monetization-analyzer

# Monetization Analyzer Skill ## Purpose This skill evaluates game concepts to identify the most monetizable opportunities based on: - **Willingness-to-Pay (WTP)** analysis from market data - **Viral potential** and organic growth mechanics - **Revenue model optimization** (premium, F2P, subscription, hybrid) - **Market demand** and addressable market size - **Competitive pricing** positioning - **Lifetime Value (LTV)** projections **Output**: Ranked list of top 3 most monetizable game concepts with detailed financial projections and go-to-market recommendations. ## When to Use This Skill Use this skill when you have: - ✅ Multiple game concepts to evaluate for investment prioritization - ✅ Market analysis data showing pricing sentiment and willingness-to-pay signals - ✅ Need to identify which concepts have highest revenue potential - ✅ Want to optimize monetization models before development - ✅ Require financial projections for pitch decks or funding proposals - ✅ Need to validate business model assumptions with market data ## Prerequisites ### Required Input Files 1. **Market Analysis Report** (from `market-analyst` skill) - Location: `/docs/market-analysis-*.md` - Must include: Sentiment data on pricing, monetization pain points, willingness-to-pay signals - Example: `market-analysis-fps-games-2025-10-26.md` 2. **Game Concepts Document** (from brainstorming/design) - Location: `/docs/*-game-concepts-*.md` or `/docs/plans/*-design.md` - Must include: Price points, target personas, distribution channels, competitors - Example: `fps-game-concepts-market-driven-2025-10-26.md` ### Optional Input Files 3. **Competitor Financial Data** (if available) - Revenue reports, player counts, ARPU data - Enhances accuracy of projections ## Core Workflow ### Phase 1: Data Extraction and Normalization **1. Load Market Analysis** Extract willingness-to-pay signals: ```javascript WTP_Signals = { price_sentiment: { "$0 (F2P)": {positive: X%, negative: Y%, mentions: N}, "$10-20": {positive: X%, negative: Y%, mentions: N}, "$20-30": {positive: X%, negative: Y%, mentions: N}, "$60-70": {positive: X%, negative: Y%, mentions: N}, "$70 + MTX": {positive: X%, negative: Y%, mentions: N} }, monetization_pain_points: [ {issue: "Premium + battle pass", severity: "CRITICAL", mentions: N}, {issue: "Loot boxes", severity: "HIGH", mentions: N} ], value_propositions: [ {model: "F2P cosmetic-only", sentiment: X%, examples: []}, {model: "Budget indie ($15-25)", sentiment: X%, examples: []} ] } ``` **2. Load Game Concepts** Extract monetization-relevant data for each concept: ```javascript GameConcept = { name: string, price_point: number | "F2P", monetization_model: string, target_audience: { primary_persona: {}, market_size_estimate: number, spending_behavior: string }, competitors: [{name, price, model, performance}], distribution_channels: [{platform, percentage, rationale}], lifecycle_commitment: string, development_cost_estimate: number } ``` ### Phase 2: Willingness-to-Pay Analysis **3. Calculate WTP Score (0-100)** ```javascript function calculateWTP(concept, marketData) { const score = { price_sentiment_alignment: 0, // Does price match positive sentiment tier? value_perception: 0, // Content/$ ratio vs. market expectations monetization_model_fit: 0, // Model aligns with audience preferences? competitive_positioning: 0, // Price competitive advantage? pain_point_avoidance: 0 // Avoids monetization red flags? }; // Price Sentiment Alignment (0-30 points) const priceТier = getPriceTier(concept.price_point); const sentiment = marketData.price_sentiment[priceTier]; score.price_sentiment_alignment = (sentiment.positive / 100) * 30; // Value Perception (0-25 points) const contentHours = estimateContentHours(concept); const pricePerHour = concept.price_point / contentHours; const marketAvgPricePerHour = calculateMarketAverage(); if (pricePerHour < marketAvgPricePerHour * 0.8) { score.value_perception = 25; // Excellent value } else if (pricePerHour < marketAvgPricePerHour) { score.value_perception = 18; // Good value } else if (pricePerHour < marketAvgPricePerHour * 1.2) { score.value_perception = 10; // Fair value } else { score.value_perception = 0; // Poor value } // Monetization Model Fit (0-20 points) const modelSentiment = marketData.value_propositions.find( vp => vp.model === concept.monetization_model ); score.monetization_model_fit = (modelSentiment.sentiment / 100) * 20; // Competitive Positioning (0-15 points) const competitorPrices = concept.competitors.map(c => c.price); const avgCompetitorPrice = average(competitorPrices); if (concept.price_point < avgCompetitorPrice * 0.7) { score.competitive_positioning = 15; // Undercut leaders } else if (concept.price_point < avgCompetitorPrice) { score.competitive_positioning = 10; // Competitive pricing } else { score.competitive_positioning = 5; // Premium positioning } // Pain Point Avoidance (0-10 points) const painPoints = marketData.monetization_pain_points; let violations = 0; painPoints.forEach(pp => { if (conceptViolatesPainPoint(concept, pp)) { violations += (pp.severity === "CRITICAL") ? 5 : 2; } }); score.pain_point_avoidance = Math.max(0, 10 - violations); return { total: Object.values(score).reduce((a, b) => a + b, 0), breakdown: score, confidence: calculateConfidence(marketData.sample_size) }; } ``` **WTP Score Interpretation:** - **90-100**: Exceptional WTP, price optimization perfect - **75-89**: Strong WTP, minor adjustments possible - **60-74**: Moderate WTP, consider price/model changes - **Below 60**: Weak WTP, major repositioning needed ### Phase 3: Viral Potential Analysis **4. Calculate Viral Score (0-100)** ```javascript function calculateViralPotential(concept, marketData) { const score = { shareability: 0, // Content naturally creates shareable moments? accessibility: 0, // Low barrier to entry? network_effects: 0, // Benefits from friend invites? streamer_appeal: 0, // Twitch/YouTube friendly? novelty_factor: 0, // Unique enough to generate buzz? social_features: 0 // Built for social play/sharing? }; // Shareability (0-20 points) const shareableGenres = ["party game", "asymmetric", "sports hybrid", "roguelike"]; if (shareableGenres.some(g => concept.genre.includes(g))) { score.shareability = 20; } else if (concept.genre.includes("competitive") || concept.genre.includes("co-op")) { score.shareability = 12; } else { score.shareability = 5; // Single-player, narrative } // Accessibility (0-20 points) if (concept.price_point === "F2P") { score.accessibility = 20; // Zero barrier } else if (concept.price_point <= 15) { score.accessibility = 15; // Impulse purchase } else if (concept.price_point <= 25) { score.accessibility = 10; // Reasonable } else { score.accessibility = 5; // Higher barrier } // Network Effects (0-20 points) if (concept.monetization_model.includes("F2P") || concept.monetization_model.includes("viral")) { score.network_effects = 20; } else if (concept.description.includes("co-op") || concept.description.includes("multiplayer")) { score.network_effects = 12; } else { score.network_effects = 0; } // Streamer Appeal (0-15 points) const streamerFriendly = [ concept.genre.includes("asymmetric"), concept.genre.includes("roguelike"), concept.genre.includes("party"), concept.description.includes("viral moments"), concept.description.includes("spectator") ]; score.streamer_appeal = streamerFriendly.filter(Boolean).length * 3; // Novelty Factor (0-15 points) const noveltyIndicators = marketData.novelty_successes || []; if (noveltyIndicators.some(n => concept.description.includes(n.innovation))) { score.novelty_factor = 15; } else if (concept.description.includes("unique") || concept.description.includes("first")) { score.novelty_factor = 10; } else { score.novelty_factor = 5; } // Social Features (0-10 points) const socialKeywords = ["co-op", "multiplayer", "friend", "clan", "team", "squad"]; const socialCount = socialKeywords.filter(kw => concept.description.toLowerCase().includes(kw) ).length; score.social_features = Math.min(10, socialCount * 2); return { total: Object.values(score).reduce((a, b) => a + b, 0), breakdown: score, viral_coefficient: estimateViralCoefficient(score.total) }; } function estimateViralCoefficient(viralScore) { // Viral coefficient: How many new users does each user bring? // K > 1 = exponential growth, K < 1 = paid acquisition needed if (viralScore >= 85) return 1.5; // Exceptional viral growth if (viralScore >= 70) return 1.2; // Strong organic growth if (viralScore >= 55) return 0.8; // Some viral mechanics if (viralScore >= 40) return 0.4; // Minimal viral spread return 0.2; // Requires paid marketing } ``` **Viral Score Interpretation:** - **85-100**: Viral hit potential (K > 1.2), minimal marketing spend - **70-84**: Strong organic growth (K ~1.0), word-of-mouth driven - **55-69**: Moderate virality (K ~0.8), some paid marketing needed - **40-54**: Low virality (K ~0.4), heavy marketing investment required - **Below 40**: No viral mechanics (K ~0.2), paid acquisition only ### Phase 4: Revenue Projection Modeling **5. Calculate Revenue Potential (Year 1-3 Projections)** ```javascript function projectRevenue(concept, wtpScore, viralScore, marketData) { const model = concept.monetization_model; // Addressable Market Size const TAM = estimateTotalAddressableMarket(concept, marketData); const SAM = TAM * 0.15; // Serviceable addressable (15% of TAM realistic) const SOM = SAM * getMarketShareEstimate(viralScore, concept.competitors.length); // Player Acquisition Model const year1Players = calculateYear1Players(concept, viralScore, SOM); const year2Players = year1Players * getRetentionMultiplier(concept.lifecycle_commitment); const year3Players = year2Players * getGrowthMultiplier(viralScore); // Revenue Calculations if (model.includes("F2P")) { return projectF2PRevenue(year1Players, year2Players, year3Players, concept); } else if (model.includes("premium") || typeof concept.price_point === "number") { return projectPremiumRevenue(year1Players, year2Players, year3Players, concept); } else { return projectHybridRevenue(year1Players, year2Players, year3Players, concept); } } function projectF2PRevenue(y1Players, y2Players, y3Players, concept) { // F2P Model: Base * Conversion Rate * ARPPU const conversionRate = 0.03; // Industry avg: 3-5% pay const ARPPU = estimateARPPU(concept); // Average revenue per paying user const y1Revenue = y1Players * conversionRate * ARPPU; const y2Revenue = y2Players * (conversionRate * 1.1) * (ARPPU * 1.15); // Improve over time const y3Revenue = y3Players * (conversionRate * 1.15) * (ARPPU * 1.25); return { year1: {players: y1Players, revenue: y1Revenue, ARPU: y1Revenue / y1Players}, year2: {players: y2Players, revenue: y2Revenue, ARPU: y2Revenue / y2Players}, year3: {players: y3Players, revenue: y3Revenue, ARPU: y3Revenue / y3Players}, total_3yr: y1Revenue + y2Revenue + y3Revenue, LTV: (y1Revenue + y2Revenue + y3Revenue) / y1Players }; } function projectPremiumRevenue(y1Players, y2Players, y3Players, concept) { // Premium Model: Units Sold * Price + Optional DLC const basePrice = concept.price_point; const dlcAttachRate = 0.25; // 25% buy DLC const avgDLCSpend = basePrice * 0.6; // DLC ~60% of base price const y1Revenue = (y1Players * basePrice) + (y1Players * dlcAttachRate * avgDLCSpend * 0.5); const y2Revenue = (y2Players * 0.3 * basePrice) + (y2Players * 0.3 * dlcAttachRate * avgDLCSpend); const y3Revenue = (y3Players * 0.1 * basePrice) + (y3Players * 0.1 * dlcAttachRate * avgDLCSpend); return { year1: {players: y1Players, revenue: y1Revenue, ARPU: basePrice}, year2: {players: y2Players * 0.3, revenue: y2Revenue, ARPU: basePrice}, year3: {players: y3Players * 0.1, revenue: y3Revenue, ARPU: basePrice}, total_3yr: y1Revenue + y2Revenue + y3Revenue, LTV: basePrice + (dlcAttachRate * avgDLCSpend) }; } function estimateARPPU(concept) { // Average Revenue Per Paying User (F2P) if (concept.genre.includes("competitive")) return 45; // Esports skin buyers spend more if (concept.genre.includes("party")) return 20; // Casual spenders