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

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

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

Competitor Financial Data (if available)
Revenue reports, player counts, ARPU data
Enhances accuracy of projections

Core Workflow

Phase 1: Data Extraction and Normalization

Load Market Analysis

Extract willingness-to-pay signals:

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: []} ] }

Load Game Concepts

Extract monetization-relevant data for each concept:

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

Calculate WTP Score (0-100)

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

Calculate Viral Score (0-100)

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

Calculate Revenue Potential (Year 1-3 Projections)

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 if (concept.genre.includes("co-op")) return 30; // Mid-tier return 25; // Default }

function estimateTotalAddressableMarket(concept, marketData) { // Use market analysis data + platform data const steamActivePlayers = 120000000; // ~120M monthly active on Steam const genreMultiplier = getGenreMarketShare(concept.genre); return steamActivePlayers * genreMultiplier; }

function getMarketShareEstimate(viralScore, competitorCount) { // Viral potential + competitive landscape determines realistic share let baseShare = 0.01; // 1% of SAM baseline

if (viralScore >= 85) baseShare *= 3; // Viral hit else if (viralScore >= 70) baseShare *= 2; // Strong growth else if (viralScore >= 55) baseShare *= 1.5;

// Competitive penalty if (competitorCount > 5) baseShare *= 0.7; else if (competitorCount > 3) baseShare *= 0.85;

return baseShare; }

Calculate Development ROI

function calculateROI(concept, revenueProjection) { const devCost = concept.development_cost_estimate || estimateDevCost(concept); const marketingCost = estimateMarketingCost(concept, revenueProjection.year1.players); const totalInvestment = devCost + marketingCost;

const grossRevenue = revenueProjection.total_3yr; const platformFees = grossRevenue * 0.30; // Steam/console take 30% const netRevenue = grossRevenue - platformFees; const netProfit = netRevenue - totalInvestment;

return { investment: totalInvestment, gross_revenue: grossRevenue, net_revenue: netRevenue, net_profit: netProfit, ROI_percentage: (netProfit / totalInvestment) * 100, payback_period_months: calculatePaybackPeriod(totalInvestment, revenueProjection), break_even_units: totalInvestment / (concept.price_point || 25) }; }

function estimateMarketingCost(concept, year1Players) { // Cost per acquisition based on viral coefficient const viralCoef = concept.viral_coefficient || 0.5;

if (viralCoef >= 1.2) { // Viral growth, minimal paid marketing return year1Players * 0.50; // $0.50 CPA (mostly organic) } else if (viralCoef >= 0.8) { return year1Players * 2; // $2 CPA } else { return year1Players * 5; // $5 CPA (heavy paid) } }

Phase 5: Monetization Optimization Recommendations

Analyze Monetization Model Fit

function analyzeMonetizationModel(concept, wtpScore, viralScore, marketData) { const currentModel = concept.monetization_model; const alternativeModels = [];

// Test F2P vs. Premium if (currentModel.includes("premium") && viralScore >= 70) { alternativeModels.push({ model: "F2P with cosmetic monetization", rationale: High viral score (${viralScore}) suggests F2P could 5-10x player base, projected_revenue_delta: "+40-80%", risk: "ARPPU uncertainty, requires cosmetic art pipeline" }); }

// Test pricing tiers if (typeof concept.price_point === "number") { const sentiment = marketData.price_sentiment; const currentTier = getPriceTier(concept.price_point);

Object.keys(sentiment).forEach(tier => {
  if (tier !== currentTier &#x26;&#x26; sentiment[tier].positive > sentiment[currentTier].positive + 10) {
    alternativeModels.push({
      model: `Price adjustment to ${tier}`,
      rationale: `${tier} has ${sentiment[tier].positive}% positive vs. current ${sentiment[currentTier].positive}%`,
      projected_revenue_delta: estimatePriceChangeDelta(concept, tier),
      risk: "Value perception vs. content ratio"
    });
  }
});

}

// Test monetization add-ons if (!currentModel.includes("DLC") && concept.lifecycle_commitment.includes("2-3 year")) { alternativeModels.push({ model: "Add expansion DLC model", rationale: "Long lifecycle supports premium content drops", projected_revenue_delta: "+15-25%", risk: "Community expectations for free updates" }); }

return { current_model: currentModel, current_model_score: scoreMonetizationModel(currentModel, wtpScore, viralScore), alternatives: alternativeModels.sort((a, b) => parseFloat(b.projected_revenue_delta) - parseFloat(a.projected_revenue_delta) ) }; }

Phase 6: Composite Monetization Score & Ranking

Calculate Total Monetization Score (0-100)

function calculateMonetizationScore(concept, wtpScore, viralScore, revenueProjection, roi) { const score = { willingness_to_pay: wtpScore.total * 0.25, // 25% weight viral_potential: viralScore.total * 0.20, // 20% weight revenue_potential: normalizeRevenue(revenueProjection.total_3yr) * 0.30, // 30% weight roi_efficiency: normalizeROI(roi.ROI_percentage) * 0.15, // 15% weight time_to_profit: normalizePaybackPeriod(roi.payback_period_months) * 0.10 // 10% weight };

return { total: Object.values(score).reduce((a, b) => a + b, 0), breakdown: score, tier: getMonetizationTier(Object.values(score).reduce((a, b) => a + b, 0)) }; }

function normalizeRevenue(revenue) { // Normalize to 0-100 scale (assuming $50M = 100) return Math.min(100, (revenue / 50000000) * 100); }

function normalizeROI(roiPercentage) { // Normalize to 0-100 scale (500% ROI = 100) return Math.min(100, (roiPercentage / 500) * 100); }

function normalizePaybackPeriod(months) { // Shorter = better (6 months = 100, 36 months = 0) return Math.max(0, 100 - ((months - 6) / 30) * 100); }

function getMonetizationTier(score) { if (score >= 90) return "S-TIER: Blockbuster potential"; if (score >= 80) return "A-TIER: Strong monetization"; if (score >= 70) return "B-TIER: Solid revenue opportunity"; if (score >= 60) return "C-TIER: Moderate monetization"; return "D-TIER: Weak monetization"; }

Rank and Select Top 3

function rankConcepts(concepts, scores) { const ranked = concepts.map((concept, i) => ({ concept: concept, scores: scores[i], monetization_score: scores[i].total, recommendation: generateRecommendation(concept, scores[i]) })).sort((a, b) => b.monetization_score - a.monetization_score);

return { top3: ranked.slice(0, 3), all_ranked: ranked, summary: generateRankingSummary(ranked) }; }

function generateRecommendation(concept, scores) { const strengths = []; const weaknesses = []; const actions = [];

// Analyze strengths if (scores.breakdown.willingness_to_pay >= 20) strengths.push("Strong price/value fit"); if (scores.breakdown.viral_potential >= 16) strengths.push("High organic growth potential"); if (scores.breakdown.revenue_potential >= 24) strengths.push("Large revenue opportunity");

// Analyze weaknesses if (scores.breakdown.willingness_to_pay < 15) weaknesses.push("Price optimization needed"); if (scores.breakdown.viral_potential < 12) weaknesses.push("Lacks viral mechanics"); if (scores.breakdown.roi_efficiency < 10) weaknesses.push("Long payback period");

// Generate actions if (weaknesses.includes("Price optimization needed")) { actions.push("Test alternative price points ($X-Y range)"); } if (weaknesses.includes("Lacks viral mechanics")) { actions.push("Add social features (friend invites, sharing, spectator mode)"); } if (scores.breakdown.revenue_potential < 20) { actions.push("Expand addressable market (additional platforms, regions)"); }

return {strengths, weaknesses, priority_actions: actions}; }

Output Format

Monetization Analysis Report Structure

Monetization Analysis Report: [Game Category]

Analysis Date: [Date] Market Analysis Source: [Filename] Game Concepts Analyzed: [Number] Top 3 Selected By: Total Monetization Score (WTP + Viral + Revenue + ROI + Time-to-Profit)

Executive Summary

[2-3 paragraphs summarizing key findings, top picks, revenue potential]

Top 3 Most Monetizable Games

🥇 #1: [Game Name] - Monetization Score: XX/100

Monetization Tier: [S/A/B/C/D-TIER]

Score Breakdown

Component	Score	Weight	Contribution
Willingness-to-Pay	XX/100	25%	XX.X
Viral Potential	XX/100	20%	XX.X
Revenue Potential	XX/100	30%	XX.X
ROI Efficiency	XX/100	15%	XX.X
Time-to-Profit	XX/100	10%	XX.X
TOTAL	XX/100	100%	XX.X

Willingness-to-Pay Analysis (XX/100)

Price Point: $XX or F2P
Market Sentiment: XX% positive for this price tier
Value Perception: $X.XX per hour (market avg: $X.XX) - [Excellent/Good/Fair/Poor]
Monetization Model: [Model name]
Model Sentiment: XX% positive (proven by [examples])
Pain Points Avoided: ✅ No premium+MTX ✅ No loot boxes ✅ [etc]
Competitive Pricing: [XX% below/above] competitor average ($XX)

Confidence: [HIGH/MEDIUM/LOW] based on [sample size] data points

Viral Potential Analysis (XX/100)

Viral Coefficient: X.X (players per player)
Growth Type: [Exponential/Organic/Paid-driven]
Shareability: XX/20 - [Why shareable]
Accessibility: XX/20 - [Barrier to entry]
Network Effects: XX/20 - [Friend invite mechanics]
Streamer Appeal: XX/15 - [Twitch/YouTube potential]
Novelty Factor: XX/15 - [Unique hook]
Social Features: XX/10 - [Co-op/multiplayer/clans]

Viral Mechanisms:

[Mechanism 1]: [How it drives viral spread]
[Mechanism 2]: [How it drives viral spread]

Revenue Projections (3-Year)

Player Acquisition:

Year 1: XXX,XXX players ([organic/paid mix])
Year 2: XXX,XXX players ([retention rate]%)
Year 3: XXX,XXX players ([growth trajectory])

Revenue Model: [Premium/F2P/Hybrid]

Year 1: $X.XM revenue, $XX ARPU, XXX,XXX paying users
Year 2: $X.XM revenue, $XX ARPU, XXX,XXX paying users
Year 3: $X.XM revenue, $XX ARPU, XXX,XXX paying users
Total 3-Year: $XX.XM gross revenue

Lifetime Value (LTV): $XX per player

Financial Projections

Investment Required:

Development: $X.XM ([team size, timeline])
Marketing: $X.XM (CPA: $X.XX based on viral coef X.X)
Total Investment: $X.XM

Returns:

Gross Revenue (3yr): $XX.XM
Platform Fees (30%): -$X.XM
Net Revenue: $XX.XM
Net Profit: $XX.XM
ROI: XXX% over 3 years
Payback Period: XX months
Break-Even Units: XX,XXX copies/players

Monetization Model Analysis

Current Model: [Model description]

Model Score: XX/100
Strengths: [Why this model works]
Market Validation: [Examples of successful similar models]

Alternative Models Considered:

[Alternative Model]
- Projected Revenue Delta: +XX%
- Rationale: [Why it could work better]
- Risk: [Implementation challenges]
[Alternative Model 2]
- Projected Revenue Delta: +XX%
- Rationale: [Why it could work better]
- Risk: [Implementation challenges]

Recommendation: [Stick with current / Switch to alternative X]

Market Demand Validation

Addressable Market:

TAM (Total): XX million players ([genre] on [platforms])
SAM (Serviceable): X.X million players (15% of TAM)
SOM (Obtainable): XXX,XXX players (X.X% market share realistic)

Demand Signals:

Market gap priority score: XX/100
Explicit demand mentions: XXX+ in market analysis
Competitor performance: [Leader doing $XXM ARR]
Growth trajectory: [Growing/Stable/Declining] market

Strengths

✅ [Strength 1 - from analysis]
✅ [Strength 2 - from analysis]
✅ [Strength 3 - from analysis]

Weaknesses

⚠️ [Weakness 1 - from analysis]
⚠️ [Weakness 2 - from analysis]

Priority Actions

🎯 [Action 1 to improve monetization]
🎯 [Action 2 to improve monetization]
🎯 [Action 3 to improve monetization]

Go-to-Market Recommendation

Launch Strategy:

Platform Priority: [Steam/Console/Multi] first
Pricing Strategy: [Launch price, discount strategy]
Marketing Budget: $X.XM ([CPA strategy])
Timeline: [Optimal launch window based on competition]

First 90 Days:

Week 1-2: [Activities]
Month 1: [Milestones]
Month 2-3: [Retention focus]

Success Metrics:

Week 1: XXK units sold / downloads
Month 1: XX% D30 retention
Month 3: $XXK MRR / ARR run-rate
Year 1: $X.XM revenue target

🥈 #2: [Game Name] - Monetization Score: XX/100

[Same detailed structure as #1]

🥉 #3: [Game Name] - Monetization Score: XX/100

[Same detailed structure as #1]

Comparative Analysis: Top 3

Quick Comparison Table

Metric	#1: [Name]	#2: [Name]	#3: [Name]
Monetization Score	XX/100	XX/100	XX/100
WTP Score	XX/100	XX/100	XX/100
Viral Score	XX/100	XX/100	XX/100
3-Year Revenue	$XXM	$XXM	$XXM
ROI %	XXX%	XXX%	XXX%
Payback Period	XX mo	XX mo	XX mo
Investment Required	$X.XM	$X.XM	$X.XM
Year 1 Players	XXXk	XXXk	XXXk
Risk Level	[Low/Med/High]	[Low/Med/High]	[Low/Med/High]

Portfolio Recommendation

If investing in ONE game: [#1/2/3] because [rationale]

If investing in TWO games: [#1 + #2/3] because [portfolio diversification rationale]

If investing in ALL THREE: [Portfolio strategy - risk balance, market coverage]

All Concepts Ranked

Rank	Game Name	Score	Tier	Revenue (3yr)	ROI	Why it ranked here
1	[Name]	XX	S	$XXM	XXX%	[1-sentence reason]
2	[Name]	XX	A	$XXM	XXX%	[1-sentence reason]
3	[Name]	XX	A	$XXM	XXX%	[1-sentence reason]
4	[Name]	XX	B	$XXM	XXX%	[1-sentence reason]
5	[Name]	XX	B	$XXM	XXX%	[1-sentence reason]
...

Market Insights

Willingness-to-Pay Patterns

Price Tiers by Sentiment:

✅ $15-25 (XX% positive): Sweet spot for indie/value games
✅ F2P (XX% positive): Works if cosmetic-only, fails if P2W
⚠️ $60-70 (XX% negative): Premium acceptable ONLY if no added MTX
❌ $70 + MTX (XX% negative): Market rejection, avoid entirely

Monetization Models Ranked:

F2P cosmetic-only (XX% positive) - Examples: CS2, [others]
Budget premium $15-25 (XX% positive) - Examples: Duckov, [others]
Premium $30-40 + expansions (XX% positive) - Examples: [Classic games]
Premium $60-70 clean (XX% positive / XX% negative) - Mixed
Premium + battle pass (XX% negative) - AVOID

Viral Mechanisms That Work

High Viral Coefficient (K > 1.0):

F2P with friend invite rewards
Asymmetric gameplay (streamers love)
Party games with spectator mode
Co-op with friend-only benefits

Moderate Viral Coefficient (K ~0.8):

Competitive with clan systems
Roguelikes with meta-progression sharing
Co-op PvE with progression

Low Viral Coefficient (K < 0.5):

Single-player narrative
Premium with no social features
Hardcore difficulty (small audience)

Recommendations by Investment Scenario

Scenario 1: Limited Budget ($500K-$1M)

Best Pick: [Game X]

Why: Highest ROI (XXX%), fastest payback (XX months)
Risk: [Low/Medium] - proven model
Expected Return: $X.XM net profit

Alternative: [Game Y]

Why: Lower risk, proven audience
Expected Return: $X.XM net profit

Scenario 2: Medium Budget ($2M-$5M)

Best Pick: [Game X]

Why: Balance of revenue potential ($XXM) and viral growth
Portfolio approach: [Game X] + [Game Y] for diversification

Scenario 3: Large Budget ($10M+)

Best Pick: [Game X]

Why: Blockbuster potential, large TAM
Competitive moat: [Unique advantages]
Market timing: [Why now is optimal]

Risk Assessment

Top 3 Monetization Risks

[Risk 1]: [Description]
- Affects: [Which games]
- Mitigation: [Strategy]
- Probability: [Low/Med/High]
[Risk 2]: [Description]
- Affects: [Which games]
- Mitigation: [Strategy]
- Probability: [Low/Med/High]
[Risk 3]: [Description]
- Affects: [Which games]
- Mitigation: [Strategy]
- Probability: [Low/Med/High]

Appendix: Methodology

Data Sources

Market Analysis: [Filename, data points, date]
Game Concepts: [Filename, concepts count]
Competitor Data: [Sources]

Scoring Formulas

WTP Score: [Components and weights]
Viral Score: [Components and weights]
Monetization Score: [Composite formula]

Assumptions

Platform fees: 30% (Steam/console standard)
F2P conversion rate: 3-5%
F2P ARPPU: $20-45 depending on genre
Viral coefficient estimates based on genre/model
TAM estimates based on [Steam/platform data]

Confidence Levels

HIGH: 100+ sentiment data points, direct competitor comp data
MEDIUM: 50-99 data points, some comp data
LOW: <50 data points, limited comp data

Report Generated By: Monetization Analyzer Skill v1.0 Analysis Date: [Date] Input Files: [List] Concepts Evaluated: [Number] Top 3 Selected: [Names]

Implementation Protocol

Step 1: Create Analysis Plan

TodoWrite([ "Load market analysis report and extract WTP signals", "Load game concepts document and extract monetization data", "Calculate WTP score for each game concept", "Calculate viral potential score for each concept", "Project 3-year revenue for each concept", "Calculate ROI and financial metrics", "Compute total monetization score", "Rank all concepts by monetization score", "Select top 3 and generate detailed analysis", "Create optimization recommendations for top 3", "Generate comprehensive monetization report" ])

Step 2: Data Loading (Parallel)

Read("/docs/[category]-game-concepts-[date].md") // Optional: Read competitor data if available

Step 3: Scoring (Sequential for each concept)

For each game concept:

Calculate WTP score (0-100)
Calculate viral score (0-100)
Project revenue (3-year model)
Calculate ROI metrics
Compute composite monetization score

Step 4: Ranking and Selection

Sort concepts by total monetization score (descending)
Select top 3
Generate detailed analysis for top 3
Create comparative analysis
Develop recommendations

Step 5: Report Generation

Save comprehensive report to: /docs/monetization-analysis-[category]-top3-[date].md

Best Practices

DO:

✅ Use actual market data for WTP signals (not assumptions) ✅ Consider viral coefficient in player acquisition costs ✅ Model both optimistic and conservative scenarios ✅ Validate assumptions against competitor performance ✅ Account for platform fees (30%) in revenue calculations ✅ Consider payback period for investment decisions ✅ Test alternative monetization models

DON'T:

❌ Ignore market sentiment on pricing ❌ Assume high prices = high revenue (volume matters) ❌ Underestimate marketing costs for low-viral games ❌ Overestimate viral growth without evidence ❌ Mix incompatible monetization models (premium + aggressive MTX) ❌ Ignore competitive pricing dynamics ❌ Project beyond 3 years (too uncertain)

Integration with Other Skills

This skill works with:

market-analyst: Primary data source for WTP and demand
reddit-sentiment-analysis: Raw sentiment on pricing/monetization
competitive-analysis: Competitor revenue benchmarking
product-roadmap: Feature prioritization by revenue impact

Summary

The Monetization Analyzer Skill transforms market data and game concepts into financial intelligence by:

✅ Quantifying willingness-to-pay from market sentiment
✅ Scoring viral potential based on game mechanics
✅ Projecting 3-year revenue with realistic models
✅ Calculating ROI and payback periods
✅ Ranking concepts by composite monetization score
✅ Selecting top 3 most monetizable opportunities
✅ Generating recommendations for optimization

Output enables data-driven decisions for:

Investment prioritization
Pricing optimization
Monetization model selection
Go-to-market strategy
Revenue forecasting
Pitch deck financial projections

monetization-analyzer

Safety Notice

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Monetization Analysis Report: [Game Category]

Executive Summary

Top 3 Most Monetizable Games

🥇 #1: [Game Name] - Monetization Score: XX/100

Score Breakdown

Willingness-to-Pay Analysis (XX/100)

Viral Potential Analysis (XX/100)

Revenue Projections (3-Year)

Financial Projections

Monetization Model Analysis

Market Demand Validation

Strengths

Weaknesses

Priority Actions

Go-to-Market Recommendation

🥈 #2: [Game Name] - Monetization Score: XX/100

🥉 #3: [Game Name] - Monetization Score: XX/100

Comparative Analysis: Top 3

Quick Comparison Table

Portfolio Recommendation

All Concepts Ranked

Market Insights

Willingness-to-Pay Patterns

Viral Mechanisms That Work

Recommendations by Investment Scenario

Scenario 1: Limited Budget ($500K-$1M)

Scenario 2: Medium Budget ($2M-$5M)

Scenario 3: Large Budget ($10M+)

Risk Assessment

Top 3 Monetization Risks

Appendix: Methodology

Data Sources

Scoring Formulas

Assumptions

Confidence Levels

Source Transparency

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