[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6X7XWP8BFTCBXZRCC2G9EAQ":3,"skill-same:01M6X7XWP8BFTCBXZRCC2G9EAQ":66},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":44,"commitSha":45,"skillDirectory":46,"forksCount":47,"repositoryHtmlUrl":48,"repositoryHomepageUrl":6,"ownerHtmlUrl":49,"ownerBlogUrl":6,"ownerDescription":50,"updatedAt":51,"install":52,"resources":65},"01M6X7XWP8BFTCBXZRCC2G9EAQ","pricing-and-wtp",null,"Models willingness to pay using Van Westendorp price sensitivity analysis, desire-premium multipliers, category benchmarks, and market_insights monetization signals. Recommends pricing model, freemium conversion estimate, and annual\u002Fmonthly strategy.","MaxKmet\u002Fidea-validation-agents","MaxKmet","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F19517077?v=4","01M6X7QNAFCSMWXN7FDJS2KFGY","Max Kmet","01M6X7QNEEA69XPH7DCQRS5NJE",511,"MIT","https:\u002F\u002Fgithub.com\u002FMaxKmet\u002Fidea-validation-agents\u002Fblob\u002Fmain\u002Fskills\u002Fpricing-and-wtp\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"pricing and wtp","wtp","pricing","monetization","estimate","pricing model","freemium conversion","pay","insights","price","sensitivity","signals","benchmarks","models","Market","conversion","strategy","model","analysis","sensitivity analysis","2026-11-10 10:12:30",false,"---\nname: pricing-and-wtp\ndescription: Models willingness to pay using Van Westendorp price sensitivity analysis, desire-premium multipliers, category benchmarks, and market_insights monetization signals. Recommends pricing model, freemium conversion estimate, and annual\u002Fmonthly strategy.\n---\n\n\u003C!-- version: 0.2.0 | outputs: memory\u002Fideas\u002F\u003Cslug>\u002Fpricing.json -->\n\n# Skill: pricing-and-wtp\n\n## Purpose\n\nDetermine what users would actually pay, not what the developer hopes to charge. Anchor WTP to pain intensity, desire strength, competitive pricing, and real monetization signals from market_insights — never to the cost of building. Pricing is the single decision with the highest leverage on unit economics: a $1\u002Fmo difference at 1,000 users is $12K\u002Fyear.\n\n## Input\n\n- Idea slug\n- `memory\u002Fideas\u002F\u003Cslug>\u002Fidea.md` (app concept, key features)\n- `memory\u002Fideas\u002F\u003Cslug>\u002Fuser_extraction.json` (pain intensity)\n- `memory\u002Fideas\u002F\u003Cslug>\u002Fcompetitors.json` (competitive pricing, pricing models in use)\n- `memory\u002Fideas\u002F\u003Cslug>\u002Fdesire_scores.json` (desire strength, primary\u002Fsecondary drivers)\n- `memory\u002Fmarket_insights\u002F\u003Cniche>-*-\u003CYYYY>-\u003CMM>.md` (trend data — **use all available platform files**)\n\n### Using Market Insights\n\nTrend analysis files provide monetization reality-checks that desk research alone cannot:\n\n| Field | How it informs pricing |\n|---|---|\n| `monetization_evidence` | The strongest signal. If competitors are visibly making money (in-app purchases mentioned in reviews, subscription pricing in App Store listings, sponsored content), the market has validated WTP. If monetization_evidence is empty across all platforms, WTP is unproven — discount aspirational price and flag risk. |\n| Platform narratives — Reddit | Users discuss pricing directly: \"I'd pay for X if...\", \"Y is overpriced because...\", \"$Z\u002Fmo is too much for just...\" These are raw WTP signals. Extract any price mentions. |\n| Platform narratives — App Store | Review complaints about pricing reveal the ceiling: \"Great app but not worth $X\u002Fmo.\" Review praise about pricing reveals the floor: \"Amazing value at $X.\" |\n| Platform narratives — TikTok | Creator recommendations often mention price as part of the hook (\"this FREE app...\", \"worth every penny of $X\"). Signals whether freemium or premium positioning resonates with the audience. |\n| `trend_velocity` | Rising-fast markets can support premium pricing (early adopters pay more). Declining markets face price pressure (users comparison-shop harder). |\n\n## Pricing Models\n\n| Model | Best for | Risk | Indie sweet spot |\n|---|---|---|---|\n| **Freemium → subscription** | Habit-forming apps with daily use, feature-gated value | Conversion friction; most users stay free forever | $3–$8\u002Fmo or $20–$50\u002Fyr |\n| **Subscription only (paywall)** | High-value, immediately obvious ROI, professional tools | High churn risk; must prove value fast | $5–$15\u002Fmo |\n| **One-time purchase** | Utilities, tools with clear one-time value, privacy-focused apps | No recurring revenue; must rely on new users | $3–$10 |\n| **Consumables \u002F credits** | Variable usage (AI generations, exports, premium content) | Unpredictable revenue; usage may decline | $1–$5 per credit pack |\n| **Freemium + consumables** | Apps where core is free but power usage costs (AI, storage) | Complex to balance free vs. paid tiers | Free tier + $2–$10 packs |\n| **Lifetime deal** | Launch phase only — use for early adopter cash and reviews | Destroys LTV; never use as primary model | $20–$60 (3–5× annual price) |\n| **Tip jar \u002F patronage** | Content-focused, community-driven, or open-source-adjacent | Very low conversion; unreliable revenue | $1–$5 tips, rare |\n\n### Model selection criteria\n\n| If the app... | Recommended model |\n|---|---|\n| Has daily usage with progressively unlocked value | Freemium → subscription |\n| Delivers immediate, obvious ROI in one session | Subscription only or one-time purchase |\n| Produces output that varies in volume (AI, exports) | Freemium + consumables |\n| Is a simple utility used occasionally | One-time purchase |\n| Requires trust before users see value (health, finance) | Extended free trial → subscription |\n| Competes with free alternatives that are \"good enough\" | Freemium (generous free tier) → subscription for power features |\n\n## Process\n\n### Step 1 — Establish competitive pricing range\n\nFrom `competitors.json`, extract pricing data for all direct competitors:\n1. List each competitor's pricing model and price point.\n2. Compute the range: `competitive_min` (cheapest) and `competitive_max` (most expensive).\n3. Identify the **modal price** — the price point most competitors converge on. This is the market's revealed equilibrium.\n4. Note any competitor using a different model (e.g., all use subscription but one uses one-time purchase) — this is a potential positioning gap.\n\nIf `competitors.json` has limited pricing data, supplement with market_insights monetization_evidence and App Store narrative pricing mentions.\n\n### Step 2 — Van Westendorp Price Sensitivity Analysis\n\nThe Van Westendorp model uses four price perception thresholds to identify the acceptable price range. Since we can't survey users directly, we estimate each threshold from available data:\n\n#### The four thresholds\n\n| Threshold | Question (conceptual) | How to estimate |\n|---|---|---|\n| **Too cheap** (floor) | Below this price, users suspect low quality | Half the cheapest competitor's price. If app is free, this is $0 (no floor). |\n| **Cheap but acceptable** (low) | Feels like a good deal | Competitive minimum price. Discount slightly if the app offers less than the cheapest competitor. |\n| **Getting expensive** (target) | Fair price — users would consider it but think carefully | Competitive modal price, adjusted by desire multiplier (Step 3). |\n| **Too expensive** (ceiling) | Above this, users won't consider it regardless of value | Competitive maximum price × 1.2. Cap at the \"pain threshold\" — the maximum users pay for similar apps in the category. |\n\n#### Acceptable price range\n\nThe **optimal price point** sits between \"cheap but acceptable\" and \"getting expensive\" — this is the target WTP.\n\n```\nwtp_low = cheap_but_acceptable\nwtp_target = getting_expensive (adjusted by desire multiplier)\nwtp_aspirational = midpoint between getting_expensive and too_expensive\n```\n\n### Step 3 — Desire-Premium Multiplier\n\nDesire strength from `desire_scores.json` directly affects pricing power. Apps that tap into primal desires command premium pricing; apps that solve mild inconveniences face price resistance.\n\n| Primary desire driver | Premium multiplier | Rationale |\n|---|---|---|\n| **Survival** (health, safety, financial security) | 1.3–1.8× | Users pay more when stakes are high. Health and money apps can charge premium. |\n| **Status** (achievement, appearance, signaling) | 1.5–2.0× | Status is inherently scarce — users pay for differentiation. Luxury\u002Fprestige positioning works. |\n| **Belonging** (community, connection) | 1.0–1.3× | Users value belonging but expect community features to be free (social norms). Premium only for exclusive communities. |\n| **Control** (mastery, organization, reducing chaos) | 1.2–1.5× | Moderate premium. Users pay for control when the alternative is stressful. |\n| **Curiosity** (learning, discovery, novelty) | 0.8–1.2× | Lowest premium. Curiosity-driven apps compete with free content (YouTube, blogs). Must add structure\u002Faccountability to charge. |\n\n**Apply the multiplier to the competitive modal price:**\n\n```\ndesire_adjusted_target = competitive_modal_price × desire_multiplier\n```\n\nIf `desire_strength_label` = \"weak\", do not apply any multiplier — the app lacks the emotional pull to justify premium pricing.\n\n#### Secondary desire bonus\n\nIf the secondary desire driver is different from the primary and scores ≥ 3, add a +10% bonus. Apps that tap two distinct desires (e.g., survival + control in a health tracker) have stronger pricing power than single-desire apps.\n\n### Step 4 — WTP Benchmarks by App Category\n\nUse these ranges as a sanity check against the Van Westendorp and desire-adjusted estimates:\n\n| App category | Subscription WTP range (monthly) | One-time WTP range | Annual discount sweet spot |\n|---|---|---|---|\n| **Health & fitness** | $5–$15\u002Fmo | $8–$30 | 40–50% off monthly |\n| **Nutrition \u002F diet** | $5–$12\u002Fmo | $8–$25 | 40–50% off monthly |\n| **Meditation \u002F mental health** | $5–$15\u002Fmo | $10–$30 | 50–60% off monthly |\n| **Finance \u002F budgeting** | $3–$10\u002Fmo | $5–$20 | 30–40% off monthly |\n| **Productivity \u002F task management** | $3–$8\u002Fmo | $5–$15 | 30–40% off monthly |\n| **Habit tracking** | $2–$6\u002Fmo | $3–$10 | 40–50% off monthly |\n| **Creative tools (photo\u002Fvideo)** | $3–$10\u002Fmo | $5–$20 | 30–40% off monthly |\n| **Education \u002F learning** | $5–$15\u002Fmo | $10–$30 | 50–60% off monthly |\n| **Dating \u002F social** | $5–$25\u002Fmo | N\u002FA (subscription dominates) | 30–40% off monthly |\n| **Parenting \u002F family** | $3–$8\u002Fmo | $5–$15 | 40–50% off monthly |\n| **Utility \u002F scanner \u002F converter** | $1–$4\u002Fmo | $2–$8 | 50–60% off monthly |\n| **AI-powered tools** | $5–$20\u002Fmo | $10–$40 consumable pack | 30–40% off monthly |\n\nIf the estimated WTP is more than 2× above the category range, it's likely too optimistic. If it's below the category minimum, the app may struggle to sustain development.\n\n### Step 5 — Freemium Conversion Estimation\n\nIf the recommended model includes a free tier, estimate what percentage of free users will convert to paid:\n\n| Factor | Higher conversion (toward 8–12%) | Lower conversion (toward 1–3%) |\n|---|---|---|\n| **Value gating** | Core value is free; premium unlocks power features or removes limits | Core value IS the paid feature — free tier feels empty |\n| **Free tier generosity** | Generous enough that users form habits before hitting the wall | Either too generous (no reason to pay) or too stingy (users leave) |\n| **Pain of free** | Free tier has clear, felt limitations (ads, watermarks, usage caps) | Free tier works fine for most users |\n| **Social proof** | Users see premium features in action (shared content with branding) | No visibility into what premium offers |\n| **Trial exposure** | Time-limited trial of premium (7–14 days) shows value upfront | No trial; users must imagine premium value |\n\n**Category benchmarks for freemium conversion:**\n\n| Category | Typical conversion rate | Top-quartile rate |\n|---|---|---|\n| Health & fitness | 2–5% | 8–12% |\n| Productivity | 3–6% | 10–15% |\n| Creative tools | 3–7% | 10–18% |\n| Education | 2–5% | 7–12% |\n| Finance | 3–6% | 8–14% |\n| Utility | 1–4% | 5–10% |\n| Social \u002F dating | 2–8% | 10–20% |\n| AI-powered | 4–8% | 12–20% |\n\nUse the typical rate as the base estimate. Adjust toward top-quartile if:\n- The desire_strength_label is \"strong\"\n- The free tier design has strong value gating\n- Category benchmarks from market_insights show successful freemium competitors\n\n### Step 6 — Annual vs. Monthly Strategy\n\nMost indie apps benefit from offering both monthly and annual plans. The annual plan serves as the anchor.\n\n#### Annual discount psychology\n\n| Discount depth | Effect |\n|---|---|\n| **20–30% off monthly** | Subtle incentive. Users who prefer monthly will stay monthly. Low commitment-capture rate. |\n| **40–50% off monthly** | Sweet spot for most B2C apps. Enough savings to feel meaningful, not so deep it signals desperation. |\n| **50–60% off monthly** | Aggressive — use for high-churn categories where locking in annual users dramatically improves LTV. |\n| **> 60% off monthly** | Signals the app isn't worth the monthly price. Avoid unless in launch\u002Fpromotional phase. |\n\n**Recommended annual price** = monthly price × 12 × (1 - discount). Present the annual plan as the default\u002Fhighlighted option.\n\n### Step 7 — B2C vs. B2B2C Pricing\n\nSome ideas have a viable path to both consumer and business revenue. Evaluate if the app concept could serve both:\n\n| Signal that B2B2C path exists | Example |\n|---|---|\n| Users would expense the app to their employer | Productivity tools, professional development |\n| A business version could serve teams | Shared dashboards, admin controls, team analytics |\n| Content or data from the app has business value | Health data for employers, fitness for corporate wellness |\n| The consumer app is a wedge into workplace adoption | Personal Slack → company Slack, personal Notion → team Notion |\n\nIf B2B2C path exists:\n- **B2C price**: Use the standard WTP methodology above (this is the consumer price).\n- **B2B2C price**: 3–5× the consumer price per seat. Businesses have larger budgets and lower price sensitivity.\n- **Recommendation**: Launch B2C first (faster validation, faster feedback). Flag B2B2C as a future monetization expansion in the output.\n- **Do NOT recommend B2B2C as the primary model** for an indie developer — enterprise sales cycles and support needs don't fit the indie model.\n\n## Output\n\nWrite to `memory\u002Fideas\u002F\u003Cslug>\u002Fpricing.json`:\n\n```json\n{\n  \"wtp_range\": {\n    \"low\": 0,\n    \"target\": 0,\n    \"aspirational\": 0,\n    \"currency\": \"USD\",\n    \"period\": \"monthly | yearly | one-time\"\n  },\n  \"van_westendorp\": {\n    \"too_cheap\": 0,\n    \"cheap_but_acceptable\": 0,\n    \"getting_expensive\": 0,\n    \"too_expensive\": 0\n  },\n  \"desire_premium_multiplier\": 0,\n  \"desire_premium_rationale\": \"\",\n  \"recommended_pricing_model\": \"\",\n  \"pricing_models_considered\": [\n    {\n      \"model\": \"\",\n      \"fit_rationale\": \"\",\n      \"risk\": \"\"\n    }\n  ],\n  \"recommended_price\": {\n    \"monthly\": 0,\n    \"annual\": 0,\n    \"annual_discount_pct\": 0,\n    \"one_time\": 0\n  },\n  \"freemium_conversion_estimate\": 0,\n  \"freemium_conversion_rationale\": \"\",\n  \"competitive_pricing_range\": {\n    \"min\": 0,\n    \"max\": 0,\n    \"modal\": 0\n  },\n  \"category_benchmark_range\": {\n    \"min\": 0,\n    \"max\": 0\n  },\n  \"b2b2c_potential\": {\n    \"exists\": false,\n    \"b2b_price_estimate\": 0,\n    \"rationale\": \"\"\n  },\n  \"market_insights_pricing_signals\": [],\n  \"pricing_rationale\": \"\"\n}\n```\n\n## Notes\n\n- The `target` WTP is the price used by downstream skills (`cac-modeler` for LTV, `tam-sam-som-builder` for revenue projections). Getting this wrong cascades errors through the entire scoring system.\n- If `monetization_evidence` from market_insights is empty across all platform files, flag pricing as \"unvalidated\" in the rationale. This is a key risk — the market may not support paid apps.\n- For apps competing with strong free alternatives (Google Keep, Apple Notes, default apps), the effective WTP ceiling may be $0 for most users. In this case, the pricing model must create value that the free alternative structurally cannot (social features, AI, specialized workflows).\n- Lifetime deal pricing should only appear as a launch tactic, never as the recommended primary model. LTD price = 3–5× annual price. Cap at $60 for consumer apps.\n- If market_insights files are past their `stale_after` date, note that competitive pricing may have shifted and recommend refreshing.\n","\u003C!-- version: 0.2.0 | outputs: memory\u002Fideas\u002F\u003Cslug>\u002Fpricing.json -->\n\n# Skill: pricing-and-wtp\n\n## Purpose\n\nDetermine what users would actually pay, not what the developer hopes to charge. Anchor WTP to pain intensity, desire strength, competitive pricing, and real monetization signals from market_insights — never to the cost of building. Pricing is the single decision with the highest leverage on unit economics: a $1\u002Fmo difference at 1,000 users is $12K\u002Fyear.\n\n## Input\n\n- Idea slug\n- `memory\u002Fideas\u002F\u003Cslug>\u002Fidea.md` (app concept, key features)\n- `memory\u002Fideas\u002F\u003Cslug>\u002Fuser_extraction.json` (pain intensity)\n- `memory\u002Fideas\u002F\u003Cslug>\u002Fcompetitors.json` (competitive pricing, pricing models in use)\n- `memory\u002Fideas\u002F\u003Cslug>\u002Fdesire_scores.json` (desire strength, primary\u002Fsecondary drivers)\n- `memory\u002Fmarket_insights\u002F\u003Cniche>-*-\u003CYYYY>-\u003CMM>.md` (trend data — **use all available platform files**)\n\n### Using Market Insights\n\nTrend analysis files provide monetization reality-checks that desk research alone cannot:\n\n| Field | How it informs pricing |\n|---|---|\n| `monetization_evidence` | The strongest signal. If competitors are visibly making money (in-app purchases mentioned in reviews, subscription pricing in App Store listings, sponsored content), the market has validated WTP. If monetization_evidence is empty across all platforms, WTP is unproven — discount aspirational price and flag risk. |\n| Platform narratives — Reddit | Users discuss pricing directly: \"I'd pay for X if...\", \"Y is overpriced because...\", \"$Z\u002Fmo is too much for just...\" These are raw WTP signals. Extract any price mentions. |\n| Platform narratives — App Store | Review complaints about pricing reveal the ceiling: \"Great app but not worth $X\u002Fmo.\" Review praise about pricing reveals the floor: \"Amazing value at $X.\" |\n| Platform narratives — TikTok | Creator recommendations often mention price as part of the hook (\"this FREE app...\", \"worth every penny of $X\"). Signals whether freemium or premium positioning resonates with the audience. |\n| `trend_velocity` | Rising-fast markets can support premium pricing (early adopters pay more). Declining markets face price pressure (users comparison-shop harder). |\n\n## Pricing Models\n\n| Model | Best for | Risk | Indie sweet spot |\n|---|---|---|---|\n| **Freemium → subscription** | Habit-forming apps with daily use, feature-gated value | Conversion friction; most users stay free forever | $3–$8\u002Fmo or $20–$50\u002Fyr |\n| **Subscription only (paywall)** | High-value, immediately obvious ROI, professional tools | High churn risk; must prove value fast | $5–$15\u002Fmo |\n| **One-time purchase** | Utilities, tools with clear one-time value, privacy-focused apps | No recurring revenue; must rely on new users | $3–$10 |\n| **Consumables \u002F credits** | Variable usage (AI generations, exports, premium content) | Unpredictable revenue; usage may decline | $1–$5 per credit pack |\n| **Freemium + consumables** | Apps where core is free but power usage costs (AI, storage) | Complex to balance free vs. paid tiers | Free tier + $2–$10 packs |\n| **Lifetime deal** | Launch phase only — use for early adopter cash and reviews | Destroys LTV; never use as primary model | $20–$60 (3–5× annual price) |\n| **Tip jar \u002F patronage** | Content-focused, community-driven, or open-source-adjacent | Very low conversion; unreliable revenue | $1–$5 tips, rare |\n\n### Model selection criteria\n\n| If the app... | Recommended model |\n|---|---|\n| Has daily usage with progressively unlocked value | Freemium → subscription |\n| Delivers immediate, obvious ROI in one session | Subscription only or one-time purchase |\n| Produces output that varies in volume (AI, exports) | Freemium + consumables |\n| Is a simple utility used occasionally | One-time purchase |\n| Requires trust before users see value (health, finance) | Extended free trial → subscription |\n| Competes with free alternatives that are \"good enough\" | Freemium (generous free tier) → subscription for power features |\n\n## Process\n\n### Step 1 — Establish competitive pricing range\n\nFrom `competitors.json`, extract pricing data for all direct competitors:\n1. List each competitor's pricing model and price point.\n2. Compute the range: `competitive_min` (cheapest) and `competitive_max` (most expensive).\n3. Identify the **modal price** — the price point most competitors converge on. This is the market's revealed equilibrium.\n4. Note any competitor using a different model (e.g., all use subscription but one uses one-time purchase) — this is a potential positioning gap.\n\nIf `competitors.json` has limited pricing data, supplement with market_insights monetization_evidence and App Store narrative pricing mentions.\n\n### Step 2 — Van Westendorp Price Sensitivity Analysis\n\nThe Van Westendorp model uses four price perception thresholds to identify the acceptable price range. Since we can't survey users directly, we estimate each threshold from available data:\n\n#### The four thresholds\n\n| Threshold | Question (conceptual) | How to estimate |\n|---|---|---|\n| **Too cheap** (floor) | Below this price, users suspect low quality | Half the cheapest competitor's price. If app is free, this is $0 (no floor). |\n| **Cheap but acceptable** (low) | Feels like a good deal | Competitive minimum price. Discount slightly if the app offers less than the cheapest competitor. |\n| **Getting expensive** (target) | Fair price — users would consider it but think carefully | Competitive modal price, adjusted by desire multiplier (Step 3). |\n| **Too expensive** (ceiling) | Above this, users won't consider it regardless of value | Competitive maximum price × 1.2. Cap at the \"pain threshold\" — the maximum users pay for similar apps in the category. |\n\n#### Acceptable price range\n\nThe **optimal price point** sits between \"cheap but acceptable\" and \"getting expensive\" — this is the target WTP.\n\n```\nwtp_low = cheap_but_acceptable\nwtp_target = getting_expensive (adjusted by desire multiplier)\nwtp_aspirational = midpoint between getting_expensive and too_expensive\n```\n\n### Step 3 — Desire-Premium Multiplier\n\nDesire strength from `desire_scores.json` directly affects pricing power. Apps that tap into primal desires command premium pricing; apps that solve mild inconveniences face price resistance.\n\n| Primary desire driver | Premium multiplier | Rationale |\n|---|---|---|\n| **Survival** (health, safety, financial security) | 1.3–1.8× | Users pay more when stakes are high. Health and money apps can charge premium. |\n| **Status** (achievement, appearance, signaling) | 1.5–2.0× | Status is inherently scarce — users pay for differentiation. Luxury\u002Fprestige positioning works. |\n| **Belonging** (community, connection) | 1.0–1.3× | Users value belonging but expect community features to be free (social norms). Premium only for exclusive communities. |\n| **Control** (mastery, organization, reducing chaos) | 1.2–1.5× | Moderate premium. Users pay for control when the alternative is stressful. |\n| **Curiosity** (learning, discovery, novelty) | 0.8–1.2× | Lowest premium. Curiosity-driven apps compete with free content (YouTube, blogs). Must add structure\u002Faccountability to charge. |\n\n**Apply the multiplier to the competitive modal price:**\n\n```\ndesire_adjusted_target = competitive_modal_price × desire_multiplier\n```\n\nIf `desire_strength_label` = \"weak\", do not apply any multiplier — the app lacks the emotional pull to justify premium pricing.\n\n#### Secondary desire bonus\n\nIf the secondary desire driver is different from the primary and scores ≥ 3, add a +10% bonus. Apps that tap two distinct desires (e.g., survival + control in a health tracker) have stronger pricing power than single-desire apps.\n\n### Step 4 — WTP Benchmarks by App Category\n\nUse these ranges as a sanity check against the Van Westendorp and desire-adjusted estimates:\n\n| App category | Subscription WTP range (monthly) | One-time WTP range | Annual discount sweet spot |\n|---|---|---|---|\n| **Health & fitness** | $5–$15\u002Fmo | $8–$30 | 40–50% off monthly |\n| **Nutrition \u002F diet** | $5–$12\u002Fmo | $8–$25 | 40–50% off monthly |\n| **Meditation \u002F mental health** | $5–$15\u002Fmo | $10–$30 | 50–60% off monthly |\n| **Finance \u002F budgeting** | $3–$10\u002Fmo | $5–$20 | 30–40% off monthly |\n| **Productivity \u002F task management** | $3–$8\u002Fmo | $5–$15 | 30–40% off monthly |\n| **Habit tracking** | $2–$6\u002Fmo | $3–$10 | 40–50% off monthly |\n| **Creative tools (photo\u002Fvideo)** | $3–$10\u002Fmo | $5–$20 | 30–40% off monthly |\n| **Education \u002F learning** | $5–$15\u002Fmo | $10–$30 | 50–60% off monthly |\n| **Dating \u002F social** | $5–$25\u002Fmo | N\u002FA (subscription dominates) | 30–40% off monthly |\n| **Parenting \u002F family** | $3–$8\u002Fmo | $5–$15 | 40–50% off monthly |\n| **Utility \u002F scanner \u002F converter** | $1–$4\u002Fmo | $2–$8 | 50–60% off monthly |\n| **AI-powered tools** | $5–$20\u002Fmo | $10–$40 consumable pack | 30–40% off monthly |\n\nIf the estimated WTP is more than 2× above the category range, it's likely too optimistic. If it's below the category minimum, the app may struggle to sustain development.\n\n### Step 5 — Freemium Conversion Estimation\n\nIf the recommended model includes a free tier, estimate what percentage of free users will convert to paid:\n\n| Factor | Higher conversion (toward 8–12%) | Lower conversion (toward 1–3%) |\n|---|---|---|\n| **Value gating** | Core value is free; premium unlocks power features or removes limits | Core value IS the paid feature — free tier feels empty |\n| **Free tier generosity** | Generous enough that users form habits before hitting the wall | Either too generous (no reason to pay) or too stingy (users leave) |\n| **Pain of free** | Free tier has clear, felt limitations (ads, watermarks, usage caps) | Free tier works fine for most users |\n| **Social proof** | Users see premium features in action (shared content with branding) | No visibility into what premium offers |\n| **Trial exposure** | Time-limited trial of premium (7–14 days) shows value upfront | No trial; users must imagine premium value |\n\n**Category benchmarks for freemium conversion:**\n\n| Category | Typical conversion rate | Top-quartile rate |\n|---|---|---|\n| Health & fitness | 2–5% | 8–12% |\n| Productivity | 3–6% | 10–15% |\n| Creative tools | 3–7% | 10–18% |\n| Education | 2–5% | 7–12% |\n| Finance | 3–6% | 8–14% |\n| Utility | 1–4% | 5–10% |\n| Social \u002F dating | 2–8% | 10–20% |\n| AI-powered | 4–8% | 12–20% |\n\nUse the typical rate as the base estimate. Adjust toward top-quartile if:\n- The desire_strength_label is \"strong\"\n- The free tier design has strong value gating\n- Category benchmarks from market_insights show successful freemium competitors\n\n### Step 6 — Annual vs. Monthly Strategy\n\nMost indie apps benefit from offering both monthly and annual plans. The annual plan serves as the anchor.\n\n#### Annual discount psychology\n\n| Discount depth | Effect |\n|---|---|\n| **20–30% off monthly** | Subtle incentive. Users who prefer monthly will stay monthly. Low commitment-capture rate. |\n| **40–50% off monthly** | Sweet spot for most B2C apps. Enough savings to feel meaningful, not so deep it signals desperation. |\n| **50–60% off monthly** | Aggressive — use for high-churn categories where locking in annual users dramatically improves LTV. |\n| **> 60% off monthly** | Signals the app isn't worth the monthly price. Avoid unless in launch\u002Fpromotional phase. |\n\n**Recommended annual price** = monthly price × 12 × (1 - discount). Present the annual plan as the default\u002Fhighlighted option.\n\n### Step 7 — B2C vs. B2B2C Pricing\n\nSome ideas have a viable path to both consumer and business revenue. Evaluate if the app concept could serve both:\n\n| Signal that B2B2C path exists | Example |\n|---|---|\n| Users would expense the app to their employer | Productivity tools, professional development |\n| A business version could serve teams | Shared dashboards, admin controls, team analytics |\n| Content or data from the app has business value | Health data for employers, fitness for corporate wellness |\n| The consumer app is a wedge into workplace adoption | Personal Slack → company Slack, personal Notion → team Notion |\n\nIf B2B2C path exists:\n- **B2C price**: Use the standard WTP methodology above (this is the consumer price).\n- **B2B2C price**: 3–5× the consumer price per seat. Businesses have larger budgets and lower price sensitivity.\n- **Recommendation**: Launch B2C first (faster validation, faster feedback). Flag B2B2C as a future monetization expansion in the output.\n- **Do NOT recommend B2B2C as the primary model** for an indie developer — enterprise sales cycles and support needs don't fit the indie model.\n\n## Output\n\nWrite to `memory\u002Fideas\u002F\u003Cslug>\u002Fpricing.json`:\n\n```json\n{\n  \"wtp_range\": {\n    \"low\": 0,\n    \"target\": 0,\n    \"aspirational\": 0,\n    \"currency\": \"USD\",\n    \"period\": \"monthly | yearly | one-time\"\n  },\n  \"van_westendorp\": {\n    \"too_cheap\": 0,\n    \"cheap_but_acceptable\": 0,\n    \"getting_expensive\": 0,\n    \"too_expensive\": 0\n  },\n  \"desire_premium_multiplier\": 0,\n  \"desire_premium_rationale\": \"\",\n  \"recommended_pricing_model\": \"\",\n  \"pricing_models_considered\": [\n    {\n      \"model\": \"\",\n      \"fit_rationale\": \"\",\n      \"risk\": \"\"\n    }\n  ],\n  \"recommended_price\": {\n    \"monthly\": 0,\n    \"annual\": 0,\n    \"annual_discount_pct\": 0,\n    \"one_time\": 0\n  },\n  \"freemium_conversion_estimate\": 0,\n  \"freemium_conversion_rationale\": \"\",\n  \"competitive_pricing_range\": {\n    \"min\": 0,\n    \"max\": 0,\n    \"modal\": 0\n  },\n  \"category_benchmark_range\": {\n    \"min\": 0,\n    \"max\": 0\n  },\n  \"b2b2c_potential\": {\n    \"exists\": false,\n    \"b2b_price_estimate\": 0,\n    \"rationale\": \"\"\n  },\n  \"market_insights_pricing_signals\": [],\n  \"pricing_rationale\": \"\"\n}\n```\n\n## Notes\n\n- The `target` WTP is the price used by downstream skills (`cac-modeler` for LTV, `tam-sam-som-builder` for revenue projections). Getting this wrong cascades errors through the entire scoring system.\n- If `monetization_evidence` from market_insights is empty across all platform files, flag pricing as \"unvalidated\" in the rationale. This is a key risk — the market may not support paid apps.\n- For apps competing with strong free alternatives (Google Keep, Apple Notes, default apps), the effective WTP ceiling may be $0 for most users. In this case, the pricing model must create value that the free alternative structurally cannot (social features, AI, specialized workflows).\n- Lifetime deal pricing should only appear as a launch tactic, never as the recommended primary model. LTD price = 3–5× annual price. Cap at $60 for consumer apps.\n- If market_insights files are past their `stale_after` date, note that competitive pricing may have shifted and recommend refreshing.\n",{"name":5,"description":7},"main","3a4c800a3022e73e9997533b603c79b60ee7597a","skills\u002Fpricing-and-wtp",58,"https:\u002F\u002Fgithub.com\u002FMaxKmet\u002Fidea-validation-agents","https:\u002F\u002Fgithub.com\u002FMaxKmet","Senior AI Engineer","2026-06-16 23:18:56",{"source":8,"skillName":5,"commands":53,"prompt":63,"zipAvailable":64},[54,57,60],{"tool":55,"command":56},"npx","npx skills add MaxKmet\u002Fidea-validation-agents --skill pricing-and-wtp",{"tool":58,"command":59},"bunx","bunx skills add MaxKmet\u002Fidea-validation-agents --skill pricing-and-wtp",{"tool":61,"command":62},"pnpm","pnpm dlx skills add MaxKmet\u002Fidea-validation-agents --skill pricing-and-wtp","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FMaxKmet\u002Fidea-validation-agents\u002Fblob\u002Fmain\u002Fskills\u002Fpricing-and-wtp\u002FSKILL.md",true,[],[67,83,109,128,154,173],{"skillNo":68,"name":69,"nameZh":6,"description":70,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":71,"categories":72,"keywords":73,"publishedAt":39},"01M6X7YZ5WBGSAB23K0DAARY1N","weakness-detection","Analyzes idea-scoring output to identify the weakest dimensions, root causes of low scores, and probable failure modes if the idea were pursued as-is.","https:\u002F\u002Fgithub.com\u002FMaxKmet\u002Fidea-validation-agents\u002Fblob\u002Fmain\u002Fskills\u002Fweakness-detection\u002FSKILL.md",[],[74,75,76,77,78,79,80,81,82],"detection","weakness detection","modes","root","output","as","dimensions","scoring","idea",{"skillNo":84,"name":85,"nameZh":6,"description":86,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":87,"categories":88,"keywords":94,"publishedAt":39},"01M6X7YRHWXGBHBT70ZY1YTCC6","user-segmentation-profiler","Classifies the user into an ICP tier (beginner\u002Fbuilder\u002Fgrowth) based on experience, constraints, and goals. Run once per user session before any workflow.","https:\u002F\u002Fgithub.com\u002FMaxKmet\u002Fidea-validation-agents\u002Fblob\u002Fmain\u002Fskills\u002Fuser-segmentation-profiler\u002FSKILL.md",[89],{"categoryNo":90,"categoryName":91,"taxonomyType":92,"taxonomyTypeDesc":93},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[95,96,97,98,99,100,101,102,103,104,105,106,107,108],"segmentation","user","user segmentation","profiler","user segmentation profiler","constraints","run","workflow","session","beginner","icp","goals","builder","growth",{"skillNo":110,"name":111,"nameZh":6,"description":112,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":113,"categories":114,"keywords":115,"publishedAt":39},"01M6X7YJKM9DRPMEDW22PETKDE","user-background-interviewer","Conducts a deep background interview to understand what types of app ideas this specific user is most likely to succeed with based on their domain knowledge, networks, and skills.","https:\u002F\u002Fgithub.com\u002FMaxKmet\u002Fidea-validation-agents\u002Fblob\u002Fmain\u002Fskills\u002Fuser-background-interviewer\u002FSKILL.md",[],[116,96,117,118,119,120,121,122,123,124,125,126,127],"background","user background interviewer","with","types","understand","app","deep","ideas","domain","interview","networks","knowledge",{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":132,"categories":133,"keywords":139,"publishedAt":39},"01M6X7YEX3NTXGXAY8SQRSK7YG","trend-to-product-mapper","Maps viral social content and trending topics to concrete app opportunities by extracting the underlying problem and validating monetization fit.","https:\u002F\u002Fgithub.com\u002FMaxKmet\u002Fidea-validation-agents\u002Fblob\u002Fmain\u002Fskills\u002Ftrend-to-product-mapper\u002FSKILL.md",[134],{"categoryNo":135,"categoryName":136,"taxonomyType":137,"taxonomyTypeDesc":138},"01JBSEEDCAT000000000000031","产品经理","PROFESSION","适用职业",[140,141,142,143,121,144,145,146,147,148,149,22,150,151,152,153],"product","mapper","trend to product mapper","trend","topics","extracting","trending","problem","fit","viral","social","maps","social content","content",{"skillNo":155,"name":156,"nameZh":6,"description":157,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":158,"categories":159,"keywords":160,"publishedAt":39},"01M6X7Y9PJGB5W9Y2RGC7MMC8T","trend-analysis","Analyzes market trends across platforms (TikTok, Reddit, App Store, Google Trends) for a given topic or category. Writes a new file to memory\u002Fmarket_insights\u002F.","https:\u002F\u002Fgithub.com\u002FMaxKmet\u002Fidea-validation-agents\u002Fblob\u002Fmain\u002Fskills\u002Ftrend-analysis\u002FSKILL.md",[],[37,143,161,162,163,164,27,165,166,121,167,168,169,33,170,171,172],"trend analysis","across","for","google trends","store","trends","app store","tiktok","reddit","file","memory","google",{"skillNo":174,"name":175,"nameZh":6,"description":176,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":177,"categories":178,"keywords":179,"publishedAt":39},"01M6X7Y5GHKXC295FGVJN4Y93R","tam-sam-som-builder","Estimates TAM, SAM, and realistic SOM for a B2C app idea using triangulated bottom-up methodology anchored to market_insights trend data, competitor revenue proxies, and community size signals. Includes indie capture rate benchmarks and growth-rate adjustments by trend velocity.","https:\u002F\u002Fgithub.com\u002FMaxKmet\u002Fidea-validation-agents\u002Fblob\u002Fmain\u002Fskills\u002Ftam-sam-som-builder\u002FSKILL.md",[],[180,181,107,182,82,183,30,143,184,108,33,31,185,27,163,186,187,188,121,189],"tam sam som builder","sam","community","velocity","up","revenue","rate","competitor","proxies","data"]