Telegram Analytics ve Veri Analizi: Kullanıcı Davranışlarını Anlama Rehberi

class GrowthAnalyzer:
    def __init__(self):
        self.metrics = {
            'daily_active_users': [],
            'new_users_daily': [],
            'user_retention': {},
            'churn_rate': {}
        }
    
    def calculate_growth_rates(self, user_data):
        daily_growth = []
        for i in range(1, len(user_data)):
            growth = ((user_data[i] - user_data[i-1]) / user_data[i-1]) * 100
            daily_growth.append(growth)
        
        return {
            'average_daily_growth': sum(daily_growth) / len(daily_growth),
            'max_growth': max(daily_growth),
            'min_growth': min(daily_growth),
            'growth_trend': self.analyze_trend(daily_growth)
        }

class EngagementAnalyzer:
    def __init__(self):
        self.engagement_metrics = {
            'message_frequency': {},
            'response_rates': {},
            'reaction_analysis': {},
            'sharing_behavior': {}
        }
    
    def analyze_engagement_patterns(self, message_data):
        patterns = {
            'peak_hours': self.find_peak_activity(message_data),
            'content_preferences': self.analyze_content_preferences(message_data),
            'user_engagement_levels': self.categorize_users(message_data),
            'viral_potential': self.calculate_viral_score(message_data)
        }
        return patterns
    
    def find_peak_activity(self, messages):
        hourly_activity = [0] * 24
        for msg in messages:
            hour = msg['timestamp'].hour
            hourly_activity[hour] += 1
        
        return {
            'peak_hour': hourly_activity.index(max(hourly_activity)),
            'off_peak': hourly_activity.index(min(hourly_activity)),
            'activity_distribution': hourly_activity
        }

class UserSegmenter:
    def __init__(self):
        self.segment_models = {
            'rfm_analysis': RFMAnalyzer(),
            'behavioral_clustering': BehavioralCluster(),
            'persona_identification': PersonaIdentifier()
        }
    
    def segment_users(self, user_data):
        segments = {}
        
        # RFM Analizi
        segments['rfm'] = self.segment_models['rfm_analysis'].analyze(
            user_data['recency'],
            user_data['frequency'], 
            user_data['monetary']
        )
        
        # Davranışsal Kümeleme
        segments['behavioral'] = self.segment_models['behavioral_clustering'].cluster(
            user_data['engagement_metrics']
        )
        
        # Kullanıcı Personası Tanımlama
        segments['personas'] = self.segment_models['persona_identification'].identify(
            user_data['demographics'],
            user_data['behavior_patterns']
        )
        
        return segments

class BehavioralPredictor:
    def __init__(self):
        self.ml_models = {
            'churn_prediction': ChurnPredictor(),
            'engagement_forecast': EngagementForecaster(),
            'content_recommendation': ContentRecommender()
        }
    
    async def predict_user_behavior(self, user_history, current_behavior):
        predictions = {}
        
        # Churn Tahmini
        predictions['churn_risk'] = await self.ml_models['churn_prediction'].predict(
            user_history, current_behavior
        )
        
        # Etkileşim Tahmini
        predictions['future_engagement'] = await self.ml_models['engagement_forecast'].forecast(
            user_history, current_behavior
        )
        
        # İçerik Önerisi
        predictions['content_recommendations'] = await self.ml_models['content_recommendation'].recommend(
            user_history, current_behavior
        )
        
        return predictions

class ContentAnalyzer:
    def __init__(self):
        self.performance_metrics = {
            'virality_score': {},
            'engagement_rate': {},
            'conversion_metrics': {},
            'audience_reach': {}
        }
    
    def analyze_content_performance(self, content_data):
        analysis = {}
        
        # Viralite Skoru
        analysis['virality'] = self.calculate_virality(
            content_data['shares'],
            content_data['views'],
            content_data['engagement']
        )
        
        # Etkileşim Oranı
        analysis['engagement_rate'] = self.calculate_engagement_rate(
            content_data['interactions'],
            content_data['reach']
        )
        
        # Dönüşüm Metrikleri
        analysis['conversion'] = self.analyze_conversions(
            content_data['cta_clicks'],
            content_data['conversions']
        )
        
        return analysis
    
    def calculate_virality(self, shares, views, engagement):
        base_score = (shares / views) * 100 if views > 0 else 0
        engagement_boost = (engagement / views) * 50 if views > 0 else 0
        return base_score + engagement_boost

class TimingOptimizer:
    def __init__(self):
        self.time_analysis = {}
        self.seasonal_patterns = {}
    
    def find_optimal_timing(self, historical_data):
        optimal_schedule = {}
        
        # Günlük Zaman Analizi
        optimal_schedule['daily'] = self.analyze_daily_patterns(
            historical_data['hourly_engagement']
        )
        
        # Haftalık Desenler
        optimal_schedule['weekly'] = self.analyze_weekly_patterns(
            historical_data['daily_engagement']
        )
        
        # Mevsimsel Trendler
        optimal_schedule['seasonal'] = self.analyze_seasonal_trends(
            historical_data['monthly_engagement']
        )
        
        return optimal_schedule
    
    def analyze_daily_patterns(self, hourly_data):
        peak_hours = []
        for hour, engagement in enumerate(hourly_data):
            if engagement > sum(hourly_data) / len(hourly_data) * 1.5:
                peak_hours.append({
                    'hour': hour,
                    'engagement_score': engagement,
                    'recommendation': 'High priority'
                })
        return sorted(peak_hours, key=lambda x: x['engagement_score'], reverse=True)

class RealTimeAnalytics:
    def __init__(self):
        self.data_stream = DataStream()
        self.alert_system = AlertSystem()
        self.visualization_engine = VisualizationEngine()
    
    async def start_monitoring(self):
        while True:
            # Gerçek zamanlı veri topla
            current_metrics = await self.data_stream.get_latest_metrics()
            
            # Anomali tespiti
            anomalies = await self.detect_anomalies(current_metrics)
            
            # Alert gönder
            if anomalies:
                await self.alert_system.send_alerts(anomalies)
            
            # Dashboard'u güncelle
            await self.visualization_engine.update_dashboard(current_metrics)
            
            await asyncio.sleep(60)  # Her dakika güncelle
    
    async def detect_anomalies(self, metrics):
        anomalies = []
        for metric, value in metrics.items():
            expected_range = self.get_expected_range(metric)
            if not expected_range[0] <= value <= expected_range[1]:
                anomalies.append({
                    'metric': metric,
                    'value': value,
                    'expected_range': expected_range,
                    'severity': self.calculate_severity(value, expected_range)
                })
        return anomalies

class PredictiveAnalyst:
    def __init__(self):
        self.time_series_models = {
            'arima': ARIMAModel(),
            'prophet': ProphetModel(),
            'lstm': LSTMModel()
        }
    
    async def forecast_metrics(self, historical_data, periods=30):
        forecasts = {}
        
        for metric, data in historical_data.items():
            # Çoklu model ile tahmin
            model_forecasts = []
            for model_name, model in self.time_series_models.items():
                forecast = await model.forecast(data, periods)
                model_forecasts.append((model_name, forecast))
            
            # Ensemble tahmin
            best_forecast = self.ensemble_predictions(model_forecasts)
            forecasts[metric] = best_forecast
        
        return forecasts
    
    def ensemble_predictions(self, predictions):
        # Model ağırlıklarını hesapla
        weights = self.calculate_model_weights(predictions)
        
        # Ağırlıklı ortalama
        ensemble_forecast = {}
        for model_name, forecast in predictions:
            weight = weights[model_name]
            for period, value in forecast.items():
                if period not in ensemble_forecast:
                    ensemble_forecast[period] = 0
                ensemble_forecast[period] += value * weight
        
        return ensemble_forecast

class BusinessIntelligence:
    def __init__(self):
        self.kpi_tracker = KPITracker()
        self.roi_calculator = ROICalculator()
        self.competitive_analyzer = CompetitiveAnalyzer()
    
    def generate_strategic_insights(self, analytics_data):
        insights = {}
        
        # KPI Performansı
        insights['kpi_performance'] = self.kpi_tracker.analyze_performance(
            analytics_data['key_metrics']
        )
        
        # ROI Hesaplama
        insights['roi_analysis'] = self.roi_calculator.calculate_roi(
            analytics_data['investments'],
            analytics_data['returns']
        )
        
        # Rekabet Analizi
        insights['competitive_position'] = self.competitive_analyzer.analyze_position(
            analytics_data['market_share'],
            analytics_data['competitor_data']
        )
        
        return insights