AI Chatbots & Engagement Metrics: The Advanced Optimization Guide

# Comprehensive chatbot engagement framework
class ChatbotEngagementFramework:
    def __init__(self):
        self.metrics_categories = {
            'conversation_quality': {
                'coherence_score': 'Logical flow and contextual understanding',
                'relevance_score': 'Response appropriateness and helpfulness',
                'completion_rate': 'Successful task completion percentage',
                'user_satisfaction': 'Direct feedback and implicit satisfaction signals'
            },
            'engagement_depth': {
                'conversation_length': 'Average turns per conversation',
                'topic_exploration': 'Breadth of topics discussed',
                'follow_up_rate': 'Users returning for additional conversations',
                'proactive_engagement': 'Bot-initiated valuable interactions'
            },
            'emotional_intelligence': {
                'sentiment_trajectory': 'User emotional journey throughout conversation',
                'empathy_score': 'Bot ability to understand and respond to emotions',
                'frustration_prevention': 'Early detection and resolution of user frustration',
                'delight_moments': 'Instances where bot exceeded user expectations'
            },
            'business_impact': {
                'conversion_rate': 'Conversations leading to desired actions',
                'customer_lifetime_value': 'Long-term value of engaged users',
                'support_cost_reduction': 'Efficiency gains from automated resolution',
                'upsell_success_rate': 'Revenue generation through conversations'
            }
        }

    def calculate_overall_engagement_score(self, conversation_data):
        """Calculate comprehensive engagement score"""
        scores = {}

        for category, metrics in self.metrics_categories.items():
            category_score = 0
            for metric in metrics:
                metric_score = self.calculate_metric_score(conversation_data, metric)
                category_score += metric_score
            scores[category] = category_score / len(metrics)

        # Weighted average based on business priorities
        weights = {
            'conversation_quality': 0.35,
            'engagement_depth': 0.25,
            'emotional_intelligence': 0.20,
            'business_impact': 0.20
        }

        overall_score = sum(scores[cat] * weights[cat] for cat in scores)
        return overall_score

import nltk
from transformers import AutoTokenizer, AutoModel
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

class ConversationCoherenceAnalyzer:
    def __init__(self):
        self.tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
        self.model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
        self.coherence_threshold = 0.7

    def analyze_conversation_coherence(self, conversation_turns):
        """Analyzes logical flow and contextual coherence"""
        coherence_scores = []

        for i in range(1, len(conversation_turns)):
            current_turn = conversation_turns[i]
            previous_turns = conversation_turns[max(0, i-3):i]  # Context window

            # Calculate contextual relevance
            context_embedding = self.get_context_embedding(previous_turns)
            current_embedding = self.get_turn_embedding(current_turn)

            coherence_score = cosine_similarity(
                [context_embedding],
                [current_embedding]
            )[0][0]

            coherence_scores.append(coherence_score)

        return {
            'average_coherence': np.mean(coherence_scores),
            'coherence_trend': self.analyze_coherence_trend(coherence_scores),
            'coherence_drops': self.identify_coherence_drops(coherence_scores),
            'overall_flow_quality': self.calculate_flow_quality(coherence_scores)
        }

    def get_context_embedding(self, turns):
        """Generate embedding for conversation context"""
        context_text = ' [SEP] '.join([turn['text'] for turn in turns])
        inputs = self.tokenizer(context_text, return_tensors='pt',
                               truncation=True, padding=True)

        with torch.no_grad():
            outputs = self.model(**inputs)
            embedding = outputs.last_hidden_state.mean(dim=1).squeeze()

        return embedding.numpy()

    def analyze_coherence_trend(self, coherence_scores):
        """Analyze if conversation coherence improves or deteriorates"""
        if len(coherence_scores) < 3:
            return 'insufficient_data'

        # Calculate trend using linear regression
        x = np.arange(len(coherence_scores))
        coefficients = np.polyfit(x, coherence_scores, 1)
        slope = coefficients[0]

        if slope > 0.01:
            return 'improving'
        elif slope < -0.01:
            return 'deteriorating'
        else:
            return 'stable'

class ResponseRelevanceScorer:
    def __init__(self):
        self.intent_classifier = self.load_intent_classifier()
        self.entity_extractor = self.load_entity_extractor()
        self.relevance_models = self.load_relevance_models()

    def score_response_relevance(self, user_message, bot_response, conversation_context):
        """Comprehensive response relevance scoring"""

        # Intent alignment score
        user_intent = self.intent_classifier.predict(user_message)
        response_intent = self.intent_classifier.predict(bot_response)
        intent_alignment = self.calculate_intent_alignment(user_intent, response_intent)

        # Entity coverage score
        user_entities = self.entity_extractor.extract(user_message)
        response_entities = self.entity_extractor.extract(bot_response)
        entity_coverage = self.calculate_entity_coverage(user_entities, response_entities)

        # Contextual appropriateness
        context_score = self.relevance_models['context_scorer'].predict({
            'user_message': user_message,
            'bot_response': bot_response,
            'conversation_history': conversation_context,
            'user_intent': user_intent,
            'extracted_entities': user_entities
        })

        # Information completeness
        completeness_score = self.calculate_information_completeness(
            user_message, bot_response, user_intent
        )

        # Composite relevance score
        relevance_score = (
            intent_alignment * 0.3 +
            entity_coverage * 0.25 +
            context_score * 0.25 +
            completeness_score * 0.2
        )

        return {
            'overall_relevance': relevance_score,
            'intent_alignment': intent_alignment,
            'entity_coverage': entity_coverage,
            'contextual_appropriateness': context_score,
            'information_completeness': completeness_score,
            'improvement_suggestions': self.generate_improvement_suggestions(
                user_message, bot_response, relevance_score
            )
        }

    def calculate_information_completeness(self, user_message, bot_response, user_intent):
        """Evaluates if response fully addresses user needs"""

        # Extract information requirements from user message
        info_requirements = self.extract_information_requirements(user_message, user_intent)

        # Analyze response coverage of requirements
        coverage_scores = []
        for requirement in info_requirements:
            coverage = self.relevance_models['information_coverage'].predict({
                'requirement': requirement,
                'response': bot_response,
                'intent_context': user_intent
            })
            coverage_scores.append(coverage)

        if not coverage_scores:
            return 0.5  # Neutral score for unclear requirements

        return np.mean(coverage_scores)

class RealTimeSatisfactionPredictor:
    def __init__(self):
        self.satisfaction_models = self.load_satisfaction_models()
        self.linguistic_analyzer = LinguisticSatisfactionAnalyzer()
        self.behavioral_analyzer = BehavioralSatisfactionAnalyzer()

    async def predict_user_satisfaction(self, conversation_data, real_time=True):
        """Predicts user satisfaction in real-time during conversation"""

        # Linguistic satisfaction indicators
        linguistic_signals = self.linguistic_analyzer.analyze(
            conversation_data['messages']
        )

        # Behavioral satisfaction indicators
        behavioral_signals = self.behavioral_analyzer.analyze(
            conversation_data['behavioral_data']
        )

        # Conversation flow satisfaction
        flow_signals = self.analyze_conversation_flow_satisfaction(
            conversation_data
        )

        # Combine signals for prediction
        combined_features = {
            **linguistic_signals,
            **behavioral_signals,
            **flow_signals
        }

        # Predict satisfaction score (0-1 scale)
        satisfaction_prediction = self.satisfaction_models['main_predictor'].predict(
            combined_features
        )

        # Generate confidence interval
        confidence_interval = self.satisfaction_models['confidence_estimator'].predict(
            combined_features
        )

        # Identify satisfaction risk factors
        risk_factors = self.identify_satisfaction_risks(
            combined_features, satisfaction_prediction
        )

        return {
            'predicted_satisfaction': satisfaction_prediction,
            'confidence_interval': confidence_interval,
            'satisfaction_trend': self.calculate_satisfaction_trend(conversation_data),
            'risk_factors': risk_factors,
            'intervention_recommendations': self.generate_intervention_recommendations(
                satisfaction_prediction, risk_factors
            )
        }

    def analyze_conversation_flow_satisfaction(self, conversation_data):
        """Analyzes satisfaction based on conversation flow patterns"""

        messages = conversation_data['messages']
        flow_indicators = {}

        # Response time satisfaction
        response_times = [msg.get('response_time', 0) for msg in messages if msg.get('sender') == 'bot']
        flow_indicators['response_time_satisfaction'] = self.score_response_times(response_times)

        # Turn-taking naturalness
        flow_indicators['turn_taking_naturalness'] = self.analyze_turn_taking(messages)

        # Topic transition smoothness
        flow_indicators['topic_transition_smoothness'] = self.analyze_topic_transitions(messages)

        # Resolution progress satisfaction
        flow_indicators['resolution_progress'] = self.analyze_resolution_progress(
            conversation_data
        )

        return flow_indicators

    def score_response_times(self, response_times):
        """Scores response times for user satisfaction impact"""
        if not response_times:
            return 0.5

        avg_response_time = np.mean(response_times)

        # Optimal response time is 1-2 seconds
        if avg_response_time <= 2:
            return 1.0
        elif avg_response_time <= 5:
            return 0.8
        elif avg_response_time <= 10:
            return 0.6
        else:
            return 0.3

    def generate_intervention_recommendations(self, satisfaction_score, risk_factors):
        """Generates real-time intervention recommendations"""

        recommendations = []

        if satisfaction_score < 0.5:
            recommendations.append({
                'type': 'immediate_intervention',
                'action': 'escalate_to_human',
                'reason': 'Low satisfaction prediction',
                'urgency': 'high'
            })

        elif satisfaction_score < 0.7:
            if 'slow_response_time' in risk_factors:
                recommendations.append({
                    'type': 'performance_optimization',
                    'action': 'optimize_response_generation',
                    'reason': 'Response time impacting satisfaction',
                    'urgency': 'medium'
                })

            if 'poor_intent_understanding' in risk_factors:
                recommendations.append({
                    'type': 'clarification_request',
                    'action': 'ask_clarifying_questions',
                    'reason': 'Intent understanding issues',
                    'urgency': 'medium'
                })

        return recommendations

class ChatbotConversionAnalytics:
    def __init__(self):
        self.attribution_models = self.load_attribution_models()
        self.conversion_predictors = self.load_conversion_predictors()

    def analyze_conversion_attribution(self, conversation_data, user_journey_data):
        """Analyzes chatbot's role in user conversion"""

        # Direct conversion attribution
        direct_conversions = self.identify_direct_conversions(conversation_data)

        # Assisted conversion attribution
        assisted_conversions = self.identify_assisted_conversions(
            conversation_data, user_journey_data
        )

        # Conversation influence scoring
        influence_scores = self.calculate_conversation_influence(
            conversation_data, user_journey_data
        )

        # Conversion probability prediction
        conversion_probability = self.predict_future_conversion(
            conversation_data, user_journey_data
        )

        return {
            'direct_conversions': direct_conversions,
            'assisted_conversions': assisted_conversions,
            'influence_scores': influence_scores,
            'conversion_probability': conversion_probability,
            'attribution_model': self.create_attribution_model(
                conversation_data, user_journey_data
            ),
            'roi_calculation': self.calculate_chatbot_roi(
                conversation_data, direct_conversions, assisted_conversions
            )
        }

    def calculate_conversation_influence(self, conversation_data, user_journey_data):
        """Calculates conversation influence on user behavior"""

        influence_factors = {}

        # Pre/post conversation behavior analysis
        pre_conversation_behavior = self.analyze_pre_conversation_behavior(user_journey_data)
        post_conversation_behavior = self.analyze_post_conversation_behavior(user_journey_data)

        # Behavior change attribution
        behavior_changes = self.calculate_behavior_changes(
            pre_conversation_behavior, post_conversation_behavior
        )

        influence_factors['behavior_change_score'] = behavior_changes['overall_change']

        # Topic influence on actions
        conversation_topics = self.extract_conversation_topics(conversation_data)
        topic_influence = self.analyze_topic_influence_on_actions(
            conversation_topics, user_journey_data
        )

        influence_factors['topic_influence'] = topic_influence

        # Timing influence
        timing_influence = self.analyze_timing_influence(
            conversation_data, user_journey_data
        )

        influence_factors['timing_influence'] = timing_influence

        return influence_factors

    def predict_future_conversion(self, conversation_data, user_journey_data):
        """Predicts likelihood of future conversion based on conversation"""

        # Extract predictive features
        conversation_features = self.extract_conversation_features(conversation_data)
        user_features = self.extract_user_features(user_journey_data)
        behavioral_features = self.extract_behavioral_features(user_journey_data)

        combined_features = {
            **conversation_features,
            **user_features,
            **behavioral_features
        }

        # Predict conversion probability
        conversion_prob = self.conversion_predictors['main_model'].predict(
            combined_features
        )

        # Predict conversion timeframe
        conversion_timeframe = self.conversion_predictors['timeframe_model'].predict(
            combined_features
        )

        # Identify conversion drivers
        conversion_drivers = self.identify_conversion_drivers(
            combined_features, conversation_data
        )

        return {
            'conversion_probability': conversion_prob,
            'predicted_timeframe': conversion_timeframe,
            'key_drivers': conversion_drivers,
            'optimization_opportunities': self.identify_conversion_optimization_opportunities(
                combined_features, conversion_prob
            )
        }

class MultiIntentConversationAnalyzer:
    def __init__(self):
        self.intent_models = self.load_multi_intent_models()
        self.intent_hierarchy = self.load_intent_hierarchy()
        self.context_manager = ConversationContextManager()

    def analyze_conversation_intents(self, conversation):
        """Analyzes complex multi-intent conversations"""

        intent_analysis = {
            'turn_by_turn_intents': [],
            'conversation_intent_journey': [],
            'unresolved_intents': [],
            'intent_satisfaction_scores': {}
        }

        conversation_context = self.context_manager.initialize_context(conversation)

        for turn in conversation:
            # Detect multiple intents in single turn
            turn_intents = self.detect_multiple_intents(turn['text'])

            # Analyze intent hierarchy and relationships
            intent_relationships = self.analyze_intent_relationships(
                turn_intents, conversation_context
            )

            # Score intent handling quality
            intent_scores = self.score_intent_handling(
                turn_intents, turn, conversation_context
            )

            # Update conversation context
            conversation_context = self.context_manager.update_context(
                conversation_context, turn, turn_intents
            )

            intent_analysis['turn_by_turn_intents'].append({
                'turn_id': turn['id'],
                'detected_intents': turn_intents,
                'intent_relationships': intent_relationships,
                'handling_scores': intent_scores
            })

        # Analyze overall intent journey
        intent_analysis['conversation_intent_journey'] = self.analyze_intent_journey(
            intent_analysis['turn_by_turn_intents']
        )

        # Identify unresolved intents
        intent_analysis['unresolved_intents'] = self.identify_unresolved_intents(
            conversation_context
        )

        return intent_analysis

    def detect_multiple_intents(self, user_text):
        """Detects and prioritizes multiple intents in user input"""

        # Primary intent detection
        primary_intent = self.intent_models['primary_classifier'].predict(user_text)

        # Secondary intent detection
        secondary_intents = self.intent_models['secondary_classifier'].predict_multiple(
            user_text, exclude_primary=primary_intent
        )

        # Intent confidence scoring
        intent_confidences = self.intent_models['confidence_scorer'].predict_all(
            user_text, [primary_intent] + secondary_intents
        )

        # Intent priority ranking
        prioritized_intents = self.prioritize_intents(
            [primary_intent] + secondary_intents,
            intent_confidences
        )

        return {
            'primary_intent': primary_intent,
            'secondary_intents': secondary_intents,
            'intent_confidences': intent_confidences,
            'prioritized_intents': prioritized_intents
        }

    def analyze_intent_journey(self, turn_by_turn_intents):
        """Analyzes the overall intent journey throughout conversation"""

        journey_analysis = {
            'intent_progression': [],
            'intent_switches': [],
            'intent_resolution_flow': [],
            'journey_coherence_score': 0.0
        }

        previous_intents = set()

        for i, turn_data in enumerate(turn_by_turn_intents):
            current_intents = set(turn_data['detected_intents']['prioritized_intents'])

            # Identify intent progression
            new_intents = current_intents - previous_intents
            continued_intents = current_intents & previous_intents
            dropped_intents = previous_intents - current_intents

            journey_analysis['intent_progression'].append({
                'turn_id': turn_data['turn_id'],
                'new_intents': list(new_intents),
                'continued_intents': list(continued_intents),
                'dropped_intents': list(dropped_intents)
            })

            # Identify intent switches
            if i > 0 and len(new_intents) > 0:
                journey_analysis['intent_switches'].append({
                    'turn_id': turn_data['turn_id'],
                    'switch_type': self.classify_intent_switch(
                        list(dropped_intents), list(new_intents)
                    ),
                    'switch_reason': self.infer_switch_reason(
                        turn_by_turn_intents[i-1], turn_data
                    )
                })

            previous_intents = current_intents

        # Calculate journey coherence
        journey_analysis['journey_coherence_score'] = self.calculate_journey_coherence(
            journey_analysis['intent_progression']
        )

        return journey_analysis

class ConversationContextManager:
    def __init__(self):
        self.entity_memory = EntityMemoryManager()
        self.topic_tracker = TopicTracker()
        self.user_state_tracker = UserStateTracker()

    def maintain_conversation_context(self, conversation):
        """Maintains comprehensive conversation context"""

        context = {
            'entities': {},
            'topics': [],
            'user_states': [],
            'conversation_goals': [],
            'unresolved_issues': []
        }

        for turn in conversation:
            # Update entity memory
            turn_entities = self.entity_memory.extract_and_update(
                turn['text'], context['entities']
            )

            # Track topic evolution
            turn_topics = self.topic_tracker.track_topics(
                turn['text'], context['topics']
            )

            # Monitor user state changes
            user_state = self.user_state_tracker.update_state(
                turn, context['user_states']
            )

            # Update conversation goals
            turn_goals = self.extract_conversation_goals(
                turn, context['conversation_goals']
            )

            # Track unresolved issues
            unresolved_issues = self.track_unresolved_issues(
                turn, context['unresolved_issues']
            )

            # Update context
            context.update({
                'entities': turn_entities,
                'topics': turn_topics,
                'user_states': context['user_states'] + [user_state],
                'conversation_goals': turn_goals,
                'unresolved_issues': unresolved_issues
            })

        return context

    def generate_context_aware_responses(self, user_input, conversation_context):
        """Generates responses that leverage conversation context"""

        # Analyze current input in context
        contextual_analysis = self.analyze_input_in_context(
            user_input, conversation_context
        )

        # Generate context-aware response candidates
        response_candidates = self.generate_response_candidates(
            user_input, contextual_analysis
        )

        # Score candidates based on context appropriateness
        scored_candidates = self.score_contextual_appropriateness(
            response_candidates, conversation_context
        )

        # Select optimal response
        optimal_response = self.select_optimal_response(scored_candidates)

        return {
            'response': optimal_response,
            'context_utilization': self.analyze_context_utilization(
                optimal_response, conversation_context
            ),
            'context_updates': self.predict_context_updates(
                user_input, optimal_response, conversation_context
            )
        }

class AdaptiveResponseGenerator:
    def __init__(self):
        self.response_models = self.load_response_models()
        self.engagement_predictor = EngagementPredictor()
        self.style_adapter = ResponseStyleAdapter()

    async def generate_optimized_response(self, user_input, conversation_context, user_profile):
        """Generates response optimized for engagement"""

        # Analyze current conversation state
        conversation_state = self.analyze_conversation_state(conversation_context)

        # Predict optimal response characteristics
        optimal_characteristics = await self.predict_optimal_response_characteristics(
            user_input, conversation_state, user_profile
        )

        # Generate multiple response candidates
        response_candidates = await self.generate_response_candidates(
            user_input, conversation_context, optimal_characteristics
        )

        # Score candidates for engagement potential
        engagement_scores = await self.score_engagement_potential(
            response_candidates, user_input, conversation_context
        )

        # Select and refine optimal response
        optimal_response = self.select_and_refine_response(
            response_candidates, engagement_scores, optimal_characteristics
        )

        return {
            'response': optimal_response,
            'engagement_prediction': engagement_scores[optimal_response['id']],
            'optimization_factors': optimal_characteristics,
            'alternative_responses': response_candidates[:3]  # Top 3 alternatives
        }

    async def predict_optimal_response_characteristics(self, user_input, conversation_state, user_profile):
        """Predicts characteristics of optimal response for current context"""

        characteristics = {}

        # Response length optimization
        characteristics['optimal_length'] = self.response_models['length_optimizer'].predict({
            'user_input_length': len(user_input.split()),
            'conversation_depth': conversation_state['depth'],
            'user_patience_level': user_profile.get('patience_level', 'medium'),
            'topic_complexity': conversation_state['topic_complexity']
        })

        # Tone and style optimization
        characteristics['optimal_tone'] = self.style_adapter.predict_optimal_tone(
            user_input, conversation_state, user_profile
        )

        # Information density optimization
        characteristics['information_density'] = self.optimize_information_density(
            user_input, conversation_state, user_profile
        )

        # Interaction style optimization
        characteristics['interaction_style'] = self.optimize_interaction_style(
            conversation_state, user_profile
        )

        return characteristics

    async def score_engagement_potential(self, response_candidates, user_input, conversation_context):
        """Scores response candidates for engagement potential"""

        scores = {}

        for candidate in response_candidates:
            # Predict user engagement
            engagement_prediction = await self.engagement_predictor.predict_engagement(
                user_input, candidate['text'], conversation_context
            )

            # Predict conversation continuation likelihood
            continuation_likelihood = self.predict_continuation_likelihood(
                candidate['text'], conversation_context
            )

            # Score response satisfaction potential
            satisfaction_potential = self.score_satisfaction_potential(
                candidate['text'], user_input, conversation_context
            )

            # Composite engagement score
            composite_score = (
                engagement_prediction * 0.4 +
                continuation_likelihood * 0.3 +
                satisfaction_potential * 0.3
            )

            scores[candidate['id']] = {
                'composite_score': composite_score,
                'engagement_prediction': engagement_prediction,
                'continuation_likelihood': continuation_likelihood,
                'satisfaction_potential': satisfaction_potential
            }

        return scores

class ConversationFlowOptimizer:
    def __init__(self):
        self.flow_models = self.load_flow_models()
        self.outcome_predictors = self.load_outcome_predictors()
        self.route_optimizer = RouteOptimizer()

    def optimize_conversation_flow(self, current_state, conversation_history, user_profile):
        """Optimizes conversation flow for maximum engagement and goal completion"""

        # Analyze current conversation state
        state_analysis = self.analyze_conversation_state(current_state, conversation_history)

        # Predict possible conversation paths
        possible_paths = self.predict_conversation_paths(
            current_state, conversation_history, user_profile
        )

        # Score each path for engagement and completion probability
        path_scores = self.score_conversation_paths(
            possible_paths, state_analysis, user_profile
        )

        # Select optimal path
        optimal_path = self.select_optimal_path(possible_paths, path_scores)

        # Generate specific routing recommendations
        routing_recommendations = self.generate_routing_recommendations(
            optimal_path, current_state
        )

        return {
            'optimal_path': optimal_path,
            'routing_recommendations': routing_recommendations,
            'path_scores': path_scores,
            'state_analysis': state_analysis,
            'optimization_rationale': self.generate_optimization_rationale(
                optimal_path, path_scores
            )
        }

    def predict_conversation_paths(self, current_state, conversation_history, user_profile):
        """Predicts possible conversation paths from current state"""

        paths = []

        # Extract current context
        current_topics = self.extract_current_topics(current_state, conversation_history)
        user_intents = self.extract_user_intents(current_state)
        unresolved_issues = self.identify_unresolved_issues(conversation_history)

        # Generate topic continuation paths
        topic_paths = self.generate_topic_continuation_paths(current_topics)

        # Generate intent resolution paths
        intent_paths = self.generate_intent_resolution_paths(user_intents)

        # Generate issue resolution paths
        issue_paths = self.generate_issue_resolution_paths(unresolved_issues)

        # Generate exploration paths
        exploration_paths = self.generate_exploration_paths(
            current_state, user_profile
        )

        # Combine and prioritize paths
        all_paths = topic_paths + intent_paths + issue_paths + exploration_paths
        prioritized_paths = self.prioritize_paths(all_paths, current_state, user_profile)

        return prioritized_paths[:10]  # Top 10 paths

    def score_conversation_paths(self, paths, state_analysis, user_profile):
        """Scores conversation paths for optimization"""

        scores = {}

        for path in paths:
            path_score = {}

            # Engagement potential score
            path_score['engagement_potential'] = self.flow_models['engagement_predictor'].predict({
                'path_type': path['type'],
                'path_complexity': path['complexity'],
                'user_engagement_history': state_analysis['engagement_history'],
                'topic_interest_alignment': self.calculate_topic_alignment(
                    path['topics'], user_profile.get('interests', [])
                )
            })

            # Goal completion probability
            path_score['completion_probability'] = self.outcome_predictors['completion_predictor'].predict({
                'path_steps': len(path['steps']),
                'path_complexity': path['complexity'],
                'user_patience_level': user_profile.get('patience_level', 'medium'),
                'historical_completion_rate': state_analysis.get('completion_rate', 0.5)
            })

            # User satisfaction potential
            path_score['satisfaction_potential'] = self.calculate_satisfaction_potential(
                path, state_analysis, user_profile
            )

            # Efficiency score (time to value)
            path_score['efficiency_score'] = self.calculate_path_efficiency(path)

            # Composite score
            weights = {
                'engagement_potential': 0.3,
                'completion_probability': 0.3,
                'satisfaction_potential': 0.25,
                'efficiency_score': 0.15
            }

            path_score['composite_score'] = sum(
                path_score[metric] * weights[metric]
                for metric in weights
            )

            scores[path['id']] = path_score

        return scores