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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.
3.7 KiB
3.7 KiB
Dealix — Pricing Discovery Worksheet
Never ask "what would you pay?" — answer is biased toward zero. Use Van Westendorp Price Sensitivity Meter (PSM) after 8+ discovery interviews. Combine with value-based sanity check.
1. Van Westendorp — four questions
Asked at end of discovery call, after pain + quantitative discovery:
- Too cheap: "At what annual price would this feel so cheap you'd question its quality or seriousness?"
- Bargain: "At what annual price would this be a good deal — strong value for the cost?"
- Getting expensive: "At what price would this start feeling expensive, but you'd still consider if the value is there?"
- Too expensive: "At what price is it simply too expensive, regardless of value?"
Record in SAR/year, unprompted. No anchoring from you.
2. Raw data table
| # | Company | Interview date | Too cheap (SAR/yr) | Bargain | Getting expensive | Too expensive |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 | ||||||
| 4 | ||||||
| 5 | ||||||
| 6 | ||||||
| 7 | ||||||
| 8 | ||||||
| 9 | ||||||
| 10 |
3. Intersections (fill after ≥8 data points)
Plot cumulative curves; find intersection points.
| Point | Definition | Value (SAR/yr) |
|---|---|---|
| Point of Marginal Cheapness | "too cheap" ∩ "getting expensive" | |
| Optimal Price Point (indifference) | "bargain" ∩ "getting expensive" | |
| Point of Marginal Expensiveness | "bargain" ∩ "too expensive" |
Acceptable pricing band: Marginal Cheapness → Marginal Expensiveness. Initial list price: start at Optimal Price Point, test both sides.
4. Value-based sanity check (per customer)
For each interviewed customer, compute:
Annual value =
(hours_saved_per_week × 52 × avg_hourly_cost_of_role)
+ (num_better_decisions × avg_decision_value)
+ (risk_avoided_per_year)
Rule: price ≤ 20% of annual value; customers rarely accept above 25%.
| Company | Hours saved/wk | Hourly cost | Better decisions/yr | Risk avoided | Annual value | Max price (25%) |
|---|---|---|---|---|---|---|
5. Pricing-model A/B experiment matrix
After first 5 interviews, prototype and test one model per prospect (never three — creates indecision).
| Model | Structure | Best when |
|---|---|---|
| Per seat | SAR/user/month | Predictable user count, horizontal role |
| Per workflow | SAR/workflow/month + seats | Workflow count drives value |
| Platform + usage | Base SAR + SAR/approval or SAR/evidence-pack | Usage tracks with realized value |
Track acceptance rate:
| Model | Offered to (# prospects) | Continued to demo | Signed pilot |
|---|---|---|---|
| Per seat | |||
| Per workflow | |||
| Platform + usage |
6. Red flags in pricing discovery
- All "too cheap" answers ≥ current plan price → pricing too low; room to raise.
- Large gap between "bargain" and "too expensive" across interviews → market isn't segmented yet; stratify by company size/sector.
- "Annual value" computed < 5× price → cannot justify the ROI pitch; either raise value or lower price.
- Customer names zero competing tools → category is unknown to them; education cost is your hidden CAC.
7. Pricing decision log
Every price change logged here with reason + evidence:
| Date | Tier | Old price | New price | Reason | Evidence source |
|---|---|---|---|---|---|