""" AI Analytics Service Advanced AI-powered analytics for content planning and performance prediction. """ from typing import Dict, Any, List, Optional, Tuple from datetime import datetime, timedelta import json from loguru import logger import asyncio from sqlalchemy.orm import Session from services.database import get_db_session from models.content_planning import ContentAnalytics, ContentStrategy, CalendarEvent from services.content_gap_analyzer.ai_engine_service import AIEngineService class AIAnalyticsService: """Advanced AI analytics service for content planning.""" def __init__(self): self.ai_engine = AIEngineService() self.db_session = None def _get_db_session(self) -> Session: """Get database session.""" if not self.db_session: self.db_session = get_db_session() return self.db_session async def analyze_content_evolution(self, strategy_id: int, time_period: str = "30d") -> Dict[str, Any]: """ Analyze content evolution over time for a specific strategy. Args: strategy_id: Content strategy ID time_period: Analysis period (7d, 30d, 90d, 1y) Returns: Content evolution analysis results """ try: logger.info(f"Analyzing content evolution for strategy {strategy_id}") # Get analytics data for the strategy analytics_data = await self._get_analytics_data(strategy_id, time_period) # Analyze content performance trends performance_trends = await self._analyze_performance_trends(analytics_data) # Analyze content type evolution content_evolution = await self._analyze_content_type_evolution(analytics_data) # Analyze audience engagement patterns engagement_patterns = await self._analyze_engagement_patterns(analytics_data) evolution_analysis = { 'strategy_id': strategy_id, 'time_period': time_period, 'performance_trends': performance_trends, 'content_evolution': content_evolution, 'engagement_patterns': engagement_patterns, 'recommendations': await self._generate_evolution_recommendations( performance_trends, content_evolution, engagement_patterns ), 'analysis_date': datetime.utcnow().isoformat() } logger.info(f"Content evolution analysis completed for strategy {strategy_id}") return evolution_analysis except Exception as e: logger.error(f"Error analyzing content evolution: {str(e)}") raise async def analyze_performance_trends(self, strategy_id: int, metrics: List[str] = None) -> Dict[str, Any]: """ Analyze performance trends for content strategy. Args: strategy_id: Content strategy ID metrics: List of metrics to analyze (engagement, reach, conversion, etc.) Returns: Performance trend analysis results """ try: logger.info(f"Analyzing performance trends for strategy {strategy_id}") if not metrics: metrics = ['engagement_rate', 'reach', 'conversion_rate', 'click_through_rate'] # Get performance data performance_data = await self._get_performance_data(strategy_id, metrics) # Analyze trends for each metric trend_analysis = {} for metric in metrics: trend_analysis[metric] = await self._analyze_metric_trend(performance_data, metric) # Generate predictive insights predictive_insights = await self._generate_predictive_insights(trend_analysis) # Calculate performance scores performance_scores = await self._calculate_performance_scores(trend_analysis) trend_results = { 'strategy_id': strategy_id, 'metrics_analyzed': metrics, 'trend_analysis': trend_analysis, 'predictive_insights': predictive_insights, 'performance_scores': performance_scores, 'recommendations': await self._generate_trend_recommendations(trend_analysis), 'analysis_date': datetime.utcnow().isoformat() } logger.info(f"Performance trend analysis completed for strategy {strategy_id}") return trend_results except Exception as e: logger.error(f"Error analyzing performance trends: {str(e)}") raise async def predict_content_performance(self, content_data: Dict[str, Any], strategy_id: int) -> Dict[str, Any]: """ Predict content performance using AI models. Args: content_data: Content details (title, description, type, platform, etc.) strategy_id: Content strategy ID Returns: Performance prediction results """ try: logger.info(f"Predicting performance for content in strategy {strategy_id}") # Get historical performance data historical_data = await self._get_historical_performance_data(strategy_id) # Analyze content characteristics content_analysis = await self._analyze_content_characteristics(content_data) # Calculate success probability success_probability = await self._calculate_success_probability({}, historical_data) # Generate optimization recommendations optimization_recommendations = await self._generate_optimization_recommendations( content_data, {}, success_probability ) prediction_results = { 'strategy_id': strategy_id, 'content_data': content_data, 'performance_prediction': {}, 'success_probability': success_probability, 'optimization_recommendations': optimization_recommendations, 'confidence_score': 0.7, 'prediction_date': datetime.utcnow().isoformat() } logger.info(f"Content performance prediction completed") return prediction_results except Exception as e: logger.error(f"Error predicting content performance: {str(e)}") raise async def generate_strategic_intelligence(self, strategy_id: int, market_data: Dict[str, Any] = None) -> Dict[str, Any]: """ Generate strategic intelligence for content planning. Args: strategy_id: Content strategy ID market_data: Additional market data for analysis Returns: Strategic intelligence results """ try: logger.info(f"Generating strategic intelligence for strategy {strategy_id}") # Get strategy data strategy_data = await self._get_strategy_data(strategy_id) # Analyze market positioning market_positioning = await self._analyze_market_positioning(strategy_data, market_data) # Identify competitive advantages competitive_advantages = await self._identify_competitive_advantages(strategy_data) # Calculate strategic scores strategic_scores = await self._calculate_strategic_scores( strategy_data, market_positioning, competitive_advantages ) intelligence_results = { 'strategy_id': strategy_id, 'market_positioning': market_positioning, 'competitive_advantages': competitive_advantages, 'strategic_scores': strategic_scores, 'risk_assessment': await self._assess_strategic_risks(strategy_data), 'opportunity_analysis': await self._analyze_strategic_opportunities(strategy_data), 'analysis_date': datetime.utcnow().isoformat() } logger.info(f"Strategic intelligence generation completed") return intelligence_results except Exception as e: logger.error(f"Error generating strategic intelligence: {str(e)}") raise # Helper methods for data retrieval and analysis async def _get_analytics_data(self, strategy_id: int, time_period: str) -> List[Dict[str, Any]]: """Get analytics data for the specified strategy and time period.""" try: session = self._get_db_session() # Calculate date range end_date = datetime.utcnow() if time_period == "7d": start_date = end_date - timedelta(days=7) elif time_period == "30d": start_date = end_date - timedelta(days=30) elif time_period == "90d": start_date = end_date - timedelta(days=90) elif time_period == "1y": start_date = end_date - timedelta(days=365) else: start_date = end_date - timedelta(days=30) # Query analytics data analytics = session.query(ContentAnalytics).filter( ContentAnalytics.strategy_id == strategy_id, ContentAnalytics.recorded_at >= start_date, ContentAnalytics.recorded_at <= end_date ).all() return [analytics.to_dict() for analytics in analytics] except Exception as e: logger.error(f"Error getting analytics data: {str(e)}") return [] async def _analyze_performance_trends(self, analytics_data: List[Dict[str, Any]]) -> Dict[str, Any]: """Analyze performance trends from analytics data.""" try: if not analytics_data: return {'trend': 'stable', 'growth_rate': 0, 'insights': 'No data available'} # Calculate trend metrics total_analytics = len(analytics_data) avg_performance = sum(item.get('performance_score', 0) for item in analytics_data) / total_analytics # Determine trend direction if avg_performance > 0.7: trend = 'increasing' elif avg_performance < 0.3: trend = 'decreasing' else: trend = 'stable' return { 'trend': trend, 'average_performance': avg_performance, 'total_analytics': total_analytics, 'insights': f'Performance is {trend} with average score of {avg_performance:.2f}' } except Exception as e: logger.error(f"Error analyzing performance trends: {str(e)}") return {'trend': 'unknown', 'error': str(e)} async def _analyze_content_type_evolution(self, analytics_data: List[Dict[str, Any]]) -> Dict[str, Any]: """Analyze how content types have evolved over time.""" try: content_types = {} for data in analytics_data: content_type = data.get('content_type', 'unknown') if content_type not in content_types: content_types[content_type] = { 'count': 0, 'total_performance': 0, 'avg_performance': 0 } content_types[content_type]['count'] += 1 content_types[content_type]['total_performance'] += data.get('performance_score', 0) # Calculate averages for content_type in content_types: if content_types[content_type]['count'] > 0: content_types[content_type]['avg_performance'] = ( content_types[content_type]['total_performance'] / content_types[content_type]['count'] ) return { 'content_types': content_types, 'most_performing_type': max(content_types.items(), key=lambda x: x[1]['avg_performance'])[0] if content_types else None, 'evolution_insights': 'Content type performance analysis completed' } except Exception as e: logger.error(f"Error analyzing content type evolution: {str(e)}") return {'error': str(e)} async def _analyze_engagement_patterns(self, analytics_data: List[Dict[str, Any]]) -> Dict[str, Any]: """Analyze audience engagement patterns.""" try: if not analytics_data: return {'patterns': {}, 'insights': 'No engagement data available'} # Analyze engagement by platform platform_engagement = {} for data in analytics_data: platform = data.get('platform', 'unknown') if platform not in platform_engagement: platform_engagement[platform] = { 'total_engagement': 0, 'count': 0, 'avg_engagement': 0 } metrics = data.get('metrics', {}) engagement = metrics.get('engagement_rate', 0) platform_engagement[platform]['total_engagement'] += engagement platform_engagement[platform]['count'] += 1 # Calculate averages for platform in platform_engagement: if platform_engagement[platform]['count'] > 0: platform_engagement[platform]['avg_engagement'] = ( platform_engagement[platform]['total_engagement'] / platform_engagement[platform]['count'] ) return { 'platform_engagement': platform_engagement, 'best_platform': max(platform_engagement.items(), key=lambda x: x[1]['avg_engagement'])[0] if platform_engagement else None, 'insights': 'Platform engagement analysis completed' } except Exception as e: logger.error(f"Error analyzing engagement patterns: {str(e)}") return {'error': str(e)} async def _generate_evolution_recommendations(self, performance_trends: Dict[str, Any], content_evolution: Dict[str, Any], engagement_patterns: Dict[str, Any]) -> List[Dict[str, Any]]: """Generate recommendations based on evolution analysis.""" recommendations = [] try: # Performance-based recommendations if performance_trends.get('trend') == 'decreasing': recommendations.append({ 'type': 'performance_optimization', 'priority': 'high', 'title': 'Improve Content Performance', 'description': 'Content performance is declining. Focus on quality and engagement.', 'action_items': [ 'Review and improve content quality', 'Optimize for audience engagement', 'Analyze competitor strategies' ] }) # Content type recommendations if content_evolution.get('most_performing_type'): best_type = content_evolution['most_performing_type'] recommendations.append({ 'type': 'content_strategy', 'priority': 'medium', 'title': f'Focus on {best_type} Content', 'description': f'{best_type} content is performing best. Increase focus on this type.', 'action_items': [ f'Increase {best_type} content production', 'Analyze what makes this content successful', 'Optimize other content types based on learnings' ] }) # Platform recommendations if engagement_patterns.get('best_platform'): best_platform = engagement_patterns['best_platform'] recommendations.append({ 'type': 'platform_strategy', 'priority': 'medium', 'title': f'Optimize for {best_platform}', 'description': f'{best_platform} shows highest engagement. Focus optimization efforts here.', 'action_items': [ f'Increase content for {best_platform}', f'Optimize content format for platform', 'Use platform-specific features' ] }) return recommendations except Exception as e: logger.error(f"Error generating evolution recommendations: {str(e)}") return [{'error': str(e)}] async def _get_performance_data(self, strategy_id: int, metrics: List[str]) -> List[Dict[str, Any]]: """Get performance data for specified metrics.""" try: session = self._get_db_session() # Get analytics data for the strategy analytics = session.query(ContentAnalytics).filter( ContentAnalytics.strategy_id == strategy_id ).all() return [analytics.to_dict() for analytics in analytics] except Exception as e: logger.error(f"Error getting performance data: {str(e)}") return [] async def _analyze_metric_trend(self, performance_data: List[Dict[str, Any]], metric: str) -> Dict[str, Any]: """Analyze trend for a specific metric.""" try: if not performance_data: return {'trend': 'no_data', 'value': 0, 'change': 0} # Extract metric values metric_values = [] for data in performance_data: metrics = data.get('metrics', {}) if metric in metrics: metric_values.append(metrics[metric]) if not metric_values: return {'trend': 'no_data', 'value': 0, 'change': 0} # Calculate trend avg_value = sum(metric_values) / len(metric_values) # Simple trend calculation if len(metric_values) >= 2: recent_avg = sum(metric_values[-len(metric_values)//2:]) / (len(metric_values)//2) older_avg = sum(metric_values[:len(metric_values)//2]) / (len(metric_values)//2) change = ((recent_avg - older_avg) / older_avg * 100) if older_avg > 0 else 0 else: change = 0 # Determine trend direction if change > 5: trend = 'increasing' elif change < -5: trend = 'decreasing' else: trend = 'stable' return { 'trend': trend, 'value': avg_value, 'change_percent': change, 'data_points': len(metric_values) } except Exception as e: logger.error(f"Error analyzing metric trend: {str(e)}") return {'trend': 'error', 'error': str(e)} async def _generate_predictive_insights(self, trend_analysis: Dict[str, Any]) -> Dict[str, Any]: """Generate predictive insights based on trend analysis.""" try: insights = { 'predicted_performance': 'stable', 'confidence_level': 'medium', 'key_factors': [], 'recommendations': [] } # Analyze trends to generate insights increasing_metrics = [] decreasing_metrics = [] for metric, analysis in trend_analysis.items(): if analysis.get('trend') == 'increasing': increasing_metrics.append(metric) elif analysis.get('trend') == 'decreasing': decreasing_metrics.append(metric) if len(increasing_metrics) > len(decreasing_metrics): insights['predicted_performance'] = 'improving' insights['confidence_level'] = 'high' if len(increasing_metrics) > 2 else 'medium' elif len(decreasing_metrics) > len(increasing_metrics): insights['predicted_performance'] = 'declining' insights['confidence_level'] = 'high' if len(decreasing_metrics) > 2 else 'medium' insights['key_factors'] = increasing_metrics + decreasing_metrics insights['recommendations'] = [ f'Focus on improving {", ".join(decreasing_metrics)}' if decreasing_metrics else 'Maintain current performance', f'Leverage success in {", ".join(increasing_metrics)}' if increasing_metrics else 'Identify new growth opportunities' ] return insights except Exception as e: logger.error(f"Error generating predictive insights: {str(e)}") return {'error': str(e)} async def _calculate_performance_scores(self, trend_analysis: Dict[str, Any]) -> Dict[str, float]: """Calculate performance scores based on trend analysis.""" try: scores = {} for metric, analysis in trend_analysis.items(): base_score = analysis.get('value', 0) change = analysis.get('change_percent', 0) # Adjust score based on trend if analysis.get('trend') == 'increasing': adjusted_score = base_score * (1 + abs(change) / 100) elif analysis.get('trend') == 'decreasing': adjusted_score = base_score * (1 - abs(change) / 100) else: adjusted_score = base_score scores[metric] = min(adjusted_score, 1.0) # Cap at 1.0 return scores except Exception as e: logger.error(f"Error calculating performance scores: {str(e)}") return {} async def _generate_trend_recommendations(self, trend_analysis: Dict[str, Any]) -> List[Dict[str, Any]]: """Generate recommendations based on trend analysis.""" recommendations = [] try: for metric, analysis in trend_analysis.items(): if analysis.get('trend') == 'decreasing': recommendations.append({ 'type': 'metric_optimization', 'priority': 'high', 'metric': metric, 'title': f'Improve {metric.replace("_", " ").title()}', 'description': f'{metric} is declining. Focus on optimization.', 'action_items': [ f'Analyze factors affecting {metric}', 'Review content strategy for this metric', 'Implement optimization strategies' ] }) elif analysis.get('trend') == 'increasing': recommendations.append({ 'type': 'metric_leverage', 'priority': 'medium', 'metric': metric, 'title': f'Leverage {metric.replace("_", " ").title()} Success', 'description': f'{metric} is improving. Build on this success.', 'action_items': [ f'Identify what\'s driving {metric} improvement', 'Apply successful strategies to other metrics', 'Scale successful approaches' ] }) return recommendations except Exception as e: logger.error(f"Error generating trend recommendations: {str(e)}") return [{'error': str(e)}] async def _analyze_single_competitor(self, url: str, analysis_period: str) -> Dict[str, Any]: """Analyze a single competitor's content strategy.""" try: # This would integrate with the competitor analyzer service # For now, return mock data return { 'url': url, 'content_frequency': 'weekly', 'content_types': ['blog', 'video', 'social'], 'engagement_rate': 0.75, 'top_performing_content': ['How-to guides', 'Industry insights'], 'publishing_schedule': ['Tuesday', 'Thursday'], 'content_themes': ['Educational', 'Thought leadership', 'Engagement'] } except Exception as e: logger.error(f"Error analyzing competitor {url}: {str(e)}") return {'url': url, 'error': str(e)} async def _compare_competitor_strategies(self, competitor_analyses: List[Dict[str, Any]]) -> Dict[str, Any]: """Compare strategies across competitors.""" try: if not competitor_analyses: return {'comparison': 'no_data'} # Analyze common patterns content_types = set() themes = set() schedules = set() for analysis in competitor_analyses: if 'content_types' in analysis: content_types.update(analysis['content_types']) if 'content_themes' in analysis: themes.update(analysis['content_themes']) if 'publishing_schedule' in analysis: schedules.update(analysis['publishing_schedule']) return { 'common_content_types': list(content_types), 'common_themes': list(themes), 'common_schedules': list(schedules), 'competitive_landscape': 'analyzed', 'insights': f'Found {len(content_types)} content types, {len(themes)} themes across competitors' } except Exception as e: logger.error(f"Error comparing competitor strategies: {str(e)}") return {'error': str(e)} async def _identify_market_trends(self, competitor_analyses: List[Dict[str, Any]]) -> Dict[str, Any]: """Identify market trends from competitor analysis.""" try: trends = { 'popular_content_types': [], 'emerging_themes': [], 'publishing_patterns': [], 'engagement_trends': [] } # Analyze trends from competitor data content_type_counts = {} theme_counts = {} for analysis in competitor_analyses: for content_type in analysis.get('content_types', []): content_type_counts[content_type] = content_type_counts.get(content_type, 0) + 1 for theme in analysis.get('content_themes', []): theme_counts[theme] = theme_counts.get(theme, 0) + 1 trends['popular_content_types'] = sorted(content_type_counts.items(), key=lambda x: x[1], reverse=True) trends['emerging_themes'] = sorted(theme_counts.items(), key=lambda x: x[1], reverse=True) return trends except Exception as e: logger.error(f"Error identifying market trends: {str(e)}") return {'error': str(e)} async def _generate_competitor_recommendations(self, competitor_analyses: List[Dict[str, Any]], strategy_comparison: Dict[str, Any], market_trends: Dict[str, Any]) -> List[Dict[str, Any]]: """Generate recommendations based on competitor analysis.""" recommendations = [] try: # Identify opportunities popular_types = [item[0] for item in market_trends.get('popular_content_types', [])] if popular_types: recommendations.append({ 'type': 'content_strategy', 'priority': 'high', 'title': 'Focus on Popular Content Types', 'description': f'Competitors are successfully using: {", ".join(popular_types[:3])}', 'action_items': [ 'Analyze successful content in these categories', 'Develop content strategy for popular types', 'Differentiate while following proven patterns' ] }) # Identify gaps all_competitor_themes = set() for analysis in competitor_analyses: all_competitor_themes.update(analysis.get('content_themes', [])) if all_competitor_themes: recommendations.append({ 'type': 'competitive_advantage', 'priority': 'medium', 'title': 'Identify Content Gaps', 'description': 'Look for opportunities competitors are missing', 'action_items': [ 'Analyze underserved content areas', 'Identify unique positioning opportunities', 'Develop differentiated content strategy' ] }) return recommendations except Exception as e: logger.error(f"Error generating competitor recommendations: {str(e)}") return [{'error': str(e)}] async def _get_historical_performance_data(self, strategy_id: int) -> List[Dict[str, Any]]: """Get historical performance data for the strategy.""" try: session = self._get_db_session() analytics = session.query(ContentAnalytics).filter( ContentAnalytics.strategy_id == strategy_id ).all() return [analytics.to_dict() for analytics in analytics] except Exception as e: logger.error(f"Error getting historical performance data: {str(e)}") return [] async def _analyze_content_characteristics(self, content_data: Dict[str, Any]) -> Dict[str, Any]: """Analyze content characteristics for performance prediction.""" try: characteristics = { 'content_type': content_data.get('content_type', 'unknown'), 'platform': content_data.get('platform', 'unknown'), 'estimated_length': content_data.get('estimated_length', 'medium'), 'complexity': 'medium', 'engagement_potential': 'medium', 'seo_potential': 'medium' } # Analyze title and description title = content_data.get('title', '') description = content_data.get('description', '') if title and description: characteristics['content_richness'] = 'high' if len(description) > 200 else 'medium' characteristics['title_optimization'] = 'good' if len(title) > 20 and len(title) < 60 else 'needs_improvement' return characteristics except Exception as e: logger.error(f"Error analyzing content characteristics: {str(e)}") return {'error': str(e)} async def _calculate_success_probability(self, performance_prediction: Dict[str, Any], historical_data: List[Dict[str, Any]]) -> float: """Calculate success probability based on prediction and historical data.""" try: base_probability = 0.5 # Adjust based on historical performance if historical_data: avg_historical_performance = sum( data.get('performance_score', 0) for data in historical_data ) / len(historical_data) if avg_historical_performance > 0.7: base_probability += 0.1 elif avg_historical_performance < 0.3: base_probability -= 0.1 return min(max(base_probability, 0.0), 1.0) except Exception as e: logger.error(f"Error calculating success probability: {str(e)}") return 0.5 async def _generate_optimization_recommendations(self, content_data: Dict[str, Any], performance_prediction: Dict[str, Any], success_probability: float) -> List[Dict[str, Any]]: """Generate optimization recommendations for content.""" recommendations = [] try: # Performance-based recommendations if success_probability < 0.5: recommendations.append({ 'type': 'content_optimization', 'priority': 'high', 'title': 'Improve Content Quality', 'description': 'Content has low success probability. Focus on quality improvements.', 'action_items': [ 'Enhance content depth and value', 'Improve title and description', 'Optimize for target audience' ] }) # Platform-specific recommendations platform = content_data.get('platform', '') if platform: recommendations.append({ 'type': 'platform_optimization', 'priority': 'medium', 'title': f'Optimize for {platform}', 'description': f'Ensure content is optimized for {platform} platform.', 'action_items': [ f'Follow {platform} best practices', 'Optimize content format for platform', 'Use platform-specific features' ] }) return recommendations except Exception as e: logger.error(f"Error generating optimization recommendations: {str(e)}") return [{'error': str(e)}] async def _get_strategy_data(self, strategy_id: int) -> Dict[str, Any]: """Get strategy data for analysis.""" try: session = self._get_db_session() strategy = session.query(ContentStrategy).filter( ContentStrategy.id == strategy_id ).first() if strategy: return strategy.to_dict() else: return {} except Exception as e: logger.error(f"Error getting strategy data: {str(e)}") return {} async def _analyze_market_positioning(self, strategy_data: Dict[str, Any], market_data: Dict[str, Any] = None) -> Dict[str, Any]: """Analyze market positioning for the strategy.""" try: positioning = { 'industry_position': 'established', 'competitive_advantage': 'content_quality', 'market_share': 'medium', 'differentiation_factors': [] } # Analyze based on strategy data industry = strategy_data.get('industry', '') if industry: positioning['industry_position'] = 'established' if industry in ['tech', 'finance', 'healthcare'] else 'emerging' # Analyze content pillars content_pillars = strategy_data.get('content_pillars', []) if content_pillars: positioning['differentiation_factors'] = [pillar.get('name', '') for pillar in content_pillars] return positioning except Exception as e: logger.error(f"Error analyzing market positioning: {str(e)}") return {'error': str(e)} async def _identify_competitive_advantages(self, strategy_data: Dict[str, Any]) -> List[Dict[str, Any]]: """Identify competitive advantages for the strategy.""" try: advantages = [] # Analyze content pillars for advantages content_pillars = strategy_data.get('content_pillars', []) for pillar in content_pillars: advantages.append({ 'type': 'content_pillar', 'name': pillar.get('name', ''), 'description': pillar.get('description', ''), 'strength': 'high' if pillar.get('frequency') == 'weekly' else 'medium' }) # Analyze target audience target_audience = strategy_data.get('target_audience', {}) if target_audience: advantages.append({ 'type': 'audience_focus', 'name': 'Targeted Audience', 'description': 'Well-defined target audience', 'strength': 'high' }) return advantages except Exception as e: logger.error(f"Error identifying competitive advantages: {str(e)}") return [] async def _calculate_strategic_scores(self, strategy_data: Dict[str, Any], market_positioning: Dict[str, Any], competitive_advantages: List[Dict[str, Any]]) -> Dict[str, float]: """Calculate strategic scores for the strategy.""" try: scores = { 'market_positioning_score': 0.7, 'competitive_advantage_score': 0.8, 'content_strategy_score': 0.75, 'overall_strategic_score': 0.75 } # Adjust scores based on analysis if market_positioning.get('industry_position') == 'established': scores['market_positioning_score'] += 0.1 if len(competitive_advantages) > 2: scores['competitive_advantage_score'] += 0.1 # Calculate overall score scores['overall_strategic_score'] = sum(scores.values()) / len(scores) return scores except Exception as e: logger.error(f"Error calculating strategic scores: {str(e)}") return {'error': str(e)} async def _assess_strategic_risks(self, strategy_data: Dict[str, Any]) -> List[Dict[str, Any]]: """Assess strategic risks for the strategy.""" try: risks = [] # Analyze potential risks content_pillars = strategy_data.get('content_pillars', []) if len(content_pillars) < 2: risks.append({ 'type': 'content_diversity', 'severity': 'medium', 'description': 'Limited content pillar diversity', 'mitigation': 'Develop additional content pillars' }) target_audience = strategy_data.get('target_audience', {}) if not target_audience: risks.append({ 'type': 'audience_definition', 'severity': 'high', 'description': 'Unclear target audience definition', 'mitigation': 'Define detailed audience personas' }) return risks except Exception as e: logger.error(f"Error assessing strategic risks: {str(e)}") return [] async def _analyze_strategic_opportunities(self, strategy_data: Dict[str, Any]) -> List[Dict[str, Any]]: """Analyze strategic opportunities for the strategy.""" try: opportunities = [] # Identify opportunities based on strategy data industry = strategy_data.get('industry', '') if industry: opportunities.append({ 'type': 'industry_growth', 'priority': 'high', 'description': f'Growing {industry} industry presents expansion opportunities', 'action_items': [ 'Monitor industry trends', 'Develop industry-specific content', 'Expand into emerging sub-sectors' ] }) content_pillars = strategy_data.get('content_pillars', []) if content_pillars: opportunities.append({ 'type': 'content_expansion', 'priority': 'medium', 'description': 'Opportunity to expand content pillar coverage', 'action_items': [ 'Identify underserved content areas', 'Develop new content pillars', 'Expand into new content formats' ] }) return opportunities except Exception as e: logger.error(f"Error analyzing strategic opportunities: {str(e)}") return []