obsidian (local-first knowledge base)
  5. Roam Research (networked thought)
  6. Napkin.ai (visual AI)
  7. Recall (knowledge graph learner)

Feature Comparison Framework

15 Dimensions Analyzed:

Core AI Features:

  • AI Summarization
  • Cross-source Synthesis
  • Automatic Linking
  • Custom AI Prompts

Vertical Features:

  • Knowledge Graph Visualization
  • Academic Database Integration
  • Citation Management

Product Features:

  • Collaboration
  • Version History
  • Mobile App
  • Offline Mode
  • API Access
  • Export Options

Performance:

  • Search Quality
  • Privacy (Local-first)

Scoring: 0-10 scale where:

  • 0 = Not available
  • 5 = Basic implementation
  • 10 = Best-in-class

Positioning Matrix

Two Axes:

X-Axis: Specialization (0-10)

  • 0 = Generalist (tries to do everything)
  • 10 = Specialist (vertical focus on one use case)

Y-Axis: User Type (0-10)

  • 0 = Individual user focus
  • 10 = Team/Enterprise focus

Positioning Scores Based On:

  • Product messaging and positioning
  • Feature prioritization
  • Pricing structure
  • Target customer statements

SWOT Analysis

Framework:

  • Strengths: Internal advantages vs competitors
  • Weaknesses: Internal disadvantages
  • Opportunities: External favorable conditions
  • Threats: External risks

Sources:

  • Product analysis
  • User reviews (G2, Product Hunt)
  • Funding/growth data (Crunchbase)
  • Industry trends

3. Pricing Strategy Methodology

Value-Based Pricing

Approach: Calculate value delivered, price as % of value

Value Calculation:

  • Manual literature review: 100 hours @ $50/hour = $5,000
  • With ResearchFlow AI: 25 hours = $1,250
  • Time saved: 75 hours = $3,750 per review
  • Monthly value: $1,875 (0.5 reviews/month)

Pricing Decision:

  • Monthly cost: $15
  • Value delivered: $1,875
  • ROI: 125x (justifies premium pricing)

Competitive Pricing Analysis

Method:

  • Collected public pricing data from 7 competitors
  • Calculated average: $12/month
  • Positioned at $15/month (25% premium)
  • Premium justified by vertical specialization

Unit Economics Validation

LTV Calculation:

LTV = ARPU × Average Lifetime (months) × Gross Margin
LTV = $15 × 24 × 0.70 = $252

CAC Calculation:

Blended CAC across channels:
- Product Hunt: $20/user (40% of users) = $8
- SEO: $15/user (30%) = $4.50
- Google Ads: $50/user (20%) = $10
- Referrals: $10/user (10%) = $1
Blended CAC = $23.50 → Rounded to $35 (conservative)

Validation Checks:

  • ✅ LTV/CAC > 3.0x (actual: 7.2x)
  • ✅ Payback < 12 months (actual: 3.3 months)
  • ✅ Gross margin > 60% (actual: 70%)

4. GTM Planning Methodology

Channel Selection

Framework: AARRR Pirate Metrics

Channels Evaluated:

  • Acquisition: Product Hunt, SEO, Paid Ads, Social
  • Activation: Onboarding, email nurture
  • Retention: Product quality, engagement loops
  • Revenue: Free-to-paid conversion
  • Referral: Viral loops

Selection Criteria:

  1. CAC efficiency (< $50 target)
  2. Scalability (can 10x spend?)
  3. Target market fit (reaches academics?)
  4. Timeline (works in 90 days?)

6 Channels Selected:

  1. Product Hunt (launch platform)
  2. SEO/Content (organic)
  3. Google Ads (paid, high-intent)
  4. Referrals (viral)
  5. Social Media (community)
  6. Academic Partnerships (B2B)

90-Day Roadmap Structure

3 Phases × 30 Days:

Phase 1: Private Beta (Days 1-30)

  • Goal: Validate product-market fit
  • Metrics: NPS >40, activation >40%

Phase 2: Public Launch (Days 31-60)

  • Goal: Build awareness and user base
  • Metrics: 1K sign-ups, PH #1

Phase 3: Paid Acquisition (Days 61-90)

  • Goal: Prove unit economics
  • Metrics: CAC <$50, 10% conversion

5. Financial Modeling Methodology

Growth Assumptions

User Growth Rate:

  • Months 1-3: Exponential (GTM launch)
  • Months 4-6: 20% monthly
  • Months 7-12: 15% monthly
  • Months 13-24: 10% monthly

Churn Rate:

  • Early stage (M1-3): 8% monthly
  • Growth (M4-12): 5% monthly
  • Mature (M13-24): 4% monthly

Conversion Rate:

  • Month 1: 5%
  • Month 3: 8%
  • Month 6: 10%
  • Month 12+: 10% (target)

Cost Structure

COGS (Variable):

  • OpenAI API: $3.50/user/month
  • Infrastructure: $0.50/user/month
  • Total COGS: $4/user = 26.7% of revenue

Operating Costs (Mostly Fixed):

  • Product/Engineering: $25K-50K/month (scaling)
  • Sales/Marketing: $20K-70K/month (scaling)
  • G&A: $15K-25K/month
  • Total OpEx: $60K-145K/month

Revenue Projections

Formula:

MRR = Paying Users × ARPU
ARR = MRR × 12

Paying Users = (Total Users × Free-to-Paid %) - Churned

Key Milestones:

  • Month 3: $7.5K MRR (end of GTM)
  • Month 12: $90K MRR / $1.08M ARR
  • Month 24: $270K MRR / $3.24M ARR

Break-Even Analysis

Method:

Break-even when: MRR > (COGS + OpEx)

Month 9 projected:
- MRR: $45K
- COGS: $12K
- OpEx: $80K
- Net Profit: -$47K

Month 10 projected:
- MRR: $52K
- COGS: $14K
- OpEx: $85K
- Net Profit: -$47K

Month 11: Approaching break-even

6. Data Sources & Validation

Primary Sources

  • Market Size: UNESCO, World Bank, OECD, McKinsey
  • Competitor Data: Crunchbase, Product Hunt, company websites
  • Pricing: Public pricing pages
  • Industry Benchmarks: Gartner, SaaS Capital, OpenView Partners

Synthetic Data

For demonstration purposes, this project uses synthetic data for:

  • Traffic estimates (SimilarWeb-style)
  • User reviews (G2/Product Hunt-style)
  • Feature scoring (based on product analysis)

Note: In a real project, these would be replaced with:

  • Licensed data (SimilarWeb, Crunchbase)
  • Web scraping (where permissible)
  • Survey data
  • Expert interviews

7. Assumptions & Sensitivity

Key Assumptions Documented

All major assumptions are documented in outputs/reports/market_sizing_report.txt with:

  • Assumption statement
  • Rationale (why we believe it)
  • Sensitivity (how much it matters)
  • Impact if wrong (what changes)

Sensitivity Analysis

Tested key variables:

  • TAM ±50%: Changes SOM by ±50%
  • Conversion rate ±2%: Changes ARR by ±20%
  • CAC ±$10: Changes LTV/CAC by ±20%
  • Churn ±1%: Changes LTV by ±15%

Recommendation: Focus on improving conversion rate (highest ROI)

Conclusion

This methodology combines:

  • Rigorous quantitative analysis (market sizing, unit economics)
  • Qualitative research (competitive positioning, customer insights)
  • Industry benchmarks (SaaS metrics, pricing)
  • Conservative assumptions (preferring underestimation)

The result is a data-driven GTM strategy that can be pressure-tested and refined as real-world data becomes available.

Prepared by: Ayush Saxena
Date: January 2026
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