What it does. 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. Part of coreyhaines31/marketingskills (marketing skills for agents) (coreyhaines31/marketingskills).

Upstream	coreyhaines31/marketingskills
Skill file	skills/pricing/SKILL.md
License	MIT
Author	Corey Haines
Fetched	2026-09-10

Install

• npx skills add coreyhaines31/marketingskills --skill pricing, or copy the skill folder into ~/.claude/skills/pricing/.
• Raw file: curl -sL https://raw.githubusercontent.com/coreyhaines31/marketingskills/HEAD/skills/pricing/SKILL.md

SKILL.md (verbatim)

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 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.

---

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.

---

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

---

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

---

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. (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

Other files in this skill

• evals/evals.json
• references/pricing-models.md
• references/pricing-page-teardown.md
• references/research-methods.md
• references/tier-structure.md

references/pricing-models.md (verbatim)

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.

references/pricing-page-teardown.md (verbatim)

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

# 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.

references/research-methods.md (verbatim)

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

references/tier-structure.md (verbatim)

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

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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

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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     │ ✗               │ ✗               │ ✓               │
└────────────────┴─────────────────┴─────────────────┴─────────────────┘

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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

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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

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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

Back to coreyhaines31/marketingskills (marketing skills for agents) or Agent skills.