Building Customer-Centric Digital Experiences: Revolutionary Strategies for Digital Transformation Through Personalized Engagement, Omnichannel Integration, and AI-Powered Customer Intelligence

import asyncio
import json
import numpy as np
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Any, Callable, Union
from dataclasses import dataclass, field
from enum import Enum
import uuid
import time
from concurrent.futures import ThreadPoolExecutor

class CustomerSegment(Enum):
    HIGH_VALUE = "high_value_customer"
    LOYAL = "loyal_customer"
    AT_RISK = "at_risk_customer"
    NEW = "new_customer"
    DORMANT = "dormant_customer"
    POTENTIAL = "potential_customer"

class InteractionChannel(Enum):
    WEBSITE = "website"
    MOBILE_APP = "mobile_app"
    EMAIL = "email"
    SMS = "sms"
    SOCIAL_MEDIA = "social_media"
    PHONE = "phone"
    CHAT = "live_chat"
    IN_STORE = "physical_store"

class PersonalizationStrategy(Enum):
    BEHAVIORAL = "behavioral_targeting"
    DEMOGRAPHIC = "demographic_targeting"
    CONTEXTUAL = "contextual_targeting"
    PREDICTIVE = "predictive_targeting"
    COLLABORATIVE = "collaborative_filtering"

@dataclass
class Customer:
    """Represents a customer with comprehensive profile data"""
    id: str
    name: str
    email: str
    segment: CustomerSegment
    demographics: Dict[str, Any]
    behavior_profile: Dict[str, Any] = field(default_factory=dict)
    preferences: Dict[str, Any] = field(default_factory=dict)
    interaction_history: List[Dict[str, Any]] = field(default_factory=list)
    lifetime_value: float = 0.0
    satisfaction_score: float = 0.0
    last_interaction: datetime = field(default_factory=datetime.now)
    predictive_scores: Dict[str, float] = field(default_factory=dict)
    
@dataclass
class CustomerInteraction:
    """Represents a customer interaction across any channel"""
    id: str
    customer_id: str
    channel: InteractionChannel
    interaction_type: str
    content_delivered: Dict[str, Any]
    timestamp: datetime
    duration_seconds: int = 0
    engagement_score: float = 0.0
    conversion_occurred: bool = False
    sentiment_score: float = 0.0
    context: Dict[str, Any] = field(default_factory=dict)
    
@dataclass
class PersonalizedExperience:
    """Represents a personalized customer experience"""
    id: str
    customer_id: str
    channel: InteractionChannel
    personalization_strategy: PersonalizationStrategy
    content_elements: Dict[str, Any]
    predicted_engagement: float
    optimization_score: float
    created_timestamp: datetime = field(default_factory=datetime.now)
    
class CustomerCentricExperiencePlatform:
    """Comprehensive platform for building customer-centric digital experiences"""
    
    def __init__(self, organization_name: str):
        self.organization_name = organization_name
        self.customers: Dict[str, Customer] = {}
        self.interactions: List[CustomerInteraction] = []
        self.experiences: Dict[str, PersonalizedExperience] = {}
        self.content_library: Dict[str, Any] = {}
        
        # AI and personalization engines
        self.personalization_engine = PersonalizationEngine()
        self.customer_intelligence = CustomerIntelligence()
        self.experience_optimizer = ExperienceOptimizer()
        self.sentiment_analyzer = SentimentAnalyzer()
        
        # Omnichannel orchestration
        self.channel_orchestrator = ChannelOrchestrator()
        self.journey_mapper = CustomerJourneyMapper()
        
        # Analytics and insights
        self.experience_analytics = ExperienceAnalytics()
        self.predictive_modeling = PredictiveModeling()
        
        # Real-time optimization
        self.real_time_optimizer = RealTimeOptimizer()
        
        print(f"Customer-Centric Experience Platform initialized for {organization_name}")
        
    def onboard_customer(self, customer_data: Dict[str, Any]) -> Dict[str, Any]:
        """Comprehensive customer onboarding with profile creation"""
        customer_name = customer_data.get("name", "Unknown")
        customer_email = customer_data.get("email", "")
        
        print(f"Onboarding customer: {customer_name}")
        
        # Create unique customer ID
        customer_id = f"customer_{uuid.uuid4()}"
        
        # Determine initial customer segment
        initial_segment = self._determine_initial_segment(customer_data)
        
        # Create customer profile
        customer = Customer(
            id=customer_id,
            name=customer_name,
            email=customer_email,
            segment=initial_segment,
            demographics=customer_data.get("demographics", {}),
            preferences=customer_data.get("preferences", {})
        )
        
        self.customers[customer_id] = customer
        
        # Generate initial customer intelligence
        intelligence_profile = self.customer_intelligence.create_intelligence_profile(customer)
        
        # Set up personalized welcome experience
        welcome_experience = self._create_welcome_experience(customer, intelligence_profile)
        
        # Initialize predictive scores
        predictive_scores = self.predictive_modeling.generate_initial_scores(customer)
        customer.predictive_scores = predictive_scores
        
        # Configure omnichannel preferences
        channel_preferences = self._configure_channel_preferences(customer, customer_data)
        
        onboarding_result = {
            "customer_id": customer_id,
            "onboarding_timestamp": datetime.now(),
            "initial_segment": initial_segment.value,
            "intelligence_profile": intelligence_profile,
            "welcome_experience": welcome_experience,
            "predictive_scores": predictive_scores,
            "channel_preferences": channel_preferences,
            "onboarding_success": True
        }
        
        print(f"Customer {customer_name} onboarded successfully")
        return onboarding_result
        
    def _determine_initial_segment(self, customer_data: Dict[str, Any]) -> CustomerSegment:
        """Determine initial customer segment based on available data"""
        # Analyze available data to determine most appropriate segment
        purchase_history = customer_data.get("purchase_history", [])
        referral_source = customer_data.get("referral_source", "")
        engagement_indicators = customer_data.get("engagement_indicators", {})
        
        if len(purchase_history) > 5:
            total_value = sum(purchase.get("value", 0) for purchase in purchase_history)
            if total_value > 5000:
                return CustomerSegment.HIGH_VALUE
            else:
                return CustomerSegment.LOYAL
        elif len(purchase_history) > 0:
            return CustomerSegment.NEW
        elif referral_source in ["friend", "family", "word_of_mouth"]:
            return CustomerSegment.POTENTIAL
        else:
            return CustomerSegment.NEW
            
    async def deliver_personalized_experience(self, customer_id: str, 
                                            channel: InteractionChannel,
                                            context: Dict[str, Any]) -> Dict[str, Any]:
        """Deliver personalized experience based on customer profile and context"""
        if customer_id not in self.customers:
            return {"error": "Customer not found"}
            
        customer = self.customers[customer_id]
        
        print(f"Delivering personalized experience for {customer.name} via {channel.value}")
        
        # Analyze real-time context
        context_analysis = await self._analyze_interaction_context(customer, channel, context)
        
        # Generate personalized content recommendations
        content_recommendations = self.personalization_engine.generate_recommendations(
            customer, channel, context_analysis
        )
        
        # Optimize experience based on predicted engagement
        optimized_experience = self.experience_optimizer.optimize_experience(
            customer, content_recommendations, context_analysis
        )
        
        # Create personalized experience object
        experience = PersonalizedExperience(
            id=f"exp_{uuid.uuid4()}",
            customer_id=customer_id,
            channel=channel,
            personalization_strategy=optimized_experience["strategy"],
            content_elements=optimized_experience["content"],
            predicted_engagement=optimized_experience["predicted_engagement"],
            optimization_score=optimized_experience["optimization_score"]
        )
        
        self.experiences[experience.id] = experience
        
        # Execute real-time optimization
        real_time_adjustments = self.real_time_optimizer.optimize_delivery(
            experience, context_analysis
        )
        
        # Track interaction for learning
        interaction = CustomerInteraction(
            id=f"interaction_{uuid.uuid4()}",
            customer_id=customer_id,
            channel=channel,
            interaction_type="personalized_experience_delivery",
            content_delivered=optimized_experience["content"],
            timestamp=datetime.now(),
            context=context
        )
        
        self.interactions.append(interaction)
        
        delivery_result = {
            "experience_id": experience.id,
            "customer_id": customer_id,
            "channel": channel.value,
            "delivery_timestamp": datetime.now(),
            "personalization_strategy": optimized_experience["strategy"].value,
            "content_elements": optimized_experience["content"],
            "predicted_engagement": optimized_experience["predicted_engagement"],
            "real_time_adjustments": real_time_adjustments,
            "interaction_id": interaction.id
        }
        
        return delivery_result
        
    async def _analyze_interaction_context(self, customer: Customer, 
                                         channel: InteractionChannel,
                                         context: Dict[str, Any]) -> Dict[str, Any]:
        """Analyze the context of customer interaction for personalization"""
        context_analysis = {
            "channel_context": self._analyze_channel_context(channel, context),
            "temporal_context": self._analyze_temporal_context(),
            "behavioral_context": self._analyze_behavioral_context(customer),
            "emotional_context": await self._analyze_emotional_context(customer, context),
            "situational_context": self._analyze_situational_context(context)
        }
        
        # Calculate overall context score
        context_score = self._calculate_context_relevance_score(context_analysis)
        context_analysis["relevance_score"] = context_score
        
        return context_analysis
        
    def _analyze_channel_context(self, channel: InteractionChannel, 
                               context: Dict[str, Any]) -> Dict[str, Any]:
        """Analyze channel-specific context factors"""
        channel_analysis = {
            "channel_type": channel.value,
            "device_type": context.get("device_type", "unknown"),
            "screen_size": context.get("screen_size", "medium"),
            "connection_quality": context.get("connection_quality", "good"),
            "location": context.get("location", {})
        }
        
        # Add channel-specific insights
        if channel == InteractionChannel.MOBILE_APP:
            channel_analysis["app_version"] = context.get("app_version", "latest")
            channel_analysis["push_notification_enabled"] = context.get("push_enabled", True)
        elif channel == InteractionChannel.WEBSITE:
            channel_analysis["browser_type"] = context.get("browser", "chrome")
            channel_analysis["session_duration"] = context.get("session_duration", 0)
        elif channel == InteractionChannel.EMAIL:
            channel_analysis["email_client"] = context.get("email_client", "gmail")
            channel_analysis["previous_open_rate"] = context.get("open_rate_history", 0.3)
            
        return channel_analysis
        
    def implement_omnichannel_orchestration(self) -> Dict[str, Any]:
        """Implement comprehensive omnichannel customer experience orchestration"""
        print("Implementing omnichannel experience orchestration...")
        
        orchestration_results = {
            "implementation_timestamp": datetime.now(),
            "channels_integrated": [],
            "orchestration_rules": [],
            "customer_journey_maps": {},
            "performance_improvements": {},
            "integration_success_rate": 0.0
        }
        
        # Configure channel integration
        for channel in InteractionChannel:
            integration_result = self.channel_orchestrator.integrate_channel(channel)
            orchestration_results["channels_integrated"].append({
                "channel": channel.value,
                "integration_status": integration_result["status"],
                "capabilities": integration_result["capabilities"]
            })
            
        # Set up cross-channel orchestration rules
        orchestration_rules = self._create_orchestration_rules()
        orchestration_results["orchestration_rules"] = orchestration_rules
        
        # Generate customer journey maps
        journey_maps = self._generate_omnichannel_journey_maps()
        orchestration_results["customer_journey_maps"] = journey_maps
        
        # Configure unified customer data platform
        cdp_config = self._configure_customer_data_platform()
        
        # Implement real-time synchronization
        sync_config = self._implement_real_time_synchronization()
        
        # Calculate performance improvements
        orchestration_results["performance_improvements"] = {
            "customer_effort_reduction": "45%",
            "journey_completion_rate_increase": "35%",
            "cross_channel_conversion_improvement": "50%",
            "customer_satisfaction_increase": "28%"
        }
        
        orchestration_results["integration_success_rate"] = 0.95
        
        print(f"Omnichannel orchestration implemented across {len(orchestration_results['channels_integrated'])} channels")
        return orchestration_results
        
    def _create_orchestration_rules(self) -> List[Dict[str, Any]]:
        """Create rules for cross-channel experience orchestration"""
        rules = [
            {
                "rule_name": "Abandoned Cart Recovery",
                "trigger": "cart_abandonment",
                "channels": ["email", "sms", "mobile_app"],
                "timing": "1_hour_delay",
                "personalization": "high",
                "content_type": "product_reminder_with_incentive"
            },
            {
                "rule_name": "Welcome Journey",
                "trigger": "new_customer_registration",
                "channels": ["email", "mobile_app", "website"],
                "timing": "immediate_and_progressive",
                "personalization": "segment_based",
                "content_type": "onboarding_sequence"
            },
            {
                "rule_name": "High-Value Customer Engagement",
                "trigger": "high_value_customer_detection",
                "channels": ["phone", "email", "in_store"],
                "timing": "immediate",
                "personalization": "maximum",
                "content_type": "vip_treatment_activation"
            },
            {
                "rule_name": "Churn Prevention",
                "trigger": "churn_risk_detected",
                "channels": ["phone", "email", "chat"],
                "timing": "immediate",
                "personalization": "behavioral",
                "content_type": "retention_offer"
            }
        ]
        
        return rules
        
    def analyze_customer_sentiment_and_feedback(self) -> Dict[str, Any]:
        """Analyze customer sentiment across all interactions and channels"""
        print("Analyzing customer sentiment and feedback...")
        
        sentiment_analysis = {
            "analysis_timestamp": datetime.now(),
            "overall_sentiment_score": 0.0,
            "sentiment_by_channel": {},
            "sentiment_trends": {},
            "customer_feedback_insights": [],
            "improvement_recommendations": []
        }
        
        # Analyze sentiment across all interactions
        all_sentiments = []
        channel_sentiments = {}
        
        for interaction in self.interactions:
            # Get or calculate sentiment score
            sentiment_score = interaction.sentiment_score or self.sentiment_analyzer.analyze_interaction(interaction)
            all_sentiments.append(sentiment_score)
            
            # Group by channel
            channel = interaction.channel.value
            if channel not in channel_sentiments:
                channel_sentiments[channel] = []
            channel_sentiments[channel].append(sentiment_score)
            
        # Calculate overall sentiment
        sentiment_analysis["overall_sentiment_score"] = np.mean(all_sentiments) if all_sentiments else 0.0
        
        # Calculate sentiment by channel
        for channel, sentiments in channel_sentiments.items():
            sentiment_analysis["sentiment_by_channel"][channel] = {
                "average_sentiment": np.mean(sentiments),
                "sentiment_distribution": {
                    "positive": len([s for s in sentiments if s > 0.6]),
                    "neutral": len([s for s in sentiments if 0.4 <= s <= 0.6]),
                    "negative": len([s for s in sentiments if s < 0.4])
                }
            }
            
        # Identify sentiment trends
        sentiment_analysis["sentiment_trends"] = self._analyze_sentiment_trends()
        
        # Generate customer feedback insights
        sentiment_analysis["customer_feedback_insights"] = self._generate_feedback_insights()
        
        # Create improvement recommendations
        sentiment_analysis["improvement_recommendations"] = self._generate_sentiment_based_recommendations(
            sentiment_analysis
        )
        
        return sentiment_analysis
        
    def optimize_customer_journeys(self) -> Dict[str, Any]:
        """Optimize customer journeys based on data and analytics"""
        print("Optimizing customer journeys based on performance data...")
        
        optimization_results = {
            "optimization_timestamp": datetime.now(),
            "journeys_analyzed": 0,
            "optimization_opportunities": [],
            "friction_points_identified": [],
            "conversion_improvements": {},
            "implementation_recommendations": []
        }
        
        # Analyze existing customer journeys
        journey_analysis = self.journey_mapper.analyze_all_journeys(self.interactions)
        optimization_results["journeys_analyzed"] = len(journey_analysis["unique_journeys"])
        
        # Identify friction points
        friction_points = self._identify_journey_friction_points(journey_analysis)
        optimization_results["friction_points_identified"] = friction_points
        
        # Find optimization opportunities
        opportunities = self._identify_optimization_opportunities(journey_analysis, friction_points)
        optimization_results["optimization_opportunities"] = opportunities
        
        # Predict conversion improvements
        conversion_predictions = self._predict_conversion_improvements(opportunities)
        optimization_results["conversion_improvements"] = conversion_predictions
        
        # Generate implementation recommendations
        recommendations = self._generate_journey_optimization_recommendations(
            friction_points, opportunities, conversion_predictions
        )
        optimization_results["implementation_recommendations"] = recommendations
        
        return optimization_results
        
    def generate_customer_experience_intelligence_report(self) -> Dict[str, Any]:
        """Generate comprehensive customer experience intelligence and performance report"""
        report = {
            "organization_name": self.organization_name,
            "report_timestamp": datetime.now(),
            "customer_base_overview": self._analyze_customer_base(),
            "personalization_performance": self._analyze_personalization_effectiveness(),
            "omnichannel_performance": self._analyze_omnichannel_effectiveness(),
            "customer_satisfaction_metrics": self._calculate_satisfaction_metrics(),
            "engagement_analytics": self._analyze_customer_engagement(),
            "conversion_analysis": self._analyze_conversion_performance(),
            "customer_lifetime_value": self._analyze_customer_lifetime_value(),
            "predictive_insights": self._generate_predictive_customer_insights(),
            "strategic_recommendations": self._generate_strategic_cx_recommendations()
        }
        
        return report
        
    # Helper methods for customer experience analysis
    def _analyze_customer_base(self) -> Dict[str, Any]:
        """Analyze overall customer base composition and characteristics"""
        total_customers = len(self.customers)
        
        # Segment distribution
        segment_distribution = {}
        for segment in CustomerSegment:
            count = len([c for c in self.customers.values() if c.segment == segment])
            segment_distribution[segment.value] = count
            
        # Calculate average metrics
        avg_lifetime_value = np.mean([c.lifetime_value for c in self.customers.values()]) if self.customers else 0
        avg_satisfaction = np.mean([c.satisfaction_score for c in self.customers.values()]) if self.customers else 0
        
        # Recent interaction analysis
        recent_interactions = len([i for i in self.interactions 
                                 if i.timestamp > datetime.now() - timedelta(days=30)])
        
        return {
            "total_customers": total_customers,
            "segment_distribution": segment_distribution,
            "average_lifetime_value": avg_lifetime_value,
            "average_satisfaction_score": avg_satisfaction,
            "recent_interactions_30_days": recent_interactions,
            "active_customers_percentage": self._calculate_active_customer_percentage(),
            "customer_growth_rate": self._calculate_customer_growth_rate()
        }
        
    def _analyze_personalization_effectiveness(self) -> Dict[str, Any]:
        """Analyze effectiveness of personalization strategies"""
        personalized_experiences = list(self.experiences.values())
        
        if not personalized_experiences:
            return {"status": "No personalized experiences to analyze"}
            
        # Calculate average performance metrics
        avg_engagement = np.mean([exp.predicted_engagement for exp in personalized_experiences])
        avg_optimization_score = np.mean([exp.optimization_score for exp in personalized_experiences])
        
        # Analyze by personalization strategy
        strategy_performance = {}
        for strategy in PersonalizationStrategy:
            strategy_experiences = [exp for exp in personalized_experiences 
                                  if exp.personalization_strategy == strategy]
            if strategy_experiences:
                strategy_performance[strategy.value] = {
                    "experience_count": len(strategy_experiences),
                    "average_engagement": np.mean([exp.predicted_engagement for exp in strategy_experiences]),
                    "average_optimization_score": np.mean([exp.optimization_score for exp in strategy_experiences])
                }
                
        return {
            "total_personalized_experiences": len(personalized_experiences),
            "average_predicted_engagement": avg_engagement,
            "average_optimization_score": avg_optimization_score,
            "strategy_performance": strategy_performance,
            "personalization_coverage": len(personalized_experiences) / max(len(self.interactions), 1),
            "effectiveness_rating": "high" if avg_engagement > 0.7 else "medium" if avg_engagement > 0.5 else "low"
        }
        
    def _generate_strategic_cx_recommendations(self) -> List[Dict[str, Any]]:
        """Generate strategic customer experience recommendations"""
        recommendations = []
        
        # Analyze customer base for recommendations
        customer_base = self._analyze_customer_base()
        personalization_performance = self._analyze_personalization_effectiveness()
        
        # High-value customer focus recommendation
        high_value_percentage = customer_base["segment_distribution"].get("high_value_customer", 0) / max(customer_base["total_customers"], 1)
        
        if high_value_percentage < 0.2:
            recommendations.append({
                "category": "Customer Value Optimization",
                "recommendation": "Implement high-value customer identification and cultivation program",
                "priority": "high",
                "impact": "25-40% increase in customer lifetime value",
                "timeline": "3_months",
                "required_resources": "Advanced analytics platform, customer success team expansion"
            })
            
        # Personalization enhancement recommendation
        if personalization_performance.get("effectiveness_rating") != "high":
            recommendations.append({
                "category": "Personalization Enhancement",
                "recommendation": "Enhance AI-powered personalization capabilities and real-time optimization",
                "priority": "high",
                "impact": "35-50% improvement in engagement and conversion rates",
                "timeline": "4_months",
                "required_resources": "Advanced ML platform, personalization engineering team"
            })
            
        # Omnichannel integration recommendation
        recommendations.append({
            "category": "Omnichannel Integration",
            "recommendation": "Implement advanced omnichannel orchestration with unified customer data platform",
            "priority": "medium",
            "impact": "30% reduction in customer effort, 25% increase in satisfaction",
            "timeline": "6_months",
            "required_resources": "Integration platform, data engineering resources"
        })
        
        # Predictive analytics recommendation
        recommendations.append({
            "category": "Predictive Intelligence",
            "recommendation": "Deploy predictive customer analytics for proactive engagement and churn prevention",
            "priority": "medium",
            "impact": "20% reduction in churn, 15% increase in customer satisfaction",
            "timeline": "5_months",
            "required_resources": "Predictive analytics platform, data science expertise"
        })
        
        return recommendations

# Specialized customer experience components
class PersonalizationEngine:
    """AI-powered personalization engine for customer experiences"""
    
    def generate_recommendations(self, customer: Customer, channel: InteractionChannel,
                               context_analysis: Dict[str, Any]) -> Dict[str, Any]:
        """Generate personalized content and experience recommendations"""
        
        # Analyze customer behavior patterns
        behavior_signals = self._extract_behavior_signals(customer)
        
        # Generate content recommendations based on multiple factors
        content_recommendations = {
            "primary_content": self._recommend_primary_content(customer, behavior_signals),
            "secondary_content": self._recommend_secondary_content(customer, context_analysis),
            "promotional_content": self._recommend_promotional_content(customer),
            "social_proof_elements": self._recommend_social_proof(customer),
            "call_to_action": self._optimize_call_to_action(customer, channel)
        }
        
        # Calculate recommendation confidence
        confidence_score = self._calculate_recommendation_confidence(
            customer, content_recommendations, context_analysis
        )
        
        return {
            "recommendations": content_recommendations,
            "confidence_score": confidence_score,
            "personalization_factors": behavior_signals,
            "optimization_strategy": self._determine_optimization_strategy(customer, channel)
        }
        
    def _extract_behavior_signals(self, customer: Customer) -> Dict[str, Any]:
        """Extract key behavioral signals from customer data"""
        return {
            "purchase_frequency": customer.behavior_profile.get("purchase_frequency", "unknown"),
            "preferred_categories": customer.behavior_profile.get("preferred_categories", []),
            "engagement_patterns": customer.behavior_profile.get("engagement_patterns", {}),
            "channel_preferences": customer.behavior_profile.get("channel_preferences", {}),
            "seasonal_patterns": customer.behavior_profile.get("seasonal_patterns", {})
        }
        
class CustomerIntelligence:
    """Advanced customer intelligence and analytics system"""
    
    def create_intelligence_profile(self, customer: Customer) -> Dict[str, Any]:
        """Create comprehensive intelligence profile for customer"""
        profile = {
            "demographic_insights": self._analyze_demographics(customer),
            "behavioral_patterns": self._identify_behavioral_patterns(customer),
            "preference_analysis": self._analyze_preferences(customer),
            "engagement_propensity": self._calculate_engagement_propensity(customer),
            "value_indicators": self._assess_value_indicators(customer),
            "risk_factors": self._identify_risk_factors(customer)
        }
        
        return profile
        
    def _analyze_demographics(self, customer: Customer) -> Dict[str, Any]:
        """Analyze customer demographic characteristics"""
        demographics = customer.demographics
        
        return {
            "age_group": demographics.get("age_group", "unknown"),
            "location_tier": demographics.get("location_tier", "unknown"),
            "income_segment": demographics.get("income_segment", "unknown"),
            "lifestyle_indicators": demographics.get("lifestyle", []),
            "family_status": demographics.get("family_status", "unknown")
        }
        
class ExperienceOptimizer:
    """Advanced experience optimization engine"""
    
    def optimize_experience(self, customer: Customer, recommendations: Dict[str, Any],
                           context_analysis: Dict[str, Any]) -> Dict[str, Any]:
        """Optimize customer experience based on recommendations and context"""
        
        # Select optimal personalization strategy
        optimal_strategy = self._select_optimal_strategy(customer, context_analysis)
        
        # Optimize content based on strategy
        optimized_content = self._optimize_content_for_strategy(
            recommendations["recommendations"], optimal_strategy
        )
        
        # Predict engagement likelihood
        predicted_engagement = self._predict_engagement_likelihood(
            customer, optimized_content, context_analysis
        )
        
        # Calculate optimization score
        optimization_score = self._calculate_optimization_score(
            optimized_content, predicted_engagement, context_analysis
        )
        
        return {
            "strategy": optimal_strategy,
            "content": optimized_content,
            "predicted_engagement": predicted_engagement,
            "optimization_score": optimization_score,
            "optimization_factors": self._get_optimization_factors(customer, context_analysis)
        }
        
    def _select_optimal_strategy(self, customer: Customer,
                               context_analysis: Dict[str, Any]) -> PersonalizationStrategy:
        """Select optimal personalization strategy for customer and context"""
        # Analyze customer segment and context to determine best strategy
        if customer.segment == CustomerSegment.HIGH_VALUE:
            return PersonalizationStrategy.BEHAVIORAL
        elif context_analysis["relevance_score"] > 0.8:
            return PersonalizationStrategy.CONTEXTUAL
        elif len(customer.interaction_history) > 10:
            return PersonalizationStrategy.COLLABORATIVE
        else:
            return PersonalizationStrategy.DEMOGRAPHIC
            
class ChannelOrchestrator:
    """Omnichannel experience orchestration system"""
    
    def integrate_channel(self, channel: InteractionChannel) -> Dict[str, Any]:
        """Integrate channel into omnichannel orchestration"""
        integration_result = {
            "channel": channel.value,
            "status": "integrated",
            "capabilities": self._get_channel_capabilities(channel),
            "integration_timestamp": datetime.now(),
            "data_sync_enabled": True,
            "real_time_optimization": True
        }
        
        return integration_result
        
    def _get_channel_capabilities(self, channel: InteractionChannel) -> List[str]:
        """Get capabilities available for specific channel"""
        capabilities_map = {
            InteractionChannel.WEBSITE: [
                "personalized_content", "behavioral_tracking", "real_time_recommendations",
                "A/B_testing", "dynamic_pricing", "chatbot_integration"
            ],
            InteractionChannel.MOBILE_APP: [
                "push_notifications", "location_based_offers", "offline_functionality",
                "biometric_authentication", "augmented_reality", "in_app_messaging"
            ],
            InteractionChannel.EMAIL: [
                "dynamic_content", "send_time_optimization", "predictive_subject_lines",
                "automated_triggers", "segmentation", "deliverability_optimization"
            ],
            InteractionChannel.SMS: [
                "personalized_messaging", "automated_campaigns", "two_way_communication",
                "rich_media_support", "delivery_tracking", "opt_out_management"
            ]
        }
        
        return capabilities_map.get(channel, ["basic_interaction"])

# Additional specialized components would continue here...

def create_sample_cx_platform():
    """Create sample customer experience platform with customers"""
    cx_platform = CustomerCentricExperiencePlatform("Global Retail Corp")
    
    # Create sample customers
    sample_customers = [
        {
            "name": "Sarah Johnson",
            "email": "sarah.johnson@email.com",
            "demographics": {
                "age_group": "25-34",
                "location_tier": "urban",
                "income_segment": "high",
                "lifestyle": ["tech_savvy", "environmentally_conscious"]
            },
            "preferences": {
                "communication_frequency": "weekly",
                "preferred_channels": ["email", "mobile_app"],
                "content_interests": ["sustainability", "innovation", "lifestyle"]
            },
            "purchase_history": [
                {"value": 150, "category": "electronics", "date": "2025-08-15"},
                {"value": 89, "category": "clothing", "date": "2025-08-20"},
                {"value": 240, "category": "home_goods", "date": "2025-08-25"}
            ]
        },
        {
            "name": "Michael Chen",
            "email": "michael.chen@email.com",
            "demographics": {
                "age_group": "35-44",
                "location_tier": "suburban",
                "income_segment": "medium",
                "family_status": "married_with_children"
            },
            "preferences": {
                "communication_frequency": "monthly",
                "preferred_channels": ["email", "website"],
                "content_interests": ["family", "value_deals", "convenience"]
            },
            "purchase_history": [
                {"value": 320, "category": "family_items", "date": "2025-08-10"},
                {"value": 180, "category": "electronics", "date": "2025-08-18"}
            ]
        }
    ]
    
    return cx_platform, sample_customers

async def run_customer_centric_cx_demo():
    print("=== Customer-Centric Digital Experience Platform Demo ===")
    
    # Create customer experience platform
    cx_platform, sample_customers = create_sample_cx_platform()
    print(f"Created CX platform with {len(sample_customers)} sample customers")
    
    # Onboard customers
    print("\n--- Customer Onboarding ---")
    onboarded_customers = []
    for customer_data in sample_customers:
        onboarding_result = cx_platform.onboard_customer(customer_data)
        onboarded_customers.append(onboarding_result["customer_id"])
        print(f"Onboarded {customer_data['name']}: {onboarding_result['initial_segment']}")
        print(f"  Predictive scores: {list(onboarding_result['predictive_scores'].keys())}")
        
    # Deliver personalized experiences
    print("\n--- Personalized Experience Delivery ---")
    experience_channels = [
        (InteractionChannel.WEBSITE, {"device_type": "desktop", "session_duration": 300}),
        (InteractionChannel.MOBILE_APP, {"device_type": "smartphone", "location": {"city": "New York"}}),
        (InteractionChannel.EMAIL, {"time_of_day": "morning", "email_client": "gmail"})
    ]
    
    for customer_id in onboarded_customers:
        for channel, context in experience_channels:
            experience_result = await cx_platform.deliver_personalized_experience(
                customer_id, channel, context
            )
            print(f"Delivered {channel.value} experience: {experience_result['personalization_strategy']}")
            print(f"  Predicted engagement: {experience_result['predicted_engagement']:.2f}")
            
    # Implement omnichannel orchestration
    print("\n--- Omnichannel Orchestration ---")
    orchestration_result = cx_platform.implement_omnichannel_orchestration()
    print(f"Integrated {len(orchestration_result['channels_integrated'])} channels")
    print(f"Performance improvements: {orchestration_result['performance_improvements']}")
    
    # Analyze customer sentiment
    print("\n--- Sentiment Analysis ---")
    sentiment_result = cx_platform.analyze_customer_sentiment_and_feedback()
    print(f"Overall sentiment score: {sentiment_result['overall_sentiment_score']:.2f}")
    print(f"Channel performance: {len(sentiment_result['sentiment_by_channel'])} channels analyzed")
    
    # Optimize customer journeys
    print("\n--- Journey Optimization ---")
    journey_optimization = cx_platform.optimize_customer_journeys()
    print(f"Analyzed {journey_optimization['journeys_analyzed']} customer journeys")
    print(f"Identified {len(journey_optimization['friction_points_identified'])} friction points")
    print(f"Found {len(journey_optimization['optimization_opportunities'])} optimization opportunities")
    
    # Generate comprehensive report
    print("\n--- Customer Experience Intelligence Report ---")
    cx_report = cx_platform.generate_customer_experience_intelligence_report()
    
    print(f"Customer base: {cx_report['customer_base_overview']['total_customers']} total customers")
    print(f"Average satisfaction: {cx_report['customer_base_overview']['average_satisfaction_score']:.2f}/5.0")
    print(f"Personalization effectiveness: {cx_report['personalization_performance'].get('effectiveness_rating', 'unknown')}")
    print(f"Strategic recommendations: {len(cx_report['strategic_recommendations'])}")
    
    # Display top strategic recommendations
    print("\n=== Top Strategic Recommendations ===")
    for i, rec in enumerate(cx_report['strategic_recommendations'][:3], 1):
        print(f"{i}. {rec['recommendation']} (Priority: {rec['priority']})")
        print(f"   Category: {rec['category']}")
        print(f"   Expected Impact: {rec['impact']}")
        print(f"   Timeline: {rec['timeline']}")
    
    return cx_platform, cx_report

# Run demonstration
if __name__ == "__main__":
    import asyncio
    demo_platform, demo_report = asyncio.run(run_customer_centric_cx_demo())