ALwrity Version 0.5.0 (Fastapi + React )
This commit is contained in:
ajaysi
626
backend/api/content_planning/docs/ENHANCED_STRATEGY_SERVICE.py
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626
backend/api/content_planning/docs/ENHANCED_STRATEGY_SERVICE.py
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"""
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Enhanced Strategy Service for Content Planning API
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Implements comprehensive improvements including onboarding data integration,
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enhanced AI prompts, and expanded input handling.
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"""
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from typing import Dict, Any, List, Optional
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from datetime import datetime
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from loguru import logger
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from sqlalchemy.orm import Session
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# Import database services
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from services.content_planning_db import ContentPlanningDBService
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from services.ai_analysis_db_service import AIAnalysisDBService
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from services.ai_analytics_service import AIAnalyticsService
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from services.onboarding_data_service import OnboardingDataService
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# Import utilities
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from ..utils.error_handlers import ContentPlanningErrorHandler
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from ..utils.response_builders import ResponseBuilder
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from ..utils.constants import ERROR_MESSAGES, SUCCESS_MESSAGES
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class EnhancedStrategyService:
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"""Enhanced service class for content strategy operations with comprehensive improvements."""
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def __init__(self):
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self.ai_analysis_db_service = AIAnalysisDBService()
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self.ai_analytics_service = AIAnalyticsService()
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self.onboarding_service = OnboardingDataService()
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async def create_enhanced_strategy(self, strategy_data: Dict[str, Any], db: Session) -> Dict[str, Any]:
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"""Create a new content strategy with enhanced inputs and AI recommendations."""
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try:
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logger.info(f"Creating enhanced content strategy: {strategy_data.get('name', 'Unknown')}")
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# Get user ID from strategy data
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user_id = strategy_data.get('user_id', 1)
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# Get personalized onboarding data
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onboarding_data = self.onboarding_service.get_personalized_ai_inputs(user_id)
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# Enhance strategy data with onboarding insights
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enhanced_data = await self._enhance_strategy_with_onboarding_data(strategy_data, onboarding_data)
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# Generate comprehensive AI recommendations
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ai_recommendations = await self._generate_comprehensive_ai_recommendations(enhanced_data)
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# Add AI recommendations to strategy data
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enhanced_data['ai_recommendations'] = ai_recommendations
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# Create strategy in database
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db_service = ContentPlanningDBService(db)
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created_strategy = await db_service.create_content_strategy(enhanced_data)
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if created_strategy:
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logger.info(f"Enhanced content strategy created successfully: {created_strategy.id}")
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return created_strategy.to_dict()
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else:
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raise Exception("Failed to create enhanced strategy")
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except Exception as e:
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logger.error(f"Error creating enhanced content strategy: {str(e)}")
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raise ContentPlanningErrorHandler.handle_general_error(e, "create_enhanced_strategy")
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async def get_enhanced_strategies(self, user_id: Optional[int] = None, strategy_id: Optional[int] = None) -> Dict[str, Any]:
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"""Get enhanced content strategies with comprehensive data and AI insights."""
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try:
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logger.info(f"🚀 Starting enhanced content strategy analysis for user: {user_id}, strategy: {strategy_id}")
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# Get personalized onboarding data
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onboarding_data = self.onboarding_service.get_personalized_ai_inputs(user_id or 1)
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# Get latest AI analysis
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latest_analysis = await self.ai_analysis_db_service.get_latest_ai_analysis(
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user_id=user_id or 1,
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analysis_type="strategic_intelligence"
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)
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if latest_analysis:
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logger.info(f"✅ Found existing strategy analysis in database: {latest_analysis.get('id', 'unknown')}")
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# Generate comprehensive strategic intelligence
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strategic_intelligence = await self._generate_comprehensive_strategic_intelligence(
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strategy_id=strategy_id or 1,
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onboarding_data=onboarding_data,
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latest_analysis=latest_analysis
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)
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# Create enhanced strategy object with comprehensive data
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enhanced_strategy = await self._create_enhanced_strategy_object(
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strategy_id=strategy_id or 1,
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strategic_intelligence=strategic_intelligence,
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onboarding_data=onboarding_data,
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latest_analysis=latest_analysis
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)
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return {
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"status": "success",
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"message": "Enhanced content strategy retrieved successfully",
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"strategies": [enhanced_strategy],
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"total_count": 1,
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"user_id": user_id,
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"analysis_date": latest_analysis.get("analysis_date"),
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"onboarding_data_utilized": True,
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"ai_enhancement_level": "comprehensive"
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}
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else:
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logger.warning("⚠️ No existing strategy analysis found in database")
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return {
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"status": "not_found",
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"message": "No enhanced content strategy found",
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"strategies": [],
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"total_count": 0,
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"user_id": user_id,
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"onboarding_data_utilized": False,
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"ai_enhancement_level": "basic"
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}
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except Exception as e:
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logger.error(f"❌ Error retrieving enhanced content strategies: {str(e)}")
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raise ContentPlanningErrorHandler.handle_general_error(e, "get_enhanced_strategies")
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async def _enhance_strategy_with_onboarding_data(self, strategy_data: Dict[str, Any], onboarding_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Enhance strategy data with onboarding insights."""
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try:
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logger.info("🔧 Enhancing strategy data with onboarding insights")
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enhanced_data = strategy_data.copy()
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# Extract website analysis data
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website_analysis = onboarding_data.get("website_analysis", {})
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research_prefs = onboarding_data.get("research_preferences", {})
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# Auto-populate missing fields from onboarding data
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if not enhanced_data.get("target_audience"):
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enhanced_data["target_audience"] = {
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"demographics": website_analysis.get("target_audience", {}).get("demographics", ["professionals"]),
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"expertise_level": website_analysis.get("target_audience", {}).get("expertise_level", "intermediate"),
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"industry_focus": website_analysis.get("target_audience", {}).get("industry_focus", "general"),
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"interests": website_analysis.get("target_audience", {}).get("interests", [])
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}
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if not enhanced_data.get("content_pillars"):
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enhanced_data["content_pillars"] = self._generate_content_pillars_from_onboarding(website_analysis)
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if not enhanced_data.get("writing_style"):
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enhanced_data["writing_style"] = website_analysis.get("writing_style", {})
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if not enhanced_data.get("content_types"):
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enhanced_data["content_types"] = website_analysis.get("content_types", ["blog", "article"])
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# Add research preferences
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enhanced_data["research_preferences"] = {
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"research_depth": research_prefs.get("research_depth", "Standard"),
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"content_types": research_prefs.get("content_types", ["blog"]),
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"auto_research": research_prefs.get("auto_research", True),
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"factual_content": research_prefs.get("factual_content", True)
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}
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# Add competitor analysis
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enhanced_data["competitor_analysis"] = onboarding_data.get("competitor_analysis", {})
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# Add gap analysis
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enhanced_data["gap_analysis"] = onboarding_data.get("gap_analysis", {})
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# Add keyword analysis
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enhanced_data["keyword_analysis"] = onboarding_data.get("keyword_analysis", {})
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logger.info("✅ Strategy data enhanced with onboarding insights")
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return enhanced_data
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except Exception as e:
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logger.error(f"Error enhancing strategy data: {str(e)}")
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return strategy_data
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async def _generate_comprehensive_ai_recommendations(self, enhanced_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Generate comprehensive AI recommendations using enhanced prompts."""
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try:
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logger.info("🤖 Generating comprehensive AI recommendations")
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# Generate different types of AI recommendations
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recommendations = {
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"strategic_recommendations": await self._generate_strategic_recommendations(enhanced_data),
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"audience_recommendations": await self._generate_audience_recommendations(enhanced_data),
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"competitive_recommendations": await self._generate_competitive_recommendations(enhanced_data),
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"performance_recommendations": await self._generate_performance_recommendations(enhanced_data),
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"calendar_recommendations": await self._generate_calendar_recommendations(enhanced_data)
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}
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logger.info("✅ Comprehensive AI recommendations generated")
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return recommendations
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except Exception as e:
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logger.error(f"Error generating comprehensive AI recommendations: {str(e)}")
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return {}
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async def _generate_strategic_recommendations(self, enhanced_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Generate strategic recommendations using enhanced prompt."""
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try:
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# Use enhanced strategic intelligence prompt
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prompt_data = {
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"business_objectives": enhanced_data.get("business_objectives", "Increase brand awareness and drive conversions"),
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"target_metrics": enhanced_data.get("target_metrics", "Traffic growth, engagement, conversions"),
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"budget": enhanced_data.get("content_budget", "Medium"),
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"team_size": enhanced_data.get("team_size", "Small"),
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"timeline": enhanced_data.get("timeline", "3 months"),
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"current_metrics": enhanced_data.get("current_performance_metrics", {}),
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"target_audience": enhanced_data.get("target_audience", {}),
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"pain_points": enhanced_data.get("audience_pain_points", []),
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"buying_journey": enhanced_data.get("buying_journey", {}),
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"content_preferences": enhanced_data.get("content_preferences", {}),
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"competitors": enhanced_data.get("competitor_analysis", {}).get("top_performers", []),
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"market_position": enhanced_data.get("market_position", {}),
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"advantages": enhanced_data.get("competitive_advantages", []),
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"market_gaps": enhanced_data.get("market_gaps", [])
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}
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# Generate strategic recommendations using AI
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strategic_recommendations = await self.ai_analytics_service.generate_strategic_intelligence(
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strategy_id=enhanced_data.get("id", 1),
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market_data=prompt_data
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)
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return strategic_recommendations
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except Exception as e:
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logger.error(f"Error generating strategic recommendations: {str(e)}")
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return {}
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async def _generate_audience_recommendations(self, enhanced_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Generate audience intelligence recommendations."""
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try:
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audience_data = {
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"demographics": enhanced_data.get("target_audience", {}).get("demographics", []),
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"behavior_patterns": enhanced_data.get("audience_behavior", {}),
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"consumption_patterns": enhanced_data.get("content_preferences", {}),
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"pain_points": enhanced_data.get("audience_pain_points", [])
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}
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# Generate audience recommendations
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audience_recommendations = {
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"personas": self._generate_audience_personas(audience_data),
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"content_preferences": self._analyze_content_preferences(audience_data),
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"buying_journey": self._map_buying_journey(audience_data),
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"engagement_patterns": self._analyze_engagement_patterns(audience_data)
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}
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return audience_recommendations
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except Exception as e:
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logger.error(f"Error generating audience recommendations: {str(e)}")
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return {}
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async def _generate_competitive_recommendations(self, enhanced_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Generate competitive intelligence recommendations."""
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try:
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competitive_data = {
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"competitors": enhanced_data.get("competitor_analysis", {}).get("top_performers", []),
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"market_position": enhanced_data.get("market_position", {}),
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"competitor_content": enhanced_data.get("competitor_content_strategies", []),
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"market_gaps": enhanced_data.get("market_gaps", [])
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}
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# Generate competitive recommendations
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competitive_recommendations = {
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"landscape_analysis": self._analyze_competitive_landscape(competitive_data),
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"differentiation_strategy": self._identify_differentiation_opportunities(competitive_data),
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"market_gaps": self._analyze_market_gaps(competitive_data),
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"partnership_opportunities": self._identify_partnership_opportunities(competitive_data)
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}
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return competitive_recommendations
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except Exception as e:
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logger.error(f"Error generating competitive recommendations: {str(e)}")
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return {}
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async def _generate_performance_recommendations(self, enhanced_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Generate performance optimization recommendations."""
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try:
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performance_data = {
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"current_metrics": enhanced_data.get("current_performance_metrics", {}),
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"top_content": enhanced_data.get("top_performing_content", []),
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"underperforming_content": enhanced_data.get("underperforming_content", []),
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"traffic_sources": enhanced_data.get("traffic_sources", {})
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}
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# Generate performance recommendations
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performance_recommendations = {
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"optimization_strategy": self._create_optimization_strategy(performance_data),
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"a_b_testing": self._generate_ab_testing_plan(performance_data),
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"traffic_optimization": self._optimize_traffic_sources(performance_data),
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"conversion_optimization": self._optimize_conversions(performance_data)
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}
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return performance_recommendations
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except Exception as e:
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logger.error(f"Error generating performance recommendations: {str(e)}")
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return {}
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async def _generate_calendar_recommendations(self, enhanced_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Generate content calendar optimization recommendations."""
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try:
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calendar_data = {
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"content_mix": enhanced_data.get("content_types", []),
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"frequency": enhanced_data.get("content_frequency", "weekly"),
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"seasonal_trends": enhanced_data.get("seasonal_trends", {}),
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"audience_behavior": enhanced_data.get("audience_behavior", {})
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}
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# Generate calendar recommendations
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calendar_recommendations = {
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"publishing_schedule": self._optimize_publishing_schedule(calendar_data),
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"content_mix": self._optimize_content_mix(calendar_data),
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"seasonal_strategy": self._create_seasonal_strategy(calendar_data),
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"engagement_calendar": self._create_engagement_calendar(calendar_data)
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}
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return calendar_recommendations
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except Exception as e:
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logger.error(f"Error generating calendar recommendations: {str(e)}")
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return {}
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def _generate_content_pillars_from_onboarding(self, website_analysis: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""Generate content pillars based on onboarding data."""
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try:
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content_type = website_analysis.get("content_type", {})
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target_audience = website_analysis.get("target_audience", {})
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purpose = content_type.get("purpose", "educational")
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industry = target_audience.get("industry_focus", "general")
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pillars = []
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if purpose == "educational":
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pillars.extend([
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{"name": "Educational Content", "description": "How-to guides and tutorials"},
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{"name": "Industry Insights", "description": "Trends and analysis"},
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{"name": "Best Practices", "description": "Expert advice and tips"}
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])
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elif purpose == "promotional":
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pillars.extend([
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{"name": "Product Updates", "description": "New features and announcements"},
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{"name": "Customer Stories", "description": "Success stories and testimonials"},
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{"name": "Company News", "description": "Updates and announcements"}
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])
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else:
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pillars.extend([
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{"name": "Industry Trends", "description": "Market analysis and insights"},
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{"name": "Expert Opinions", "description": "Thought leadership content"},
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{"name": "Resource Library", "description": "Tools, guides, and resources"}
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])
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return pillars
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except Exception as e:
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logger.error(f"Error generating content pillars: {str(e)}")
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return [{"name": "General Content", "description": "Mixed content types"}]
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async def _create_enhanced_strategy_object(self, strategy_id: int, strategic_intelligence: Dict[str, Any],
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onboarding_data: Dict[str, Any], latest_analysis: Dict[str, Any]) -> Dict[str, Any]:
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"""Create enhanced strategy object with comprehensive data."""
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try:
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# Extract data from strategic intelligence
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market_positioning = strategic_intelligence.get("market_positioning", {})
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strategic_scores = strategic_intelligence.get("strategic_scores", {})
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risk_assessment = strategic_intelligence.get("risk_assessment", [])
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opportunity_analysis = strategic_intelligence.get("opportunity_analysis", [])
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# Create comprehensive strategy object
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enhanced_strategy = {
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"id": strategy_id,
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"name": "Enhanced Digital Marketing Strategy",
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"industry": onboarding_data.get("website_analysis", {}).get("target_audience", {}).get("industry_focus", "technology"),
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"target_audience": onboarding_data.get("website_analysis", {}).get("target_audience", {}),
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"content_pillars": self._generate_content_pillars_from_onboarding(onboarding_data.get("website_analysis", {})),
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"writing_style": onboarding_data.get("website_analysis", {}).get("writing_style", {}),
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"content_types": onboarding_data.get("website_analysis", {}).get("content_types", ["blog", "article"]),
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"research_preferences": onboarding_data.get("research_preferences", {}),
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"competitor_analysis": onboarding_data.get("competitor_analysis", {}),
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"gap_analysis": onboarding_data.get("gap_analysis", {}),
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"keyword_analysis": onboarding_data.get("keyword_analysis", {}),
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"ai_recommendations": {
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# Market positioning data expected by frontend
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"market_score": market_positioning.get("positioning_score", 75),
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"strengths": [
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"Strong brand voice",
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"Consistent content quality",
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"Data-driven approach",
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"AI-powered insights",
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"Personalized content delivery"
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],
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"weaknesses": [
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"Limited video content",
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"Slow content production",
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"Limited social media presence",
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"Need for more interactive content"
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],
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# Competitive advantages expected by frontend
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"competitive_advantages": [
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{
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"advantage": "AI-powered content creation",
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"impact": "High",
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||||
"implementation": "In Progress"
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||||
},
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{
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"advantage": "Data-driven strategy",
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"impact": "Medium",
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||||
"implementation": "Complete"
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||||
},
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||||
{
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||||
"advantage": "Personalized content delivery",
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"impact": "High",
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||||
"implementation": "Planning"
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||||
},
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||||
{
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||||
"advantage": "Comprehensive audience insights",
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||||
"impact": "High",
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||||
"implementation": "Complete"
|
||||
}
|
||||
],
|
||||
# Strategic risks expected by frontend
|
||||
"strategic_risks": [
|
||||
{
|
||||
"risk": "Content saturation in market",
|
||||
"probability": "Medium",
|
||||
"impact": "High"
|
||||
},
|
||||
{
|
||||
"risk": "Algorithm changes affecting reach",
|
||||
"probability": "High",
|
||||
"impact": "Medium"
|
||||
},
|
||||
{
|
||||
"risk": "Competition from AI tools",
|
||||
"probability": "High",
|
||||
"impact": "High"
|
||||
},
|
||||
{
|
||||
"risk": "Rapid industry changes",
|
||||
"probability": "Medium",
|
||||
"impact": "Medium"
|
||||
}
|
||||
],
|
||||
# Strategic insights
|
||||
"strategic_insights": strategic_intelligence.get("strategic_insights", []),
|
||||
# Market positioning details
|
||||
"market_positioning": {
|
||||
"industry_position": market_positioning.get("industry_position", "emerging"),
|
||||
"competitive_advantage": market_positioning.get("competitive_advantage", "AI-powered content"),
|
||||
"market_share": market_positioning.get("market_share", "2.5%"),
|
||||
"positioning_score": market_positioning.get("positioning_score", 4)
|
||||
},
|
||||
# Strategic scores
|
||||
"strategic_scores": {
|
||||
"overall_score": strategic_scores.get("overall_score", 7.2),
|
||||
"content_quality_score": strategic_scores.get("content_quality_score", 8.1),
|
||||
"engagement_score": strategic_scores.get("engagement_score", 6.8),
|
||||
"conversion_score": strategic_scores.get("conversion_score", 7.5),
|
||||
"innovation_score": strategic_scores.get("innovation_score", 8.3)
|
||||
},
|
||||
# Opportunity analysis
|
||||
"opportunity_analysis": opportunity_analysis,
|
||||
# Recommendations
|
||||
"recommendations": strategic_intelligence.get("recommendations", [])
|
||||
},
|
||||
"created_at": latest_analysis.get("created_at", datetime.utcnow().isoformat()),
|
||||
"updated_at": latest_analysis.get("updated_at", datetime.utcnow().isoformat()),
|
||||
"enhancement_level": "comprehensive",
|
||||
"onboarding_data_utilized": True
|
||||
}
|
||||
|
||||
return enhanced_strategy
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error creating enhanced strategy object: {str(e)}")
|
||||
return {}
|
||||
|
||||
# Helper methods for generating specific recommendations
|
||||
def _generate_audience_personas(self, audience_data: Dict[str, Any]) -> List[Dict[str, Any]]:
|
||||
"""Generate audience personas based on data."""
|
||||
return [
|
||||
{
|
||||
"name": "Professional Decision Maker",
|
||||
"demographics": audience_data.get("demographics", []),
|
||||
"behavior": "Researches extensively before decisions",
|
||||
"content_preferences": ["In-depth guides", "Case studies", "Expert analysis"]
|
||||
}
|
||||
]
|
||||
|
||||
def _analyze_content_preferences(self, audience_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Analyze content preferences."""
|
||||
return {
|
||||
"preferred_formats": ["Blog posts", "Guides", "Case studies"],
|
||||
"preferred_topics": ["Industry trends", "Best practices", "How-to guides"],
|
||||
"preferred_tone": "Professional and authoritative"
|
||||
}
|
||||
|
||||
def _map_buying_journey(self, audience_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Map buying journey stages."""
|
||||
return {
|
||||
"awareness": ["Educational content", "Industry insights"],
|
||||
"consideration": ["Product comparisons", "Case studies"],
|
||||
"decision": ["Product demos", "Testimonials"]
|
||||
}
|
||||
|
||||
def _analyze_engagement_patterns(self, audience_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Analyze engagement patterns."""
|
||||
return {
|
||||
"peak_times": ["Tuesday 10-11 AM", "Thursday 2-3 PM"],
|
||||
"preferred_channels": ["Email", "LinkedIn", "Company blog"],
|
||||
"content_length": "Medium (1000-2000 words)"
|
||||
}
|
||||
|
||||
def _analyze_competitive_landscape(self, competitive_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Analyze competitive landscape."""
|
||||
return {
|
||||
"market_share": "2.5%",
|
||||
"competitive_position": "Emerging leader",
|
||||
"key_competitors": competitive_data.get("competitors", []),
|
||||
"differentiation_opportunities": ["AI-powered content", "Personalization"]
|
||||
}
|
||||
|
||||
def _identify_differentiation_opportunities(self, competitive_data: Dict[str, Any]) -> List[str]:
|
||||
"""Identify differentiation opportunities."""
|
||||
return [
|
||||
"AI-powered content personalization",
|
||||
"Data-driven content optimization",
|
||||
"Comprehensive audience insights",
|
||||
"Advanced analytics integration"
|
||||
]
|
||||
|
||||
def _analyze_market_gaps(self, competitive_data: Dict[str, Any]) -> List[Dict[str, Any]]:
|
||||
"""Analyze market gaps."""
|
||||
return [
|
||||
{
|
||||
"gap": "Video content in technology sector",
|
||||
"opportunity": "High",
|
||||
"competition": "Low",
|
||||
"implementation": "Medium"
|
||||
}
|
||||
]
|
||||
|
||||
def _identify_partnership_opportunities(self, competitive_data: Dict[str, Any]) -> List[Dict[str, Any]]:
|
||||
"""Identify partnership opportunities."""
|
||||
return [
|
||||
{
|
||||
"partner": "Industry influencers",
|
||||
"opportunity": "Guest content collaboration",
|
||||
"impact": "High",
|
||||
"effort": "Medium"
|
||||
}
|
||||
]
|
||||
|
||||
def _create_optimization_strategy(self, performance_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Create performance optimization strategy."""
|
||||
return {
|
||||
"priority_areas": ["Content quality", "SEO optimization", "Engagement"],
|
||||
"optimization_timeline": "30-60 days",
|
||||
"expected_improvements": ["20% traffic increase", "15% engagement boost"]
|
||||
}
|
||||
|
||||
def _generate_ab_testing_plan(self, performance_data: Dict[str, Any]) -> List[Dict[str, Any]]:
|
||||
"""Generate A/B testing plan."""
|
||||
return [
|
||||
{
|
||||
"test": "Headline optimization",
|
||||
"hypothesis": "Action-oriented headlines perform better",
|
||||
"timeline": "2 weeks",
|
||||
"metrics": ["CTR", "Time on page"]
|
||||
}
|
||||
]
|
||||
|
||||
def _optimize_traffic_sources(self, performance_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Optimize traffic sources."""
|
||||
return {
|
||||
"organic_search": "Focus on long-tail keywords",
|
||||
"social_media": "Increase LinkedIn presence",
|
||||
"email": "Improve subject line optimization",
|
||||
"direct": "Enhance brand recognition"
|
||||
}
|
||||
|
||||
def _optimize_conversions(self, performance_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Optimize conversions."""
|
||||
return {
|
||||
"cta_optimization": "Test different call-to-action buttons",
|
||||
"landing_page_improvement": "Enhance page load speed",
|
||||
"content_optimization": "Add more conversion-focused content"
|
||||
}
|
||||
|
||||
def _optimize_publishing_schedule(self, calendar_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Optimize publishing schedule."""
|
||||
return {
|
||||
"optimal_days": ["Tuesday", "Thursday"],
|
||||
"optimal_times": ["10:00 AM", "2:00 PM"],
|
||||
"frequency": "2-3 times per week",
|
||||
"seasonal_adjustments": "Increase frequency during peak periods"
|
||||
}
|
||||
|
||||
def _optimize_content_mix(self, calendar_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Optimize content mix."""
|
||||
return {
|
||||
"blog_posts": "60%",
|
||||
"video_content": "20%",
|
||||
"infographics": "10%",
|
||||
"case_studies": "10%"
|
||||
}
|
||||
|
||||
def _create_seasonal_strategy(self, calendar_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Create seasonal content strategy."""
|
||||
return {
|
||||
"q1": "Planning and strategy content",
|
||||
"q2": "Implementation and best practices",
|
||||
"q3": "Results and case studies",
|
||||
"q4": "Year-end reviews and predictions"
|
||||
}
|
||||
|
||||
def _create_engagement_calendar(self, calendar_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Create engagement calendar."""
|
||||
return {
|
||||
"daily": "Social media engagement",
|
||||
"weekly": "Email newsletter",
|
||||
"monthly": "Comprehensive blog post",
|
||||
"quarterly": "Industry report"
|
||||
}
|
||||
Reference in New Issue
Block a user