class EmojiAnalyticsCollector {
  constructor() {
    this.dataSchema = {
      usageEvent: {
        emojiId: 'string',
        userId: 'hashed_string',
        timestamp: 'datetime',
        platform: 'string',
        context: {
          messageType: 'string',
          conversationLength: 'number',
          participantCount: 'number',
          timeOfDay: 'number',
          dayOfWeek: 'number'
        },
        userContext: {
          ageGroup: 'string',
          locationRegion: 'string',
          languagePreference: 'string',
          userTier: 'string'
        },
        engagement: {
          responseTime: 'milliseconds',
          followUpActions: 'array',
          recipientReactions: 'array'
        }
      }
    };
    
    this.privacyFilters = new PrivacyManager();
  }
  
  collectUsageData(emojiUsage) {
    const anonymizedData = this.privacyFilters.anonymize(emojiUsage);
    const enrichedData = this.enrichWithContext(anonymizedData);
    
    return this.validateAndStore(enrichedData);
  }
  
  enrichWithContext(usageData) {
    return {
      ...usageData,
      semanticContext: this.analyzeSentiment(usageData.messageText),
      temporalPatterns: this.detectTimePatterns(usageData.timestamp),
      socialContext: this.analyzeSocialDynamics(usageData.conversation),
      deviceContext: this.enrichDeviceInformation(usageData.platform)
    };
  }
}

class EmojiDashboardSystem {
  constructor() {
    this.visualizations = {
      usageHeatmap: new HeatmapRenderer(),
      trendAnalysis: new TrendlineRenderer(),
      demographicBreakdown: new DemographicRenderer(),
      sentimentFlow: new SentimentRenderer(),
      platformComparison: new ComparisonRenderer()
    };
    
    this.realTimeProcessor = new StreamProcessor();
  }
  
  generateUsageHeatmap(timeRange, filters) {
    const usageData = this.queryUsageData(timeRange, filters);
    const heatmapData = this.processForHeatmap(usageData);
    
    return {
      visualization: this.visualizations.usageHeatmap.render(heatmapData),
      insights: this.generateHeatmapInsights(heatmapData),
      recommendations: this.suggestOptimizations(heatmapData)
    };
  }
  
  createTrendAnalysis(metrics, period) {
    const trendData = this.calculateTrends(metrics, period);
    
    return {
      growthRates: this.calculateGrowthRates(trendData),
      seasonalPatterns: this.identifySeasonalPatterns(trendData),
      anomalies: this.detectUsageAnomalies(trendData),
      predictions: this.generateTrendPredictions(trendData)
    };
  }
}

class UserSegmentationEngine {
  constructor() {
    this.clusteringAlgorithm = new KMeansClusterer();
    this.segmentationFeatures = [
      'emojiDiversity', 'usageFrequency', 'contextualVariation',
      'temporalPatterns', 'socialEngagement', 'platformPreference'
    ];
  }
  
  segmentUserBase(userData) {
    const features = this.extractFeatures(userData);
    const clusters = this.clusteringAlgorithm.fit(features);
    
    return clusters.map(cluster => ({
      segmentId: cluster.id,
      characteristics: this.analyzeClusterCharacteristics(cluster),
      size: cluster.members.length,
      emojiPreferences: this.identifyPreferredEmojis(cluster),
      engagementPatterns: this.analyzeEngagementPatterns(cluster),
      businessValue: this.calculateSegmentValue(cluster)
    }));
  }
  
  trackCohortEvolution(cohortId, timeframe) {
    const cohortData = this.getCohortData(cohortId, timeframe);
    
    return {
      retentionRates: this.calculateRetention(cohortData),
      engagementEvolution: this.trackEngagementChanges(cohortData),
      emojiAdoption: this.analyzeNewEmojiAdoption(cohortData),
      churnIndicators: this.identifyChurnSignals(cohortData)
    };
  }
}

import pandas as pd
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
from sklearn.neural_network import MLPRegressor
from sklearn.model_selection import TimeSeriesSplit
import numpy as np

class EmojiAdoptionPredictor:
    def __init__(self):
        self.models = {
            'random_forest': RandomForestRegressor(n_estimators=100),
            'gradient_boost': GradientBoostingRegressor(n_estimators=100),
            'neural_network': MLPRegressor(hidden_layer_sizes=(100, 50))
        }
        
        self.feature_engineering = FeatureEngineer()
        self.ensemble_weights = {'random_forest': 0.4, 'gradient_boost': 0.4, 'neural_network': 0.2}
    
    def prepare_features(self, emoji_data):
        features = {
            'visual_complexity': self.calculate_visual_complexity(emoji_data.design),
            'color_appeal': self.analyze_color_psychology(emoji_data.colors),
            'semantic_clarity': self.measure_semantic_clarity(emoji_data.meaning),
            'cultural_resonance': self.assess_cultural_fit(emoji_data.cultural_context),
            'platform_compatibility': self.check_platform_support(emoji_data.technical_specs),
            'timing_factors': self.analyze_release_timing(emoji_data.release_date),
            'market_saturation': self.measure_category_saturation(emoji_data.category),
            'user_demand_signals': self.analyze_demand_indicators(emoji_data.pre_release_data)
        }
        
        return pd.DataFrame([features])
    
    def predict_adoption_curve(self, emoji_data, prediction_horizon=90):
        features = self.prepare_features(emoji_data)
        
        predictions = {}
        for name, model in self.models.items():
            pred = model.predict(features)[0]
            predictions[name] = pred
        
        # Ensemble prediction
        ensemble_prediction = sum(
            pred * self.ensemble_weights[name] 
            for name, pred in predictions.items()
        )
        
        # Generate adoption curve
        adoption_curve = self.generate_adoption_timeline(
            ensemble_prediction, 
            prediction_horizon
        )
        
        return {
            'peak_adoption_rate': ensemble_prediction,
            'adoption_timeline': adoption_curve,
            'confidence_interval': self.calculate_confidence_interval(predictions),
            'success_probability': self.calculate_success_probability(ensemble_prediction)
        }

from statsmodels.tsa.arima.model import ARIMA
from statsmodels.tsa.seasonal import seasonal_decompose
import pmdarima as pm

class EmojiEngagementForecaster:
    def __init__(self):
        self.seasonal_models = {}
        self.trend_models = {}
        self.external_factors = ExternalFactorAnalyzer()
    
    def decompose_usage_patterns(self, usage_timeseries):
        decomposition = seasonal_decompose(
            usage_timeseries, 
            model='additive', 
            period=7  # Weekly seasonality
        )
        
        return {
            'trend': decomposition.trend,
            'seasonal': decomposition.seasonal,
            'residual': decomposition.resid,
            'observed': decomposition.observed
        }
    
    def build_forecasting_model(self, emoji_id, historical_data):
        # Auto ARIMA model selection
        model = pm.auto_arima(
            historical_data,
            start_p=1, start_q=1,
            max_p=3, max_q=3,
            seasonal=True,
            stepwise=True,
            suppress_warnings=True,
            error_action='ignore'
        )
        
        self.seasonal_models[emoji_id] = model
        return model
    
    def forecast_engagement(self, emoji_id, forecast_horizon=30):
        model = self.seasonal_models[emoji_id]
        forecast, conf_int = model.predict(
            n_periods=forecast_horizon,
            return_conf_int=True
        )
        
        # Adjust for external factors
        external_adjustments = self.external_factors.calculate_adjustments(
            emoji_id, 
            forecast_horizon
        )
        
        adjusted_forecast = forecast * external_adjustments
        
        return {
            'forecast': adjusted_forecast,
            'confidence_interval': conf_int,
            'external_factors': external_adjustments,
            'model_accuracy': self.evaluate_model_accuracy(model)
        }

class EmojiChurnPredictor:
    def __init__(self):
        self.churn_model = GradientBoostingClassifier()
        self.feature_importance_tracker = {}
        self.intervention_recommender = InterventionRecommender()
    
    def extract_churn_features(self, user_data):
        recent_activity = user_data[-30:]  # Last 30 days
        historical_activity = user_data[:-30]
        
        features = {
            'usage_decline_rate': self.calculate_decline_rate(user_data),
            'emoji_diversity_change': self.measure_diversity_change(user_data),
            'engagement_frequency_drop': self.measure_frequency_change(user_data),
            'platform_switching': self.detect_platform_changes(user_data),
            'social_isolation_score': self.calculate_isolation_score(user_data),
            'feature_abandonment': self.identify_abandoned_features(user_data),
            'support_interactions': self.count_support_contacts(user_data),
            'competitor_usage_signals': self.detect_competitor_usage(user_data)
        }
        
        return features
    
    def predict_churn_risk(self, user_id):
        user_data = self.get_user_data(user_id)
        features = self.extract_churn_features(user_data)
        
        churn_probability = self.churn_model.predict_proba([list(features.values())])[0][1]
        
        return {
            'churn_risk': churn_probability,
            'risk_level': self.categorize_risk_level(churn_probability),
            'key_risk_factors': self.identify_key_factors(features),
            'recommended_interventions': self.intervention_recommender.suggest(features),
            'timeline_to_churn': self.estimate_churn_timeline(features)
        }

class EmojiBusinessIntelligence {
  constructor() {
    this.revenueTracker = new RevenueAttributionSystem();
    this.conversionAnalyzer = new ConversionAnalyzer();
    this.ltv_calculator = new LifetimeValueCalculator();
  }
  
  calculateEmojiROI(emojiFeature, timeframe) {
    const costs = this.calculateFeatureCosts(emojiFeature);
    const benefits = this.calculateFeatureBenefits(emojiFeature, timeframe);
    
    return {
      developmentCost: costs.development,
      maintenanceCost: costs.maintenance,
      marketingCost: costs.marketing,
      
      revenueAttribution: benefits.directRevenue,
      retentionImpact: benefits.retentionValue,
      engagementLift: benefits.engagementIncrease,
      
      roi: (benefits.totalBenefit - costs.totalCost) / costs.totalCost,
      paybackPeriod: costs.totalCost / benefits.monthlyBenefit
    };
  }
  
  generateStrategicInsights(businessData) {
    return {
      marketOpportunities: this.identifyMarketGaps(businessData),
      competitiveAdvantages: this.analyzeCompetitivePosition(businessData),
      investmentPriorities: this.rankInvestmentOpportunities(businessData),
      riskAssessment: this.evaluateBusinessRisks(businessData),
      growthProjections: this.projectGrowthScenarios(businessData)
    };
  }
}

class ProductIntelligenceSystem {
  constructor() {
    this.featureGapAnalyzer = new FeatureGapAnalyzer();
    this.userNeedsMiner = new UserNeedsMiner();
    this.competitorAnalyzer = new CompetitorAnalyzer();
  }
  
  generateProductRecommendations(usageData, userFeedback) {
    const insights = {
      unmetNeeds: this.userNeedsMiner.identifyUnmetNeeds(usageData, userFeedback),
      usagePatterns: this.analyzeUsagePatterns(usageData),
      featureGaps: this.featureGapAnalyzer.identifyGaps(usageData),
      competitorFeatures: this.competitorAnalyzer.analyzeFeatures()
    };
    
    return {
      recommendedFeatures: this.prioritizeFeatureRecommendations(insights),
      technicalRequirements: this.generateTechnicalSpecs(insights),
      marketValidation: this.assessMarketDemand(insights),
      implementationRoadmap: this.createImplementationPlan(insights)
    };
  }
  
  trackFeatureSuccess(featureId, launchDate) {
    const prelaunchMetrics = this.getPreLaunchBaseline(featureId, launchDate);
    const postlaunchMetrics = this.getPostLaunchMetrics(featureId, launchDate);
    
    return {
      adoptionRate: this.calculateAdoptionRate(prelaunchMetrics, postlaunchMetrics),
      userSatisfaction: this.measureUserSatisfaction(featureId),
      businessImpact: this.assessBusinessImpact(featureId),
      iterationNeeds: this.identifyIterationOpportunities(featureId)
    };
  }
}

class StrategicPlanningSystem {
  constructor() {
    this.marketAnalyzer = new MarketAnalyzer();
    this.trendPredictor = new TrendPredictor();
    this.scenarioPlanner = new ScenarioPlanner();
  }
  
  generateStrategicPlan(timeHorizon, objectives) {
    const marketInsights = this.marketAnalyzer.analyzeMarketConditions();
    const trendPredictions = this.trendPredictor.forecastTrends(timeHorizon);
    const scenarios = this.scenarioPlanner.generateScenarios(marketInsights, trendPredictions);
    
    return {
      marketPosition: this.assessCurrentPosition(marketInsights),
      opportunities: this.identifyStrategicOpportunities(scenarios),
      threats: this.assessStrategicThreats(scenarios),
      recommendations: this.generateStrategicRecommendations(objectives, scenarios),
      resourceAllocation: this.optimizeResourceAllocation(scenarios),
      successMetrics: this.defineSuccessMetrics(objectives, scenarios)
    };
  }
}