Founder Os

• Frequency < monthly AND severity < painful
• Zero willingness to pay after 20 conversations
• Market size < $100M TAM (won't attract investors or sustain growth)
• You can't explain the problem in one sentence
• Existing solutions are "good enough" and switching cost is high

Customer Discovery Interview Script

Opening (2 min): "Tell me about the last time you dealt with [PROBLEM]. Walk me through what happened."

Deep dive (15 min):

1. "How often does this come up?"
2. "What do you currently do about it?" (existing behavior = real demand)
3. "What's the most annoying part of your current approach?"
4. "Have you tried anything else? What happened?"
5. "If you could wave a magic wand, what would change?"
6. "How much time/money does this cost you per [week/month]?"

Commitment test (3 min):

• "Can I add you to our beta list?" (email = weak signal)
• "Would you pay $X/month for this?" (verbal = medium signal)
• "Can I charge you $X now for early access?" (payment = strong signal)
• "Can you intro me to 3 others with this problem?" (referral = strongest signal)

Rules:

• NEVER pitch your solution during discovery
• NEVER ask "would you use X?" — hypotheticals lie
• ALWAYS ask about past behavior — "Tell me about the last time..."
• Record exact quotes — "mom test" phrasing matters
• 20 interviews minimum before building anything

Interview Synthesis Template

After every 5 interviews, update:

discovery_synthesis:
  interviews_completed: 0
  top_3_problems:
    - problem: ""
      frequency: ""
      quotes: ["", ""]
      mentioned_by: "X of Y"
  patterns:
    consistent: [""]  # same across all interviews
    surprising: [""]  # didn't expect this
    contradictory: [""]  # different people say opposite things
  existing_solutions_used: [""]
  price_sensitivity: "anchored at $X-Y/mo"
  decision: "proceed | pivot-problem | pivot-customer | kill"
  confidence: "low | medium | high"

Phase 2: MVP & Launch (Week 3-8)

MVP Scope Decision Matrix

Rules:

• If you can test the hypothesis WITHOUT code, do that first
• MVP must test ONE hypothesis — not "will people use this?" but "will [segment] pay $X for [specific value]?"
• Maximum 6-week build — if it takes longer, scope is too big
• Ship to 10 users, not 10,000 — intimate feedback beats vanity metrics

Launch Checklist

pre_launch:
  - [ ] 20+ discovery interviews completed
  - [ ] Problem validated (frequency + severity + WTP)
  - [ ] MVP tests primary hypothesis
  - [ ] 10+ beta users committed (by name)
  - [ ] Pricing set (see pricing section)
  - [ ] Analytics installed (activation event defined)
  - [ ] Feedback channel open (Slack, email, Intercom)

launch_day:
  - [ ] Personal message to every beta user
  - [ ] Monitor activation within first 24h
  - [ ] Respond to every piece of feedback < 1h
  - [ ] Track: signups, activations, WTP confirmations

post_launch_week_1:
  - [ ] Call every activated user — what worked?
  - [ ] Call every churned user — what failed?
  - [ ] Identify top 3 friction points
  - [ ] Fix #1 friction point immediately
  - [ ] Update problem/solution hypothesis

Phase 3: Product-Market Fit (Month 2-12)

PMF Measurement Framework

Sean Ellis Test (Primary): Ask: "How would you feel if you could no longer use [product]?"

• Very disappointed → Count these
• Somewhat disappointed
• Not disappointed
• N/A (no longer use)

Threshold: 40%+ "Very Disappointed" = PMF

Run this survey after users have experienced core value (not day 1).

Supporting Metrics:

Pre-PMF Operating Rules:

1. Talk to users every single day
2. Ship updates weekly minimum
3. Don't hire non-essential roles
4. Don't spend on paid marketing
5. Don't optimize onboarding (fix the product first)
6. Measure learning velocity, not revenue

PMF Search Process

Week 1-2: Ship feature/change
Week 2-3: Measure impact (retention, NPS, Ellis test)
Week 3-4: Interview users about change
Week 4: Decide → double down or try something else

Repeat until 40%+ "very disappointed"

Pivot Decision Framework

pivot_assessment:
  current_retention_trend: "improving | flat | declining"
  months_of_runway: 0
  customer_segments_tested: 0
  pivots_remaining: "runway_months / 3"  # each pivot needs ~3 months
  
  pivot_types:
    zoom_in: "One feature IS the product — kill the rest"
    zoom_out: "Product is one feature of something bigger"
    customer_segment: "Same product, different buyer"
    customer_need: "Same customer, different problem"
    channel: "Same product, different distribution"
    pricing: "Same product, different business model"
    technology: "Same problem, different solution"
  
  decision_rules:
    - "If retention is improving (even slowly) → stay the course"
    - "If flat for 3+ months after real iteration → pivot"
    - "If < 6 months runway → pivot NOW or raise bridge"
    - "If you've tested 3+ segments with same product → pivot product"
    - "If users love it but won't pay → pricing/segment pivot"

Phase 4: Unit Economics & Pricing

Startup Pricing Framework

Step 1: Value-based price anchor

Annual value delivered to customer: $________
Price = 10-20% of value delivered
Example: Save customer $50K/year → price at $5K-10K/year

Step 2: Pricing model selection

Step 3: Three-tier architecture

pricing_tiers:
  starter:
    price: "$X/mo"  # anchor low, capture market
    features: "core value only"
    target: "individual / small team"
    purpose: "land"
    
  professional:
    price: "$3-4X/mo"  # this is where margin lives
    features: "core + collaboration + integrations"
    target: "growing team"
    purpose: "expand (should be 60-70% of revenue)"
    highlight: true  # "Most Popular" badge
    
  enterprise:
    price: "Custom ($10X+)"
    features: "everything + SSO + SLA + dedicated support"
    target: "large org"
    purpose: "signal legitimacy + capture whales"

Pricing Rules:

• Price HIGHER than you think — you can always discount, can't easily raise
• Annual discount = 2 months free (16% off) — not more
• Never compete on price — compete on value, speed, or experience
• Grandfather early users — loyalty matters
• Review pricing quarterly — most startups underprice for too long

Unit Economics Health Check

unit_economics:
  CAC: "$___"  # total sales+marketing spend / new customers
  LTV: "$___"  # avg revenue per customer × avg lifespan in months
  LTV_CAC_ratio: "___"  # target: 3:1+
  CAC_payback_months: "___"  # target: <12
  gross_margin: "___%"  # target: >70% for SaaS
  burn_multiple: "___"  # net burn / net new ARR — target: <2
  magic_number: "___"  # net new ARR / S&M spend last quarter — target: >0.75
  
  health_assessment:
    - "LTV:CAC > 3:1 → healthy, can invest in growth"
    - "LTV:CAC 1-3:1 → cautious, optimize before scaling"  
    - "LTV:CAC < 1:1 → STOP — losing money on every customer"
    - "Payback > 18mo → cash flow problem, even if profitable long-term"
    - "Burn multiple > 3 → spending too much for growth achieved"

Phase 5: Fundraising

Fundraising Readiness Checklist

raise_when:
  - [ ] You have momentum (growing MoM, not flatlined)
  - [ ] You know what the money is for (specific milestones, not "general")
  - [ ] You have 6+ months runway (raising from strength, not desperation)
  - [ ] Your story is crisp (problem → solution → traction → vision in 60 seconds)

do_not_raise_when:
  - "Pre-PMF with no traction (unless deep tech / biotech)"
  - "To avoid hard decisions about business model"
  - "Because competitors raised"
  - "When you have < 3 months runway (terms will be terrible)"

Fundraising Math

round_benchmarks:
  pre_seed:
    raise: "$250K-$1M"
    valuation: "$3-6M"
    dilution: "10-20%"
    what_you_need: "idea + team + early signal"
    timeline: "2-4 weeks"
    
  seed:
    raise: "$1-4M"
    valuation: "$8-15M"
    dilution: "15-25%"
    what_you_need: "$10-50K MRR or strong engagement metrics"
    timeline: "4-8 weeks"
    
  series_a:
    raise: "$5-15M"
    valuation: "$30-80M"
    dilution: "15-25%"
    what_you_need: "$1-3M ARR, 3x+ YoY growth, clear PMF"
    timeline: "8-16 weeks"