Base code
This commit is contained in:
Kunthawat Greethong
974
backend/services/ai_analytics_service.py
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974
backend/services/ai_analytics_service.py
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@@ -0,0 +1,974 @@
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"""
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AI Analytics Service
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Advanced AI-powered analytics for content planning and performance prediction.
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"""
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from typing import Dict, Any, List, Optional, Tuple
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from datetime import datetime, timedelta
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import json
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from loguru import logger
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import asyncio
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from sqlalchemy.orm import Session
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from services.database import get_db_session
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from models.content_planning import ContentAnalytics, ContentStrategy, CalendarEvent
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from services.content_gap_analyzer.ai_engine_service import AIEngineService
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class AIAnalyticsService:
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"""Advanced AI analytics service for content planning."""
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def __init__(self):
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self.ai_engine = AIEngineService()
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self.db_session = None
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def _get_db_session(self) -> Session:
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"""Get database session."""
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if not self.db_session:
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self.db_session = get_db_session()
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return self.db_session
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async def analyze_content_evolution(self, strategy_id: int, time_period: str = "30d") -> Dict[str, Any]:
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"""
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Analyze content evolution over time for a specific strategy.
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Args:
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strategy_id: Content strategy ID
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time_period: Analysis period (7d, 30d, 90d, 1y)
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Returns:
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Content evolution analysis results
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"""
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try:
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logger.info(f"Analyzing content evolution for strategy {strategy_id}")
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# Get analytics data for the strategy
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analytics_data = await self._get_analytics_data(strategy_id, time_period)
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# Analyze content performance trends
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performance_trends = await self._analyze_performance_trends(analytics_data)
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# Analyze content type evolution
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content_evolution = await self._analyze_content_type_evolution(analytics_data)
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# Analyze audience engagement patterns
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engagement_patterns = await self._analyze_engagement_patterns(analytics_data)
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evolution_analysis = {
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'strategy_id': strategy_id,
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'time_period': time_period,
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'performance_trends': performance_trends,
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'content_evolution': content_evolution,
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'engagement_patterns': engagement_patterns,
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'recommendations': await self._generate_evolution_recommendations(
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performance_trends, content_evolution, engagement_patterns
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),
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'analysis_date': datetime.utcnow().isoformat()
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}
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logger.info(f"Content evolution analysis completed for strategy {strategy_id}")
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return evolution_analysis
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except Exception as e:
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logger.error(f"Error analyzing content evolution: {str(e)}")
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raise
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async def analyze_performance_trends(self, strategy_id: int, metrics: List[str] = None) -> Dict[str, Any]:
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"""
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Analyze performance trends for content strategy.
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Args:
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strategy_id: Content strategy ID
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metrics: List of metrics to analyze (engagement, reach, conversion, etc.)
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Returns:
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Performance trend analysis results
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"""
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try:
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logger.info(f"Analyzing performance trends for strategy {strategy_id}")
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if not metrics:
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metrics = ['engagement_rate', 'reach', 'conversion_rate', 'click_through_rate']
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# Get performance data
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performance_data = await self._get_performance_data(strategy_id, metrics)
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# Analyze trends for each metric
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trend_analysis = {}
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for metric in metrics:
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trend_analysis[metric] = await self._analyze_metric_trend(performance_data, metric)
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# Generate predictive insights
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predictive_insights = await self._generate_predictive_insights(trend_analysis)
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# Calculate performance scores
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performance_scores = await self._calculate_performance_scores(trend_analysis)
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trend_results = {
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'strategy_id': strategy_id,
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'metrics_analyzed': metrics,
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'trend_analysis': trend_analysis,
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'predictive_insights': predictive_insights,
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'performance_scores': performance_scores,
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'recommendations': await self._generate_trend_recommendations(trend_analysis),
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'analysis_date': datetime.utcnow().isoformat()
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}
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logger.info(f"Performance trend analysis completed for strategy {strategy_id}")
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return trend_results
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except Exception as e:
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logger.error(f"Error analyzing performance trends: {str(e)}")
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raise
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async def predict_content_performance(self, content_data: Dict[str, Any],
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strategy_id: int) -> Dict[str, Any]:
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"""
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Predict content performance using AI models.
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Args:
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content_data: Content details (title, description, type, platform, etc.)
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strategy_id: Content strategy ID
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Returns:
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Performance prediction results
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"""
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try:
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logger.info(f"Predicting performance for content in strategy {strategy_id}")
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# Get historical performance data
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historical_data = await self._get_historical_performance_data(strategy_id)
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# Analyze content characteristics
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content_analysis = await self._analyze_content_characteristics(content_data)
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# Calculate success probability
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success_probability = await self._calculate_success_probability({}, historical_data)
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# Generate optimization recommendations
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optimization_recommendations = await self._generate_optimization_recommendations(
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content_data, {}, success_probability
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)
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prediction_results = {
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'strategy_id': strategy_id,
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'content_data': content_data,
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'performance_prediction': {},
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'success_probability': success_probability,
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'optimization_recommendations': optimization_recommendations,
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'confidence_score': 0.7,
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'prediction_date': datetime.utcnow().isoformat()
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}
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logger.info(f"Content performance prediction completed")
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return prediction_results
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except Exception as e:
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logger.error(f"Error predicting content performance: {str(e)}")
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raise
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async def generate_strategic_intelligence(self, strategy_id: int,
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market_data: Dict[str, Any] = None) -> Dict[str, Any]:
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"""
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Generate strategic intelligence for content planning.
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Args:
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strategy_id: Content strategy ID
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market_data: Additional market data for analysis
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Returns:
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Strategic intelligence results
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"""
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try:
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logger.info(f"Generating strategic intelligence for strategy {strategy_id}")
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# Get strategy data
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strategy_data = await self._get_strategy_data(strategy_id)
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# Analyze market positioning
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market_positioning = await self._analyze_market_positioning(strategy_data, market_data)
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# Identify competitive advantages
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competitive_advantages = await self._identify_competitive_advantages(strategy_data)
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# Calculate strategic scores
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strategic_scores = await self._calculate_strategic_scores(
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strategy_data, market_positioning, competitive_advantages
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)
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intelligence_results = {
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'strategy_id': strategy_id,
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'market_positioning': market_positioning,
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'competitive_advantages': competitive_advantages,
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'strategic_scores': strategic_scores,
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'risk_assessment': await self._assess_strategic_risks(strategy_data),
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'opportunity_analysis': await self._analyze_strategic_opportunities(strategy_data),
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'analysis_date': datetime.utcnow().isoformat()
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}
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logger.info(f"Strategic intelligence generation completed")
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return intelligence_results
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except Exception as e:
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logger.error(f"Error generating strategic intelligence: {str(e)}")
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raise
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# Helper methods for data retrieval and analysis
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async def _get_analytics_data(self, strategy_id: int, time_period: str) -> List[Dict[str, Any]]:
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"""Get analytics data for the specified strategy and time period."""
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try:
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session = self._get_db_session()
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# Calculate date range
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end_date = datetime.utcnow()
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if time_period == "7d":
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start_date = end_date - timedelta(days=7)
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elif time_period == "30d":
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start_date = end_date - timedelta(days=30)
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elif time_period == "90d":
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start_date = end_date - timedelta(days=90)
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elif time_period == "1y":
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start_date = end_date - timedelta(days=365)
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else:
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start_date = end_date - timedelta(days=30)
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# Query analytics data
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analytics = session.query(ContentAnalytics).filter(
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ContentAnalytics.strategy_id == strategy_id,
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ContentAnalytics.recorded_at >= start_date,
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ContentAnalytics.recorded_at <= end_date
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).all()
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return [analytics.to_dict() for analytics in analytics]
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except Exception as e:
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logger.error(f"Error getting analytics data: {str(e)}")
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return []
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async def _analyze_performance_trends(self, analytics_data: List[Dict[str, Any]]) -> Dict[str, Any]:
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"""Analyze performance trends from analytics data."""
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try:
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if not analytics_data:
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return {'trend': 'stable', 'growth_rate': 0, 'insights': 'No data available'}
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# Calculate trend metrics
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total_analytics = len(analytics_data)
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avg_performance = sum(item.get('performance_score', 0) for item in analytics_data) / total_analytics
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# Determine trend direction
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if avg_performance > 0.7:
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trend = 'increasing'
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elif avg_performance < 0.3:
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trend = 'decreasing'
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else:
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trend = 'stable'
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return {
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'trend': trend,
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'average_performance': avg_performance,
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'total_analytics': total_analytics,
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'insights': f'Performance is {trend} with average score of {avg_performance:.2f}'
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}
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except Exception as e:
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logger.error(f"Error analyzing performance trends: {str(e)}")
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return {'trend': 'unknown', 'error': str(e)}
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async def _analyze_content_type_evolution(self, analytics_data: List[Dict[str, Any]]) -> Dict[str, Any]:
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"""Analyze how content types have evolved over time."""
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try:
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content_types = {}
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for data in analytics_data:
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content_type = data.get('content_type', 'unknown')
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if content_type not in content_types:
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content_types[content_type] = {
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'count': 0,
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'total_performance': 0,
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'avg_performance': 0
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}
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content_types[content_type]['count'] += 1
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content_types[content_type]['total_performance'] += data.get('performance_score', 0)
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# Calculate averages
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for content_type in content_types:
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if content_types[content_type]['count'] > 0:
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content_types[content_type]['avg_performance'] = (
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content_types[content_type]['total_performance'] /
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content_types[content_type]['count']
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)
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return {
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'content_types': content_types,
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'most_performing_type': max(content_types.items(), key=lambda x: x[1]['avg_performance'])[0] if content_types else None,
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'evolution_insights': 'Content type performance analysis completed'
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}
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except Exception as e:
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logger.error(f"Error analyzing content type evolution: {str(e)}")
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return {'error': str(e)}
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async def _analyze_engagement_patterns(self, analytics_data: List[Dict[str, Any]]) -> Dict[str, Any]:
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"""Analyze audience engagement patterns."""
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try:
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if not analytics_data:
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return {'patterns': {}, 'insights': 'No engagement data available'}
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# Analyze engagement by platform
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platform_engagement = {}
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for data in analytics_data:
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platform = data.get('platform', 'unknown')
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if platform not in platform_engagement:
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platform_engagement[platform] = {
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'total_engagement': 0,
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'count': 0,
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'avg_engagement': 0
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}
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metrics = data.get('metrics', {})
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engagement = metrics.get('engagement_rate', 0)
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platform_engagement[platform]['total_engagement'] += engagement
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platform_engagement[platform]['count'] += 1
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# Calculate averages
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for platform in platform_engagement:
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if platform_engagement[platform]['count'] > 0:
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platform_engagement[platform]['avg_engagement'] = (
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platform_engagement[platform]['total_engagement'] /
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platform_engagement[platform]['count']
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)
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return {
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'platform_engagement': platform_engagement,
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'best_platform': max(platform_engagement.items(), key=lambda x: x[1]['avg_engagement'])[0] if platform_engagement else None,
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'insights': 'Platform engagement analysis completed'
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}
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except Exception as e:
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logger.error(f"Error analyzing engagement patterns: {str(e)}")
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return {'error': str(e)}
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async def _generate_evolution_recommendations(self, performance_trends: Dict[str, Any],
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content_evolution: Dict[str, Any],
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engagement_patterns: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""Generate recommendations based on evolution analysis."""
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recommendations = []
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try:
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# Performance-based recommendations
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if performance_trends.get('trend') == 'decreasing':
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recommendations.append({
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'type': 'performance_optimization',
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'priority': 'high',
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'title': 'Improve Content Performance',
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'description': 'Content performance is declining. Focus on quality and engagement.',
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'action_items': [
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'Review and improve content quality',
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'Optimize for audience engagement',
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'Analyze competitor strategies'
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]
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})
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# Content type recommendations
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if content_evolution.get('most_performing_type'):
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best_type = content_evolution['most_performing_type']
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recommendations.append({
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'type': 'content_strategy',
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'priority': 'medium',
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'title': f'Focus on {best_type} Content',
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'description': f'{best_type} content is performing best. Increase focus on this type.',
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'action_items': [
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f'Increase {best_type} content production',
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'Analyze what makes this content successful',
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'Optimize other content types based on learnings'
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]
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})
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# Platform recommendations
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if engagement_patterns.get('best_platform'):
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best_platform = engagement_patterns['best_platform']
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recommendations.append({
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'type': 'platform_strategy',
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'priority': 'medium',
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'title': f'Optimize for {best_platform}',
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'description': f'{best_platform} shows highest engagement. Focus optimization efforts here.',
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'action_items': [
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f'Increase content for {best_platform}',
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f'Optimize content format for platform',
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'Use platform-specific features'
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]
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})
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return recommendations
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except Exception as e:
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logger.error(f"Error generating evolution recommendations: {str(e)}")
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return [{'error': str(e)}]
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async def _get_performance_data(self, strategy_id: int, metrics: List[str]) -> List[Dict[str, Any]]:
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"""Get performance data for specified metrics."""
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try:
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session = self._get_db_session()
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# Get analytics data for the strategy
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analytics = session.query(ContentAnalytics).filter(
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ContentAnalytics.strategy_id == strategy_id
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).all()
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return [analytics.to_dict() for analytics in analytics]
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except Exception as e:
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logger.error(f"Error getting performance data: {str(e)}")
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return []
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async def _analyze_metric_trend(self, performance_data: List[Dict[str, Any]], metric: str) -> Dict[str, Any]:
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"""Analyze trend for a specific metric."""
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try:
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if not performance_data:
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return {'trend': 'no_data', 'value': 0, 'change': 0}
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# Extract metric values
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metric_values = []
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for data in performance_data:
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metrics = data.get('metrics', {})
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if metric in metrics:
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metric_values.append(metrics[metric])
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if not metric_values:
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return {'trend': 'no_data', 'value': 0, 'change': 0}
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# Calculate trend
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avg_value = sum(metric_values) / len(metric_values)
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# Simple trend calculation
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if len(metric_values) >= 2:
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recent_avg = sum(metric_values[-len(metric_values)//2:]) / (len(metric_values)//2)
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older_avg = sum(metric_values[:len(metric_values)//2]) / (len(metric_values)//2)
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change = ((recent_avg - older_avg) / older_avg * 100) if older_avg > 0 else 0
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else:
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change = 0
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# Determine trend direction
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if change > 5:
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trend = 'increasing'
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elif change < -5:
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trend = 'decreasing'
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else:
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trend = 'stable'
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return {
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'trend': trend,
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'value': avg_value,
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'change_percent': change,
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'data_points': len(metric_values)
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}
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except Exception as e:
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logger.error(f"Error analyzing metric trend: {str(e)}")
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return {'trend': 'error', 'error': str(e)}
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async def _generate_predictive_insights(self, trend_analysis: Dict[str, Any]) -> Dict[str, Any]:
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"""Generate predictive insights based on trend analysis."""
|
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try:
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insights = {
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'predicted_performance': 'stable',
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'confidence_level': 'medium',
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'key_factors': [],
|
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'recommendations': []
|
||||
}
|
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# Analyze trends to generate insights
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increasing_metrics = []
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decreasing_metrics = []
|
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|
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for metric, analysis in trend_analysis.items():
|
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if analysis.get('trend') == 'increasing':
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increasing_metrics.append(metric)
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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 []
|
||||
Reference in New Issue
Block a user