Pricing & Monetization Strategy

Recommend a pricing model and structure, or brainstorm 3–5 monetization options, anchored in what customers actually said about price — their willingness-to-pay signals, objections, and the value they name. Competitor pricing is a secondary check, not the anchor. Value-based pricing built on customer quotes beats guessing from competitor screenshots.

Step 0 — Relevance & availability

Confirm this is pricing/monetization work and Evermuse is connected. If not, produce a framework-only recommendation labeled ⚠ ungrounded — Evermuse not connected and tell the user to authorize the MCP. See ${CLAUDE_PLUGIN_ROOT}/skills/using-evermuse/SKILL.md Step 0.

Evermuse Grounding (required)

Follow ${CLAUDE_PLUGIN_ROOT}/skills/using-evermuse/SKILL.md.

Required — one parallel batch of searches. Verify the product (Rule 1), then fire in a single parallel batch:

• 3–4 evidence searches, each worded from a different angle (limit up to 50). Evidence comes back rich and varied — expect large, useful result sets.
• one guidance search and one context search (limit up to 50). These are usually sparse or empty; run them anyway and note when they're thin.

Read each response's digest — it reports how many more results exist. Use judgment on whether a query is worth pulling deeper (raise limit toward the 100 max and/or page with next_offset to avoid repeats), weighing payload size, remaining context, task complexity, and the value of the data. For deep pulls, consider spawning sub-agents — instruct them to return every citation with the same metadata the tools return (url, who_said_it, meeting_name, created_at) so you can still cite.

Optional — considered use. Once grounded, reach for the other tools only when they add value: a quote-angled search for verbatim voice, read_source for a single deep dive, inline citations (references/citations.md), and add_source to save the deliverable. Sources, citations, and saving are optional — not required.

Instructions

You are a pricing strategist for $ARGUMENTS. Ground everything in value delivered and evidenced willingness to pay.

A) Pricing strategy (default)

1. Value delivered — core value prop; the customer's alternative and its cost; quantifiable outcome (time saved, revenue gained, cost cut); WTP from evidence, quoted.
2. Pick a model — Flat-rate · Per-seat · Usage-based · Tiered · Freemium · Freemium+usage · Value-based. Recommend the best fit and say why, in terms of the value metric customers care about.
3. Competitive pricing (secondary) — map competitor tiers via list_competitors/get_competitor_capabilities + context; place your product (premium / mid / budget); find gaps. Label secondary.
4. Structure — 2–4 tiers with clear differentiation; feature-gate on value metrics (not arbitrary limits); pick the value metric (seats, events, storage, API calls); anchor the popular tier; ~15–20% annual discount.
5. Price sensitivity — Van Westendorp if survey data exists; otherwise estimate from evidenced WTP + competitor pricing. Surface pricing objections from evidence as the sensitivity signal.
6. Experiments — pricing-page A/Bs, founder-led sales conversations, landing-page anchor tests, conversion cohort analysis.

B) Monetization brainstorm (when the user wants options, not one answer)

Generate 3–5 distinct models (Freemium, Subscription, Usage-based, Seat-based, One-time, Marketplace fee, Ads). For each: how it works for this product · audience fit (grounded in evidence) · unit economics (CAC/LTV/break-even) · risks · competitive position (context, secondary) · a low-cost validation experiment with a success metric. Recommend 1–2 to test first.

Where the source method reaches for "web search / competitor pricing / survey files," use Evermuse evidence (WTP, objections, value language) as the primary source; competitor pricing and market context are secondary.

Deliverable format

# Pricing Recommendation — [product]

**Recommended model:** [model] · **Value metric:** [unit]
*Grounded in WTP:* > "[quote about price/value]" — [attribution] [`1`](URL)

| Tier | Price | Target segment | Key features | Positioning |
|---|---|---|---|---|

**Objections heard (sensitivity signal):** [cited [`2`](URL)]
**Competitor pricing (secondary):** [table] — via list_competitors/context
**Assumptions → tests:** [assumption] → [experiment]
**Risks → mitigations:** …

Further reading

• Adapted from phuryn/pm-skills (MIT), merging its pricing-strategy and monetization-strategy skills; grounding is Evermuse-native.
• Product Pricing Strategies 101