Market Intelligence

Market Intelligence

Pipeline stage: ResearchAgent | Merged from: market-research-reports, startup-trend-prediction, startup-analyst

When to Use

• Sizing a market opportunity (TAM/SAM/SOM)
• Analyzing market structure, dynamics, or competitive forces
• Predicting trend trajectory (rising / peaking / declining)
• Determining adoption curve stage and market-entry timing (enter / wait / avoid)
• Evaluating market drivers, barriers, and risks
• Running PESTLE, Porter's Five Forces, BCG Matrix, or SWOT at the market level
• Segmenting customers or mapping value chains
• Assessing Technology Readiness Levels for a market
• Performing industry-specific market analysis (SaaS, marketplace, consumer, B2B, fintech)

Do NOT use for: competitive positioning / moat analysis (use competitive-strategy), financial modeling / unit economics (use financial-modeling), team planning / hiring (use financial-modeling).

1. Market Sizing Methodology

Three complementary approaches. Use at least two and cross-check results.

1.1 Top-Down Approach

Start from total industry size, narrow by segments, geography, and reachable share.

TAM = Total industry revenue (from analyst reports, government data)
SAM = TAM x % addressable by your solution (segment, geo, channel fit)
SOM = SAM x realistic capture rate (1-5% for startups, year 1-3)

Sample Calculation (B2B SaaS, Construction PM):

Common data sources: Gartner, Forrester, IDC, Statista, IBISWorld, government census/BLS, industry associations, public company filings (10-K).

1.2 Bottom-Up Approach

Build from individual customer economics upward. More defensible for investors.

SOM = # reachable customers x avg revenue per customer x conversion rate
SAM = # total potential customers in segment x avg revenue per customer
TAM = # all possible customers globally x avg revenue per customer

Sample Calculation (Vertical SaaS):

1.3 Value Theory Approach

For new market categories where no existing market data exists.

Market Value = # people with problem x willingness to pay x frequency

Steps:

1. Quantify the pain: hours wasted, dollars lost, opportunities missed
2. Survey or interview 20-50 target users for willingness to pay
3. Estimate frequency of purchase/usage
4. Apply conservative adoption rates

Sample Calculation (AI Recruiting Platform):

1.4 Sizing Sanity Checks

Always validate market sizing with:

• Comparable company revenue: If no public company in the space exceeds $100M ARR, a $10B TAM claim needs scrutiny
• Adjacent market benchmarks: Similar markets in adjacent verticals
• Bottom-up vs top-down delta: If they differ by more than 3x, re-examine assumptions
• Growth rate plausibility: CAGR > 30% sustained for 10+ years is rare outside AI/biotech
• Explicit assumptions: Document who pays, how much, and why you can reach them

1.5 Market Sizing by Stage

2. Market Analysis Frameworks

2.1 Porter's Five Forces

Rate each force High / Medium / Low with specific rationale.

Overall Market Attractiveness:

• 0-1 forces High: Very attractive market
• 2 forces High: Moderately attractive
• 3+ forces High: Challenging market, need strong differentiation

2.2 PESTLE Analysis

Analyze each dimension with current trends and 1-3 year impact.

2.3 BCG Growth-Share Matrix

Plot market segments or product lines on two axes:

                    HIGH Market Share    LOW Market Share
HIGH Market Growth   Stars               Question Marks
LOW Market Growth    Cash Cows           Dogs

2.4 Value Chain Analysis

Map the industry value chain to identify where value is created and captured.

[Raw Inputs] -> [Component Makers] -> [Assembly/Platform] -> [Distribution] -> [End Customer]
     5%              15%                   35%                  25%              20%

Key questions per stage:

• Where are margins highest?
• Where is consolidation occurring?
• Where can technology disrupt?
• Where are switching costs highest?

2.5 SWOT (Market-Level)

Market-level SWOT focuses on the market itself, not a specific company.

3. Trend Analysis & Timing

3.1 Leading vs Lagging Indicators

Separate signal types to avoid confusing hype with real adoption.

3.2 Signal vs Noise Framework

Strong Signals (High Confidence):

Moderate Signals (Validate):

Weak Signals (Monitor Only):

Noise Filters -- Exclude from prediction:

• Single viral tweet without follow-up
• PR-driven announcements without product
• Predictions from parties with financial interest
• Old data recycled as "new trend"

Multiple signals required: Funding + Hiring + GitHub activity = Strong signal. Just media coverage = Hype, validate further.

3.3 Rogers Diffusion Model

3.4 Bass Diffusion Model (Quantitative)

Mathematical model for predicting adoption timing.

F(t) = [1 - e^(-(p+q)*t)] / [1 + (q/p) * e^(-(p+q)*t)]

Where:
  F(t) = Fraction of market adopted by time t
  p    = Coefficient of innovation (external influence)
  q    = Coefficient of imitation (internal/word-of-mouth)
  t    = Time since introduction

Parameters by Market Type:

3.5 Gartner Hype Cycle Mapping

3.6 Hype-Cycle Defenses

Before accepting any trend as real, apply these filters:

• Falsification: What evidence would prove the trend is NOT real?
• Base rates: How often do similar trends reach mass adoption? Use reference class forecasting.
• Adoption constraints: Distribution channels, customer budgets, switching costs, compliance requirements, implementation complexity.

3.7 Cycle Pattern Library

Technology Cycles (7-10 years):

Market Cycles (5-7 years):

Business Model Cycles (3-5 years):

4. Adoption Curve Positioning

4.1 Position Identification

4.2 Timing Decision Framework

4.3 Market Timing ROI Impact

Timing ROI Formula:

Timing_ROI = (Baseline_CAC / Actual_CAC) x Market_Share_Captured

Example: Enter at Early Majority (CAC = $100) vs Late Majority (CAC = $250):

• Early: $100 CAC, 15% share -> ROI factor = 1.0 x 0.15 = 0.15
• Late: $250 CAC, 5% share -> ROI factor = 0.4 x 0.05 = 0.02
• 7.5x better outcome from optimal timing

4.4 Key Timing Principles

History rhymes. Past patterns repeat with new technology:

• Client-server -> Web apps -> Mobile -> On-device
• Mainframe -> PC -> Cloud -> Distributed
• Manual -> Scripted -> Automated -> Autonomous

Timing beats being right. Being right about a trend but wrong about timing = failure:

• Too early: Market not ready, burn runway
• Too late: Established players, commoditized
• Just right: Ride the wave

Predictions are living documents:

• Revisit quarterly
• Track accuracy over time
• Adjust for new data
• Document what changed and why

5. Market Dynamics

5.1 Growth Drivers & Inhibitors

Driver categories to evaluate:

Top 5-10 drivers should be quantified with estimated impact on market growth rate.

5.2 Customer Segmentation

5.3 Technology Readiness Levels (TRL)

5.4 Risk Assessment (Market-Level)

Risk Heatmap: Probability vs Impact

Risk categories to evaluate:

• Market risk: Demand fails to materialize, market contracts
• Competitive risk: Incumbent response, new entrant flooding
• Regulatory risk: Unfavorable regulation, compliance cost
• Technology risk: Platform dependency, tech obsolescence
• Execution risk: Talent scarcity, channel access
• Financial risk: Funding environment, customer budget cuts

6. Data Sources & Validation

6.1 Primary Data Sources

6.2 Data Quality Requirements

• Currency: Data no older than 2 years (prefer current year)
• Sourcing: All statistics attributed to specific sources with dates
• Validation: Cross-reference 2+ independent sources for key claims
• Assumptions: All projections state underlying assumptions explicitly
• Limitations: Acknowledge data gaps and uncertainty ranges

6.3 Reference Class Forecasting (Outside View)

Use historical analogs to calibrate predictions and avoid overconfidence.

6.4 Hype-Cycle Defenses Checklist

Before committing to a trend thesis:

• Falsification criteria documented: what would disprove this trend?
• Base rate checked: how often do analogous trends succeed?
• Adoption constraints listed: distribution, budget, switching costs, compliance, implementation complexity
• Leading indicators identified (not just lagging/media)
• 3+ independent signal types confirm the trend
• At least 1 primary source (regulators, standards bodies, platform docs, filings)
• Assumptions are explicit and time-bound
• Review cadence set (quarterly recommended)

7. Industry-Specific Approaches

7.1 SaaS

Key metrics to size and analyze:

• MRR/ARR, Net Dollar Retention (NDR), CAC payback period
• Logo churn vs revenue churn
• ACV distribution: SMB $1-10K, Mid-market $10-100K, Enterprise $100K+

Market sizing approach:

SAM = # companies in ICP x avg seats x price/seat/year

Benchmarks by stage:

SaaS-specific market signals:

• Category creation on G2/Capterra
• Gartner Magic Quadrant appearance
• Enterprise pilot programs

7.2 Marketplace

Key metrics to size and analyze:

• GMV, take rate, liquidity, supply/demand balance
• Typical take rates: 5-15% (goods), 15-30% (services), 20-40% (managed)

Market sizing approach:

TAM = Total spend in category (both sides)
SAM = Addressable spend that can flow through marketplace
SOM = SAM x realistic take rate x penetration

Marketplace-specific dynamics:

• Chicken-and-egg: Which side to seed first?
• Multi-homing risk: Can users easily use competitors simultaneously?
• Geographic density requirements for local marketplaces
• Disintermediation risk: Will users go direct after matching?

7.3 Consumer

Key metrics to size and analyze:

• DAU/MAU ratio, retention curves (D1/D7/D30), virality coefficient (K-factor)
• ARPU, conversion rate (free to paid), engagement depth

Market sizing approach:

TAM = # people with problem x frequency x willingness to pay
SAM = TAM x smartphone/internet penetration x demographic filter
SOM = SAM x realistic download/signup rate x activation rate

Consumer-specific signals:

• App store ranking trends
• Social media organic mentions (not sponsored)
• Influencer adoption without sponsorship
• Retention curve shape: concave = good, convex = churning

7.4 B2B (Non-SaaS)

Key metrics to size and analyze:

• ACV, sales cycle length, win rate, pipeline velocity
• Typical sales cycles: SMB 1-3 months, Mid-market 3-6 months, Enterprise 6-18 months

Market sizing approach:

SAM = # companies in target segment x avg deal size
SOM = SAM x realistic win rate x sales capacity

B2B-specific dynamics:

• Champion/economic buyer alignment
• Procurement process complexity
• Implementation/integration requirements
• Reference customer importance

7.5 Fintech

Key metrics to size and analyze:

• Transaction volume, revenue per transaction, regulatory cost
• Net interest margin (lending), AUM growth (wealth), loss ratios (insurance)

Market sizing approach:

TAM = Total financial flows in category
SAM = Flows addressable by your license/geography/segment
SOM = SAM x realistic capture rate (often <1% in Year 1)

Fintech-specific considerations:

• Regulatory licensing requirements and timeline (6-24 months)
• Compliance cost as % of revenue (often 15-25% early stage)
• Partnership vs direct licensing trade-offs
• Embedded finance as distribution channel
• Trust and security as adoption barriers

8. Prediction Methodology

8.1 Building a Trend View

Step 1: Define the Decision

• What decision: enter / wait / avoid?
• Horizon: 1-2 years (standard for startups)
• Target buyer and market segment

Step 2: Collect Signals

• Gather 3+ independent signals including at least 1 primary source
• Separate leading vs lagging indicators (see section 3.1)
• Apply signal weights (see section 3.2)

Step 3: Apply Hype-Cycle Defenses

• Document falsification criteria
• Check base rates via reference class forecasting
• List adoption constraints with severity

Step 4: Position on Adoption Curve

• Map current penetration to Rogers model (section 3.3)
• Estimate timing via Bass model parameters (section 3.4)
• Cross-reference with Gartner Hype Cycle phase (section 3.5)

Step 5: Generate Prediction

## Prediction: [TOPIC]

Thesis: [1-2 sentence prediction]
Confidence: High / Medium / Low
Timing: [When this will happen]
Evidence: [3-5 supporting data points]
Counter-evidence: [What could invalidate]
Decision: Enter / Wait / Avoid
Review cadence: Quarterly

Step 6: Identify Opportunities

8.2 Trend Awareness Protocol

When analyzing market trends or timing, use current data:

1. Search for "[technology/market] trends [current year]"
2. Search for "[technology] adoption curve [current year]"
3. Search for "[market] market size forecast [current year]"
4. Search for "[technology] vs alternatives [current year]"

Report after searching:

• Current state: Where is the technology/market NOW on adoption curve
• Trajectory: Growing, peaking, or declining based on data
• Timing window: Is now early, optimal, or late to enter
• Evidence quality: Distinguish hype from real adoption signals

8.3 What Good Looks Like

A complete market intelligence analysis includes:

• Decision: One clear enter/wait/avoid call with horizon and owner
• Evidence: 3+ independent signal types (not just media) with explicit confidence
• Market size: TAM/SAM/SOM with assumptions, both bottom-up and top-down cross-checked
• Sensitivity ranges: Optimistic/base/pessimistic scenarios
• Falsification criteria: Documented and reviewable
• Constraints: Adoption blockers listed with mitigations
• Capital efficiency: Break-even path documented (2026 investor priority)
• Cadence: Quarterly refresh with "what changed" and accuracy notes

9. Quality Standards

Analysis Requirements

• Use credible, cited data sources with publication dates
• Document all assumptions clearly and explicitly
• Provide realistic, conservative estimates (not optimistic)
• Validate with multiple methods when possible (bottom-up + top-down)
• Include relevant industry benchmarks
• Present findings in structured tables and frameworks
• Offer actionable recommendations tied to decisions
• Acknowledge limitations, risks, and data gaps

Common Pitfalls to Avoid

• Treating "attention" (media/social) as "adoption" (revenue/usage)
• Market sizing without explicit assumptions and bottom-up checks
• Extrapolating from a single platform, influencer, or funding headline
• Using overly optimistic penetration rates (>5% Year 1 for most startups)
• Ignoring adoption constraints (distribution, budget, switching, compliance)
• Conflating TAM with SAM -- investors see through this immediately
• Presenting a single point estimate without ranges or scenarios
• Making unsupported claims or skipping validation steps
• Providing generic advice without stage and industry context

Stage Awareness

• Pre-seed: Focus on product-market fit signals, not revenue optimization
• Seed: Balance growth and efficiency, establish unit economics baseline
• Series A: Prove scalable, repeatable model with strong unit economics

Investor Expectations by Round

10. Behavioral Traits & Response Methodology

10.1 Analyst Behavioral Traits

When performing market intelligence work, activate these traits:

• Startup-focused: Understand early-stage constraints and realities (budget, team size, time pressure)
• Data-driven: Always ground recommendations in data and benchmarks
• Conservative: Use realistic, defensible assumptions -- not optimistic projections
• Pragmatic: Balance rigor with speed and resource constraints
• Transparent: Document assumptions and limitations clearly
• Founder-friendly: Communicate in plain language, not jargon
• Action-oriented: Provide specific next steps and recommendations
• Investor-aware: Understand what VCs look for in each analysis at each stage
• Rigorous: Validate assumptions and triangulate findings from 2+ sources
• Honest: Acknowledge risks, data gaps, and limitations upfront

10.2 10-Step Response Methodology

When answering market intelligence questions, follow this sequence:

1. Understand context -- Company stage, business model, specific question, decision to be made
2. Activate relevant section -- Reference the appropriate framework section from this skill
3. Gather necessary data -- Use web search when current data is needed; cite sources with dates
4. Apply frameworks -- Use proven methodologies (Porter's, PESTLE, Bass, Rogers, etc.)
5. Calculate and analyze -- Show work, document assumptions, include formulas
6. Validate findings -- Cross-check with benchmarks, alternatives, and bottom-up vs top-down
7. Present clearly -- Use tables, structured output, clear section headers
8. Provide recommendations -- Actionable next steps with rationale
9. Cite sources -- Always include data sources and publication dates
10. Acknowledge limitations -- Be transparent about assumptions, data quality, and confidence level

10.3 Output Format Guidance

For Market Sizing Analysis:

• Clear headers and subheaders
• Tables for data presentation
• Formulas shown explicitly with step-by-step calculation
• Sources cited with URLs and dates
• Assumptions documented in a dedicated section
• Benchmarks referenced for comparison
• Next steps provided

For Calculations:

• Formula used
• Input values with sources
• Step-by-step calculation
• Result with units
• Interpretation of result
• Benchmark comparison

For Recommendations:

• Specific, actionable steps
• Rationale for each recommendation
• Expected outcomes
• Resource requirements
• Timeline or sequencing
• Risks and mitigation

10.4 Example Interactions

Market Sizing:

• "What's the TAM for a B2B SaaS project management tool for construction companies?"
• "Calculate the addressable market for an AI-powered recruiting platform"
• "Help me size the opportunity for a marketplace connecting freelance designers with startups"

Trend Analysis:

• "Is the AI agent market rising, peaking, or declining?"
• "When should we enter the vertical SaaS market for logistics?"
• "What adoption stage is the climate tech sector in right now?"

Competitive Landscape:

• "Analyze the competitive landscape for email marketing automation"
• "How should we position against Salesforce in the construction vertical?"
• "What are the barriers to entry in the fintech lending space?"

Market Timing:

• "Should we enter the AI coding tools market now or wait?"
• "Is the developer tools market too crowded for a new entrant?"
• "What's the timing window for embedded finance in healthcare?"

Metrics & Benchmarks:

• "What metrics should I track for my marketplace startup?"
• "Is my CAC of $2,500 and LTV of $8,000 good for enterprise SaaS?"
• "What growth rate do Series A investors expect for B2B SaaS?"

Strategy:

• "Should I target SMBs or enterprise customers first?"
• "How do I decide between freemium and sales-led go-to-market?"
• "What pricing strategy makes sense for my stage?"

10.5 Special Considerations

Founder Context:

• First-time founders need more education and framework explanation
• Repeat founders want faster, more tactical analysis
• Technical founders may need GTM and business model guidance
• Business founders may need product and technical strategy help

Industry Nuances:

• SaaS: Focus on MRR, NDR, CAC payback
• Marketplace: Emphasize GMV, take rate, liquidity
• Consumer: Prioritize retention, virality, engagement
• B2B: Highlight ACV, sales efficiency, win rate
• Fintech: Regulatory licensing, compliance cost, trust barriers

11. Do / Avoid Checklist

Do

• Use a decision horizon (enter / wait / avoid) and revisit quarterly
• Track leading indicators and adoption constraints, not just hype
• Write assumptions explicitly and update them when data changes
• Require 3+ independent signals, including at least 1 primary source (standards, regulators, platform docs)
• Separate leading vs lagging indicators; don't overfit to social/media noise
• Add hype-cycle defenses: falsification, base rates, and adoption constraints
• Cross-check bottom-up and top-down market sizing (flag if delta > 3x)
• Document confidence levels explicitly (strong / medium / weak)
• Include sensitivity ranges (optimistic / base / pessimistic scenarios)
• Set a quarterly review cadence with "what changed" and accuracy notes

Avoid

• Extrapolating from a single platform, influencer, or funding headline
• Treating "attention" (media/social mentions) as "adoption" (revenue/usage)
• Market sizing without explicit assumptions and bottom-up checks
• Using overly optimistic penetration rates (> 5% Year 1 for most startups)
• Conflating TAM with SAM -- investors see through this immediately
• Ignoring adoption constraints (distribution, budget, switching, compliance)
• Presenting a single point estimate without ranges or scenarios
• Making unsupported claims or skipping validation steps
• Providing generic advice without stage and industry context
• Forgetting to cite data sources with publication dates

12. Quick Reference Templates

12.1 Trend View Template

Use this template to quickly structure a trend analysis:

## Decision Context

- Decision: enter / wait / avoid
- Horizon: {{HORIZON}}
- Buyer and market: {{BUYER}} / {{MARKET}}

## Signals Collected

| Signal | Type | What it indicates | Source | Confidence |
|--------|------|-------------------|--------|------------|
| {{SIGNAL_1}} | Leading / Lagging / Weak | {{INDICATION}} | {{SOURCE}} | High / Med / Low |
| {{SIGNAL_2}} | | | | |
| {{SIGNAL_3}} | | | | |

## Hype-Cycle Defenses

- Falsification: {{WHAT_WOULD_DISPROVE_THIS}}
- Base rate: {{HOW_OFTEN_DO_SIMILAR_TRENDS_SUCCEED}}
- Adoption constraints: {{DISTRIBUTION, BUDGET, SWITCHING, COMPLIANCE}}

## Market Sizing Sanity Check

- Bottom-up: #customers x WTP x realistic penetration = {{RESULT}}
- Top-down: Total market x segment % x capture rate = {{RESULT}}
- Delta: {{RATIO}} (flag if > 3x)

12.2 Reference Class Forecasting Template

Use historical analogs to calibrate predictions and avoid overconfidence.

## Reference Class Forecast: {{TOPIC}}

| Item | Notes |
|------|-------|
| Milestone | {{e.g., 10% enterprise adoption, $100M ARR category, regulatory clearance}} |
| Analog set | {{List 5-10 similar past trends (same buyer, budget, compliance, distribution)}} |
| Base rate | {{x/y analogs reached milestone within horizon}} |
| Timing range | p10: {{FAST}} / p50: {{MEDIAN}} / p90: {{SLOW}} |
| Adjustment factors | {{What differs now vs analogs: distribution, budgets, compliance, infra}} |
| Net assessment | {{Higher / Lower / Same probability as base rate, and why}} |

12.3 Prediction Template

## Prediction: {{TOPIC}}

Domain: {{Technology / Market / Business Model}}
Lookback Period: {{2-3 years}}
Prediction Horizon: {{1-2 years}}
Geography: {{Global / Region-specific}}
Industry: {{Horizontal / Specific vertical}}

Thesis: {{1-2 sentence prediction}}
Confidence: High / Medium / Low
Timing: {{When this will happen}}
Evidence:
  1. {{Supporting data point 1}}
  2. {{Supporting data point 2}}
  3. {{Supporting data point 3}}
Counter-evidence: {{What could invalidate this prediction}}
Decision: Enter / Wait / Avoid
Review cadence: Quarterly
Next review: {{DATE}}

12.4 Opportunity Matrix Template

| Opportunity | Timing Window | Competition | Constraints | Action |
|-------------|---------------|-------------|-------------|--------|
| {{OPP_1}} | {{WINDOW}} | Low/Med/High | {{Key blockers}} | Build / Watch / Avoid |
| {{OPP_2}} | {{WINDOW}} | Low/Med/High | {{Key blockers}} | Build / Watch / Avoid |
| {{OPP_3}} | {{WINDOW}} | Low/Med/High | {{Key blockers}} | Build / Watch / Avoid |

13. Deep Research Report Framework

Absorbed from the market-research-reports skill. Use this section when producing comprehensive, consulting-firm-quality market research reports (50+ pages).

13.1 Report Structure (11 Core Chapters)

Front Matter (~5 pages)

Core Analysis (~35 pages)

…(truncated)