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Evan Hu ed595230cf feat(hands): improve 6 lower-scoring hands — system prompts and SKILL.md depth

- browser: 5→7 phases, SPA detection, error recovery decision tree, 3 new settings
- strategist: framework integration methodology, 7 anti-patterns, uncertainty quantification
- lead: remove clip language, add BANT/MEDDIC qualification, 3 new settings + CRM export
- researcher: CRAAP→CRAAP+, 7-step conflict resolution, 6-item cognitive bias audit
- collector: concrete change classification (structural/content/metadata), 5-factor scoring, 2 new settings
- apitester: OWASP Top 10 checklist, 4 load test profiles, contract testing phase, GraphQL/Webhook patterns

2026-03-23 00:31:13 +09:00

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name, version, description, runtime

name	version	description	runtime
strategist-hand-skill	1.0.0	Expert knowledge for AI business strategy -- frameworks, market analysis, competitive intelligence, and strategic planning methodologies	prompt_only

Business Strategy Expert Knowledge

Strategic Analysis Frameworks

SWOT Analysis

Map internal and external factors:

	Helpful	Harmful
Internal	Strengths	Weaknesses
External	Opportunities	Threats

Best practices:

Be specific: "Strong brand recognition in enterprise segment" not just "Good brand"
Prioritize: Rank items by impact
Cross-reference: Look for SO (strength-opportunity) and WT (weakness-threat) combinations
Action-oriented: Every SWOT item should suggest a strategic response
Time-bound: Note whether each factor is stable, strengthening, or weakening

SWOT Cross-Impact Matrix — The real value of SWOT is in the intersections:

	Opportunities	Threats
Strengths	SO strategies: Use strengths to capture opportunities (offensive)	ST strategies: Use strengths to neutralize threats (defensive)
Weaknesses	WO strategies: Fix weaknesses to unlock opportunities (investment)	WT strategies: Minimize weaknesses exposed by threats (survival)

Prioritize: SO strategies first (highest ROI), then ST (protect position), then WO (selective investment), last WT (only if existential).

Porter's Five Forces

Analyze industry attractiveness:

Threat of New Entrants: Capital requirements, economies of scale, brand loyalty, access to distribution, regulatory barriers
Bargaining Power of Suppliers: Concentration, switching costs, differentiation, forward integration threat
Bargaining Power of Buyers: Concentration, switching costs, price sensitivity, backward integration threat
Threat of Substitutes: Performance trade-offs, switching costs, buyer propensity to substitute
Competitive Rivalry: Number of competitors, industry growth, fixed costs, differentiation, exit barriers

Rate each force: Low / Medium / High with supporting evidence.

Dynamic Five Forces: Forces change over time. For each force, note the trend direction (strengthening/stable/weakening) and the trigger event that could shift it. A force rated "Low" today with a strengthening trend deserves more attention than a stable "Medium" force.

PESTEL Analysis

Macro-environmental scanning:

Factor	Key Questions
Political	Government stability? Trade policies? Regulation changes?
Economic	GDP growth? Interest rates? Inflation? Exchange rates?
Social	Demographics? Cultural trends? Consumer behavior shifts?
Technological	Innovation pace? R&D spending? Automation trends?
Environmental	Climate regulations? Sustainability demands? Resource scarcity?
Legal	Employment law? IP protection? Competition law? Data privacy?

Framework Integration Methodology

Individual frameworks are lenses. Strategic insight comes from combining them. Here is how to synthesize multiple frameworks into a unified analysis:

The Integration Cascade — Use frameworks in dependency order:

Step 1: PESTEL (macro context)
  → Identifies external forces shaping the industry
  → Output: Which macro factors matter most? What is changing?

Step 2: Porter's Five Forces (industry structure)
  → PESTEL outputs feed directly into Porter's forces
  → Example: "AI adoption accelerating" (PESTEL-Tech) → "Threat of new entrants rising" (Porter)
  → Output: How attractive is this industry? Where is structural power?

Step 3: SWOT (company positioning within industry)
  → Porter's outputs define the external O/T quadrants
  → Internal assessment (S/W) is company-specific
  → Output: Where does this company sit relative to industry forces?

Step 4: Strategic Options Generation
  → SWOT cross-impact matrix generates candidate strategies
  → Porter's forces identify which strategies are structurally viable
  → PESTEL trends determine timing and urgency

Cross-Framework Contradiction Resolution: When frameworks disagree, do not average or ignore — investigate:

PESTEL says favorable + Porter says unattractive → Macro tailwind but bad industry structure (e.g., restaurant industry: everyone eats, but margins are terrible)
SWOT says strong + Porter says high rivalry → Company advantage may erode faster than expected
Resolution: State both findings, explain the tension, and let the tension inform the recommendation (e.g., "Enter but with a differentiation strategy that exploits the macro trend while avoiding head-on competition")

Synthesis Quality Checklist:

Does the conclusion follow logically from framework outputs, or did you skip to a preferred answer?
Did you weight frameworks by relevance (PESTEL matters more for market entry; Porter matters more for competitive strategy)?
Are the frameworks consistent? If not, is the inconsistency explained?
Could someone reconstruct your reasoning by reading the framework outputs alone?

Market Sizing (TAM-SAM-SOM)

TAM (Total Addressable Market): Total market demand for a product/service.

TAM = (Total potential customers) x (Annual revenue per customer)

SAM (Serviceable Addressable Market): TAM segment you can reach.

SAM = TAM x (% you can realistically serve given geography, channels, capability)

SOM (Serviceable Obtainable Market): SAM you can realistically capture.

SOM = SAM x (Expected market share %)

Methods:

Top-down: Start with industry reports, narrow to your segment
Bottom-up: Start with unit economics, multiply by reachable customers
Value theory: How much value does the solution create? What % can you capture?

Worked Example: Netflix vs Blockbuster (2007)

SWOT Analysis for Netflix:

Category	Item	Evidence
Strength	Streaming technology	First-mover in online streaming; DVD-by-mail eliminated late fees
Strength	Recommendation engine	Personalized suggestions increased engagement 60%
Weakness	Limited content library	Dependent on studio licensing deals
Weakness	High content acquisition cost	Margins compressed by licensing fees
Opportunity	Broadband adoption	US broadband penetration growing 30% YoY
Opportunity	International expansion	Untapped markets in Europe and Asia
Threat	Studio-owned platforms	Studios could bypass Netflix and go direct-to-consumer
Threat	Piracy	Illegal streaming as free alternative

Porter's Five Forces for Video Streaming (2007):

Force	Rating	Rationale
New Entrants	2/5	High capital needed for content + tech infrastructure
Supplier Power	4/5	Studios control content; few alternatives
Buyer Power	3/5	Low switching cost but high engagement reduces churn
Substitutes	2/5	No equivalent convenience at the time
Rivalry	3/5	Blockbuster dominant but slow to innovate

Strategic Insight: Netflix's technology moat + Blockbuster's organizational inertia = classic disruption pattern. Blockbuster's $6B revenue masked its vulnerability to a $1B challenger with superior unit economics. Confidence: High (90%) — outcome confirmed by Blockbuster's 2010 bankruptcy.

Competitive Positioning

Positioning Map: Plot competitors on 2 key dimensions (e.g., price vs. quality, breadth vs. depth).

Competitive Advantage Sources:

Cost leadership: Lower cost structure than competitors
Differentiation: Unique value proposition
Focus/Niche: Serve a narrow segment exceptionally well
Network effects: Value increases with more users
Switching costs: Expensive or difficult for customers to leave

Strategic Planning Methodologies

OKR Framework (Objectives and Key Results)

Objective: [What you want to achieve -- qualitative, inspiring]
  KR1: [Measurable outcome 1]
  KR2: [Measurable outcome 2]
  KR3: [Measurable outcome 3]

Rules:

3-5 objectives per period
2-5 key results per objective
Key results must be measurable (not tasks)
Score 0.0 to 1.0; target 0.7 average (stretch goals)

Strategy Canvas (Blue Ocean)

Compare your offering vs competitors across key factors:

Factor          | Competitor A | Competitor B | Your Offering
Price           | High         | Medium       | Low
Quality         | High         | Medium       | High
Ease of Use     | Low          | Medium       | High
Features        | Many         | Few          | Moderate
Support         | Good         | Poor         | Excellent

Identify factors to:

Eliminate: Remove factors the industry takes for granted
Reduce: Lower factors below industry standard
Raise: Increase factors above industry standard
Create: Introduce factors the industry has never offered

Decision Matrix

Option	Criterion 1 (w:30%)	Criterion 2 (w:25%)	Criterion 3 (w:25%)	Criterion 4 (w:20%)	Weighted Score
A	4	3	5	2	3.55
B	3	5	3	4	3.70
C	5	2	4	3	3.55

Competitive Intelligence

Information Sources

Source Type	Examples	Reliability
Public filings	SEC filings, annual reports	High
Press releases	Company announcements	Medium-High
Job postings	LinkedIn, careers pages	Medium
Product pages	Websites, pricing pages	Medium
Review sites	G2, Capterra, Trustpilot	Medium
Social media	LinkedIn, Twitter, Reddit	Medium-Low
Industry reports	Gartner, Forrester, McKinsey	High
Patents	USPTO, Google Patents	High
News coverage	TechCrunch, Bloomberg	Medium

Competitor Tracking Template

Company: [Name]
Last Updated: YYYY-MM-DD

Product: [Core offering]
Pricing: [Model and price points]
Positioning: [How they describe themselves]
Target Market: [Who they sell to]
Key Differentiators: [What makes them unique]
Recent Moves: [Product launches, funding, hires, partnerships]
Strengths: [What they do well]
Weaknesses: [Where they fall short]
Estimated Revenue: [If available]
Employee Count: [Growth indicator]

Report Templates

Executive Brief Template

# Strategic Brief: [Topic]
**Date**: YYYY-MM-DD | **Author**: Strategist Hand

## Situation
[2-3 sentences describing the current state]

## Key Findings
1. [Most important finding]
2. [Second finding]
3. [Third finding]

## Recommendation
[Clear, actionable recommendation with rationale]

## Next Steps
- [ ] [Action item 1] -- [Owner] -- [Due date]
- [ ] [Action item 2] -- [Owner] -- [Due date]

## Risk Factors
- [Key risk 1 and mitigation]
- [Key risk 2 and mitigation]

Strategy Memo Template (SCR Format)

# Strategy Memo: [Topic]

## Situation
[What is happening -- neutral facts]

## Complication
[Why this matters -- the challenge or opportunity]

## Resolution
[What we should do about it -- the recommendation]

## Evidence
[Supporting data and analysis]

## Implementation
[How to execute the recommendation]

Worked Examples

Example 1: B2B SaaS Market Entry into Japan

Context: A US-based B2B SaaS company (project management tool, $15M ARR, 200 employees) evaluating entry into the Japanese market.

PESTEL Analysis — Japan B2B SaaS (2025):

Factor	Assessment	Impact	Score (1-5)
Political	Stable democracy; strong US-Japan trade relations; Digital Agency pushing government digitization	Positive	4
Economic	GDP $4.2T; weak yen (150 JPY/USD) makes USD-priced SaaS expensive; enterprise IT spend growing 4% YoY	Mixed	3
Social	Aging workforce accelerates automation need; consensus-driven decision making lengthens sales cycles (avg 6-9 months); strong preference for local-language support	Critical constraint	2
Technological	High internet penetration (93%); cloud adoption lagging US by 3-5 years but accelerating; 5G rollout complete in urban areas	Opportunity	4
Environmental	ESG reporting mandated for listed companies from 2023; sustainability-linked procurement gaining traction	Moderate opportunity	3
Legal	APPI (Act on Protection of Personal Information) requires data residency consideration; strict labor laws affect HR SaaS	Compliance cost	2

PESTEL Score: 18/30 — Moderately favorable. Key risk: social/cultural factors demand significant localization investment.

Porter's Five Forces — Japan Project Management SaaS:

Force	Rating	Evidence
New Entrants	2/5	High localization cost ($500K-$1M); relationship-driven market favors incumbents
Supplier Power	1/5	Cloud infrastructure (AWS Tokyo, Azure Japan) is commodity; no supplier concentration
Buyer Power	4/5	Enterprise buyers demand customization; long procurement cycles give buyers leverage; RFP-driven purchasing
Substitutes	3/5	Excel/spreadsheet culture deeply entrenched; domestic tools (Backlog, Jooto) have cultural fit advantage
Rivalry	4/5	Asana, Monday.com, Notion already present; domestic players Backlog (Nulab) and Redmine have loyal bases

Go-to-Market Recommendation:

Strategy: Partner-Led Entry (not direct sales)
Timeline: 18 months to first enterprise deal

Phase 1 (Months 1-6): Foundation
  - Hire Country Manager (must be bilingual Japanese national)
  - Full UI/UX localization (not just translation — date formats, name order, honorifics)
  - Achieve ISMAP certification (required for government/enterprise procurement)
  - Data residency: Deploy on AWS Tokyo region
  - Budget: $800K

Phase 2 (Months 4-12): Channel Development
  - Sign 2-3 SIer (System Integrator) partners: target NTT Data, Fujitsu, NEC
  - Japanese SIers control 60% of enterprise software purchasing decisions
  - Co-develop integration with domestic tools (kintone, Sansan, freee)
  - Budget: $600K (partner enablement + integration development)

Phase 3 (Months 8-18): Market Penetration
  - Target mid-market first (500-2000 employees) — faster decision cycles than enterprise
  - Launch at Japan IT Week (Spring/Autumn) and SaaS Industry Conference
  - Content marketing: Japanese-language case studies, webinars with local customers
  - Target: 20 paying customers, $500K ARR by month 18
  - Budget: $400K

Total Investment: $1.8M over 18 months
Break-even: Month 30 (projected)

Decision: Proceed with caution. The $4.2T economy and cloud adoption tailwind justify the investment, but only with proper localization and channel strategy. Direct sales without SIer partnerships has a historically high failure rate (>70% for foreign SaaS in Japan).

Example 2: Competitive Response — Major Player Enters Your Niche

Context: You run a $5M ARR vertical SaaS for veterinary clinics (500 customers, 15% market share). Salesforce just announced "Salesforce for Veterinary" — a vertical solution built on their platform.

Threat Assessment:

Dimension	Your Position	Salesforce	Gap
Brand recognition	Niche leader	Global enterprise brand	Large — but irrelevant in vet niche
Product depth	Purpose-built (8 years domain expertise)	Horizontal platform with vertical skin	Strong advantage
Price point	$200/mo per clinic	$500/mo estimated (Salesforce pricing)	2.5x cheaper
Implementation time	2 weeks	3-6 months (typical SF implementation)	Strong advantage
Integration depth	Deep PMS/PIMS integration	API-based, requires middleware	Strong advantage
Sales motion	Direct + word-of-mouth	Enterprise sales team + SI partners	Different segments
Switching cost for your customers	Moderate (data migration + retraining)	High (Salesforce ecosystem lock-in)	Neutral

Strategic Response Framework:

IMMEDIATE (Week 1-4): Defend the Base
  1. Customer communication campaign
     - CEO letter to all 500 customers: "Our commitment to veterinary"
     - Emphasize: purpose-built > horizontal platform
     - Announce product roadmap acceleration

  2. Lock in at-risk accounts
     - Identify top 50 accounts by revenue
     - Offer annual contract discounts (15-20% for 2-year commitment)
     - Schedule QBRs with all enterprise accounts within 30 days

  3. Competitive battle card
     - Create internal sales doc: feature-by-feature comparison
     - "Why vets choose us over Salesforce" — 5 key differentiators
     - Objection handling for "shouldn't we go with the safe choice?"

SHORT-TERM (Month 2-6): Deepen the Moat
  4. Accelerate domain-specific features
     - AI-powered treatment plan suggestions (Salesforce can't match this)
     - Telemedicine integration (vertical-specific)
     - Inventory management tied to treatment protocols

  5. Build switching costs
     - Launch data analytics dashboard (clinics depend on historical trends)
     - Introduce multi-location management (target growing chains)
     - API marketplace for vet-specific integrations (lab equipment, imaging)

  6. Community defense
     - Launch "Vet Tech Community" — user forum + knowledge base
     - Annual user conference (even virtual — creates tribal loyalty)
     - Customer advisory board (top 10 clinics = co-development partners)

MEDIUM-TERM (Month 6-18): Counterattack
  7. Move upmarket selectively
     - Enterprise tier for 10+ location chains ($500/mo — match SF pricing)
     - Offer white-glove migration from legacy systems
     - This is the segment Salesforce will target — contest it

  8. Geographic expansion
     - Salesforce announcement creates awareness of the category
     - Ride the wave: "Already purpose-built, already proven"
     - Target UK, Australia, Canada (English-speaking, similar vet market structure)

Pricing Response Decision Matrix:

Option	Revenue Impact	Competitive Effect	Risk
No change	Neutral	Salesforce still 2.5x more expensive	Low — price isn't the battleground
Cut prices 20%	-$1M ARR	Signals weakness; Salesforce won't match	High
Add premium tier	+$500K potential	Compete at enterprise level; justify R&D	Medium
Usage-based addon	+$300K potential	Expand ARPU without base price war	Low

Recommendation: Add premium tier + usage-based addons. Do NOT cut base prices. Salesforce's entry validates your market — use it to raise your valuation narrative ("Salesforce sees a $2B market opportunity in vet SaaS — we already own 15%").

Confidence: Medium-High (75%) — Historical pattern: when Salesforce enters verticals, purpose-built incumbents retain 80%+ of existing customers. Risk is in new customer acquisition where brand matters more.

Example 3: Platform Sunset Decision — Migrate or Maintain Legacy Product

Context: A mid-stage startup ($20M ARR) runs two products: a legacy desktop app (60% of revenue, declining 10% YoY) and a modern cloud product (40% of revenue, growing 50% YoY). Should they sunset the desktop app?

Decision Matrix:

Criterion (Weight)	Option A: Maintain Both	Option B: Sunset in 12mo	Option C: Sunset in 24mo
Revenue protection (30%)	5 — No disruption	2 — Lose 40% of legacy revenue	4 — Gradual migration
Engineering efficiency (25%)	1 — Two codebases drain resources	5 — Full focus on cloud	3 — Phased transition
Customer satisfaction (20%)	3 — Legacy stagnates	2 — Forced migration angers users	4 — Supported migration path
Market positioning (15%)	2 — Confused narrative	5 — Clear cloud-first story	4 — Transitional narrative
Financial risk (10%)	3 — Slow bleed sustainable	2 — Revenue cliff risk	4 — Manageable decline
Weighted Score	2.95	3.35	3.75

Recommendation: Option C — 24-month sunset with structured migration program.

Migration Program:
  Months 1-6:   Feature parity audit; build top 20 missing cloud features
  Months 7-12:  Migration incentive (20% discount for annual cloud commitment)
  Months 13-18: Desktop enters maintenance-only mode; no new features
  Months 19-24: End-of-life announcement; dedicated migration support team
  Month 24:     Desktop product sunsets; legacy support for 6 more months

Financial Model:
  Current state:     $12M desktop + $8M cloud = $20M ARR
  Month 12 (projected): $9M desktop + $14M cloud = $23M ARR
  Month 24 (projected): $2M desktop + $22M cloud = $24M ARR
  Month 30 (projected): $0 desktop + $26M cloud = $26M ARR

  Net ARR risk: ~$3M from non-migrating desktop customers
  Offset: Engineering savings of $1.5M/yr + faster cloud feature velocity

Financial Analysis Frameworks

Unit Economics

Core metrics every strategy should quantify:

CAC (Customer Acquisition Cost)
  = Total Sales & Marketing Spend / New Customers Acquired
  Example: $500K spend / 100 new customers = $5,000 CAC

LTV (Lifetime Value)
  = ARPU x Gross Margin % x (1 / Churn Rate)
  Example: $500/mo x 80% x (1 / 0.03) = $13,333 LTV

LTV:CAC Ratio
  Target: > 3:1 for healthy SaaS
  Example: $13,333 / $5,000 = 2.67:1 (below target — reduce CAC or increase retention)

CAC Payback Period
  = CAC / (ARPU x Gross Margin %)
  Example: $5,000 / ($500 x 0.80) = 12.5 months
  Target: < 18 months for SaaS

Unit Economics Health Check:

Metric	Danger Zone	Acceptable	Excellent
LTV:CAC	< 1:1	3:1	> 5:1
CAC Payback	> 24 months	12-18 months	< 12 months
Gross Margin	< 60%	70-80%	> 80%
Net Revenue Retention	< 90%	100-110%	> 120%
Logo Churn (monthly)	> 5%	2-3%	< 1%

Revenue Modeling

SaaS Revenue Waterfall:

Beginning ARR:                    $10,000,000
  + New Business:                 +$3,000,000  (new logos)
  + Expansion:                    +$1,500,000  (upsell/cross-sell)
  - Contraction:                    -$500,000  (downgrades)
  - Churn:                        -$1,200,000  (lost customers)
  = Ending ARR:                   $12,800,000

Net New ARR:         $2,800,000
Net Revenue Retention:   113%  = ($10M + $1.5M - $0.5M - $1.2M) / $10M
Gross Revenue Retention:  88%  = ($10M - $0.5M - $1.2M) / $10M

MRR Growth Decomposition:

MRR Growth Rate = New MRR + Expansion MRR - Churned MRR - Contraction MRR
                  ─────────────────────────────────────────────────────────
                                     Beginning MRR

Quick Ratio = (New MRR + Expansion MRR) / (Churned MRR + Contraction MRR)
Target: > 4 for high-growth SaaS

Break-Even Analysis

Break-Even Revenue = Fixed Costs / Gross Margin %

Example:
  Fixed Costs (monthly): $200K (salaries, rent, tools)
  Gross Margin: 80%
  Break-Even Revenue = $200K / 0.80 = $250K/month = $3M ARR

Break-Even Customers = Break-Even Revenue / ARPU
  = $250K / $500 = 500 customers

Scenario Table:

Scenario	Fixed Costs	Gross Margin	Break-Even ARR	Break-Even Customers
Lean	$150K/mo	85%	$2.1M	353
Base	$200K/mo	80%	$3.0M	500
Growth	$350K/mo	75%	$5.6M	933

Project Evaluation — Simplified DCF

Use for evaluating strategic investments (new market entry, build vs buy, major feature investment):

NPV = Σ [Cash Flow_t / (1 + r)^t] - Initial Investment

Where:
  r = discount rate (typically 10-15% for startups, 8-10% for established companies)
  t = year (0, 1, 2, ... n)

Worked Example — Should we build a mobile app?

Initial Investment: $500K (development cost)
Discount Rate: 12%

Year  | Incremental Revenue | Incremental Cost | Net Cash Flow | PV Factor | Present Value
------|--------------------|--------------------|---------------|-----------|-------------
  0   | $0                 | $500,000           | -$500,000     | 1.000     | -$500,000
  1   | $200,000           | $80,000            | $120,000      | 0.893     | $107,143
  2   | $400,000           | $100,000           | $300,000      | 0.797     | $239,158
  3   | $600,000           | $120,000           | $480,000      | 0.712     | $341,655
  4   | $700,000           | $130,000           | $570,000      | 0.636     | $362,204

NPV = $550,160  → Positive NPV → Project is financially justified
Payback Period: ~2.3 years (cumulative cash flow turns positive in Year 3)

Decision Rule:

NPV > 0 → Proceed (project creates value)
NPV < 0 → Reject (project destroys value)
Compare NPV across mutually exclusive options; pick highest

Go-to-Market Strategy Patterns

Growth Motion Selection

Growth Motion	Best For	Sales Cycle	CAC	Key Metric
Product-Led Growth (PLG)	Self-serve products; low price point (<$500/mo); individual users	Minutes to days	Low ($50-$500)	Activation rate, PQL conversion
Sales-Led Growth	Enterprise products; complex deployment; >$50K ACV	Weeks to months	High ($5K-$50K)	Pipeline velocity, win rate
Community-Led Growth	Developer tools; open-source; platform products	Varies	Very low ($10-$100)	Community size, contribution rate
Partner-Led Growth	Market entry; regulated industries; ecosystem products	Varies	Medium ($1K-$10K)	Partner-sourced revenue %

PLG Funnel:

Visitor → Sign-up → Activated User → PQL → Paid Customer → Expanded Account
  100%     10%        40%             25%      15%            30%

Key levers:
  - Sign-up friction: Reduce form fields, add SSO
  - Time-to-value: Get user to "aha moment" in < 5 minutes
  - PQL definition: User hits usage threshold that correlates with purchase
  - Expansion trigger: Team features, usage limits, premium capabilities

Sales-Led Funnel:

Lead → MQL → SQL → Opportunity → Proposal → Closed Won
 100%   20%   50%     60%          70%        30%

Key levers:
  - Lead quality: ICP fit scoring
  - MQL→SQL handoff: Alignment between marketing and sales
  - Discovery: Deep pain identification
  - Champion building: Enable internal advocate
  - Procurement: Legal/security review preparation

Pricing Strategy Frameworks

Value-Based Pricing (recommended for most SaaS):

1. Quantify customer value created
   Example: Your tool saves 10 hours/week per user
   Value = 10 hrs x $75/hr x 52 weeks = $39,000/year

2. Capture 10-20% of value created
   Price = $39,000 x 15% = $5,850/year = $487/month

3. Validate with willingness-to-pay research
   Van Westendorp Price Sensitivity Meter:
   - "At what price is this too expensive?" → $600/mo
   - "At what price is this a bargain?" → $200/mo
   - "At what price does it seem expensive but you'd still consider?" → $450/mo
   - "At what price does it seem too cheap to trust?" → $100/mo
   → Optimal price range: $200-$450/mo

Pricing Tier Architecture:

Tier Structure (Good-Better-Best):

| | Starter | Professional | Enterprise |
|---|---------|-------------|------------|
| Target | Individual/SMB | Mid-market team | Large organization |
| Price | $29/mo | $99/mo/user | Custom (>$500/mo) |
| Anchor role | Drive adoption | Revenue driver (~60% of revenue) | Margin driver |
| Features | Core functionality | Full platform | Custom + SLA + support |
| Support | Self-serve/email | Priority email + chat | Dedicated CSM + phone |
| Billing | Monthly/Annual | Annual preferred | Annual contract |

Design principles:
  - Middle tier should be the obvious best value
  - Top tier exists to make middle tier look reasonable (anchoring effect)
  - Feature gates should align with natural usage growth
  - Price metric should scale with value received (per user, per GB, per transaction)

Competitive Pricing Analysis:

Competitor Price Map:

Competitor    | Entry Price | Mid-Tier | Enterprise | Price Metric
-------------|-------------|----------|------------|-------------
Competitor A  | $49/mo      | $149/mo  | Custom     | Per user
Competitor B  | $0 (free)   | $99/mo   | $299/mo    | Flat rate
Competitor C  | $29/mo      | $79/mo   | Custom     | Per user
Your Product  | ???         | ???      | ???        | ???

Positioning options:
  - Price leader: 20-30% below average → requires cost advantage
  - Value leader: At or above average → requires clear differentiation
  - Premium: 30%+ above average → requires brand and feature superiority

Channel Strategy

Channel	Margin	Control	Scale	Best For
Direct sales	High (85-95%)	Full	Slow	Enterprise, complex products
Inside sales	High (80-90%)	Full	Medium	Mid-market, $5K-$50K ACV
Self-serve	Highest (95%+)	Full	Fast	PLG, low ACV
Reseller/VAR	Low (60-70%)	Medium	Medium	Regional coverage, compliance
Marketplace (AWS/Azure)	Low (70-85%)	Low	Fast	Enterprise procurement shortcuts
System Integrator	Low (50-70%)	Low	Medium	Complex implementations
Affiliate/Referral	High (80-90%)	Low	Fast	Consumer, SMB

Launch Playbook Template

LAUNCH PLAYBOOK: [Product/Feature Name]
Launch Date: YYYY-MM-DD
Launch Type: [Major / Minor / Feature / Beta]

PRE-LAUNCH (T-8 weeks to T-0)
  Week -8: Finalize positioning and messaging
  Week -6: Create sales enablement materials (battle cards, one-pagers, demo script)
  Week -4: Brief analyst relations (Gartner, Forrester) if applicable
  Week -3: Seed beta customers (5-10 design partners); collect testimonials
  Week -2: Pre-brief press/media under embargo
  Week -1: Internal all-hands; sales team training; support team training

LAUNCH DAY (T-0)
  - Blog post (SEO-optimized)
  - Email to customer base
  - Social media campaign (LinkedIn, Twitter/X)
  - Press release (if major launch)
  - Product Hunt submission (if applicable)
  - In-app announcement for existing users
  - Founder/CEO LinkedIn post (highest engagement channel)

POST-LAUNCH (T+1 to T+8 weeks)
  Week +1: Monitor activation metrics; respond to all feedback
  Week +2: Publish customer case study
  Week +4: Webinar / live demo for pipeline
  Week +6: Analyze launch metrics vs targets
  Week +8: Retrospective and iteration plan

METRICS TO TRACK:
  - Awareness: Blog views, social impressions, press mentions
  - Activation: Sign-ups, trial starts, feature adoption rate
  - Revenue: Pipeline generated, deals influenced, new ARR
  - Sentiment: NPS from beta users, social sentiment, support ticket volume

Scenario Planning

Best / Base / Worst Case Framework

Structure every major strategic decision with three scenarios:

SCENARIO PLANNING: [Decision or Initiative]

                    | Worst Case      | Base Case       | Best Case
--------------------|-----------------|-----------------|------------------
Revenue impact      | [quantify]      | [quantify]      | [quantify]
Timeline            | [duration]      | [duration]      | [duration]
Key assumption      | [what goes wrong]| [most likely]   | [what goes right]
Probability         | [15-25%]        | [50-60%]        | [15-25%]
Trigger indicators  | [early signals] | [tracking metrics]| [early signals]
Response plan       | [pivot/exit]    | [continue/adjust]| [accelerate/expand]

Worked Example — Launching a New Product Line:

SCENARIO PLANNING: Launch enterprise analytics add-on ($200/mo)

                    | Worst Case (20%)  | Base Case (55%)   | Best Case (25%)
--------------------|-------------------|-------------------|-------------------
Adoption rate       | 5% of customers   | 15% of customers  | 30% of customers
Year 1 revenue      | $120K             | $360K             | $720K
Development cost    | $400K             | $400K             | $400K
Year 1 ROI          | -70%              | -10%              | +80%
Break-even          | Never (kill it)   | Month 18          | Month 8
Key assumption      | Customers don't   | Moderate demand;  | Strong demand;
                    | see value; churn  | gradual adoption  | pulls forward
                    | increases 2%      |                   | enterprise deals

Trigger Indicators:
  Worst: < 3% adoption after 3 months; NPS < 20 for add-on
  Base:  8-12% adoption after 3 months; positive but slow pipeline
  Best:  > 20% adoption after 3 months; inbound enterprise interest

Response Plans:
  Worst: Pivot to bundling analytics into existing plan (retention play)
  Base:  Continue; invest in onboarding and customer education
  Best:  Hire dedicated analytics PM; accelerate roadmap; raise prices 20%

Expected Value Calculation:

Expected Revenue = (Worst Revenue x Worst Prob) + (Base Revenue x Base Prob) + (Best Revenue x Best Prob)
                 = ($120K x 0.20) + ($360K x 0.55) + ($720K x 0.25)
                 = $24K + $198K + $180K
                 = $402K

Expected ROI = ($402K - $400K) / $400K = 0.5%
→ Marginal on expected value alone — proceed only if strategic upside justifies the bet

Sensitivity Analysis

Identify which variables have the highest impact on outcomes:

SENSITIVITY ANALYSIS: New Market Entry

Base Case NPV: $550K

Variable            | -20% Change    | Base     | +20% Change    | Sensitivity
--------------------|---------------|----------|----------------|------------
Customer price      | $280K (-49%)  | $550K    | $820K (+49%)   | HIGH
Customer volume     | $310K (-44%)  | $550K    | $790K (+44%)   | HIGH
Churn rate          | $720K (+31%) | $550K    | $380K (-31%)   | HIGH
Development cost    | $650K (+18%) | $550K    | $450K (-18%)   | MEDIUM
CAC                 | $610K (+11%) | $550K    | $490K (-11%)   | MEDIUM
Discount rate       | $590K (+7%)  | $550K    | $510K (-7%)    | LOW

Interpretation: Price and volume are the highest-leverage variables. Strategy should prioritize pricing power and demand generation over cost optimization.

Tornado Chart Format (text representation):

Variable Impact on NPV (base = $550K):

Customer price     |████████████████████| -49% to +49%
Customer volume    |███████████████████ | -44% to +44%
Churn rate         |██████████████      | -31% to +31%
Development cost   |█████████           | -18% to +18%
CAC                |██████              | -11% to +11%
Discount rate      |████                | -7% to +7%

Risk-Adjusted Decision Making

Risk Register Template:

Risk	Probability (1-5)	Impact (1-5)	Risk Score	Mitigation	Residual Risk
Key hire doesn't work out	3	4	12	Pipeline of 2 backup candidates	6
Competitor launches first	4	3	12	Focus on differentiation not speed	8
Technical architecture fails to scale	2	5	10	Prototype load test at 10x before commit	4
Regulatory change blocks approach	1	5	5	Legal review + pivot plan documented	3
Customer demand lower than projected	3	4	12	Pre-sell to 10 design partners before building	6

Risk-Adjusted NPV:

Risk-Adjusted NPV = Base NPV x (1 - Risk Discount)

Where Risk Discount = Σ (Probability x Impact x Weight) for all material risks

Example:
  Base NPV: $550K
  Combined risk score: 0.15 (derived from risk register)
  Risk-Adjusted NPV: $550K x (1 - 0.15) = $467.5K

Industry Analysis Templates

Market Landscape Map

Plot all players in a market on two strategic dimensions:

MARKET LANDSCAPE: [Industry/Category]

                        Enterprise-Grade
                              |
                    Quadrant 2|  Quadrant 1
                    Niche     |  Market Leaders
                    Enterprise|
         Narrow ──────────────┼────────────── Broad
         Solution             |               Platform
                    Quadrant 3|  Quadrant 4
                    Point     |  Mass-Market
                    Solutions |  Platforms
                              |
                         SMB-Focused

Example — Project Management SaaS (2025):

Quadrant 1 (Leaders):   Asana, Monday.com, Smartsheet
Quadrant 2 (Niche):     Targetprocess (SAFe), Planview (PPM), Kantata (services)
Quadrant 3 (Point):     Todoist, Basecamp, Trello
Quadrant 4 (Platforms): Notion, ClickUp, Microsoft Planner

Your Position: [X]
Desired Position: [→ direction of strategic movement]

Building a Landscape Map:

Select two dimensions that represent the most important strategic trade-offs in the market
Commonly used axes:
- Price / Complexity
- Breadth of platform / Depth of solution
- Enterprise / SMB focus
- Horizontal / Vertical specialization
- Self-serve / High-touch
Plot all known competitors (minimum 8-10 for useful map)
Identify white space — under-served quadrant combinations
Draw your strategic vector — where are you moving and why?

Technology Adoption Lifecycle Positioning

THE ADOPTION CURVE:

  Innovators   Early        Early       Late        Laggards
  (2.5%)      Adopters     Majority    Majority     (16%)
              (13.5%)      (34%)       (34%)
     ___
    /   \
   /     \____
  /            \________
 /                      \_________
/                                  \___

       ↑                ↑
    THE CHASM      MAINSTREAM
    (biggest       (revenue
     risk point)    acceleration)

Positioning by Stage:

Stage	Customer Profile	Sales Approach	Pricing Strategy	Key Risk
Innovators	Tech enthusiasts; will tolerate bugs	Community; direct outreach	Free/very low; usage-based	Building for wrong use case
Early Adopters	Visionaries; want competitive advantage	Consultative selling; pilots	Value-based; ROI-justified	Chasm — can't cross to mainstream
Early Majority	Pragmatists; want proven solutions	References; case studies; demos	Competitive; published pricing	Scaling sales and support
Late Majority	Conservatives; want complete solutions	Standard procurement; RFPs	Bundled; enterprise agreements	Margin compression
Laggards	Skeptics; forced by circumstance	Compliance-driven; mandates	Legacy pricing; long contracts	Market is commoditizing

Chasm-Crossing Checklist:

□ Whole product: Does the product solve the complete use case without workarounds?
□ References: Do you have 3-5 referenceable customers in the target segment?
□ Repeatability: Can you sell and implement without founder involvement?
□ Support: Can you support customers at scale (not just white-glove)?
□ Positioning: Is the messaging pragmatist-friendly (ROI, risk reduction) not visionary?
□ Competition: Have you defined the competitive set for pragmatist comparison?
□ Pricing: Is pricing simple, transparent, and aligned with buyer expectations?

Value Chain Analysis

Decompose industry activities to find competitive advantage:

VALUE CHAIN: [Industry]

PRIMARY ACTIVITIES:
┌─────────────┬──────────────┬──────────────┬──────────────┬──────────────┐
│  Inbound    │  Operations  │  Outbound    │  Marketing   │  Service     │
│  Logistics  │              │  Logistics   │  & Sales     │              │
├─────────────┼──────────────┼──────────────┼──────────────┼──────────────┤
│ Sourcing    │ Production   │ Distribution │ Branding     │ Support      │
│ Inventory   │ Quality      │ Delivery     │ Pricing      │ Maintenance  │
│ Supplier    │ Assembly     │ Warehousing  │ Channel mgmt │ Returns      │
│ management  │ Testing      │ Order mgmt   │ Positioning  │ Training     │
└─────────────┴──────────────┴──────────────┴──────────────┴──────────────┘

SUPPORT ACTIVITIES:
┌──────────────────────────────────────────────────────────────────────────┐
│ Infrastructure: Finance, Legal, Management, Planning                     │
│ Human Resources: Recruiting, Training, Compensation, Culture             │
│ Technology: R&D, IT systems, Automation, Data analytics                  │
│ Procurement: Vendor selection, Negotiation, Contract management          │
└──────────────────────────────────────────────────────────────────────────┘

Analysis Process:

For each activity:
  1. Cost: What % of total cost does this activity represent?
  2. Value: How much does this activity contribute to customer willingness-to-pay?
  3. Capability: Rate your performance vs competitors (1-5)
  4. Strategic importance: Is this a source of differentiation? (Yes/No)

Activity             | Cost % | Value Contribution | Capability | Differentiator?
---------------------|--------|-------------------|------------|----------------
Inbound logistics    | 15%    | Low               | 3/5        | No
Operations           | 25%    | High              | 4/5        | Yes
Outbound logistics   | 10%    | Medium            | 3/5        | No
Marketing & Sales    | 30%    | High              | 2/5        | Needs improvement
Service              | 20%    | High              | 5/5        | Yes

Strategic Implications:
  - Invest: Operations (current strength + high value) and Service (strength to protect)
  - Improve: Marketing & Sales (high cost + low capability = drag on growth)
  - Optimize: Logistics (non-differentiating — minimize cost)

SaaS-Specific Value Chain:

┌────────────┬───────────────┬──────────────┬────────────────┬─────────────┐
│ Product    │ Customer      │ Customer     │ Customer       │ Expansion   │
│ Development│ Acquisition   │ Onboarding   │ Success        │ & Retention │
├────────────┼───────────────┼──────────────┼────────────────┼─────────────┤
│ R&D        │ Marketing     │ Implementation│ Support       │ Upsell      │
│ Design     │ Sales         │ Training     │ Account mgmt  │ Cross-sell  │
│ QA         │ Partnerships  │ Migration    │ Health scoring │ Renewals    │
│ Platform   │ Growth/PLG    │ Integration  │ Community      │ Advocacy    │
└────────────┴───────────────┴──────────────┴────────────────┴─────────────┘

Key insight for SaaS: The majority of LTV is created AFTER the initial sale.
Disproportionate investment should go to Onboarding → Success → Expansion.

Strategic Analysis Anti-Patterns

Common cognitive traps that produce bad strategy. Actively check for these in every analysis:

Anti-Pattern	Detection Question	Countermeasure
Confirmation Bias — Seeking data that supports pre-existing beliefs; ignoring contradictory evidence	"Did I search for disconfirming evidence with equal effort?"	For every key conclusion, explicitly search for the strongest counterargument
Anchoring — First number encountered dominates all later estimates (first source says "$10B market" and final estimate drifts toward $10B)	"Is my final estimate suspiciously close to the first number I found?"	Collect 3+ independent estimates; use both bottom-up and top-down methods; investigate any 2x+ divergence
Strategy-by-Analogy — "Uber did X, so we should do X in healthcare" without testing structural similarity	"What are the 3 most important differences between this situation and the analogy?"	Use analogies to generate hypotheses, never to validate conclusions
Missing Causal Chain — Clear start and desirable end, but no credible mechanism connecting them (Step 1 → ??? → Profit)	"What specifically happens between 'launch' and 'achieve outcome'?"	Every recommendation needs a testable causal chain: A → B → C → D
Denominator Neglect — Citing impressive absolutes while ignoring base rates ("10,000 users!" out of 2M impressions = 0.5%)	"Relative to what?"	Always present metrics as ratios/rates; compare to benchmarks
Survivorship Bias — Deriving strategy from winners only; ignoring that failed companies tried the same thing	"How many companies tried this and failed?"	Seek failure case studies; note success AND failure rates
Planning Fallacy — Timelines assuming everything goes right	"Does this plan require performing better than we ever have?"	Use reference class forecasting; add 30-50% buffer; present best/base/worst timelines

Uncertainty Quantification

Expressing Uncertainty

For quantitative estimates (market size, revenue, costs):

Never give a single number. Always give a range: "Market size: $8-12B (base estimate $10B)"
State the confidence interval: "80% confident the market is between $8B and $12B"
Identify the key variable driving the range: "Range is driven primarily by uncertainty in adoption rate (15-25%)"

For qualitative assessments:

Use the calibrated confidence scale consistently:
- Very High (>90%): Would be genuinely surprised if wrong. Multiple high-quality sources agree.
- High (70-90%): Strong evidence, but plausible alternative interpretations exist.
- Medium (50-70%): Balanced evidence. Reasonable people could disagree.
- Low (30-50%): More uncertain than certain. Treat as hypothesis, not finding.
- Very Low (<30%): Speculative. Useful for scenario planning but not for action.

Assumption Tracking

Every analysis rests on assumptions. Make them explicit:

ASSUMPTION REGISTER:

| # | Assumption | Confidence | Impact if Wrong | Validation Method |
|---|-----------|------------|-----------------|-------------------|
| 1 | Market grows 15% YoY | High | Changes TAM by +/- 30% | Track quarterly industry reports |
| 2 | No new regulation in 12mo | Medium | Could block market entry | Monitor regulatory pipeline |
| 3 | Key hire joins by Q2 | Medium | Delays launch 3-6 months | Pipeline status check monthly |
| 4 | Competitor does not cut price | Low | Margin compression 10-15% | Track competitor pricing weekly |

Flag any assumption rated "Low" that has "High" impact — these are the strategic landmines that deserve contingency plans.

When to Say "We Don't Know"

It is better to say "insufficient data to assess" than to fabricate a confident-sounding answer. Specifically:

If fewer than 2 independent sources support a data point, flag it as unverified
If the key variable has a range wider than 3x (e.g., market could be $5B or $15B), call out that the analysis is highly sensitive to this input
If you are extrapolating a trend beyond the data range, state the extrapolation explicitly

Industry-Specific Strategic Patterns

Certain strategic dynamics recur within industry categories. Recognizing these patterns accelerates analysis:

Platform / Marketplace Businesses

Winner-take-most dynamics: Network effects create power-law outcomes. Market share of #1 player often exceeds #2 + #3 combined.
Chicken-and-egg problem: Must solve supply and demand simultaneously. Common solutions: single-player mode, subsidize one side, constrain geography first.
Multi-homing risk: If users can easily use multiple platforms, network effects weaken. Strategy must increase switching costs or exclusive value.
Key metric: Liquidity (match rate between supply and demand). Revenue follows liquidity, not the reverse.

B2B SaaS

Land-and-expand: Initial deal size matters less than expansion potential. Net revenue retention >120% can drive growth even at 0 new logos.
Switching cost lifecycle: Switching costs increase with integration depth, data accumulation, and workflow embedding. Year 1 churn is always highest.
Category creation vs. category entry: Creating a new category requires 3-5x more marketing spend but yields pricing power. Entering an existing category is cheaper but forces competitive positioning.
Key metric: Net Revenue Retention (NRR). Above 130% = exceptional. Below 100% = leaky bucket that marketing cannot fill.

Consumer / D2C

Acquisition cost spiral: As easy-to-reach audiences saturate, CAC rises. Growth requires channel diversification or organic/viral mechanics.
Brand as moat: In commoditized categories, brand is the primary differentiation. Brand building requires consistency over years, not campaigns over months.
Retention curve shape: If the retention curve flattens (users who stay past day 30 tend to stay indefinitely), invest in onboarding. If it keeps declining, the product has a retention problem, not an acquisition problem.
Key metric: Cohort retention at day 30/60/90. Payback period on CAC.

Regulated Industries (Healthcare, Finance, Insurance)

Compliance as moat: Regulatory requirements (HIPAA, SOC2, PCI-DSS) are expensive to achieve but create durable barriers to entry.
Sales cycle reality: Enterprise sales cycles of 6-18 months are normal. Budget accordingly. Premature scaling of sales teams is the #1 killer.
Build vs. partner: In heavily regulated industries, partnering with incumbents (who have regulatory relationships) often beats trying to disrupt them directly.
Key metric: Sales cycle length, regulatory approval timeline, compliance cost as % of revenue.

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