pricing-strategy
The pricing-strategy skill analyzes and designs pricing strategies by evaluating value proposition, selecting appropriate pricing models (flat-rate, per-seat, usage-based, tiered, freemium, or value-based), conducting competitive analysis, and estimating customer willingness to pay. Use it when setting initial prices for a product, evaluating whether to change pricing, comparing different monetization approaches, or preparing to launch new pricing tiers.
git clone --depth 1 https://github.com/phuryn/pm-skills /tmp/pricing-strategy && cp -r /tmp/pricing-strategy/pm-product-strategy/skills/pricing-strategy ~/.claude/skills/pricing-strategySKILL.md
## Pricing Strategy
Design a pricing strategy grounded in value delivery, competitive positioning, and willingness to pay.
### Context
You are developing a pricing strategy for **$ARGUMENTS**.
If the user provides files (competitor pricing, survey data, financial models, or usage data), read them first. Use web search to research competitor pricing if needed.
### Instructions
1. **Understand the value delivered**:
- What is the core value proposition?
- What is the customer's alternative (and its cost)?
- What quantifiable outcomes does the product deliver? (time saved, revenue gained, cost reduced)
- What is the customer's willingness to pay based on that value?
2. **Evaluate pricing models** — recommend the best fit:
| Model | Best For | Example |
|---|---|---|
| **Flat-rate** | Simple products, predictable costs | Basecamp ($99/mo flat) |
| **Per-seat** | Collaboration tools, team products | Slack, Figma |
| **Usage-based** | Infrastructure, API products | AWS, Twilio |
| **Tiered** | Products with distinct user segments | Most SaaS (Free/Pro/Enterprise) |
| **Freemium** | Products with viral/network effects | Spotify, Notion |
| **Freemium + usage** | Platform products | Vercel, OpenAI API |
| **Value-based** | High-impact enterprise tools | Salesforce, Palantir |
3. **Analyze competitive pricing**:
- Map competitor pricing tiers and what's included
- Identify where your product sits (premium, mid-market, budget)
- Find pricing gaps or opportunities
- Note any industry pricing conventions
4. **Design the pricing structure**:
- **Tiers**: Define 2-4 tiers with clear differentiation
- **Feature gating**: Which features go in which tier? (Use value metrics, not arbitrary limits)
- **Value metric**: What unit do you charge on? (users, events, storage, API calls)
- **Anchor pricing**: Set the most popular tier to feel like the obvious choice
- **Annual discount**: Typically 15-20% off monthly pricing
5. **Estimate price sensitivity**:
- Van Westendorp Price Sensitivity Meter (if survey data available):
- Too cheap → quality concerns
- Cheap → good value
- Expensive → starting to hesitate
- Too expensive → won't buy
- Alternatively, estimate based on competitor pricing and value delivered
6. **Plan pricing experiments**:
- A/B test pricing pages (different price points, tier names, feature bundles)
- Founder-led sales conversations to test willingness to pay
- Landing page tests with different price anchors
- Cohort analysis of conversion rates by price point
7. **Output a pricing recommendation**:
```
Recommended Model: [Model type]
Value Metric: [What you charge on]
| Tier | Price | Target Segment | Key Features | Positioning |
|---|---|---|---|---|
Key Assumptions:
- [Assumption] → [How to test]
Risks:
- [Risk] → [Mitigation]
```
Think step by step. Save as markdown. Flag any assumptions that need validation before launch.
---
### Further Reading
- [Product Pricing Strategies 101](https://www.productcompass.pm/p/product-pricing-strategies-101)
- [The AI Product Pricing Masterclass: OpenAI Product Lead on Why SaaS Pricing Fails in AI (and How to Fix It)](https://www.productcompass.pm/p/ai-product-pricing) (video course)The method for finding the gap between what a system is supposed to do and what the code actually does — the class of bug generic scanners miss because they have no model of intent. Defines what counts as documented intent, what counts as implementation evidence, which mismatches matter, and how to avoid hand-wavy findings. Use when auditing AI-built code, reviewing access control against documented permissions, or checking whether a codebase matches its own documentation.
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