Day Parting Strategies

Day Parting Strategies

Optimize lead distribution and pricing based on time patterns. Master day parting to route leads when buyers want them most, maximize conversion rates, and implement sophisticated time-based pricing.

📍 You Are Here

You need to implement time-based routing, pricing, or capacity management. Day parting helps you align lead flow with buyer schedules, optimize for conversion windows, and maximize revenue through temporal strategies.

🎯 What You'll Learn

• Day parting fundamentals
• Time-based routing
• Dynamic pricing by time
• Conversion optimization
• Advanced scheduling patterns
• Performance analysis

⏰ Understanding Day Parting

What is Day Parting?

Day parting divides time into segments for optimization:

const dayPartSegments = {
  // Traditional broadcast day parts
  broadcast: {
    'early_morning': { start: '06:00', end: '09:00' },
    'daytime': { start: '09:00', end: '16:00' },
    'early_fringe': { start: '16:00', end: '19:00' },
    'prime_time': { start: '19:00', end: '23:00' },
    'late_night': { start: '23:00', end: '02:00' },
    'overnight': { start: '02:00', end: '06:00' }
  },
  
  // Business-focused segments
  business: {
    'pre_work': { start: '06:00', end: '09:00' },
    'morning': { start: '09:00', end: '12:00' },
    'lunch': { start: '12:00', end: '13:00' },
    'afternoon': { start: '13:00', end: '17:00' },
    'after_work': { start: '17:00', end: '20:00' },
    'evening': { start: '20:00', end: '23:00' },
    'overnight': { start: '23:00', end: '06:00' }
  },
  
  // Custom segments
  custom: {
    'peak_conversion': { start: '10:00', end: '14:00' },
    'high_value': { start: '18:00', end: '21:00' },
    'low_competition': { start: '05:00', end: '08:00' }
  }
};

Why Day Parting Matters

Benefits of temporal optimization:

const dayPartingBenefits = {
  conversion_optimization: {
    metric: 'conversion_rate',
    improvement: '35%',
    reason: 'Leads delivered when buyers are most responsive'
  },
  
  cost_efficiency: {
    metric: 'cost_per_acquisition',
    improvement: '-22%',
    reason: 'Lower prices during off-peak hours'
  },
  
  buyer_satisfaction: {
    metric: 'buyer_nps',
    improvement: '+15 points',
    reason: 'Leads arrive when buyers want them'
  },
  
  operational_efficiency: {
    metric: 'staff_utilization',
    improvement: '28%',
    reason: 'Aligned volume with staffing levels'
  }
};

🔀 Time-Based Routing

Basic Time Routing

Route leads by time of day:

class TimeBasedRouter {
  constructor(config) {
    this.routes = config.routes;
    this.timezone = config.timezone || 'America/New_York';
    this.fallbackRoute = config.fallback;
  }
  
  route(lead) {
    const currentTime = this.getCurrentTime(lead.timezone || this.timezone);
    const dayOfWeek = currentTime.getDay();
    const hour = currentTime.getHours();
    const minute = currentTime.getMinutes();
    const timeValue = hour * 100 + minute; // HHMM format
    
    // Find matching route
    for (const route of this.routes) {
      if (this.matchesTimeCondition(route, dayOfWeek, timeValue)) {
        return route.destination;
      }
    }
    
    return this.fallbackRoute;
  }
  
  matchesTimeCondition(route, dayOfWeek, timeValue) {
    // Check day of week
    if (route.days && !route.days.includes(dayOfWeek)) {
      return false;
    }
    
    // Check time range
    const startTime = this.parseTime(route.start);
    const endTime = this.parseTime(route.end);
    
    // Handle overnight periods
    if (endTime < startTime) {
      return timeValue >= startTime || timeValue < endTime;
    } else {
      return timeValue >= startTime && timeValue < endTime;
    }
  }
}

// Configuration example
const routingConfig = {
  timezone: 'America/New_York',
  routes: [
    {
      name: 'Business Hours Premium',
      days: [1, 2, 3, 4, 5], // Mon-Fri
      start: '09:00',
      end: '17:00',
      destination: 'premium_buyer_pool'
    },
    {
      name: 'Evening Special',
      days: [1, 2, 3, 4, 5], // Mon-Fri
      start: '17:00',
      end: '21:00',
      destination: 'evening_buyer_pool'
    },
    {
      name: 'Weekend',
      days: [0, 6], // Sat-Sun
      start: '10:00',
      end: '18:00',
      destination: 'weekend_buyer_pool'
    }
  ],
  fallback: 'overflow_buyer_pool'
};

Multi-Timezone Routing

Handle leads across time zones:

class MultiTimezoneRouter {
  constructor() {
    this.buyerSchedules = new Map();
  }
  
  addBuyer(buyer) {
    this.buyerSchedules.set(buyer.id, {
      ...buyer,
      schedule: this.parseSchedule(buyer.schedule),
      timezone: buyer.timezone
    });
  }
  
  findAvailableBuyers(lead) {
    const leadTime = new Date();
    const availableBuyers = [];
    
    for (const [buyerId, buyer] of this.buyerSchedules) {
      // Convert to buyer's timezone
      const buyerTime = this.convertToTimezone(leadTime, buyer.timezone);
      
      if (this.isBuyerAvailable(buyer, buyerTime)) {
        availableBuyers.push({
          ...buyer,
          localTime: buyerTime,
          preference: this.calculatePreference(buyer, buyerTime)
        });
      }
    }
    
    // Sort by preference
    return availableBuyers.sort((a, b) => b.preference - a.preference);
  }
  
  isBuyerAvailable(buyer, time) {
    const dayOfWeek = time.getDay();
    const timeOfDay = time.getHours() * 100 + time.getMinutes();
    
    const daySchedule = buyer.schedule[dayOfWeek];
    if (!daySchedule || !daySchedule.open) return false;
    
    return timeOfDay >= daySchedule.start && timeOfDay < daySchedule.end;
  }
  
  calculatePreference(buyer, time) {
    let score = 100;
    
    // Peak hours bonus
    const hour = time.getHours();
    if (hour >= 10 && hour <= 14) score += 20;
    else if (hour >= 18 && hour <= 20) score += 15;
    
    // Capacity utilization
    const utilization = buyer.getCurrentUtilization();
    if (utilization < 0.5) score += 30;
    else if (utilization > 0.8) score -= 20;
    
    // Historical performance
    const hourlyConversion = buyer.getConversionRate(hour);
    score += hourlyConversion * 50;
    
    return score;
  }
}

Intelligent Queue Management

Queue leads for optimal delivery:

class TimeOptimizedQueue {
  constructor() {
    this.queues = new Map();
    this.predictions = new PredictionEngine();
  }
  
  queueLead(lead, options = {}) {
    const optimalTime = this.calculateOptimalDeliveryTime(lead);
    
    const queueEntry = {
      lead,
      queued_at: new Date(),
      deliver_at: optimalTime,
      priority: this.calculatePriority(lead),
      attempts: 0,
      options
    };
    
    this.addToQueue(queueEntry);
    
    return {
      queued: true,
      position: this.getQueuePosition(queueEntry),
      estimated_delivery: optimalTime
    };
  }
  
  calculateOptimalDeliveryTime(lead) {
    const factors = {
      buyer_availability: this.getBuyerAvailabilityWindows(lead),
      conversion_probability: this.predictions.getConversionByHour(lead),
      competition_level: this.getCompetitionByHour(),
      lead_decay: this.calculateLeadDecay(lead)
    };
    
    // Score each future hour
    const hourScores = [];
    const now = new Date();
    
    for (let h = 0; h < 24; h++) {
      const checkTime = new Date(now.getTime() + h * 3600000);
      const score = this.scoreDeliveryTime(checkTime, factors, lead);
      
      hourScores.push({
        time: checkTime,
        score,
        factors: this.explainScore(checkTime, factors)
      });
    }
    
    // Select best time
    const optimal = hourScores.reduce((best, current) => 
      current.score > best.score ? current : best
    );
    
    return optimal.time;
  }
  
  scoreDeliveryTime(time, factors, lead) {
    let score = 0;
    
    // Buyer availability (highest weight)
    const availability = factors.buyer_availability
      .filter(window => this.timeInWindow(time, window))
      .length;
    score += availability * 40;
    
    // Conversion probability
    const conversionProb = factors.conversion_probability[time.getHours()];
    score += conversionProb * 30;
    
    // Competition (inverse relationship)
    const competition = factors.competition_level[time.getHours()];
    score += (1 - competition) * 20;
    
    // Lead freshness decay
    const age = time - lead.created_at;
    const decayFactor = Math.exp(-age / factors.lead_decay.halfLife);
    score *= decayFactor;
    
    return score;
  }
}

💰 Dynamic Pricing Strategies

Time-Based Pricing

Adjust prices by time:

class DayPartPricing {
  constructor(config) {
    this.basePrices = config.basePrices;
    this.adjustments = config.adjustments;
    this.dynamicPricing = config.enableDynamic || false;
  }
  
  calculatePrice(lead, destination) {
    const basePrice = this.getBasePrice(lead, destination);
    const timeAdjustment = this.getTimeAdjustment(lead);
    const demandAdjustment = this.dynamicPricing ? 
      this.getDemandAdjustment() : 1.0;
    
    const finalPrice = basePrice * timeAdjustment * demandAdjustment;
    
    return {
      base: basePrice,
      adjustments: {
        time: timeAdjustment,
        demand: demandAdjustment
      },
      final: Math.round(finalPrice * 100) / 100,
      explanation: this.explainPricing(basePrice, timeAdjustment, demandAdjustment)
    };
  }
  
  getTimeAdjustment(lead) {
    const hour = new Date().getHours();
    const day = new Date().getDay();
    
    // Hour-based adjustments
    const hourlyAdjustments = {
      // Early morning discount
      0: 0.7, 1: 0.7, 2: 0.7, 3: 0.7, 4: 0.7, 5: 0.8,
      
      // Morning ramp up
      6: 0.9, 7: 0.95, 8: 1.0, 9: 1.1,
      
      // Peak hours premium
      10: 1.2, 11: 1.25, 12: 1.3, 13: 1.3, 14: 1.25,
      
      // Afternoon
      15: 1.15, 16: 1.1, 17: 1.05,
      
      // Evening premium
      18: 1.15, 19: 1.2, 20: 1.15, 21: 1.0,
      
      // Late night discount
      22: 0.85, 23: 0.75
    };
    
    let adjustment = hourlyAdjustments[hour] || 1.0;
    
    // Weekend adjustment
    if (day === 0 || day === 6) {
      adjustment *= 0.85; // 15% weekend discount
    }
    
    // Special day adjustments
    const specialAdjustment = this.getSpecialDayAdjustment(new Date());
    adjustment *= specialAdjustment;
    
    return adjustment;
  }
  
  getDemandAdjustment() {
    // Real-time demand-based pricing
    const metrics = {
      current_volume: this.getCurrentHourlyVolume(),
      current_capacity: this.getAvailableCapacity(),
      conversion_rate: this.getRecentConversionRate(),
      competitor_pricing: this.getCompetitorPricing()
    };
    
    let adjustment = 1.0;
    
    // High demand, low capacity = higher prices
    const utilization = metrics.current_volume / metrics.current_capacity;
    if (utilization > 0.9) {
      adjustment *= 1.15;
    } else if (utilization > 0.8) {
      adjustment *= 1.1;
    } else if (utilization < 0.3) {
      adjustment *= 0.9;
    }
    
    // High conversion = higher prices
    if (metrics.conversion_rate > 0.8) {
      adjustment *= 1.1;
    }
    
    return adjustment;
  }
}

// Pricing configuration
const pricingConfig = {
  basePrices: {
    default: 45.00,
    premium: 65.00,
    economy: 25.00
  },
  
  adjustments: {
    enableTimeOfDay: true,
    enableDayOfWeek: true,
    enableDemandBased: true,
    enableSpecialDays: true
  },
  
  specialDays: [
    { date: '2024-07-04', name: 'Independence Day', adjustment: 0.5 },
    { date: '2024-12-25', name: 'Christmas', adjustment: 0.3 },
    { date: '2024-11-29', name: 'Black Friday', adjustment: 1.5 }
  ]
};

Surge Pricing

Dynamic pricing for peak demand:

class SurgePricingEngine {
  constructor() {
    this.baseMultiplier = 1.0;
    this.surgeThresholds = [
      { utilization: 0.95, multiplier: 1.5, name: 'Extreme' },
      { utilization: 0.85, multiplier: 1.3, name: 'High' },
      { utilization: 0.75, multiplier: 1.15, name: 'Moderate' },
      { utilization: 0.65, multiplier: 1.0, name: 'Normal' }
    ];
    this.metrics = new MetricsCollector();
  }
  
  calculateSurge() {
    const realTimeMetrics = {
      lead_velocity: this.metrics.getLeadVelocity('5m'),
      buyer_capacity: this.metrics.getBuyerCapacity(),
      queue_depth: this.metrics.getQueueDepth(),
      conversion_rate: this.metrics.getConversionRate('1h')
    };
    
    // Calculate utilization
    const utilization = this.calculateUtilization(realTimeMetrics);
    
    // Find applicable surge level
    const surgeLevel = this.surgeThresholds.find(level => 
      utilization >= level.utilization
    ) || { multiplier: 1.0, name: 'Normal' };
    
    // Apply smoothing to prevent rapid changes
    const smoothedMultiplier = this.smoothMultiplier(
      surgeLevel.multiplier,
      this.currentMultiplier
    );
    
    return {
      multiplier: smoothedMultiplier,
      level: surgeLevel.name,
      utilization: utilization * 100,
      metrics: realTimeMetrics,
      next_update: new Date(Date.now() + 60000) // Update every minute
    };
  }
  
  smoothMultiplier(target, current, factor = 0.3) {
    // Exponential smoothing
    return current + (target - current) * factor;
  }
  
  applySurgePricing(basePrice, lead) {
    const surge = this.calculateSurge();
    const timeMultiplier = this.getTimeMultiplier();
    const qualityMultiplier = this.getQualityMultiplier(lead);
    
    const finalPrice = basePrice * surge.multiplier * timeMultiplier * qualityMultiplier;
    
    return {
      base_price: basePrice,
      surge_multiplier: surge.multiplier,
      surge_level: surge.level,
      time_multiplier: timeMultiplier,
      quality_multiplier: qualityMultiplier,
      final_price: Math.round(finalPrice * 100) / 100,
      valid_until: surge.next_update
    };
  }
}

📋 Conversion Optimization

Optimal Delivery Windows

Identify best times for conversion:

class ConversionOptimizer {
  constructor() {
    this.historicalData = new HistoricalDatabase();
    this.mlModel = new ConversionPredictionModel();
  }
  
  async analyzeConversionPatterns(segment) {
    const data = await this.historicalData.getConversions(segment, '90days');
    
    const patterns = {
      by_hour: this.analyzeHourlyPatterns(data),
      by_day: this.analyzeDailyPatterns(data),
      by_hour_and_day: this.analyzeCombinedPatterns(data),
      
      optimal_windows: this.findOptimalWindows(data),
      avoid_windows: this.findPoorWindows(data),
      
      insights: this.generateInsights(data)
    };
    
    return patterns;
  }
  
  analyzeHourlyPatterns(data) {
    const hourlyStats = new Array(24).fill(null).map(() => ({
      attempts: 0,
      conversions: 0,
      revenue: 0,
      avg_time_to_convert: 0
    }));
    
    data.forEach(record => {
      const hour = new Date(record.delivered_at).getHours();
      hourlyStats[hour].attempts++;
      
      if (record.converted) {
        hourlyStats[hour].conversions++;
        hourlyStats[hour].revenue += record.revenue;
        hourlyStats[hour].avg_time_to_convert += record.time_to_convert;
      }
    });
    
    // Calculate rates and averages
    return hourlyStats.map((stats, hour) => ({
      hour,
      conversion_rate: stats.conversions / stats.attempts || 0,
      avg_revenue: stats.revenue / stats.conversions || 0,
      avg_time_to_convert: stats.avg_time_to_convert / stats.conversions || 0,
      volume: stats.attempts,
      
      score: this.calculateHourScore(stats),
      recommendation: this.getHourRecommendation(stats, hour)
    }));
  }
  
  findOptimalWindows(data) {
    const windows = [];
    const threshold = this.calculateThreshold(data);
    
    let currentWindow = null;
    
    this.analyzeHourlyPatterns(data).forEach((hour, index) => {
      if (hour.score > threshold) {
        if (!currentWindow) {
          currentWindow = {
            start: index,
            end: index,
            avg_score: hour.score,
            total_volume: hour.volume
          };
        } else {
          currentWindow.end = index;
          currentWindow.avg_score = 
            (currentWindow.avg_score + hour.score) / 2;
          currentWindow.total_volume += hour.volume;
        }
      } else if (currentWindow) {
        windows.push(currentWindow);
        currentWindow = null;
      }
    });
    
    if (currentWindow) windows.push(currentWindow);
    
    return windows.map(w => ({
      ...w,
      description: `${this.formatHour(w.start)} - ${this.formatHour(w.end + 1)}`,
      quality: this.assessWindowQuality(w)
    }));
  }
}

Contact Strategy Optimization

Best times to contact leads:

class ContactStrategyOptimizer {
  optimizeContactSchedule(lead, buyerPreferences) {
    const factors = {
      lead_data: {
        timezone: this.detectTimezone(lead),
        occupation: this.inferOccupation(lead),
        age_group: this.categorizeAge(lead.age),
        previous_engagement: this.getEngagementHistory(lead)
      },
      
      buyer_data: {
        operating_hours: buyerPreferences.hours,
        preferred_times: buyerPreferences.preferred_contact_times,
        capacity_by_hour: buyerPreferences.hourly_capacity
      },
      
      historical_data: {
        contact_success_by_hour: this.getContactSuccessRates(),
        conversion_by_delay: this.getConversionByDelay()
      }
    };
    
    const strategy = {
      immediate: this.shouldContactImmediately(factors),
      optimal_time: this.calculateOptimalContactTime(factors),
      
      schedule: this.buildContactSchedule(factors),
      
      messaging: {
        tone: this.recommendTone(factors),
        channel: this.recommendChannel(factors),
        urgency: this.calculateUrgency(factors)
      }
    };
    
    return strategy;
  }
  
  buildContactSchedule(factors) {
    const schedule = [];
    const maxAttempts = 5;
    let delay = 0;
    
    for (let attempt = 1; attempt <= maxAttempts; attempt++) {
      const timing = this.calculateAttemptTiming(attempt, factors);
      
      schedule.push({
        attempt,
        delay_minutes: delay,
        time: new Date(Date.now() + delay * 60000),
        method: timing.method,
        script_tone: timing.tone,
        
        success_probability: timing.probability,
        
        if_no_answer: timing.voicemail ? 'leave_voicemail' : 'no_message',
        
        next_attempt_delay: timing.next_delay
      });
      
      delay += timing.next_delay;
    }
    
    return schedule;
  }
}

📈 Performance Analysis

Day Part Analytics

Measure and optimize:

class DayPartAnalytics {
  constructor() {
    this.metrics = new MetricsStore();
    this.reports = new ReportGenerator();
  }
  
  async generateDayPartReport(dateRange) {
    const data = await this.metrics.getDataForRange(dateRange);
    
    const report = {
      summary: this.generateSummary(data),
      
      performance_by_hour: this.analyzeHourlyPerformance(data),
      performance_by_day: this.analyzeDailyPerformance(data),
      
      day_part_comparison: this.compareDayParts(data),
      
      optimal_schedules: this.recommendSchedules(data),
      
      financial_impact: this.calculateFinancialImpact(data),
      
      visualizations: this.generateCharts(data)
    };
    
    return report;
  }
  
  analyzeHourlyPerformance(data) {
    const hourlyMetrics = new Array(24).fill(null).map((_, hour) => {
      const hourData = data.filter(d => 
        new Date(d.timestamp).getHours() === hour
      );
      
      return {
        hour,
        volume: hourData.length,
        
        conversion_rate: this.calculateConversionRate(hourData),
        avg_revenue: this.calculateAvgRevenue(hourData),
        avg_response_time: this.calculateAvgResponseTime(hourData),
        
        quality_metrics: {
          avg_quality_score: this.calculateAvgQuality(hourData),
          high_quality_percentage: this.calculateHighQualityPercentage(hourData)
        },
        
        buyer_metrics: {
          acceptance_rate: this.calculateAcceptanceRate(hourData),
          buyer_satisfaction: this.calculateBuyerSatisfaction(hourData)
        },
        
        efficiency_score: this.calculateEfficiencyScore(hourData),
        
        recommendations: this.generateHourlyRecommendations(hourData, hour)
      };
    });
    
    return hourlyMetrics;
  }
  
  compareDayParts(data) {
    const dayParts = {
      'morning': { hours: [6, 7, 8, 9, 10, 11] },
      'afternoon': { hours: [12, 13, 14, 15, 16, 17] },
      'evening': { hours: [18, 19, 20, 21, 22, 23] },
      'overnight': { hours: [0, 1, 2, 3, 4, 5] }
    };
    
    const comparison = {};
    
    Object.entries(dayParts).forEach(([name, config]) => {
      const partData = data.filter(d => 
        config.hours.includes(new Date(d.timestamp).getHours())
      );
      
      comparison[name] = {
        volume: partData.length,
        volume_percentage: (partData.length / data.length) * 100,
        
        performance: {
          conversion_rate: this.calculateConversionRate(partData),
          revenue: this.calculateTotalRevenue(partData),
          avg_value: this.calculateAvgValue(partData)
        },
        
        quality: {
          avg_score: this.calculateAvgQuality(partData),
          distribution: this.calculateQualityDistribution(partData)
        },
        
        optimal_use: this.recommendDayPartUsage(partData, name)
      };
    });
    
    return comparison;
  }
}

ROI Optimization

Maximize return through timing:

class DayPartROI {
  optimizeScheduleForROI(historicalData, constraints) {
    const model = {
      objective: 'maximize_profit',
      
      variables: this.defineScheduleVariables(),
      
      constraints: [
        ...this.volumeConstraints(constraints),
        ...this.capacityConstraints(constraints),
        ...this.budgetConstraints(constraints)
      ],
      
      coefficients: this.calculateProfitCoefficients(historicalData)
    };
    
    const solution = this.solver.solve(model);
    
    return {
      optimal_schedule: this.interpretSolution(solution),
      expected_roi: solution.objective_value,
      
      implementation: {
        routing_rules: this.generateRoutingRules(solution),
        pricing_rules: this.generatePricingRules(solution),
        capacity_allocation: this.generateCapacityPlan(solution)
      },
      
      sensitivity_analysis: this.analyzeSensitivity(solution, model)
    };
  }
  
  calculateProfitCoefficients(data) {
    const coefficients = {};
    
    // For each hour and buyer combination
    this.buyers.forEach(buyer => {
      for (let hour = 0; hour < 24; hour++) {
        const key = `${buyer.id}_hour_${hour}`;
        
        const hourlyData = data.filter(d => 
          d.buyer === buyer.id && 
          new Date(d.timestamp).getHours() === hour
        );
        
        // Expected profit = revenue * conversion_rate - cost
        const avgRevenue = this.calculateAvgRevenue(hourlyData);
        const conversionRate = this.calculateConversionRate(hourlyData);
        const avgCost = this.calculateAvgCost(hourlyData);
        
        coefficients[key] = (avgRevenue * conversionRate) - avgCost;
      }
    });
    
    return coefficients;
  }
}

💡 Best Practices

Implementation Guidelines

1. Start Simple: Begin with basic time blocks
2. Test Thoroughly: A/B test schedule changes
3. Monitor Continuously: Track performance shifts
4. Adjust Gradually: Avoid dramatic changes
5. Consider Holidays: Plan for special days

Common Patterns

const commonDayPartPatterns = {
  b2c: {
    peak_hours: ['10-12', '18-20'],
    avoid_hours: ['22-06'],
    weekend_premium: false
  },
  
  b2b: {
    peak_hours: ['09-11', '14-16'],
    avoid_hours: ['18-08', 'weekends'],
    weekend_premium: true
  },
  
  financial: {
    peak_hours: ['09-17'],
    avoid_hours: ['20-07'],
    lunch_dip: '12-13'
  },
  
  insurance: {
    peak_hours: ['10-14', '18-20'],
    saturday_morning: true,
    sunday_closed: true
  }
};

Testing Strategies

class DayPartTester {
  async runDayPartTest(config) {
    const control = config.current_schedule;
    const variant = config.test_schedule;
    const duration = config.test_duration || '2_weeks';
    
    // Split traffic
    const test = {
      name: config.name,
      hypothesis: config.hypothesis,
      
      allocation: {
        control: 0.5,
        variant: 0.5
      },
      
      metrics: [
        'conversion_rate',
        'revenue_per_lead',
        'buyer_satisfaction',
        'operational_cost'
      ],
      
      success_criteria: config.success_criteria
    };
    
    // Run test
    const results = await this.abTest.run(test, duration);
    
    // Analyze results
    return {
      winner: results.winner,
      confidence: results.statistical_significance,
      impact: results.estimated_impact,
      recommendation: this.generateRecommendation(results)
    };
  }
}

📚 Related Documentation

• Hours of Operation - Schedule configuration
• Volume Caps Advanced - Time-based caps
• Revenue Optimization - Pricing strategies

⏰ Timing Excellence: Day parting aligns your lead operations with natural rhythms of business and buyer behavior. Use data to find your optimal patterns and continuously refine!