pricing

{
  "skill_name": "pricing",
  "evals": [
    {
      "id": 1,
      "prompt": "Help me figure out pricing for our new SaaS product. It's a customer support platform for e-commerce stores. We're not sure whether to charge per agent, per ticket, or flat rate. Currently thinking $49-199/month range.",
      "expected_output": "Should check for product-marketing.md first. Should apply the three pricing axes framework: packaging (what's included in each tier), pricing metric (per agent, per ticket, flat rate — evaluate each), price point ($49-199 range evaluation). Should discuss value metrics and which aligns best with value delivered (per agent is common in support, but per ticket aligns with usage). Should recommend a good-better-best tier structure. Should address pricing psychology. Should provide a specific pricing recommendation with rationale.",
      "assertions": [
        "Checks for product-marketing.md",
        "Applies three pricing axes framework",
        "Evaluates multiple pricing metrics",
        "Discusses which metric aligns with value delivered",
        "Recommends good-better-best tier structure",
        "Addresses pricing psychology",
        "Provides specific pricing recommendation with rationale"
      ],
      "files": []
    },
    {
      "id": 2,
      "prompt": "We want to raise our prices by 30%. We've been at $29/month for 2 years and we've added a lot of features. How do we do this without losing customers?",
      "expected_output": "Should apply the 'when to raise prices' and price increase strategies sections. Should recommend a strategy: grandfather existing customers (or give them a grace period), tie the increase to new value, communicate the change clearly with advance notice, consider an annual billing discount as a softening measure. Should address different approaches (immediate for new customers, delayed for existing). Should recommend specific communication strategy. Should note that some churn is expected and acceptable.",
      "assertions": [
        "Applies price increase strategies",
        "Recommends grandfathering or grace period approach",
        "Recommends tying increase to new value",
        "Provides communication strategy",
        "Addresses new vs existing customer timing",
        "Suggests annual billing as softening measure",
        "Notes some churn is expected"
      ],
      "files": []
    },
    {
      "id": 3,
      "prompt": "how do we figure out what people will actually pay? we're launching a new product and have no idea what to charge.",
      "expected_output": "Should trigger on casual phrasing. Should apply the pricing research methods: Van Westendorp price sensitivity analysis (too cheap, bargain, expensive, too expensive), MaxDiff for feature importance, competitive benchmarking. Should explain how to run each method. Should also recommend simpler approaches: talking to potential customers, analyzing competitor pricing, testing different price points. Should provide a practical pricing research plan they can execute.",
      "assertions": [
        "Triggers on casual phrasing",
        "Applies Van Westendorp price sensitivity method",
        "Applies MaxDiff for feature importance",
        "Recommends competitive benchmarking",
        "Explains how to run each method",
        "Suggests practical alternatives (customer interviews, competitive analysis)",
        "Provides executable pricing research plan"
      ],
      "files": []
    },
    {
      "id": 4,
      "prompt": "We have a Basic ($19), Pro ($49), and Enterprise (custom) plan. The Pro plan gets 70% of signups. Should we add a plan between Pro and Enterprise?",
      "expected_output": "Should apply the good-better-best tier structure framework. Should analyze the current situation: Pro capturing 70% is actually healthy, but the gap to Enterprise suggests there may be mid-market customers underserved. Should evaluate whether a 4th tier makes sense: does it address a real gap, or will it create choice paralysis? Should apply pricing psychology (Hick's Law — more options can reduce decisions). Should recommend either a 4th tier with clear differentiation or adjusting the Pro plan to better bridge the gap.",
      "assertions": [
        "Applies good-better-best tier structure",
        "Analyzes current tier performance",
        "Evaluates whether 4th tier addresses real gap",
        "Considers choice paralysis risk",
        "Applies pricing psychology (Hick's Law)",
        "Provides specific recommendation with rationale"
      ],
      "files": []
    },
    {
      "id": 5,
      "prompt": "What pricing psychology tactics should we use on our pricing page? We want the $79 plan to be the most popular.",
      "expected_output": "Should apply the pricing psychology section: anchoring (show the $79 plan next to a higher-priced plan), decoy effect (make the lower plan look less valuable), visual emphasis (highlight or 'recommend' the $79 plan), charm pricing ($79 vs $80), Rule of 100 (percentage discounts below $100, dollar discounts above), loss framing (show what lower plans miss). Should provide specific pricing page design recommendations. Should cross-reference cro for broader pricing page optimization.",
      "assertions": [
        "Applies pricing psychology tactics",
        "Applies anchoring effect",
        "Applies decoy effect or visual emphasis",
        "Applies charm pricing or Rule of 100",
        "Provides specific pricing page recommendations",
        "Cross-references cro or marketing-psychology"
      ],
      "files": []
    },
    {
      "id": 6,
      "prompt": "Our pricing page conversion rate is only 1.5%. Can you review the page and suggest improvements?",
      "expected_output": "Should recognize this is a pricing page conversion optimization task, not a pricing strategy task. Should defer to or cross-reference the cro skill, which handles pricing page conversion rate optimization including plan comparison clarity, CTA optimization, and trust signals. Pricing-strategy focuses on the actual pricing decisions (what to charge, how to package), not the page design.",
      "assertions": [
        "Recognizes this as pricing page CRO, not pricing strategy",
        "References or defers to cro skill",
        "Explains that pricing is about pricing decisions",
        "Does not attempt full page CRO audit"
      ],
      "files": []
    },
    {
      "id": 7,
      "prompt": "Can you tear down our pricing page? I want to know if it is clear for buyers, and also whether AI tools like ChatGPT or Perplexity can actually read our prices when someone asks them to compare tools in our category.",
      "expected_output": "Should run the two-axis pricing page teardown (references/pricing-page-teardown.md), not a generic CRO audit. Axis 1 (human buyer experience): value-prop clarity, plan differentiation, cognitive load, trust signals, pricing psychology, price transparency. Axis 2 (AI-agent readiness): machine-readable pricing (real numbers in HTML/text, not locked in an image, JS-only render, or behind Contact us), extractable FAQ/objection coverage, per-tier depth stated in text, and structured data (Product/Offer schema) + AI-bot crawlability. Should recommend the paste test (paste the URL into an LLM and ask for plans and prices; if it cannot answer, an AI shopping for the buyer cannot either). Should prioritize fixes by impact x effort and note AI-readiness fixes are often high-impact/low-effort. Should hand implementation to schema (Product/Offer JSON-LD) and ai-seo (extractability, AI-bot access, llms.txt). May credit the AI-agent-readiness lens to Kyle Poyar.",
      "assertions": [
        "Runs the two-axis teardown (human buyer experience AND AI-agent readiness)",
        "Checks machine-readable pricing (not locked in an image / JS-only / behind Contact us)",
        "Recommends the paste test (an LLM can correctly quote plans and prices)",
        "Hands off to schema (Product/Offer structured data) and ai-seo (extractability / AI-bot access / llms.txt)",
        "Prioritizes fixes by impact x effort; flags AI-readiness fixes as often high-impact low-effort",
        "Does not treat this as pure conversion-rate CRO"
      ],
      "files": []
    },
    {
      "id": 8,
      "prompt": "We're launching an AI writing tool for solo creators next week and I genuinely have no idea what to charge on day one. I was going to just do $9/month to get people in the door. What price should I pick and how should I even structure it?",
      "expected_output": "Should treat this as an INITIAL pricing question, not a price-optimization one — the goal of a first price is learning, not precision ('pick a price you can learn from'). Should apply the $10/$100/$1,000 rule of thumb and place a solo-creator tool near the ~$10 bucket. Should warn against the $9 trap (false traction, hard to raise later, cheapest customers churn most). May cite the Intercom/Jason Fried 'just charge $50 and see what happens' idea — ship a price and get real signal. Should reject competitor-based and cost-based anchoring in favor of value + differentiation. Should recommend a pricing MODEL/structure: for an AI actions-based tool, credit-based or usage-based (or a hybrid) is a natural fit; may reference the 8 models. May mention the ~10:1 value/price ratio and the low-price-hurts-retention counterpoint (when in doubt, price higher).",
      "assertions": [
        "Frames the first price as a learning bet, not an optimization",
        "Applies the $10/$100/$1,000 rule of thumb and buckets the tool appropriately",
        "Warns against the $9 / ultra-low trap (false traction, hard to raise, low-price churn)",
        "References 'just charge $50 and see' / getting a real signal (Intercom/Jason Fried)",
        "Rejects competitor-based and cost-based pricing in favor of value + differentiation",
        "Recommends a pricing model/structure (e.g. credit-based or usage-based for an AI tool)",
        "Notes value/price ratio (~10:1) or that low prices hurt retention"
      ],
      "files": []
    }
  ]
}

# Pricing Models

The eight core ways to structure *how* you charge. This is distinct from the value metric (what unit you charge on) and the tier structure (how you package). Most real products **combine** two or more of these.

## Contents
- The 8 Pricing Models
- Combining Models
- The Value/Price Ratio
- The Low-Price Retention Counterpoint

---

## The 8 Pricing Models

| Model | How it works | Best when | Reference |
|-------|-------------|-----------|-----------|
| **Flat-rate** | One price, one product, everyone pays the same | Simple product, one persona, you want zero pricing friction | Basecamp |
| **Usage-based** | Pay for what you consume (metered) | Value scales directly with volume; consumption is variable and easy to meter | Stripe |
| **Tier-based** | Good-better-best packages at set prices | Distinct segments with different needs and budgets | Kinsta |
| **User-based** | Price per seat/user | Value grows as more people in the org use it (collaboration) | Notion |
| **Feature-based** | Price gated by which capabilities are unlocked | Clear feature tiers map to willingness to pay | Intercom |
| **Credit-based** | Buy a bucket of credits, spend them on actions | Usage is lumpy or bursty; you want prepaid commitment and simple mental accounting | Audible |
| **Outcome-based** | Pay per result delivered (resolution, task completed) | You can measure and attribute the outcome, and the outcome is what the buyer actually wants | Intercom Fin, Zapier |
| **Hybrid** | Deliberate mix (e.g. platform fee + usage, or seats + credits) | A single model under- or over-charges different customers | Drift |

### When to reach for each

- **Flat-rate** — reach for it first if you can. It's the easiest to sell, easiest to understand, easiest to forecast. The tradeoff: you leave money on the table with your biggest customers.
- **Usage-based** — the fairest model when consumption tracks value, but revenue is less predictable and buyers fear a surprise bill. Pair with spend caps or alerts.
- **Tier-based** — the default for self-serve SaaS. Lets one page serve SMB through mid-market.
- **User-based** — only if value genuinely rises with headcount. If it doesn't, seats punish adoption (teams share logins to avoid paying).
- **Feature-based** — powerful for segmentation, but don't gate the feature that delivers your core value; gate the ones that separate casual from serious users.
- **Credit-based** — good for AI/actions-based products where each action has a cost. Credits decouple price from a single unit and make prepayment feel natural.
- **Outcome-based** — the emerging model for AI agents (charge per resolved ticket, per automation run). Highest trust because the buyer only pays when they win — but only viable when the outcome is measurable and clearly attributable to you.
- **Hybrid** — where most mature products end up. A base platform fee for predictability plus a usage/outcome component for upside.

---

## Combining Models

These aren't mutually exclusive. Common combinations:

- **Tiers + per-user** — seats within each package (most B2B SaaS)
- **Platform fee + usage** — predictable base, variable upside (Twilio-style)
- **Seats + credits** — pay per person, then top up credits for heavy actions
- **Feature tiers + outcome** — unlock capabilities by tier, charge per result on top

Pick the primary model from the value metric, then layer a second only if a single model clearly mis-prices a real segment.

---

## The Value/Price Ratio

Aim for roughly a **10:1 value-to-price ratio** (Ryan Kulp): the customer should perceive about **10x more value than they pay**. This is the buffer that makes the purchase feel obvious rather than negotiated, and it leaves headroom to raise prices later as you add value.

If you can't articulate 10x value, the problem is usually the offer or the positioning, not the price point.

---

## The Low-Price Retention Counterpoint

Charging too little is not the safe choice. **Low prices hurt retention** (Patrick Campbell / ProfitWell data, echoed by operators like Josh Pigford of SpyFu and Tyler Tringas): under-priced customers churn *more*, not less, because a low price signals low value and attracts the least-committed, most price-sensitive buyers.

Related: the **discount-asker signal** — customers who negotiate for a discount tend to churn at roughly **2x** the rate of full-price customers. Discounting to close a deal often buys a customer who leaves anyway.

**Implication:** when in doubt, price higher. It's easier to grandfather a price down than to claw one up, and a higher price selects for better-fit, longer-retained customers.

# Pricing Page Teardown

A structured way to score a live pricing page and return prioritized fixes. It grades **two axes**: the classic **human buyer experience**, and — the newer, higher-leverage lens — **AI-agent readiness**: whether the LLMs and agents that increasingly shortlist and compare tools can actually read, quote, and recommend your pricing.

> **Framework credit:** the two-axis structure and especially the AI-agent-readiness lens are adapted from **Kyle Poyar's** (Growth Unhinged) pricing-page teardown. Learn-from-only — this rubric is authored independently; credit the framing to Poyar.

## Why the second axis matters now

Buyers increasingly ask ChatGPT, Perplexity, and Claude *"what's the best [category] tool and what does it cost?"* before they ever hit your site. If your price is trapped in an image, rendered only by JavaScript, or missing from the page's text, a text-fetching agent often can't read it — some agents render JS or fall back to vision/OCR, but many don't, so don't count on it. And a "Contact us" tier gives an agent no public number to quote at all. When the agent can't read your price, it recommends and quotes the competitor whose pricing it *can*. This axis is the pricing-page complement to `ai-seo` and `schema` — neither *guarantees* a citation, but a page a fetcher can't parse makes one much less likely.

**The 30-second test — the "paste test":** give the pricing URL to a **browsing-capable** AI (Perplexity, ChatGPT with search, or Claude with web) — or paste the page's *rendered* text — and ask *"What are the plans and prices?"* If it can't answer correctly and completely, agents fetching your page the same way will struggle too. It's a heuristic, not proof every agent fails (some render JS or use vision), but a clean miss is a real finding worth fixing.

## The rubric

Score each dimension **Pass / Partial / Gap** (or 1–5 if you want a number). Two sub-scores (one per axis) plus a prioritized fix list is the deliverable — not a single vanity number.

### Axis 1 — Human buyer experience

| # | Dimension | Passing looks like | Common gaps |
|---|---|---|---|
| 1 | **Value-prop clarity** | Above the fold: what you get + why it's worth it, in the buyer's words | Feature list with no outcome; "flexible plans for every team" |
| 2 | **Plan clarity / differentiation** | Obvious which plan is for whom and exactly how they differ | Feature-soup tables; tiers that blur together; no "who it's for" |
| 3 | **Cognitive load** | A buyer can decide in <30s | Too many tiers (5+), unexplained jargon, decision paralysis |
| 4 | **Trust signals** | Logos, testimonials, security/compliance, a guarantee near the CTA | No proof; trust content buried below the fold |
| 5 | **Pricing psychology** | A recommended/anchor tier, sensible anchoring, coherent charm vs. round pricing | No recommended tier; highest price hidden last; random price endings |
| 6 | **Transparency** | The actual price is shown; what's in/out is clear; no surprise fees | "Contact us" on every tier; hidden overages; usage limits omitted |

### Axis 2 — AI-agent readiness (the novel lens)

| # | Dimension | Passing looks like | Common gaps |
|---|---|---|---|
| 7 | **Machine-readable pricing** | The real numbers are in the page's HTML/text | Price in an image/SVG, JS-only render, or a PDF — text-fetching crawlers get nothing reliable; "Contact sales" leaves no public number to quote |
| 8 | **FAQ / objection coverage** | Extractable answers to "does it do X," "what's the limit," "can I cancel," "is there a free trial" | No FAQ, or answers only in a support portal an agent won't reach |
| 9 | **Per-tier depth in text** | Each plan's inclusions, limits, and quotas stated in words | Differences shown only as checkmark columns in an image; limits unnamed |
| 10 | **Structured data & extractability** | `Product`/`Offer` schema markup, clean semantic HTML, AI search/agent bots allowed to crawl (`llms.txt` is a nice-to-have, not yet a standard) | No schema; pricing behind auth/interaction; AI *search* bots blocked in robots.txt |

Dimensions 7 and 10 hand off to **`schema`** (Product/Offer JSON-LD) and **`ai-seo`** (extractability, AI-bot access, `llms.txt`) for implementation.

## How to run it

1. **Load context** — read `.agents/product-marketing.md` (ICP, positioning) so "clarity" is judged against the *right* buyer.
2. **Fetch the page as an agent would** — get the rendered text/HTML, not a screenshot. Note immediately whether prices appear in the text (that's dimension 7).
3. **Run the paste test** — ask an LLM for the plans and prices from the URL; record what it gets wrong or misses.
4. **Score all 10 dimensions** Pass/Partial/Gap with a one-line reason each.
5. **Prioritize fixes** by impact × effort. AI-readiness gaps are often *high impact, low effort* (add text prices, add Offer schema) — surface those first.

## Output template

```markdown
# Pricing Page Teardown — [url] — [date]

## Scores
- Human buyer experience: [X/6 passing]
- AI-agent readiness:      [X/4 passing]

## Paste test
[What an LLM returned for "plans and prices" — and what it got wrong/missed]

## Dimension-by-dimension
| # | Dimension | Verdict | Note |
|---|-----------|---------|------|
| 1 | Value-prop clarity | Pass/Partial/Gap | ... |
| … | … | … | … |

## Prioritized fixes (impact × effort)
1. [High/low] — [fix] — [why it matters] — [→ schema / ai-seo / cro if handing off]
2. ...

## The one thing
[The single highest-leverage fix — often "put your actual prices in text + add Offer schema so AI can quote you."]
```

## Common failure patterns

- **The image-price** — a beautiful pricing graphic with the numbers baked in. Humans love it; text-fetching agents (and screen readers) usually can't read it. Put prices in text; the image can stay as decoration.
- **"Contact us" everywhere** — sometimes right for true enterprise, but if *all* tiers hide price, both humans and agents bounce to a competitor with numbers. Show at least a starting price or a representative range.
- **Checkmark-only tables** — feature differences shown only as ✓/✗ columns in an image or icon font. State the actual limits and inclusions in words.
- **JS-only render / auth wall** — if the price only appears after interaction or login, most fetchers won't see it (only JS-rendering agents might).
- **Blocked AI *search* bots** — the crawlers that feed AI *answers* are the search agents, not the training crawlers: OpenAI's `OAI-SearchBot`, Anthropic's `Claude-SearchBot` / `Claude-User`, Perplexity's `PerplexityBot`. Blocking `GPTBot` only opts out of model *training*, not ChatGPT Search — so check which bots your robots.txt actually blocks. (Bot access is `ai-seo`'s domain — hand it off there.)

## Related
- `schema` — Product/Offer JSON-LD so machines read your tiers and prices.
- `ai-seo` — extractability, AI-bot access, `llms.txt`, getting cited by AI answers.
- `cro` — converting the human once the page is clear.
- `copywriting` — the value-prop and tier copy the teardown flags.

# Pricing Research Methods

## Contents
- Van Westendorp Price Sensitivity Meter (The Four Questions, How to Analyze, Survey Tips, Sample Output)
- MaxDiff Analysis (How It Works, Example Survey Question, Analyzing Results, Using MaxDiff for Packaging)
- Willingness to Pay Surveys
- Usage-Value Correlation Analysis

## Van Westendorp Price Sensitivity Meter

The Van Westendorp survey identifies the acceptable price range for your product.

### The Four Questions

Ask each respondent:
1. "At what price would you consider [product] to be so expensive that you would not consider buying it?" (Too expensive)
2. "At what price would you consider [product] to be priced so low that you would question its quality?" (Too cheap)
3. "At what price would you consider [product] to be starting to get expensive, but you still might consider it?" (Expensive/high side)
4. "At what price would you consider [product] to be a bargain—a great buy for the money?" (Cheap/good value)

### How to Analyze

1. Plot cumulative distributions for each question
2. Find the intersections:
   - **Point of Marginal Cheapness (PMC):** "Too cheap" crosses "Expensive"
   - **Point of Marginal Expensiveness (PME):** "Too expensive" crosses "Cheap"
   - **Optimal Price Point (OPP):** "Too cheap" crosses "Too expensive"
   - **Indifference Price Point (IDP):** "Expensive" crosses "Cheap"

**The acceptable price range:** PMC to PME
**Optimal pricing zone:** Between OPP and IDP

### Survey Tips
- Need 100-300 respondents for reliable data
- Segment by persona (different willingness to pay)
- Use realistic product descriptions
- Consider adding purchase intent questions

### Sample Output

```
Price Sensitivity Analysis Results:
─────────────────────────────────
Point of Marginal Cheapness:  $29/mo
Optimal Price Point:          $49/mo
Indifference Price Point:     $59/mo
Point of Marginal Expensiveness: $79/mo

Recommended range: $49-59/mo
Current price: $39/mo (below optimal)
Opportunity: 25-50% price increase without significant demand impact
```

---

## MaxDiff Analysis (Best-Worst Scaling)

MaxDiff identifies which features customers value most, informing packaging decisions.

### How It Works

1. List 8-15 features you could include
2. Show respondents sets of 4-5 features at a time
3. Ask: "Which is MOST important? Which is LEAST important?"
4. Repeat across multiple sets until all features compared
5. Statistical analysis produces importance scores

### Example Survey Question

```
Which feature is MOST important to you?
Which feature is LEAST important to you?

□ Unlimited projects
□ Custom branding
□ Priority support
□ API access
□ Advanced analytics
```

### Analyzing Results

Features are ranked by utility score:
- High utility = Must-have (include in base tier)
- Medium utility = Differentiator (use for tier separation)
- Low utility = Nice-to-have (premium tier or cut)

### Using MaxDiff for Packaging

| Utility Score | Packaging Decision |
|---------------|-------------------|
| Top 20% | Include in all tiers (table stakes) |
| 20-50% | Use to differentiate tiers |
| 50-80% | Higher tiers only |
| Bottom 20% | Consider cutting or premium add-on |

---

## Willingness to Pay Surveys

**Direct method (simple but biased):**
"How much would you pay for [product]?"

**Better: Gabor-Granger method:**
"Would you buy [product] at [$X]?" (Yes/No)
Vary price across respondents to build demand curve.

**Even better: Conjoint analysis:**
Show product bundles at different prices
Respondents choose preferred option
Statistical analysis reveals price sensitivity per feature

---

## Usage-Value Correlation Analysis

### 1. Instrument usage data
Track how customers use your product:
- Feature usage frequency
- Volume metrics (users, records, API calls)
- Outcome metrics (revenue generated, time saved)

### 2. Correlate with customer success
- Which usage patterns predict retention?
- Which usage patterns predict expansion?
- Which customers pay the most, and why?

### 3. Identify value thresholds
- At what usage level do customers "get it"?
- At what usage level do they expand?
- At what usage level should price increase?

### Example Analysis

```
Usage-Value Correlation Analysis:
─────────────────────────────────
Segment: High-LTV customers (>$10k ARR)
Average monthly active users: 15
Average projects: 8
Average integrations: 4

Segment: Churned customers
Average monthly active users: 3
Average projects: 2
Average integrations: 0

Insight: Value correlates with team adoption (users)
        and depth of use (integrations)

Recommendation: Price per user, gate integrations to higher tiers
```

# Tier Structure and Packaging

## Contents
- How Many Tiers?
- Good-Better-Best Framework
- Tier Differentiation Strategies
- Example Tier Structure
- Packaging for Personas (Identifying Pricing Personas, Persona-Based Packaging)
- Freemium vs. Free Trial (When to Use Freemium, When to Use Free Trial, Hybrid Approaches)
- Enterprise Pricing (When to Add Custom Pricing, Enterprise Tier Elements, Enterprise Pricing Strategies)

## How Many Tiers?

**2 tiers:** Simple, clear choice
- Works for: Clear SMB vs. Enterprise split
- Risk: May leave money on table

**3 tiers:** Industry standard
- Good tier = Entry point
- Better tier = Recommended (anchor to best)
- Best tier = High-value customers

**4+ tiers:** More granularity
- Works for: Wide range of customer sizes
- Risk: Decision paralysis, complexity

---

## Good-Better-Best Framework

**Good tier (Entry):**
- Purpose: Remove barriers to entry
- Includes: Core features, limited usage
- Price: Low, accessible
- Target: Small teams, try before you buy

**Better tier (Recommended):**
- Purpose: Where most customers land
- Includes: Full features, reasonable limits
- Price: Your "anchor" price
- Target: Growing teams, serious users

**Best tier (Premium):**
- Purpose: Capture high-value customers
- Includes: Everything, advanced features, higher limits
- Price: Premium (often 2-3x "Better")
- Target: Larger teams, power users, enterprises

---

## Tier Differentiation Strategies

**Feature gating:**
- Basic features in all tiers
- Advanced features in higher tiers
- Works when features have clear value differences

**Usage limits:**
- Same features, different limits
- More users, storage, API calls at higher tiers
- Works when value scales with usage

**Support level:**
- Email support → Priority support → Dedicated success
- Works for products with implementation complexity

**Access and customization:**
- API access, SSO, custom branding
- Works for enterprise differentiation

---

## Example Tier Structure

```
┌────────────────┬─────────────────┬─────────────────┬─────────────────┐
│                │ Starter         │ Pro             │ Business        │
│                │ $29/mo          │ $79/mo          │ $199/mo         │
├────────────────┼─────────────────┼─────────────────┼─────────────────┤
│ Users          │ Up to 5         │ Up to 20        │ Unlimited       │
│ Projects       │ 10              │ Unlimited       │ Unlimited       │
│ Storage        │ 5 GB            │ 50 GB           │ 500 GB          │
│ Integrations   │ 3               │ 10              │ Unlimited       │
│ Analytics      │ Basic           │ Advanced        │ Custom          │
│ Support        │ Email           │ Priority        │ Dedicated       │
│ API Access     │ ✗               │ ✓               │ ✓               │
│ SSO            │ ✗               │ ✗               │ ✓               │
│ Audit logs     │ ✗               │ ✗               │ ✓               │
└────────────────┴─────────────────┴─────────────────┴─────────────────┘
```

---

## Packaging for Personas

### Identifying Pricing Personas

Different customers have different:
- Willingness to pay
- Feature needs
- Buying processes
- Value perception

**Segment by:**
- Company size (solopreneur → SMB → enterprise)
- Use case (marketing vs. sales vs. support)
- Sophistication (beginner → power user)
- Industry (different budget norms)

### Persona-Based Packaging

**Step 1: Define personas**

| Persona | Size | Needs | WTP | Example |
|---------|------|-------|-----|---------|
| Freelancer | 1 person | Basic features | Low | $19/mo |
| Small Team | 2-10 | Collaboration | Medium | $49/mo |
| Growing Co | 10-50 | Scale, integrations | Higher | $149/mo |
| Enterprise | 50+ | Security, support | High | Custom |

**Step 2: Map features to personas**

| Feature | Freelancer | Small Team | Growing | Enterprise |
|---------|------------|------------|---------|------------|
| Core features | ✓ | ✓ | ✓ | ✓ |
| Collaboration | — | ✓ | ✓ | ✓ |
| Integrations | — | Limited | Full | Full |
| API access | — | — | ✓ | ✓ |
| SSO/SAML | — | — | — | ✓ |
| Audit logs | — | — | — | ✓ |
| Custom contract | — | — | — | ✓ |

**Step 3: Price to value for each persona**
- Research willingness to pay per segment
- Set prices that capture value without blocking adoption
- Consider segment-specific landing pages

---

## Freemium vs. Free Trial

### When to Use Freemium

**Freemium works when:**
- Product has viral/network effects
- Free users provide value (content, data, referrals)
- Large market where % conversion drives volume
- Low marginal cost to serve free users
- Clear feature/usage limits for upgrade trigger

**Freemium risks:**
- Free users may never convert
- Devalues product perception
- Support costs for non-paying users
- Harder to raise prices later

### When to Use Free Trial

**Free trial works when:**
- Product needs time to demonstrate value
- Onboarding/setup investment required
- B2B with buying committees
- Higher price points
- Product is "sticky" once configured

**Trial best practices:**
- 7-14 days for simple products
- 14-30 days for complex products
- Full access (not feature-limited)
- Clear countdown and reminders
- Credit card optional vs. required trade-off

**Credit card upfront:**
- Higher trial-to-paid conversion (40-50% vs. 15-25%)
- Lower trial volume
- Better qualified leads

### Hybrid Approaches

**Freemium + Trial:**
- Free tier with limited features
- Trial of premium features
- Example: Zoom (free 40-min, trial of Pro)

**Reverse trial:**
- Start with full access
- After trial, downgrade to free tier
- Example: See premium value, live with limitations until ready

---

## Enterprise Pricing

### When to Add Custom Pricing

Add "Contact Sales" when:
- Deal sizes exceed $10k+ ARR
- Customers need custom contracts
- Implementation/onboarding required
- Security/compliance requirements
- Procurement processes involved

### Enterprise Tier Elements

**Table stakes:**
- SSO/SAML
- Audit logs
- Admin controls
- Uptime SLA
- Security certifications

**Value-adds:**
- Dedicated support/success
- Custom onboarding
- Training sessions
- Custom integrations
- Priority roadmap input

### Enterprise Pricing Strategies

**Per-seat at scale:**
- Volume discounts for large teams
- Example: $15/user (standard) → $10/user (100+)

**Platform fee + usage:**
- Base fee for access
- Usage-based above thresholds
- Example: $500/mo base + $0.01 per API call

**Value-based contracts:**
- Price tied to customer's revenue/outcomes
- Example: % of transactions, revenue share

---
name: pricing
description: "When the user wants help with pricing decisions, packaging, or monetization strategy. Also use when the user mentions 'pricing,' 'pricing tiers,' 'freemium,' 'free trial,' 'packaging,' 'price increase,' 'value metric,' 'Van Westendorp,' 'willingness to pay,' 'monetization,' 'how much should I charge,' 'my pricing is wrong,' 'pricing page,' 'annual vs monthly,' 'per seat pricing,' 'should I offer a free plan,' 'pricing page teardown,' 'pricing page audit,' 'is my pricing page AI-readable,' or 'can AI read my pricing.' Use this whenever someone is figuring out what to charge, how to structure their plans, or wants to audit a pricing page (for humans and for the AI agents that shortlist tools). For in-app upgrade screens, see paywalls. For offer construction (bonuses, guarantees, value framing, naming) on services/courses/coaching/high-ticket B2B, see offers."
metadata:
  version: 2.1.1
---

# Pricing Strategy

You are an expert in SaaS pricing and monetization strategy. Your goal is to help design pricing that captures value, drives growth, and aligns with customer willingness to pay.

## Before Starting

**Check for product marketing context first:**
If `.agents/product-marketing.md` exists (or `.claude/product-marketing.md`, or the legacy `product-marketing-context.md` filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.

Gather this context (ask if not provided):

### 1. Business Context
- What type of product? (SaaS, marketplace, e-commerce, service)
- What's your current pricing (if any)?
- What's your target market? (SMB, mid-market, enterprise)
- What's your go-to-market motion? (self-serve, sales-led, hybrid)

### 2. Value & Competition
- What's the primary value you deliver?
- What alternatives do customers consider?
- How do competitors price?

### 3. Current Performance
- What's your current conversion rate?
- What's your ARPU and churn rate?
- Any feedback on pricing from customers/prospects?

### 4. Goals
- Optimizing for growth, revenue, or profitability?
- Moving upmarket or expanding downmarket?

---

## Pricing Fundamentals

### The Three Pricing Axes

**1. Packaging** — What's included at each tier?
- Features, limits, support level
- How tiers differ from each other

**2. Pricing Metric** — What do you charge for?
- Per user, per usage, flat fee
- How price scales with value

**3. Price Point** — How much do you charge?
- The actual dollar amounts
- Perceived value vs. cost

### Value-Based Pricing

Price should be based on value delivered, not cost to serve:

- **Customer's perceived value** — The ceiling
- **Your price** — Between alternatives and perceived value
- **Next best alternative** — The floor for differentiation
- **Your cost to serve** — Only a baseline, not the basis

**Key insight:** Price between the next best alternative and perceived value.

**Don't anchor on the wrong things:**
- **Not competitor-based** — matching a competitor's price copies their strategy, not their economics. It's a data point, not a target.
- **Not cost-based** — cost is a floor, never the basis. Value + differentiation set the price.

---

## Initial Pricing — "Pick a Price You Can Learn From"

The frameworks below (value metrics, tiers, Van Westendorp) are for optimizing a price. **On day one you don't have a price to optimize — you have a bet to place.** The goal of your first price is *learning*, not precision. Pick a number, ship it, and let real buyers tell you if it's wrong.

### The $10 / $100 / $1,000 rule of thumb

When you have nothing to go on, start with the order of magnitude that matches who you serve:

- **~$10/mo** — prosumer / individual, high volume, low touch
- **~$100/mo** — SMB / team tool, the SaaS default
- **~$1,000/mo** — mid-market / business-critical / sales-assisted

Pick the bucket by **who the customer is and how much value you deliver**, then start near the round number. You can move within the bucket fast once you have signal.

### Avoid the $9 trap

Resist the urge to price ultra-low (e.g. **$9/mo**) to reduce friction. Ultra-low pricing:
- Creates **false traction** — signups that look like validation but come from people who'd never pay a real price
- **Traps you** — it's far harder to raise a price 5–10x later than to have started higher, and your cheapest customers churn most and complain loudest (see [references/pricing-models.md](references/pricing-models.md) on low-price retention)

Round-and-slightly-higher beats clever-and-cheap.

### "Just charge $50 and see what happens"

When early Intercom agonized over pricing, Jason Fried's advice was essentially: **just charge $50 and see what happens.** Stop modeling; get a real signal. If people pay without flinching, raise it. If nobody bites, you've learned something for the cost of a week, not a quarter.

**For the eight ways to structure how you charge (flat, usage, tier, user, feature, credit, outcome, hybrid) and the value/price ratio:** See [references/pricing-models.md](references/pricing-models.md).

---

## Value Metrics

### What is a Value Metric?

The value metric is what you charge for—it should scale with the value customers receive.

**Good value metrics:**
- Align price with value delivered
- Are easy to understand
- Scale as customer grows
- Are hard to game

### Common Value Metrics

| Metric | Best For | Example |
|--------|----------|---------|
| Per user/seat | Collaboration tools | Slack, Notion |
| Per usage | Variable consumption | AWS, Twilio |
| Per feature | Modular products | HubSpot add-ons |
| Per contact/record | CRM, email tools | Mailchimp |
| Per transaction | Payments, marketplaces | Stripe |
| Flat fee | Simple products | Basecamp |

### Choosing Your Value Metric

Ask: "As a customer uses more of [metric], do they get more value?"
- If yes → good value metric
- If no → price doesn't align with value

**The value metric picks the pricing model.** Once you know what scales with value, choose how to charge on it — flat, usage, tier, user, feature, credit, outcome, or a hybrid. See [references/pricing-models.md](references/pricing-models.md).

---

## Tier Structure Overview

### Good-Better-Best Framework

**Good tier (Entry):** Core features, limited usage, low price
**Better tier (Recommended):** Full features, reasonable limits, anchor price
**Best tier (Premium):** Everything, advanced features, 2-3x Better price

### Tier Differentiation

- **Feature gating** — Basic vs. advanced features
- **Usage limits** — Same features, different limits
- **Support level** — Email → Priority → Dedicated
- **Access** — API, SSO, custom branding

**For detailed tier structures and persona-based packaging**: See [references/tier-structure.md](references/tier-structure.md)

---

## Pricing Research

### Van Westendorp Method

Four questions that identify acceptable price range:
1. Too expensive (wouldn't consider)
2. Too cheap (question quality)
3. Expensive but might consider
4. A bargain

Analyze intersections to find optimal pricing zone.

### MaxDiff Analysis

Identifies which features customers value most:
- Show sets of features
- Ask: Most important? Least important?
- Results inform tier packaging

**For detailed research methods**: See [references/research-methods.md](references/research-methods.md)

---

## When to Raise Prices

### Signs It's Time

**Market signals:**
- Competitors have raised prices
- Prospects don't flinch at price
- "It's so cheap!" feedback

**Business signals:**
- Very high conversion rates (>40%)
- Very low churn (<3% monthly)
- Strong unit economics

**Product signals:**
- Significant value added since last pricing
- Product more mature/stable

### Price Increase Strategies

1. **Grandfather existing** — New price for new customers only
2. **Delayed increase** — Announce 3-6 months out
3. **Tied to value** — Raise price but add features
4. **Plan restructure** — Change plans entirely

### Rollout Methodology

A price change is a rollout, not a switch you flip. Sequence it to de-risk:

1. **Test on new customers first.** Raise the price only for *new* signups and watch conversion. New customers have no anchor and no relationship at stake, so they give you a clean read on whether the market accepts the number — before you touch a single existing account.
2. **Don't reflexively grandfather forever.** Grandfathering feels kind, but it can leave enormous money on the table. Run the math: a customer paying **$50/mo** who *should* be at **$250/mo** is a **$2,400/yr** gap — and $200/mo you're subsidizing indefinitely across your whole base. Grandfather as a *transition* (a grace period), not a permanent exemption.
3. **Roll out small, then gradually.** Move **5–10%** of existing customers to the new price first. Watch churn and support volume for a cycle, then expand in staggered waves. A staggered rollout contains the blast radius and gives you an off-ramp if churn spikes.
4. **Communicate the *why*, months ahead, with a generous offer.** Tell customers why the price is changing (usually: more value shipped) well in advance. Soften it: lock-in-the-old-price-if-you-upgrade-to-annual-now, an extended grace window, or a one-time credit. Advance notice + a generous option converts a resentment moment into a loyalty one.

Expect — and accept — some churn. The customers most likely to leave over a justified increase are usually your least-profitable, highest-support, most price-sensitive accounts.

---

## Pricing Page Best Practices

### Above the Fold
- Clear tier comparison table
- Recommended tier highlighted
- Monthly/annual toggle
- Primary CTA for each tier

### Common Elements
- Feature comparison table
- Who each tier is for
- FAQ section
- Annual discount callout (17-20%)
- Money-back guarantee
- Customer logos/trust signals

### Pricing Psychology
- **Anchoring:** Show higher-priced option first
- **Decoy effect:** Middle tier should be best value
- **Charm pricing:** $49 vs. $50 (for value-focused)
- **Round pricing:** $50 vs. $49 (for premium)

---

## Pricing Page Teardown

When someone wants to audit an existing pricing *page* for **clarity, transparency, and AI-readability** (not the pricing strategy itself, and not conversion-rate optimization — that's `cro`), run a **teardown** that scores it across two axes and returns prioritized fixes:

- **Human buyer experience** — value-prop clarity, plan differentiation, cognitive load, trust signals, pricing psychology, and price transparency.
- **AI-agent readiness** — whether the LLMs and agents that increasingly shortlist and compare tools can actually read and quote your pricing: machine-readable prices (not locked in an image or behind "Contact us"), extractable FAQ/objection coverage, per-tier depth stated in text, and structured data. Buyers now ask ChatGPT/Perplexity/Claude "what's the best X and what does it cost?" *before* visiting — a pricing page an agent can't parse loses deals you never see.

**Fast check — the "paste test":** give the pricing URL to a browsing-capable AI (Perplexity, ChatGPT with search, Claude with web) — or paste the rendered page text — and ask "what are the plans and prices?" A clean miss means agents fetching your page will struggle too (a heuristic, not proof every agent fails).

The AI-readiness fixes are usually high-impact, low-effort (put prices in text, add `Offer` schema). Hand implementation to **schema** (Product/Offer JSON-LD) and **ai-seo** (extractability, AI-bot access, `llms.txt`).

**For the full 10-dimension rubric, scoring, and report template:** See [references/pricing-page-teardown.md](references/pricing-page-teardown.md). *(AI-agent-readiness lens adapted from Kyle Poyar / Growth Unhinged.)*

---

## Pricing Checklist

### Before Setting Prices
- [ ] Defined target customer personas
- [ ] Researched competitor pricing
- [ ] Identified your value metric
- [ ] Conducted willingness-to-pay research
- [ ] Mapped features to tiers

### Pricing Structure
- [ ] Chosen number of tiers
- [ ] Differentiated tiers clearly
- [ ] Set price points based on research
- [ ] Created annual discount strategy
- [ ] Planned enterprise/custom tier

---

## Task-Specific Questions

1. What pricing research have you done?
2. What's your current ARPU and conversion rate?
3. What's your primary value metric?
4. Who are your main pricing personas?
5. Are you self-serve, sales-led, or hybrid?
6. What pricing changes are you considering?

---

## Related Skills

- **churn-prevention**: For cancel flows, save offers, and reducing revenue churn
- **cro**: For optimizing pricing page conversion
- **ai-seo**: For making the pricing page extractable/citable by AI (the teardown's AI-agent-readiness axis)
- **schema**: For Product/Offer structured data so machines can read your tiers and prices
- **copywriting**: For pricing page copy
- **marketing-psychology**: For pricing psychology principles
- **ab-testing**: For testing pricing changes
- **revops**: For deal desk processes and pipeline pricing
- **sales-enablement**: For proposal templates and pricing presentations