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aa024703fc
Saves the DEALIX_BUSINESS_VIABILITY_KIT.md (Weeks 4-12 customer discovery
operating manual) and produces only the operational artifacts it explicitly
names. Per the kit's Appendix C: no new plan documents, no Wave A-E work,
no features without customer pull.
Added:
Customer Viability operating artifacts
- docs/customer_learnings/hypotheses.yaml - 12 hypotheses tracked
to SUPPORTED/FALSIFIED/AMBIGUOUS with interview-log citations
- docs/customer_learnings/interviews/_template_ar.md - 45-min Arabic
discovery script + post-call log schema
- docs/customer_learnings/interviews/_template_en.md - English version
- docs/customer_learnings/founder_dashboard.md - weekly Monday printable
dashboard (kit Sec 8)
- docs/customer_learnings/pricing_discovery.md - Van Westendorp PSM +
value-based sanity check + A/B model matrix
- docs/customer_learnings/unit_economics.md - per-customer economics,
LTV/CAC ratios, 12-month scenario template
- docs/customer_learnings/defensibility_scorecard.md - 5 moats x 2
questions, quarterly re-measurement
Registry updates
- docs/registry/TRUTH.yaml customer_validation section: hypothesis
counters + discovery-interview counter + kit reference
- docs/customer_learnings/README.md updated to link new artifacts
Gates after change:
architecture_brief.py 40/40
release_readiness_matrix 102/102 (added 8 new BVK artifact checks)
v005_truth_registry_audit 19/19 SUPPORTED
Agent scope going forward per kit Appendix C: customer-surfaced P0 defects,
UX polish appearing in 2+ interviews, perf issues on staging, pentest
remediations. No new plans. No Wave tasks.
109 lines
3.5 KiB
Markdown
109 lines
3.5 KiB
Markdown
# Dealix — Unit Economics Worksheet
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> Fill in ONLY after 3 paying customers. Filling it earlier is fiction.
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> All figures in SAR unless stated.
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> Re-run at end of each month once populated.
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---
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## 1. Per-customer monthly economics
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### Revenue
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- MRR per customer (avg): ______
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### Cost to serve (per customer / month)
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| Line | Amount (SAR) |
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|------|--------------|
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| Infrastructure (AWS + DB + Redis) | ______ |
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| LLM API (Anthropic + OpenAI + Groq, at observed usage) | ______ |
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| Third-party services (Sentry, OTel backend, etc.) | ______ |
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| CS time (hours × hourly cost) | ______ |
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| Support time (eng on-call, tickets) | ______ |
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| **Total Cost to Serve** | ______ |
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### Gross Margin
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- GM per customer (SAR): ______
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- GM %: ______ %
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- **Target**: GM ≥ 70% (SaaS healthy)
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- **Red flag**: GM < 60% → LLM spend dominates; audit model routing.
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---
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## 2. Customer Acquisition Cost (CAC)
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| Line | Amount (SAR) |
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|------|--------------|
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| Founder time spent closing (hrs × opportunity cost) | ______ |
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| Marketing spend (ads, events) | ______ |
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| Sales tools (CRM, LinkedIn Sales Nav, etc.) | ______ |
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| Referral incentives | ______ |
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| **Total CAC (amortized across paid customers)** | ______ |
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---
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## 3. Lifetime Value (LTV)
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- Expected contract length (months, honest not aspirational): ______
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- Monthly gross margin per customer: ______
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- **LTV** = months × monthly GM = ______
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---
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## 4. Health ratios
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| Metric | Value | Target | Red flag |
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|--------|-------|--------|----------|
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| LTV / CAC | ______ | ≥ 3× | < 2× |
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| CAC payback period (months) | ______ | < 18 | > 24 |
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| Gross margin % | ______ | ≥ 70% | < 60% |
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| Net revenue retention (12m forward) | ______ | ≥ 120% | < 100% |
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---
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## 5. 12-month scenario (fill after M3)
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Re-forecast every month.
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| Month | New logos | Churn | MRR | Cumulative revenue | Burn | Cash position |
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|-------|-----------|-------|-----|--------------------|----|-----|
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| M3 (today) | ___ | 0 | ___ | ___ | ___ | ___ |
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| M4 | ___ | ___ | ___ | ___ | ___ | ___ |
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| M5 | ___ | ___ | ___ | ___ | ___ | ___ |
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| M6 | ___ | ___ | ___ | ___ | ___ | ___ |
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| M7 | ___ | ___ | ___ | ___ | ___ | ___ |
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| M8 | ___ | ___ | ___ | ___ | ___ | ___ |
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| M9 | ___ | ___ | ___ | ___ | ___ | ___ |
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| M10 | ___ | ___ | ___ | ___ | ___ | ___ |
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| M11 | ___ | ___ | ___ | ___ | ___ | ___ |
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| M12 | ___ | ___ | ___ | ___ | ___ | ___ |
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---
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## 6. Red-flag diagnostics
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If a ratio is off, diagnose in this order (cheapest fix first):
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| Symptom | First suspect | Diagnostic |
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|---------|--------------|------------|
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| GM < 60% | LLM spend | Review model_routing dashboard; downgrade non-reasoning calls |
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| GM < 60% | Infra waste | k6 baseline says p95 vs actual load — overprovisioned? |
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| CAC too high | Founder-only sales | Is anyone else closing? if not, motion is not yet repeatable |
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| CAC too high | Lead mix | Cold outbound vs warm referrals ratio — shift if skewed |
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| LTV/CAC < 3 | Contract length | Are pilots renewing verbally but not signing annual? Why? |
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| Payback > 24 | Pricing | Van Westendorp says prices could be higher — test |
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| NRR < 100 | Churn | Exit interview every churn — real reason, not stated reason |
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---
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## 7. Evidence pointer
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Do not fill in this worksheet from memory. Sources of truth:
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- Revenue: Stripe / invoice ledger
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- LLM spend: `backend/app/services/model_routing_dashboard.py` monthly export
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- Infra: AWS cost explorer export
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- CS/Support hours: weekly time logs
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- CAC: founder calendar + marketing ledger
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Cite the source file/export in the margin when filling each row.
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