User Intent Tracking

User intent tracking leverages LLMs as intelligent classifiers to understand what users really want to accomplish, enabling more effective routing, personalization, and user experience optimization. This principle is central to the LLM-in-Product track of GISE methodology.

Core Concepts

Intent as the Foundation of User Experience

Understanding user intent enables systems to provide more relevant, efficient, and satisfying interactions:

Intent Classification Hierarchy

Intent classification works at multiple levels of granularity:

Level 1: Primary Intent Categories

• Transactional: User wants to complete an action or transaction
• Informational: User seeks information or wants to learn
• Navigational: User wants to find or access a specific resource
• Support: User needs help or has a problem to resolve

Level 2: Domain-Specific Intent

• E-commerce: Browse, compare, purchase, track, return
• SaaS Platform: Onboard, configure, use features, upgrade, get support
• Content Platform: Discover, consume, create, share, manage

Level 3: Contextual Intent

• User Journey Stage: New user, active user, churning user, returning user
• Urgency Level: Immediate, routine, exploratory, critical
• Complexity: Simple query, multi-step process, expert-level task

Implementation Architecture

Intent Classification Pipeline

LLM Prompt Design for Intent Classification

Multi-Level Classification Prompt

# System Message
You are an intent classifier for a SaaS platform. Classify user inputs into primary intent categories and provide confidence scores.

## Classification Categories

### Primary Intents:
- TRANSACTIONAL: User wants to complete an action or transaction
- INFORMATIONAL: User seeks information or wants to learn
- NAVIGATIONAL: User wants to find or access a specific resource  
- SUPPORT: User needs help or has a problem to resolve

### Domain-Specific Intents (SaaS):
- ONBOARDING: Getting started, initial setup, account creation
- FEATURE_USE: Using specific platform features
- CONFIGURATION: Settings, preferences, customization
- BILLING: Payments, subscriptions, invoicing
- INTEGRATION: Connecting to other tools or services
- TROUBLESHOOTING: Resolving issues or errors

## Response Format
Respond with JSON:
{
  "primary_intent": "CATEGORY",
  "domain_intent": "SPECIFIC_CATEGORY", 
  "confidence": 0.95,
  "reasoning": "Brief explanation",
  "suggested_routing": "recommended_handler",
  "context_needed": ["additional", "context", "items"]
}

# User Message
[USER_INPUT]

Context-Aware Classification

# System Message with Context
You are classifying user intent with the following context:

## User Context:
- User Type: {{user_type}}
- Account Status: {{account_status}}
- Previous Interactions: {{recent_interactions}}
- Current Page/Feature: {{current_context}}
- Time Since Last Activity: {{activity_recency}}

## Session Context:
- Previous Intents: {{session_intents}}
- Conversation History: {{conversation_summary}}
- Failed Attempts: {{failure_history}}

Classify the user's intent considering this context for more accurate routing.

# User Message
{{user_input}}

Implementation Patterns

Real-Time Classification Service

interface IntentClassificationService {
  async classifyIntent(input: string, context: UserContext): Promise<IntentResult> {
    // 1. Prepare context and clean input
    const processedInput = this.preprocessInput(input, context);
    
    // 2. Call LLM for classification
    const classification = await this.llmService.classify({
      prompt: this.buildClassificationPrompt(processedInput, context),
      temperature: 0.1, // Low temperature for consistent classification
      maxTokens: 200
    });
    
    // 3. Parse and validate result
    const result = this.parseClassificationResult(classification);
    
    // 4. Apply business rules and confidence thresholds
    const finalResult = this.applyBusinessLogic(result, context);
    
    // 5. Log for analytics and improvement
    await this.logClassification(input, context, finalResult);
    
    return finalResult;
  }
}

interface IntentResult {
  primaryIntent: PrimaryIntent;
  domainIntent: DomainIntent;
  confidence: number;
  reasoning: string;
  suggestedRouting: string;
  contextNeeded: string[];
  metadata: {
    processingTime: number;
    modelVersion: string;
    fallbackUsed: boolean;
  };
}

Batch Classification for Analytics

interface BatchIntentAnalyzer {
  async analyzeUserJourney(userId: string, timeRange: TimeRange): Promise<IntentJourney> {
    // Analyze sequence of user intents over time
    const interactions = await this.getUserInteractions(userId, timeRange);
    
    const intentSequence = await Promise.all(
      interactions.map(interaction => 
        this.classifyIntent(interaction.input, interaction.context)
      )
    );
    
    return {
      userId,
      timeRange,
      intentProgression: this.analyzeIntentProgression(intentSequence),
      satisfactionSignals: this.extractSatisfactionSignals(intentSequence),
      optimizationOpportunities: this.identifyOptimizations(intentSequence)
    };
  }
}

Context Management

Context Types and Sources

Context Assembly Strategy

interface ContextAssembler {
  assembleContext(userId: string, sessionId: string): Promise<ClassificationContext> {
    return {
      user: await this.getUserContext(userId),
      session: await this.getSessionContext(sessionId),
      system: this.getSystemContext(),
      temporal: this.getTemporalContext()
    };
  }
  
  private async getUserContext(userId: string): Promise<UserContext> {
    return {
      profile: await this.userService.getProfile(userId),
      behaviorPatterns: await this.analyticsService.getBehaviorPatterns(userId),
      preferences: await this.preferenceService.getPreferences(userId),
      accountStatus: await this.accountService.getStatus(userId),
      recentActivity: await this.activityService.getRecentActivity(userId, '24h')
    };
  }
  
  private getSessionContext(sessionId: string): Promise<SessionContext> {
    return {
      currentPage: this.sessionService.getCurrentPage(sessionId),
      navigationHistory: this.sessionService.getNavigationHistory(sessionId),
      previousQueries: this.sessionService.getQueries(sessionId),
      timeOnPage: this.sessionService.getTimeOnPage(sessionId),
      interactionCount: this.sessionService.getInteractionCount(sessionId)
    };
  }
}

Routing and Response Strategies

Intent-Based Routing

Response Generation by Intent Type

Transactional Intent Response

class TransactionalResponseGenerator {
  async generateResponse(intent: TransactionalIntent, context: UserContext): Promise<Response> {
    switch(intent.domainIntent) {
      case 'PURCHASE':
        return this.generatePurchaseFlow(intent, context);
      case 'ACCOUNT_UPDATE':
        return this.generateAccountUpdateFlow(intent, context);
      case 'SUBSCRIPTION_CHANGE':
        return this.generateSubscriptionFlow(intent, context);
      default:
        return this.generateGenericTransactionResponse(intent, context);
    }
  }
  
  private generatePurchaseFlow(intent: TransactionalIntent, context: UserContext): Response {
    return {
      type: 'ACTION_SEQUENCE',
      steps: [
        'Product selection confirmation',
        'Pricing display',
        'Payment method selection',
        'Order confirmation'
      ],
      ui_components: ['ProductCard', 'PricingTable', 'PaymentForm'],
      analytics_tags: ['purchase_intent', 'conversion_funnel']
    };
  }
}

Informational Intent Response

class InformationalResponseGenerator {
  async generateResponse(intent: InformationalIntent, context: UserContext): Promise<Response> {
    // Use RAG system for knowledge retrieval
    const relevantKnowledge = await this.ragService.retrieve(
      intent.originalQuery, 
      context
    );
    
    // Generate contextual response
    const response = await this.llmService.generate({
      system: "You are a helpful product assistant. Provide accurate, helpful information based on the retrieved knowledge.",
      user: intent.originalQuery,
      context: relevantKnowledge,
      temperature: 0.3
    });
    
    return {
      type: 'INFORMATION_RESPONSE',
      content: response,
      sources: relevantKnowledge.sources,
      followUpSuggestions: await this.generateFollowUps(intent, context),
      analytics_tags: ['information_provided', intent.domainIntent]
    };
  }
}

Performance Optimization

Caching Strategies

Fallback Mechanisms

class IntentClassificationWithFallbacks {
  async classifyWithFallbacks(input: string, context: UserContext): Promise<IntentResult> {
    try {
      // Primary: LLM classification
      return await this.llmClassifier.classify(input, context);
    } catch (error) {
      this.logger.warn('LLM classification failed, trying rule-based fallback');
      
      try {
        // Fallback 1: Rule-based classification
        return await this.ruleBasedClassifier.classify(input, context);
      } catch (fallbackError) {
        this.logger.warn('Rule-based classification failed, using default routing');
        
        // Fallback 2: Default routing based on keywords
        return this.getDefaultClassification(input, context);
      }
    }
  }
  
  private getDefaultClassification(input: string, context: UserContext): IntentResult {
    // Simple keyword-based fallback
    const keywords = this.extractKeywords(input);
    const primaryIntent = this.mapKeywordsToPrimaryIntent(keywords);
    
    return {
      primaryIntent,
      domainIntent: 'GENERAL',
      confidence: 0.5, // Low confidence for fallback
      reasoning: 'Fallback classification based on keywords',
      suggestedRouting: 'general_handler',
      contextNeeded: [],
      metadata: {
        processingTime: Date.now() - this.startTime,
        modelVersion: 'fallback',
        fallbackUsed: true
      }
    };
  }
}

Analytics and Improvement

Intent Analytics Dashboard

Track key metrics for intent classification effectiveness:

Classification Accuracy Metrics

• Intent classification accuracy by category
• Confidence score distributions
• Fallback usage rates
• User satisfaction by intent type

User Experience Metrics

• Resolution rate by intent category
• Average interaction time by intent
• Intent progression patterns
• Abandonment rates by routing

System Performance Metrics

• Classification latency percentiles
• Cache hit rates
• Error rates and failure modes
• Resource utilization by intent volume

Continuous Improvement Loop

Feedback Collection Mechanisms

interface FeedbackCollector {
  // Explicit feedback
  async collectExplicitFeedback(
    intentId: string, 
    userRating: number, 
    comments?: string
  ): Promise<void> {
    await this.feedbackService.store({
      intentId,
      feedbackType: 'explicit',
      rating: userRating,
      comments,
      timestamp: new Date(),
      userId: this.getCurrentUserId()
    });
  }
  
  // Implicit feedback from user behavior
  async collectImplicitFeedback(
    intentId: string,
    userActions: UserAction[]
  ): Promise<void> {
    const satisfactionSignals = this.analyzeBehaviorSignals(userActions);
    
    await this.feedbackService.store({
      intentId,
      feedbackType: 'implicit',
      satisfactionScore: satisfactionSignals.score,
      behaviorPatterns: satisfactionSignals.patterns,
      timestamp: new Date(),
      userId: this.getCurrentUserId()
    });
  }
}

Implementation Checklist

Phase 1: Basic Intent Classification

• Define intent taxonomy for your domain
• Create classification prompts and test with sample data
• Implement basic LLM classification service
• Set up logging and basic analytics
• Create fallback mechanisms for service failures

Phase 2: Context Integration

• Identify relevant context sources (user, session, system)
• Implement context assembly and caching
• Update classification prompts to use context
• Test classification accuracy improvement with context
• Monitor performance impact of context processing

Phase 3: Response Optimization

• Implement intent-based routing system
• Create response generators for each intent type
• Set up A/B testing for different response strategies
• Implement user feedback collection mechanisms
• Optimize response generation based on user feedback

Phase 4: Advanced Analytics

• Create intent analytics dashboard
• Implement continuous improvement pipelines
• Set up automated model retraining workflows
• Create business intelligence reports on user intent patterns
• Optimize system performance based on usage patterns

Business Impact

Key Performance Indicators

• User Satisfaction: Improved task completion rates and user ratings
• Operational Efficiency: Reduced support ticket volume and resolution time
• Conversion Optimization: Higher conversion rates through better intent routing
• Cost Reduction: Lower operational costs through automated intent handling

ROI Measurement

interface IntentTrackingROI {
  calculateROI(timeRange: TimeRange): Promise<ROIMetrics> {
    const metrics = await this.analyticsService.getMetrics(timeRange);
    
    return {
      supportCostReduction: this.calculateSupportSavings(metrics),
      conversionImprovement: this.calculateConversionGains(metrics),
      operationalEfficiency: this.calculateEfficiencyGains(metrics),
      userSatisfactionImprovement: this.calculateSatisfactionGains(metrics),
      totalROI: this.calculateTotalROI(metrics)
    };
  }
}

User intent tracking transforms user interactions from reactive responses to proactive, intelligent assistance. When implemented as part of the GISE methodology, it creates a foundation for superior user experiences and business outcomes.

Ready to implement user intent tracking? Start with Phase 1: Basic Intent Classification and build systematically toward advanced analytics and optimization.