Pricing Strategist

# pricing-strategist ## Purpose Help Commercial, Product Marketing, and CMO functions answer three questions at the pricing-design moment: 1. **Which pricing model fits this product + customer + market?** (subscription seat-based, usage-based, value-based, freemium, hybrid) 2. **What does the customer actually pay before it feels too expensive?** (Van Westendorp PSM on WTP survey responses) 3. **How should we package this into tiers?** (Good / Better / Best — with anti-pattern detection) The skill recommends **a model and a range**. The human picks the number, owns the trade-offs, and runs the GTM. ## When to use - Launching a new SaaS / API / AI tool and choosing the first pricing model - Revisiting pricing after 18+ months of GTM data (model shift, not just price increase) - Designing or redesigning tier packaging (Good/Better/Best, Bronze/Silver/Gold) - You have Van Westendorp survey data and want the optimal price range - A board / exec is asking "what should we charge?" and you need the structured answer - You suspect your packaging has anti-patterns (decoy tier, feature dump, no upgrade trigger) **Do not use for:** - Per-deal discount approval → `deal-desk` - Strategic CMO positioning, brand, category creation → `c-level-advisor/cmo-advisor` - Whole-company revenue strategy → `c-level-advisor/cro-advisor` - Technical-sale enablement → `business-growth/sales-engineer` ## Workflow ### Step 1 — Assess customer context Fill `assets/pricing_brief_template.md` (≈ 20 min). Capture: industry, deal size avg, customer count, value drivers, adoption curve, consumption pattern (seat / usage / value / hybrid), competitor models. ### Step 2 — Pick the pricing model Run `scripts/pricing_model_picker.py --input brief.json --profile saas --output markdown`. Output ranks 5 models by fit-score 0-100 with trade-offs. Decision logic is deterministic: low usage variance + high seat-attach → subscription wins; power-law usage + variable customer value → usage-based wins. ### Step 3 — Validate WTP with Van Westendorp PSM If you have survey data (≥ 4 questions per respondent: too cheap / bargain / getting expensive / too expensive), run `scripts/wtp_analyzer.py --input survey.json --output markdown`. Output: 4 intersection points (OPP, IDP, PMC, PME) and the Range of Acceptable Prices. PSM gives a **range**, not the price. See `references/van_westendorp_methodology.md` for common misinterpretations. ### Step 4 — Design packaging Run `scripts/packaging_designer.py --input features.json --profile saas --output markdown`. Output: 3-tier Good/Better/Best assignment with anti-pattern flags (decoy tier, feature dump, no upgrade trigger, Bronze loss leader, Enterprise no-anchor). ### Step 5 — Decide Take model + range + packaging into the pricing committee. Skill does not commit the number — you do. ## Scripts - `scripts/pricing_model_picker.py` — 5-model fit scorer (subscription / usage / value / freemium / hybrid) - `scripts/wtp_analyzer.py` — Van Westendorp PSM implementation - `scripts/packaging_designer.py` — Good/Better/Best tier designer with anti-pattern detection All scripts: stdlib only. `--help` and `--sample` work on all three. ## Quick example ```bash # Emits a scored 5-model pricing-fit recommendation (subscription / usage / value / freemium / hybrid) for the built-in example cd commercial/skills/pricing-strategist && python3 scripts/pricing_model_picker.py --sample ``` ## References - `references/saas_pricing_canon.md` — Skok, Tunguz, Campbell, Ramanujam, BVP, Shevlin, Stanford GSB - `references/van_westendorp_methodology.md` — original 1976 paper, NMS refinement, Conjoint.ly, Sawtooth, ESOMAR, Lipovetsky, Decision Analyst - `references/packaging_anti_patterns.md` — ProfitWell, OpenView, BVP vertical SaaS, Ramanujam, Poyar, SaaS Capital ## Assumptions - Pricing decisions are joint: Commercial owns the model + tier shape, Product owns the features-per-tier, Finance owns the discount envelope, Legal owns the contract. - Van Westendorp PSM is a **directional** tool. N ≥ 30 minimum, N ≥ 100 preferred. Below 30, the script emits a sample-size warning. - "Value-based pricing" requires a measurable customer value driver (revenue lift, cost saved, time recovered). If you can't measure it, don't pick value-based. - Industry profiles tune defaults — they don't override your data. - This is a decision-support skill, not a price oracle. Output is a model + range, never the number. ## Anti-patterns - **Recommending a specific number.** This skill emits a model and a range. Final price is a human commercial decision involving deal-desk policy, competitive intel, and strategic intent that this skill cannot know. - **Using PSM with N < 30.** Statistical noise dominates. The script warns; respect the warning. - **Treating PSM as "the price."** PSM gives a Range of Acceptable Prices (RAP) and an Optimal Price Point (OPP). Test the range in market, don't anchor on a single intersection. - **Picking value-based pricing without a measurable value metric.** Without instrumentation to show customer ROI, value-based collapses into "whatever they'll pay" — which is just bad usage-based pricing. - **Designing tiers before picking a model.** Tier structure depends on the model. Run pricing_model_picker first. - **Packaging "feature dumps" into the Best tier.** If Best has 3x the features for 2x the price, customers buy Better and never upgrade. See `packaging_anti_patterns.md`. - **Hidden usage-based pricing inside subscription tiers.** "Up to 100k API calls/mo, then $X per 1k" disguised as a "Pro tier" is two pricing models in one. Customers notice. Pick one. - **Confusing this skill with deal-desk.** Pricing strategy = the menu. Deal-desk = approving discounts off the menu. Different decision, different cadence, different owner. ## Distinct from - **deal-desk** — per-deal discount approval, MEDDIC, deal scoring. Operates daily on existing pricing. - **c-level-advisor/cmo-advisor** — strategic positioning, brand, category. Pricing strategist consumes positioning as input, doesn't generate it. - **c-level-advisor/cro-advisor** — full-funnel revenue strategy, comp plans, territory design. Pricing strategist is one input to CRO. - **business-growth/sales-engineer** — technical sale, POC scoping. Sales engineering operates after pricing is set. ## Forcing-question library (Matt Pocock grill discipline) Walked one at a time by `/cs:grill-commercial` or the orchestrator. Recommended answer + canon citation per question. Never bundled. 1. **"Is your customer paying for outcomes, seats, or usage?"** Recommended: outcomes (value-based) if you can measure them; usage if marginal cost is variable; seats only if usage is roughly flat per user. Canon: Ramanujam 2016 (*Monetizing Innovation*) — Mistake #1 of 9: seat-based pricing on a usage-variable product caps TAM at ~20% of WTP. 2. **"Do you have a measurable value metric, or are you guessing?"** Recommended: instrument the value metric BEFORE going to market with value-based pricing. Canon: Patrick Campbell / ProfitWell research — value-based without instrumentation collapses into bad usage-based pricing. 3. **"What's the variance in customer usage across your top decile vs. median?"** Recommended: variance > 10x → usage-based wins; variance < 3x → subscription wins; in between → hybrid with usage overage. Canon: Kyle Poyar (*Growth Unhinged*) — high-variance products lose 60%+ of revenue on flat-rate plans. 4. **"What's your competitor's pricing model, and why are you choosing the same or different?"** Recommended: surface the differentiation hypothesis explicitly. Identical pricing = identical value claim. Canon: David Skok (*For Entrepreneurs*) — pricing is a positioning signal. 5. **"What sample size do you have for WTP analysis, and is it segmented?"** Recommended: N≥30 per segment for PSM, N≥100 for conjoint. Canon: van Westendorp 1976 / Sawtooth Software methodology — sub-30 PSM is statistical noise. 6. **"What's the ONE feature that forces a tier upgrade?"** Recommended: every Better and Best tier needs a single non-negotiable upgrade trigger. Canon: Ramanujam (*Monetizing Innovation*) — Mistake #4: tiers with no clear differentiator make 70% of customers pick the cheapest. Walk depth-first. Lock 1-3 before opening 4-6. After all 6 are answered, invoke `pricing_model_picker.py` → `wtp_analyzer.py` → `packaging_designer.py` in sequence.