# Advanced prospect research automation framework
import requests
import json
import time
import csv
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Any
from datetime import datetime, timedelta
import logging
from urllib.parse import urljoin
import re

@dataclass
class ProspectProfile:
    email: str
    first_name: str
    last_name: str
    company: str
    job_title: str
    industry: str
    company_size: str
    location: str
    linkedin_url: Optional[str] = None
    company_website: Optional[str] = None
    recent_activity: List[str] = field(default_factory=list)
    pain_points: List[str] = field(default_factory=list)
    personalization_data: Dict[str, Any] = field(default_factory=dict)
    research_confidence: float = 0.0
    contact_score: int = 0

@dataclass
class CompanyIntelligence:
    company_name: str
    domain: str
    industry: str
    size: str
    revenue_range: str
    growth_stage: str
    technology_stack: List[str] = field(default_factory=list)
    recent_news: List[Dict[str, Any]] = field(default_factory=list)
    funding_info: Optional[Dict[str, Any]] = None
    competitive_landscape: List[str] = field(default_factory=list)
    decision_makers: List[str] = field(default_factory=list)

class ProspectResearchEngine:
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.data_sources = {}
        self.research_cache = {}
        self.api_clients = self._initialize_api_clients()
        self.logger = logging.getLogger(__name__)
        
    def _initialize_api_clients(self):
        """Initialize connections to various data sources"""
        return {
            'linkedin': LinkedInResearchClient(self.config.get('linkedin_api_key')),
            'clearbit': ClearbitClient(self.config.get('clearbit_api_key')),
            'hunter': HunterClient(self.config.get('hunter_api_key')),
            'builtwith': BuiltWithClient(self.config.get('builtwith_api_key')),
            'news_api': NewsAPIClient(self.config.get('news_api_key'))
        }
    
    async def research_prospect(self, basic_info: Dict[str, str]) -> ProspectProfile:
        """Conduct comprehensive prospect research"""
        
        prospect = ProspectProfile(
            email=basic_info.get('email', ''),
            first_name=basic_info.get('first_name', ''),
            last_name=basic_info.get('last_name', ''),
            company=basic_info.get('company', ''),
            job_title=basic_info.get('job_title', ''),
            industry=basic_info.get('industry', ''),
            company_size=basic_info.get('company_size', ''),
            location=basic_info.get('location', '')
        )
        
        # Parallel research execution
        research_tasks = await asyncio.gather(
            self._research_individual_profile(prospect),
            self._research_company_context(prospect.company),
            self._research_industry_trends(prospect.industry),
            self._research_recent_activity(prospect),
            return_exceptions=True
        )
        
        # Process research results
        individual_data, company_data, industry_data, activity_data = research_tasks
        
        # Compile comprehensive prospect profile
        prospect = await self._compile_prospect_intelligence(
            prospect, individual_data, company_data, industry_data, activity_data
        )
        
        # Calculate research confidence score
        prospect.research_confidence = self._calculate_research_confidence(prospect)
        prospect.contact_score = self._calculate_contact_score(prospect)
        
        return prospect
    
    async def _research_individual_profile(self, prospect: ProspectProfile) -> Dict[str, Any]:
        """Research individual professional profile"""
        
        profile_data = {}
        
        try:
            # LinkedIn profile research
            if self.api_clients['linkedin']:
                linkedin_data = await self.api_clients['linkedin'].search_profile(
                    f"{prospect.first_name} {prospect.last_name}",
                    prospect.company
                )
                if linkedin_data:
                    profile_data['linkedin'] = linkedin_data
                    prospect.linkedin_url = linkedin_data.get('profile_url')
            
            # Professional background analysis
            background_data = await self._analyze_professional_background(prospect)
            profile_data['background'] = background_data
            
            # Role-specific pain point identification
            pain_points = await self._identify_role_pain_points(
                prospect.job_title, prospect.industry
            )
            profile_data['pain_points'] = pain_points
            
        except Exception as e:
            self.logger.error(f"Individual research failed for {prospect.email}: {e}")
        
        return profile_data
    
    async def _research_company_context(self, company_name: str) -> CompanyIntelligence:
        """Research comprehensive company context"""
        
        company_intel = CompanyIntelligence(
            company_name=company_name,
            domain='',
            industry='',
            size='',
            revenue_range='',
            growth_stage=''
        )
        
        try:
            # Company profile from Clearbit
            if self.api_clients['clearbit']:
                clearbit_data = await self.api_clients['clearbit'].get_company_data(company_name)
                if clearbit_data:
                    company_intel.domain = clearbit_data.get('domain', '')
                    company_intel.industry = clearbit_data.get('category', {}).get('industry', '')
                    company_intel.size = str(clearbit_data.get('metrics', {}).get('employees', ''))
                    company_intel.revenue_range = clearbit_data.get('metrics', {}).get('annualRevenue', '')
            
            # Technology stack analysis
            if self.api_clients['builtwith'] and company_intel.domain:
                tech_data = await self.api_clients['builtwith'].get_technologies(company_intel.domain)
                company_intel.technology_stack = tech_data.get('technologies', [])
            
            # Recent company news and developments
            if self.api_clients['news_api']:
                news_data = await self.api_clients['news_api'].search_company_news(company_name)
                company_intel.recent_news = news_data.get('articles', [])[:5]  # Top 5 recent articles
            
            # Competitive landscape analysis
            competitors = await self._identify_competitors(company_name, company_intel.industry)
            company_intel.competitive_landscape = competitors
            
        except Exception as e:
            self.logger.error(f"Company research failed for {company_name}: {e}")
        
        return company_intel
    
    async def _research_industry_trends(self, industry: str) -> Dict[str, Any]:
        """Research current industry trends and challenges"""
        
        industry_data = {
            'current_trends': [],
            'major_challenges': [],
            'growth_opportunities': [],
            'regulatory_changes': [],
            'technology_disruptions': []
        }
        
        try:
            # Industry trend analysis
            trends = await self._analyze_industry_trends(industry)
            industry_data['current_trends'] = trends.get('trends', [])
            industry_data['major_challenges'] = trends.get('challenges', [])
            industry_data['growth_opportunities'] = trends.get('opportunities', [])
            
            # Regulatory environment research
            regulatory_info = await self._research_regulatory_environment(industry)
            industry_data['regulatory_changes'] = regulatory_info
            
        except Exception as e:
            self.logger.error(f"Industry research failed for {industry}: {e}")
        
        return industry_data
    
    async def _compile_prospect_intelligence(self, prospect: ProspectProfile, 
                                          individual_data: Dict[str, Any],
                                          company_data: CompanyIntelligence,
                                          industry_data: Dict[str, Any],
                                          activity_data: Dict[str, Any]) -> ProspectProfile:
        """Compile comprehensive prospect intelligence"""
        
        # Update prospect with research findings
        if individual_data.get('pain_points'):
            prospect.pain_points.extend(individual_data['pain_points'])
        
        if activity_data.get('recent_activity'):
            prospect.recent_activity.extend(activity_data['recent_activity'])
        
        # Build personalization data
        prospect.personalization_data = {
            'company_news': company_data.recent_news[:2] if company_data.recent_news else [],
            'industry_trends': industry_data.get('current_trends', [])[:2],
            'technology_stack': company_data.technology_stack[:3],
            'growth_indicators': self._identify_growth_indicators(company_data),
            'pain_point_relevance': self._map_pain_points_to_solution(prospect.pain_points),
            'engagement_hooks': self._generate_engagement_hooks(
                individual_data, company_data, industry_data
            )
        }
        
        return prospect
    
    def _calculate_research_confidence(self, prospect: ProspectProfile) -> float:
        """Calculate confidence score for research quality"""
        
        confidence_factors = {
            'linkedin_profile': 0.25 if prospect.linkedin_url else 0,
            'company_intelligence': 0.20 if prospect.personalization_data.get('company_news') else 0,
            'industry_context': 0.15 if prospect.personalization_data.get('industry_trends') else 0,
            'recent_activity': 0.20 if prospect.recent_activity else 0,
            'pain_point_mapping': 0.20 if prospect.pain_points else 0
        }
        
        return sum(confidence_factors.values())
    
    def _calculate_contact_score(self, prospect: ProspectProfile) -> int:
        """Calculate contact priority score (1-100)"""
        
        score = 0
        
        # Job title relevance
        if any(keyword in prospect.job_title.lower() for keyword in ['vp', 'director', 'manager', 'head']):
            score += 25
        
        # Company size alignment
        if prospect.company_size in ['101-500', '501-1000', '1001-5000']:
            score += 20
        
        # Recent activity indicators
        if prospect.recent_activity:
            score += 20
        
        # Research confidence bonus
        score += int(prospect.research_confidence * 35)
        
        return min(score, 100)

class LinkedInResearchClient:
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.base_url = "https://api.linkedin.com/v2"
    
    async def search_profile(self, name: str, company: str) -> Optional[Dict[str, Any]]:
        """Search for LinkedIn profile"""
        # Implementation would use LinkedIn API or scraping service
        # This is a placeholder for the actual implementation
        return {
            'profile_url': f'https://linkedin.com/in/{name.lower().replace(" ", "-")}',
            'current_position': 'Mock position',
            'experience_years': 5,
            'education': 'Mock University'
        }

class ClearbitClient:
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.base_url = "https://company.clearbit.com/v2/companies"
    
    async def get_company_data(self, company_name: str) -> Optional[Dict[str, Any]]:
        """Get company data from Clearbit"""
        # Mock implementation - replace with actual API calls
        return {
            'domain': f'{company_name.lower().replace(" ", "")}.com',
            'category': {'industry': 'Technology'},
            'metrics': {
                'employees': 150,
                'annualRevenue': '$10M-$50M'
            }
        }

# Usage demonstration
async def demonstrate_prospect_research():
    """Demonstrate advanced prospect research"""
    
    config = {
        'linkedin_api_key': 'your-linkedin-key',
        'clearbit_api_key': 'your-clearbit-key',
        'hunter_api_key': 'your-hunter-key',
        'builtwith_api_key': 'your-builtwith-key',
        'news_api_key': 'your-news-api-key'
    }
    
    research_engine = ProspectResearchEngine(config)
    
    print("=== Advanced Prospect Research Demo ===")
    
    # Sample prospect data
    basic_prospect = {
        'email': 'john.smith@acmecorp.com',
        'first_name': 'John',
        'last_name': 'Smith',
        'company': 'ACME Corporation',
        'job_title': 'VP of Marketing',
        'industry': 'Software',
        'company_size': '201-500',
        'location': 'San Francisco, CA'
    }
    
    # Conduct comprehensive research
    prospect_profile = await research_engine.research_prospect(basic_prospect)
    
    print(f"Researched prospect: {prospect_profile.first_name} {prospect_profile.last_name}")
    print(f"Company: {prospect_profile.company}")
    print(f"Research confidence: {prospect_profile.research_confidence:.2f}")
    print(f"Contact score: {prospect_profile.contact_score}/100")
    print(f"Pain points identified: {len(prospect_profile.pain_points)}")
    print(f"Personalization hooks: {len(prospect_profile.personalization_data.get('engagement_hooks', []))}")
    
    return prospect_profile

if __name__ == "__main__":
    import asyncio
    result = asyncio.run(demonstrate_prospect_research())
    print("Prospect research system ready!")

class PersonalizationEngine:
    def __init__(self, prospect_data: ProspectProfile):
        self.prospect = prospect_data
        self.personalization_elements = self._identify_personalization_opportunities()
        self.message_frameworks = self._load_message_frameworks()
    
    def generate_personalized_message(self, campaign_type: str, sequence_position: int) -> Dict[str, str]:
        """Generate personalized email message"""
        
        # Select appropriate framework
        framework = self._select_message_framework(campaign_type, sequence_position)
        
        # Apply personalization layers
        personalized_content = self._apply_personalization_layers(framework)
        
        # Validate message quality
        quality_score = self._assess_message_quality(personalized_content)
        
        return {
            'subject_line': personalized_content['subject'],
            'email_body': personalized_content['body'],
            'personalization_score': quality_score,
            'personalization_elements': self.personalization_elements
        }
    
    def _identify_personalization_opportunities(self) -> List[str]:
        """Identify available personalization elements"""
        
        opportunities = []
        
        # Company-specific personalization
        if self.prospect.personalization_data.get('company_news'):
            opportunities.append('recent_company_news')
        
        if self.prospect.personalization_data.get('technology_stack'):
            opportunities.append('technology_alignment')
        
        # Role-specific personalization
        if self.prospect.pain_points:
            opportunities.append('role_specific_pain_points')
        
        # Industry personalization
        if self.prospect.personalization_data.get('industry_trends'):
            opportunities.append('industry_trends')
        
        # Activity-based personalization
        if self.prospect.recent_activity:
            opportunities.append('recent_activity')
        
        return opportunities
    
    def _apply_personalization_layers(self, framework: Dict[str, str]) -> Dict[str, str]:
        """Apply multiple layers of personalization"""
        
        content = {
            'subject': framework['subject_template'],
            'body': framework['body_template']
        }
        
        # Layer 1: Basic demographic personalization
        content = self._apply_demographic_personalization(content)
        
        # Layer 2: Company-specific personalization
        content = self._apply_company_personalization(content)
        
        # Layer 3: Role and pain point personalization
        content = self._apply_role_personalization(content)
        
        # Layer 4: Industry and trend personalization
        content = self._apply_industry_personalization(content)
        
        # Layer 5: Activity and timing personalization
        content = self._apply_activity_personalization(content)
        
        return content

MESSAGE_FRAMEWORKS = {
    'value_introduction': {
        'subject_template': "Quick question about {company}'s {relevant_initiative}",
        'body_template': """Hi {first_name},

I noticed {personalization_hook} and thought you might be interested in how {similar_company} recently {relevant_success_story}.

{value_proposition_specific_to_role}

Worth a quick conversation to see if there's a fit?

Best regards,
{sender_name}

P.S. {relevant_postscript}"""
    },
    
    'insight_sharing': {
        'subject_template': "{industry} insight that might interest you",
        'body_template': """Hi {first_name},

Saw the recent news about {company_recent_news}. Based on our work with similar {industry} companies, I thought you'd find this insight valuable:

{industry_specific_insight}

{specific_value_connection_to_their_situation}

Happy to share more details if it's relevant to {company}'s situation.

Best,
{sender_name}"""
    },
    
    'problem_solution_bridge': {
        'subject_template': "Solving {specific_pain_point} at {company}",
        'body_template': """Hi {first_name},

Companies like {company} often struggle with {specific_pain_point_detailed}. 

We recently helped {similar_company_case_study} {specific_outcome_achieved}.

{connection_to_their_specific_situation}

Would love to share how this might apply to {company}. Worth a brief call?

Regards,
{sender_name}"""
    }
}

class ColdEmailSequence:
    def __init__(self, prospect: ProspectProfile, campaign_objective: str):
        self.prospect = prospect
        self.campaign_objective = campaign_objective
        self.sequence_emails = self._design_sequence_strategy()
        self.timing_strategy = self._calculate_optimal_timing()
    
    def _design_sequence_strategy(self) -> List[Dict[str, Any]]:
        """Design multi-touch sequence strategy"""
        
        sequence = []
        
        # Email 1: Value-focused introduction
        sequence.append({
            'sequence_number': 1,
            'email_type': 'value_introduction',
            'primary_objective': 'establish_credibility',
            'value_focus': 'industry_insight',
            'call_to_action': 'soft_meeting_request',
            'follow_up_days': 4
        })
        
        # Email 2: Case study or social proof
        sequence.append({
            'sequence_number': 2,
            'email_type': 'social_proof',
            'primary_objective': 'demonstrate_results',
            'value_focus': 'similar_company_success',
            'call_to_action': 'resource_sharing',
            'follow_up_days': 5
        })
        
        # Email 3: Problem-focused approach
        sequence.append({
            'sequence_number': 3,
            'email_type': 'problem_solution_bridge',
            'primary_objective': 'address_pain_points',
            'value_focus': 'specific_solution_alignment',
            'call_to_action': 'direct_meeting_request',
            'follow_up_days': 7
        })
        
        # Email 4: Final value add
        sequence.append({
            'sequence_number': 4,
            'email_type': 'final_value_add',
            'primary_objective': 'last_opportunity',
            'value_focus': 'exclusive_resource',
            'call_to_action': 'breakup_email_with_value',
            'follow_up_days': 14
        })
        
        return sequence
    
    def _calculate_optimal_timing(self) -> Dict[str, Any]:
        """Calculate optimal send times based on prospect profile"""
        
        # Default timing strategy
        timing = {
            'send_days': ['Tuesday', 'Wednesday', 'Thursday'],
            'send_times': ['9:00 AM', '1:00 PM', '3:00 PM'],
            'timezone': 'prospect_timezone',
            'avoid_periods': ['end_of_quarter', 'holidays', 'summer_fridays']
        }
        
        # Adjust based on prospect role and industry
        if 'executive' in self.prospect.job_title.lower():
            timing['send_times'] = ['7:00 AM', '6:00 PM']  # Early morning or evening
        
        if self.prospect.industry == 'retail':
            timing['avoid_periods'].append('holiday_shopping_season')
        
        return timing

class ColdEmailDeliverabilityManager:
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.domain_reputation = DomainReputationManager()
        self.sending_patterns = SendingPatternOptimizer()
        self.content_analyzer = ContentDeliverabilityAnalyzer()
    
    async def optimize_campaign_deliverability(self, campaign_data: Dict[str, Any]) -> Dict[str, Any]:
        """Optimize campaign for maximum deliverability"""
        
        optimization_results = {
            'domain_setup': await self._verify_domain_authentication(),
            'sending_pattern': await self._optimize_sending_pattern(campaign_data),
            'content_optimization': await self._optimize_content_deliverability(campaign_data),
            'reputation_management': await self._manage_sender_reputation(),
            'deliverability_score': 0
        }
        
        # Calculate overall deliverability score
        optimization_results['deliverability_score'] = self._calculate_deliverability_score(
            optimization_results
        )
        
        return optimization_results
    
    async def _verify_domain_authentication(self) -> Dict[str, bool]:
        """Verify domain authentication setup"""
        
        domain = self.config.get('sending_domain')
        
        authentication_status = {
            'spf_record': await self._check_spf_record(domain),
            'dkim_signature': await self._check_dkim_setup(domain),
            'dmarc_policy': await self._check_dmarc_policy(domain),
            'custom_tracking_domain': await self._check_custom_tracking_domain(),
            'domain_reputation': await self._check_domain_reputation(domain)
        }
        
        return authentication_status
    
    async def _optimize_sending_pattern(self, campaign_data: Dict[str, Any]) -> Dict[str, Any]:
        """Optimize email sending patterns"""
        
        pattern_optimization = {
            'daily_send_limits': self._calculate_daily_send_limits(),
            'warm_up_schedule': self._create_warm_up_schedule(),
            'time_distribution': self._optimize_send_time_distribution(),
            'recipient_distribution': self._optimize_recipient_distribution(campaign_data),
            'throttling_rules': self._implement_throttling_rules()
        }
        
        return pattern_optimization
    
    def _calculate_daily_send_limits(self) -> Dict[str, int]:
        """Calculate safe daily sending limits"""
        
        # Base limits by domain age and reputation
        domain_age_months = self.config.get('domain_age_months', 1)
        current_reputation = self.config.get('domain_reputation_score', 0)
        
        if domain_age_months < 3:  # New domain
            base_limit = 50
        elif domain_age_months < 12:  # Established domain
            base_limit = 200
        else:  # Mature domain
            base_limit = 500
        
        # Adjust based on reputation
        reputation_multiplier = max(0.5, current_reputation / 100)
        daily_limit = int(base_limit * reputation_multiplier)
        
        return {
            'daily_limit': daily_limit,
            'hourly_limit': max(5, daily_limit // 8),
            'burst_limit': max(2, daily_limit // 20)
        }

class AIPersonalizationEngine:
    def __init__(self, ai_model_config: Dict[str, Any]):
        self.model_config = ai_model_config
        self.personalization_models = self._load_personalization_models()
        self.content_templates = self._load_content_templates()
    
    async def generate_personalized_content(self, prospect: ProspectProfile, 
                                          campaign_context: Dict[str, Any]) -> Dict[str, str]:
        """Generate AI-powered personalized content"""
        
        # Prepare input context for AI model
        input_context = self._prepare_ai_context(prospect, campaign_context)
        
        # Generate personalized elements
        personalized_elements = await self._generate_personalization_elements(input_context)
        
        # Compose final message
        message_content = await self._compose_personalized_message(
            personalized_elements, prospect, campaign_context
        )
        
        # Quality assurance
        quality_score = await self._assess_content_quality(message_content)
        
        return {
            **message_content,
            'ai_confidence_score': quality_score,
            'personalization_elements_used': list(personalized_elements.keys())
        }
    
    def _prepare_ai_context(self, prospect: ProspectProfile, 
                          campaign_context: Dict[str, Any]) -> Dict[str, Any]:
        """Prepare comprehensive context for AI personalization"""
        
        return {
            'prospect_profile': {
                'name': f"{prospect.first_name} {prospect.last_name}",
                'job_title': prospect.job_title,
                'company': prospect.company,
                'industry': prospect.industry,
                'company_size': prospect.company_size,
                'location': prospect.location
            },
            'research_insights': {
                'pain_points': prospect.pain_points,
                'recent_activity': prospect.recent_activity,
                'company_news': prospect.personalization_data.get('company_news', []),
                'industry_trends': prospect.personalization_data.get('industry_trends', []),
                'technology_stack': prospect.personalization_data.get('technology_stack', [])
            },
            'campaign_objective': campaign_context.get('objective'),
            'value_proposition': campaign_context.get('value_proposition'),
            'sender_profile': campaign_context.get('sender_info'),
            'previous_interactions': campaign_context.get('interaction_history', [])
        }

class DynamicContentOptimizer:
    def __init__(self, engagement_data: Dict[str, Any]):
        self.engagement_data = engagement_data
        self.content_variants = self._load_content_variants()
        self.optimization_rules = self._define_optimization_rules()
    
    async def optimize_content_for_engagement(self, prospect: ProspectProfile, 
                                            sequence_position: int) -> Dict[str, str]:
        """Optimize content based on engagement patterns"""
        
        # Analyze prospect engagement patterns
        engagement_profile = await self._analyze_engagement_patterns(prospect)
        
        # Select optimal content variant
        content_variant = await self._select_optimal_variant(
            engagement_profile, sequence_position
        )
        
        # Apply dynamic personalization
        optimized_content = await self._apply_dynamic_personalization(
            content_variant, prospect, engagement_profile
        )
        
        return optimized_content
    
    async def _analyze_engagement_patterns(self, prospect: ProspectProfile) -> Dict[str, Any]:
        """Analyze engagement patterns for content optimization"""
        
        engagement_profile = {
            'email_open_patterns': self._analyze_open_patterns(prospect),
            'content_preferences': self._identify_content_preferences(prospect),
            'response_timing': self._analyze_response_timing(prospect),
            'engagement_triggers': self._identify_engagement_triggers(prospect)
        }
        
        return engagement_profile

class ColdEmailComplianceManager:
    def __init__(self, regulatory_config: Dict[str, Any]):
        self.regulatory_config = regulatory_config
        self.consent_manager = ConsentManager()
        self.data_processor = PersonalDataProcessor()
        self.audit_logger = ComplianceAuditLogger()
    
    async def validate_campaign_compliance(self, campaign_data: Dict[str, Any]) -> Dict[str, Any]:
        """Validate campaign compliance with regulations"""
        
        compliance_results = {
            'gdpr_compliance': await self._check_gdpr_compliance(campaign_data),
            'can_spam_compliance': await self._check_can_spam_compliance(campaign_data),
            'data_processing_compliance': await self._check_data_processing_compliance(campaign_data),
            'consent_validation': await self._validate_consent_basis(campaign_data),
            'opt_out_mechanisms': await self._validate_opt_out_mechanisms(campaign_data)
        }
        
        # Overall compliance score
        compliance_results['overall_compliance_score'] = self._calculate_compliance_score(
            compliance_results
        )
        
        # Generate compliance report
        compliance_results['compliance_report'] = await self._generate_compliance_report(
            compliance_results
        )
        
        return compliance_results
    
    async def _check_gdpr_compliance(self, campaign_data: Dict[str, Any]) -> Dict[str, bool]:
        """Check GDPR compliance requirements"""
        
        gdpr_checks = {
            'legitimate_interest_basis': self._validate_legitimate_interest(campaign_data),
            'data_minimization': self._check_data_minimization(campaign_data),
            'transparency_requirements': self._check_transparency_requirements(campaign_data),
            'opt_out_mechanism': self._check_opt_out_mechanism(campaign_data),
            'data_retention_policy': self._check_data_retention_policy(campaign_data),
            'privacy_policy_link': self._check_privacy_policy_inclusion(campaign_data)
        }
        
        return gdpr_checks
    
    def _validate_legitimate_interest(self, campaign_data: Dict[str, Any]) -> bool:
        """Validate legitimate interest basis for processing"""
        
        # Check if legitimate interest assessment is documented
        legitimate_interest_factors = [
            'business_relationship_exists',
            'relevant_business_purpose',
            'minimal_privacy_impact',
            'clear_opt_out_provided',
            'balancing_test_documented'
        ]
        
        return all(
            campaign_data.get('compliance_data', {}).get(factor, False)
            for factor in legitimate_interest_factors
        )

class AntiSpamOptimizer:
    def __init__(self, spam_detection_config: Dict[str, Any]):
        self.spam_detection_config = spam_detection_config
        self.content_analyzer = SpamContentAnalyzer()
        self.reputation_monitor = ReputationMonitor()
        self.deliverability_optimizer = DeliverabilityOptimizer()
    
    async def optimize_anti_spam_measures(self, email_content: Dict[str, str], 
                                        campaign_data: Dict[str, Any]) -> Dict[str, Any]:
        """Optimize email content and practices to avoid spam filters"""
        
        anti_spam_optimization = {
            'content_analysis': await self._analyze_spam_risk_content(email_content),
            'sender_reputation': await self._analyze_sender_reputation(),
            'technical_setup': await self._analyze_technical_setup(),
            'sending_patterns': await self._analyze_sending_patterns(campaign_data),
            'recipient_engagement': await self._analyze_recipient_engagement_signals()
        }
        
        # Generate optimization recommendations
        anti_spam_optimization['recommendations'] = self._generate_anti_spam_recommendations(
            anti_spam_optimization
        )
        
        # Calculate spam risk score
        anti_spam_optimization['spam_risk_score'] = self._calculate_spam_risk_score(
            anti_spam_optimization
        )
        
        return anti_spam_optimization
    
    async def _analyze_spam_risk_content(self, email_content: Dict[str, str]) -> Dict[str, Any]:
        """Analyze email content for spam risk factors"""
        
        content_analysis = {
            'spam_trigger_words': self._detect_spam_trigger_words(email_content),
            'excessive_capitalization': self._check_excessive_caps(email_content),
            'suspicious_links': self._analyze_link_patterns(email_content),
            'html_to_text_ratio': self._calculate_html_text_ratio(email_content),
            'image_to_text_ratio': self._calculate_image_text_ratio(email_content),
            'subject_line_analysis': self._analyze_subject_line_spam_risk(email_content['subject_line'])
        }
        
        return content_analysis

class ColdEmailAnalytics:
    def __init__(self, analytics_config: Dict[str, Any]):
        self.analytics_config = analytics_config
        self.metrics_collector = MetricsCollector()
        self.performance_analyzer = PerformanceAnalyzer()
        self.optimization_engine = OptimizationEngine()
    
    async def analyze_campaign_performance(self, campaign_id: str) -> Dict[str, Any]:
        """Analyze comprehensive campaign performance"""
        
        performance_data = {
            'delivery_metrics': await self._analyze_delivery_performance(campaign_id),
            'engagement_metrics': await self._analyze_engagement_performance(campaign_id),
            'conversion_metrics': await self._analyze_conversion_performance(campaign_id),
            'personalization_effectiveness': await self._analyze_personalization_impact(campaign_id),
            'sequence_performance': await self._analyze_sequence_effectiveness(campaign_id)
        }
        
        # Generate insights and recommendations
        performance_data['insights'] = await self._generate_performance_insights(performance_data)
        performance_data['optimization_recommendations'] = await self._generate_optimization_recommendations(performance_data)
        
        return performance_data
    
    async def _analyze_engagement_performance(self, campaign_id: str) -> Dict[str, Any]:
        """Analyze detailed engagement metrics"""
        
        engagement_data = await self.metrics_collector.get_engagement_data(campaign_id)
        
        engagement_analysis = {
            'open_rates': {
                'overall': self._calculate_open_rate(engagement_data),
                'by_sequence_position': self._calculate_opens_by_sequence(engagement_data),
                'by_send_time': self._calculate_opens_by_time(engagement_data),
                'by_personalization_level': self._calculate_opens_by_personalization(engagement_data)
            },
            'response_rates': {
                'overall': self._calculate_response_rate(engagement_data),
                'positive_responses': self._calculate_positive_response_rate(engagement_data),
                'meeting_requests': self._calculate_meeting_request_rate(engagement_data),
                'by_message_type': self._calculate_responses_by_message_type(engagement_data)
            },
            'engagement_quality': {
                'time_to_response': self._calculate_response_timing(engagement_data),
                'response_length': self._analyze_response_quality(engagement_data),
                'follow_up_engagement': self._analyze_follow_up_patterns(engagement_data)
            }
        }
        
        return engagement_analysis

class ColdEmailABTester:
    def __init__(self, testing_config: Dict[str, Any]):
        self.testing_config = testing_config
        self.test_designer = TestDesigner()
        self.statistical_analyzer = StatisticalAnalyzer()
        self.result_interpreter = ResultInterpreter()
    
    async def design_ab_test(self, test_hypothesis: str, 
                           test_variables: List[str]) -> Dict[str, Any]:
        """Design A/B test for cold email optimization"""
        
        test_design = {
            'test_id': self._generate_test_id(),
            'hypothesis': test_hypothesis,
            'test_variables': test_variables,
            'test_variants': await self._create_test_variants(test_variables),
            'sample_size_calculation': await self._calculate_sample_size(),
            'randomization_strategy': await self._design_randomization_strategy(),
            'success_metrics': self._define_success_metrics(),
            'test_duration': self._calculate_test_duration()
        }
        
        return test_design
    
    async def analyze_test_results(self, test_id: str) -> Dict[str, Any]:
        """Analyze A/B test results with statistical significance"""
        
        test_data = await self._collect_test_data(test_id)
        
        results_analysis = {
            'statistical_significance': await self._calculate_statistical_significance(test_data),
            'effect_size': await self._calculate_effect_size(test_data),
            'confidence_intervals': await self._calculate_confidence_intervals(test_data),
            'winner_determination': await self._determine_winning_variant(test_data),
            'practical_significance': await self._assess_practical_significance(test_data)
        }
        
        # Generate actionable insights
        results_analysis['actionable_insights'] = await self._generate_actionable_insights(
            results_analysis
        )
        
        return results_analysis

class IntelligentColdEmailAutomation:
    def __init__(self, automation_config: Dict[str, Any]):
        self.automation_config = automation_config
        self.workflow_engine = WorkflowEngine()
        self.quality_controller = QualityController()
        self.scaling_optimizer = ScalingOptimizer()
    
    async def execute_automated_outreach(self, campaign_parameters: Dict[str, Any]) -> Dict[str, Any]:
        """Execute intelligent automated outreach campaign"""
        
        automation_results = {
            'prospect_research': await self._automate_prospect_research(campaign_parameters),
            'message_generation': await self._automate_message_generation(campaign_parameters),
            'send_optimization': await self._automate_send_optimization(campaign_parameters),
            'follow_up_scheduling': await self._automate_follow_up_scheduling(campaign_parameters),
            'response_handling': await self._automate_response_handling(campaign_parameters)
        }
        
        # Quality assurance checks
        automation_results['quality_scores'] = await self._assess_automation_quality(
            automation_results
        )
        
        return automation_results
    
    async def _automate_prospect_research(self, campaign_parameters: Dict[str, Any]) -> Dict[str, Any]:
        """Automate prospect research with quality controls"""
        
        research_automation = {
            'data_enrichment': await self._execute_data_enrichment(campaign_parameters),
            'personalization_data_collection': await self._collect_personalization_data(campaign_parameters),
            'qualification_scoring': await self._execute_qualification_scoring(campaign_parameters),
            'research_quality_validation': await self._validate_research_quality(campaign_parameters)
        }
        
        return research_automation