# Pricing strategist Human Guide

## What This Is For
Help Commercial, Product Marketing, and CMO functions answer three questions at the pricing-design moment: It gives the agent a clearer input/output frame for go-to-market work: what context to ask for, what decisions to make, and what usable artifact to return.

Use this as a human-readable version of the Pricing strategist agent skill. It is meant for marketers, operators, founders, and other non-coders who want the workflow without reading agent-specific implementation instructions.

## When To Use This
- Use this when you need a repeatable process for pricing strategist.
- Use this when the task needs judgment, examples, constraints, or a clear output format rather than a one-off prompt.
- Use this when you want to hand an AI assistant enough context to produce a usable marketing artifact.

## When Not To Use This
- Do not use this when you only need a quick factual answer.
- Do not use this when the work depends on private data you cannot share with the assistant.
- Do not use this as a replacement for legal, compliance, financial, or medical review.

## What You Need Before Starting
- The goal or business outcome you want.
- The audience, customer segment, or market context.
- Any source material the assistant should respect, such as notes, briefs, examples, URLs, or brand guidance.
- Constraints such as tone, length, channel, deadline, region, or approval requirements.
- A clear definition of what a good final answer should look like.

## Step-By-Step Workflow
1. State the job clearly: "Use the Pricing strategist guide to help me with..."
2. Add context: audience, goal, offer, channel, source material, and constraints.
3. Ask the assistant to identify missing inputs before producing the final output.
4. Have the assistant follow the skill-specific guidance below.
5. Review the result against the final checklist and ask for revisions where needed.

## Skill-Specific Guidance
- **Which pricing model fits this product + customer + market?** (subscription seat-based, usage-based, value-based, freemium, hybrid)
- **What does the customer actually pay before it feels too expensive?** (Van Westendorp PSM on WTP survey responses)
- **How should we package this into tiers?** (Good / Better / Best — with anti-pattern detection)
- 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)
- 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`

## Decision Points And Nuance
The original skill emphasizes: Purpose, When to use, Workflow, Step 1 — Assess customer context, Step 2 — Pick the pricing model, Step 3 — Validate WTP with Van Westendorp PSM, Step 4 — Design packaging, Step 5 — Decide, Scripts, Quick example.

Use these questions to steer the work:
- What is the intended audience or buyer?
- What source material must be preserved?
- What should the assistant optimize for: clarity, persuasion, accuracy, speed, creativity, or conversion?
- What examples represent the desired quality bar?
- What should the assistant avoid?

## Common Mistakes
- 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.
- **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.
- **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`.
- Walked one at a time by `/cs:grill-commercial` or the orchestrator. Recommended answer + canon citation per question. Never bundled.

## Copy-And-Paste Prompt
```text
Use the Pricing strategist human guide.

My goal:
[Describe the business outcome]

Audience:
[Describe who this is for]

Context and source material:
[Paste notes, examples, links, or existing copy]

Constraints:
[Tone, length, channel, timeline, must-include items, must-avoid items]

Before producing the final output, ask me for any missing information that would materially improve the result.
```

## Final Checklist
- [ ] The output matches the original goal.
- [ ] The audience and context are reflected in the answer.
- [ ] Important constraints and source material were preserved.
- [ ] The assistant made the relevant decisions explicit.
- [ ] The final artifact is ready to use, review, or hand to the next person.

## Source
This guide was generated from the alirezarezvani/claude-skills skill entry for `pricing-strategist`.

## Source Skill Notes
These notes preserve the nuance from the original skill. Use them as supporting reference when the workflow above feels too generic.

# 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.