mirror of
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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.
110 lines
3.7 KiB
Markdown
110 lines
3.7 KiB
Markdown
# Dealix — Pricing Discovery Worksheet
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> **Never ask "what would you pay?"** — answer is biased toward zero.
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> Use Van Westendorp Price Sensitivity Meter (PSM) after 8+ discovery interviews.
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> Combine with value-based sanity check.
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---
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## 1. Van Westendorp — four questions
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Asked at end of discovery call, after pain + quantitative discovery:
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1. **Too cheap**: "At what annual price would this feel so cheap you'd question its quality or seriousness?"
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2. **Bargain**: "At what annual price would this be a good deal — strong value for the cost?"
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3. **Getting expensive**: "At what price would this start feeling expensive, but you'd still consider if the value is there?"
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4. **Too expensive**: "At what price is it simply too expensive, regardless of value?"
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Record in SAR/year, unprompted. No anchoring from you.
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---
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## 2. Raw data table
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| # | Company | Interview date | Too cheap (SAR/yr) | Bargain | Getting expensive | Too expensive |
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|---|---------|----------------|--------------------|----|-------------------|---------------|
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| 1 | | | | | | |
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---
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## 3. Intersections (fill after ≥8 data points)
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Plot cumulative curves; find intersection points.
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| Point | Definition | Value (SAR/yr) |
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|-------|------------|----------------|
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| Point of Marginal Cheapness | "too cheap" ∩ "getting expensive" | |
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| Optimal Price Point (indifference) | "bargain" ∩ "getting expensive" | |
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| Point of Marginal Expensiveness | "bargain" ∩ "too expensive" | |
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**Acceptable pricing band**: Marginal Cheapness → Marginal Expensiveness.
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**Initial list price**: start at Optimal Price Point, test both sides.
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---
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## 4. Value-based sanity check (per customer)
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For each interviewed customer, compute:
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```
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Annual value =
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(hours_saved_per_week × 52 × avg_hourly_cost_of_role)
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+ (num_better_decisions × avg_decision_value)
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+ (risk_avoided_per_year)
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```
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**Rule**: price ≤ 20% of annual value; customers rarely accept above 25%.
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| Company | Hours saved/wk | Hourly cost | Better decisions/yr | Risk avoided | Annual value | Max price (25%) |
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|---------|---------------|-------------|---------------------|--------------|--------------|-----------------|
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---
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## 5. Pricing-model A/B experiment matrix
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After first 5 interviews, prototype and test **one model per prospect** (never three — creates indecision).
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| Model | Structure | Best when |
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|-------|-----------|-----------|
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| Per seat | SAR/user/month | Predictable user count, horizontal role |
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| Per workflow | SAR/workflow/month + seats | Workflow count drives value |
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| Platform + usage | Base SAR + SAR/approval or SAR/evidence-pack | Usage tracks with realized value |
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Track acceptance rate:
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| Model | Offered to (# prospects) | Continued to demo | Signed pilot |
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|-------|---------------------------|-------------------|--------------|
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| Per seat | | | |
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| Per workflow | | | |
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| Platform + usage | | | |
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---
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## 6. Red flags in pricing discovery
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- All "too cheap" answers ≥ current plan price → pricing too low; room to raise.
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- Large gap between "bargain" and "too expensive" across interviews → market isn't segmented yet; stratify by company size/sector.
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- "Annual value" computed < 5× price → cannot justify the ROI pitch; either raise value or lower price.
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- Customer names zero competing tools → category is unknown to them; education cost is your hidden CAC.
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---
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## 7. Pricing decision log
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Every price change logged here with reason + evidence:
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| Date | Tier | Old price | New price | Reason | Evidence source |
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|------|------|-----------|-----------|--------|-----------------|
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