Initial commit: frontend + backend integration
This commit is contained in:
Ignacio
648
frontend/utils/realDataAnalysis.ts
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648
frontend/utils/realDataAnalysis.ts
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/**
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* Generación de análisis con datos reales (no sintéticos)
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*/
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import type { AnalysisData, Kpi, DimensionAnalysis, HeatmapDataPoint, Opportunity, RoadmapInitiative, EconomicModelData, BenchmarkDataPoint, Finding, Recommendation, TierKey, CustomerSegment, RawInteraction, AgenticReadinessResult, SubFactor, SkillMetrics } from '../types';
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import { RoadmapPhase } from '../types';
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import { BarChartHorizontal, Zap, Smile, DollarSign, Target, Globe } from 'lucide-react';
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import { calculateAgenticReadinessScore, type AgenticReadinessInput } from './agenticReadinessV2';
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import { classifyQueue } from './segmentClassifier';
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/**
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* Generar análisis completo con datos reales
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*/
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export function generateAnalysisFromRealData(
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tier: TierKey,
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interactions: RawInteraction[],
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costPerHour: number,
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avgCsat: number,
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segmentMapping?: { high_value_queues: string[]; medium_value_queues: string[]; low_value_queues: string[] }
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): AnalysisData {
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console.log(`🔄 Generating analysis from ${interactions.length} real interactions`);
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// PASO 1: Limpieza de ruido (duration < 10s)
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const cleanedInteractions = interactions.filter(i => {
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const totalDuration = i.duration_talk + i.hold_time + i.wrap_up_time;
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return totalDuration >= 10;
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});
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console.log(`🧹 Cleaned: ${interactions.length} → ${cleanedInteractions.length} (removed ${interactions.length - cleanedInteractions.length} noise)`);
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// PASO 2: Calcular métricas por skill
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const skillMetrics = calculateSkillMetrics(cleanedInteractions, costPerHour);
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console.log(`📊 Calculated metrics for ${skillMetrics.length} skills`);
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// PASO 3: Generar heatmap data con dimensiones
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const heatmapData = generateHeatmapFromMetrics(skillMetrics, avgCsat, segmentMapping);
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// PASO 4: Calcular métricas globales
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const totalInteractions = cleanedInteractions.length;
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const avgAHT = Math.round(skillMetrics.reduce((sum, s) => sum + s.aht_mean, 0) / skillMetrics.length);
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const avgFCR = Math.round((skillMetrics.reduce((sum, s) => sum + (100 - s.transfer_rate), 0) / skillMetrics.length));
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const totalCost = Math.round(skillMetrics.reduce((sum, s) => sum + s.total_cost, 0));
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// KPIs principales
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const summaryKpis: Kpi[] = [
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{ label: "Interacciones Totales", value: totalInteractions.toLocaleString('es-ES') },
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{ label: "AHT Promedio", value: `${avgAHT}s` },
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{ label: "Tasa FCR", value: `${avgFCR}%` },
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{ label: "CSAT", value: `${(avgCsat / 20).toFixed(1)}/5` }
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];
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// Health Score basado en métricas reales
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const overallHealthScore = calculateHealthScore(heatmapData);
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// Dimensiones (simplificadas para datos reales)
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const dimensions: DimensionAnalysis[] = generateDimensionsFromRealData(
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cleanedInteractions,
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skillMetrics,
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avgCsat,
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avgAHT
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);
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// Agentic Readiness Score
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const agenticReadiness = calculateAgenticReadinessFromRealData(skillMetrics);
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// Findings y Recommendations
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const findings = generateFindingsFromRealData(skillMetrics, cleanedInteractions);
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const recommendations = generateRecommendationsFromRealData(skillMetrics);
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// Opportunities
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const opportunities = generateOpportunitiesFromRealData(skillMetrics, costPerHour);
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// Roadmap
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const roadmap = generateRoadmapFromRealData(opportunities);
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// Economic Model
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const economicModel = generateEconomicModelFromRealData(skillMetrics, costPerHour);
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// Benchmark
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const benchmarkData = generateBenchmarkFromRealData(skillMetrics);
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return {
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tier,
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overallHealthScore,
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summaryKpis,
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dimensions,
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heatmapData,
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agenticReadiness,
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findings,
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recommendations,
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opportunities,
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roadmap,
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economicModel,
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benchmarkData
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};
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}
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/**
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* PASO 2: Calcular métricas base por skill
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*/
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interface SkillMetrics {
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skill: string;
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volume: number;
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aht_mean: number;
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aht_std: number;
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cv_aht: number;
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transfer_rate: number;
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total_cost: number;
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hold_time_mean: number;
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cv_talk_time: number;
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}
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function calculateSkillMetrics(interactions: RawInteraction[], costPerHour: number): SkillMetrics[] {
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// Agrupar por skill
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const skillGroups = new Map<string, RawInteraction[]>();
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interactions.forEach(i => {
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if (!skillGroups.has(i.queue_skill)) {
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skillGroups.set(i.queue_skill, []);
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}
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skillGroups.get(i.queue_skill)!.push(i);
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});
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// Calcular métricas para cada skill
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const metrics: SkillMetrics[] = [];
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skillGroups.forEach((group, skill) => {
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const volume = group.length;
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if (volume === 0) return; // Evitar división por cero
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// AHT = duration_talk + hold_time + wrap_up_time
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const ahts = group.map(i => i.duration_talk + i.hold_time + i.wrap_up_time);
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const aht_mean = ahts.reduce((sum, v) => sum + v, 0) / volume;
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const aht_variance = ahts.reduce((sum, v) => sum + Math.pow(v - aht_mean, 2), 0) / volume;
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const aht_std = Math.sqrt(aht_variance);
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const cv_aht = aht_mean > 0 ? aht_std / aht_mean : 0;
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// Talk time CV
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const talkTimes = group.map(i => i.duration_talk);
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const talk_mean = talkTimes.reduce((sum, v) => sum + v, 0) / volume;
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const talk_std = Math.sqrt(talkTimes.reduce((sum, v) => sum + Math.pow(v - talk_mean, 2), 0) / volume);
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const cv_talk_time = talk_mean > 0 ? talk_std / talk_mean : 0;
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// Transfer rate
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const transfers = group.filter(i => i.transfer_flag).length;
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const transfer_rate = (transfers / volume) * 100;
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// Hold time promedio
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const hold_time_mean = group.reduce((sum, i) => sum + i.hold_time, 0) / volume;
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// Coste total (AHT en horas * coste por hora * volumen)
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const total_cost = (aht_mean / 3600) * costPerHour * volume;
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metrics.push({
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skill,
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volume,
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aht_mean,
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aht_std,
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cv_aht,
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transfer_rate,
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total_cost,
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hold_time_mean,
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cv_talk_time
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});
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});
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return metrics.sort((a, b) => b.volume - a.volume); // Ordenar por volumen descendente
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}
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/**
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* PASO 3: Transformar métricas a dimensiones (0-10)
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*/
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function generateHeatmapFromMetrics(
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metrics: SkillMetrics[],
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avgCsat: number,
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segmentMapping?: { high_value_queues: string[]; medium_value_queues: string[]; low_value_queues: string[] }
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): HeatmapDataPoint[] {
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console.log('🔍 generateHeatmapFromMetrics called with:', {
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metricsLength: metrics.length,
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firstMetric: metrics[0],
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avgCsat,
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hasSegmentMapping: !!segmentMapping
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});
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const result = metrics.map(m => {
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// Dimensión 1: Predictibilidad (CV AHT)
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const predictability = Math.max(0, Math.min(10, 10 - ((m.cv_aht - 0.3) / 1.2 * 10)));
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// Dimensión 2: Complejidad Inversa (Transfer Rate)
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const complexity_inverse = Math.max(0, Math.min(10, 10 - ((m.transfer_rate / 100 - 0.05) / 0.25 * 10)));
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// Dimensión 3: Repetitividad (Volumen)
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let repetitiveness = 0;
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if (m.volume >= 5000) {
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repetitiveness = 10;
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} else if (m.volume <= 100) {
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repetitiveness = 0;
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} else {
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// Interpolación lineal entre 100 y 5000
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repetitiveness = ((m.volume - 100) / (5000 - 100)) * 10;
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}
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// Agentic Readiness Score (promedio ponderado)
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const agentic_readiness = (
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predictability * 0.40 +
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complexity_inverse * 0.35 +
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repetitiveness * 0.25
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);
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// Categoría
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let category: 'automate' | 'assist' | 'optimize';
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if (agentic_readiness >= 8.0) {
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category = 'automate';
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} else if (agentic_readiness >= 5.0) {
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category = 'assist';
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} else {
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category = 'optimize';
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}
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// Segmentación
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const segment = segmentMapping
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? classifyQueue(m.skill, segmentMapping.high_value_queues, segmentMapping.medium_value_queues, segmentMapping.low_value_queues)
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: 'medium' as CustomerSegment;
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// Scores de performance (normalizados 0-100)
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const fcr_score = Math.round(100 - m.transfer_rate);
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const aht_score = Math.round(Math.max(0, Math.min(100, 100 - ((m.aht_mean - 240) / 310) * 100)));
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const csat_score = avgCsat;
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const hold_time_score = Math.round(Math.max(0, Math.min(100, 100 - (m.hold_time_mean / 60) * 10)));
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const transfer_rate_score = Math.round(100 - m.transfer_rate);
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return {
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skill: m.skill,
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volume: m.volume,
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aht_seconds: Math.round(m.aht_mean),
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metrics: {
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fcr: fcr_score,
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aht: aht_score,
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csat: csat_score,
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hold_time: hold_time_score,
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transfer_rate: transfer_rate_score
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},
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automation_readiness: Math.round(agentic_readiness * 10),
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variability: {
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cv_aht: Math.round(m.cv_aht * 100),
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cv_talk_time: Math.round(m.cv_talk_time * 100),
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cv_hold_time: Math.round(m.cv_talk_time * 80), // Aproximación
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transfer_rate: Math.round(m.transfer_rate)
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},
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dimensions: {
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predictability: Math.round(predictability * 10) / 10,
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complexity_inverse: Math.round(complexity_inverse * 10) / 10,
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repetitiveness: Math.round(repetitiveness * 10) / 10
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},
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agentic_readiness: Math.round(agentic_readiness * 10) / 10,
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category,
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segment
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};
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});
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console.log('📊 Heatmap data generated from real data:', {
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length: result.length,
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firstItem: result[0],
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objectKeys: result[0] ? Object.keys(result[0]) : [],
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hasMetricsObject: result[0] && typeof result[0].metrics !== 'undefined',
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metricsKeys: result[0] && result[0].metrics ? Object.keys(result[0].metrics) : [],
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firstMetrics: result[0] && result[0].metrics ? result[0].metrics : null,
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automation_readiness: result[0] ? result[0].automation_readiness : null
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});
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return result;
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}
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/**
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* Calcular Health Score global
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*/
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function calculateHealthScore(heatmapData: HeatmapDataPoint[]): number {
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if (heatmapData.length === 0) return 50;
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const avgFCR = heatmapData.reduce((sum, d) => sum + (d.metrics?.fcr || 0), 0) / heatmapData.length;
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const avgAHT = heatmapData.reduce((sum, d) => sum + (d.metrics?.aht || 0), 0) / heatmapData.length;
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const avgCSAT = heatmapData.reduce((sum, d) => sum + (d.metrics?.csat || 0), 0) / heatmapData.length;
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const avgVariability = heatmapData.reduce((sum, d) => sum + (100 - (d.variability?.cv_aht || 0)), 0) / heatmapData.length;
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return Math.round((avgFCR + avgAHT + avgCSAT + avgVariability) / 4);
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}
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/**
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* Generar dimensiones desde datos reales
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*/
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function generateDimensionsFromRealData(
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interactions: RawInteraction[],
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metrics: SkillMetrics[],
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avgCsat: number,
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avgAHT: number
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): DimensionAnalysis[] {
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const totalVolume = interactions.length;
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const avgCV = metrics.reduce((sum, m) => sum + m.cv_aht, 0) / metrics.length;
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const avgTransferRate = metrics.reduce((sum, m) => sum + m.transfer_rate, 0) / metrics.length;
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return [
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{
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id: 'volumetry_distribution',
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name: 'volumetry_distribution',
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title: 'Análisis de la Demanda',
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score: Math.min(100, Math.round((totalVolume / 200))), // Score basado en volumen
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percentile: 65,
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summary: `Se procesaron ${totalVolume.toLocaleString('es-ES')} interacciones distribuidas en ${metrics.length} skills diferentes.`,
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kpi: { label: 'Volumen Total', value: totalVolume.toLocaleString('es-ES') },
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icon: BarChartHorizontal
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},
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{
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id: 'performance',
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name: 'performance',
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title: 'Rendimiento Operativo',
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score: Math.round(100 - (avgCV * 100)),
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percentile: 70,
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summary: avgCV < 0.4
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? 'El AHT muestra baja variabilidad, indicando procesos estandarizados.'
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: 'La variabilidad del AHT es alta, sugiriendo inconsistencia en procesos.',
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kpi: { label: 'AHT Promedio', value: `${avgAHT}s` },
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icon: Zap
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},
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{
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id: 'satisfaction',
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name: 'satisfaction',
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title: 'Voz del Cliente',
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score: avgCsat,
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percentile: 60,
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summary: `CSAT promedio de ${(avgCsat / 20).toFixed(1)}/5.`,
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kpi: { label: 'CSAT', value: `${(avgCsat / 20).toFixed(1)}/5` },
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icon: Smile
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},
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{
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id: 'economy',
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name: 'economy',
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title: 'Rentabilidad del Servicio',
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score: Math.round(100 - avgTransferRate),
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percentile: 55,
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summary: `Tasa de transferencia del ${avgTransferRate.toFixed(1)}%.`,
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kpi: { label: 'Transfer Rate', value: `${avgTransferRate.toFixed(1)}%` },
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icon: DollarSign
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},
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{
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id: 'efficiency',
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name: 'efficiency',
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title: 'Resolución y Calidad',
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score: Math.round(100 - avgTransferRate),
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percentile: 68,
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summary: `FCR estimado del ${(100 - avgTransferRate).toFixed(1)}%.`,
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kpi: { label: 'FCR', value: `${(100 - avgTransferRate).toFixed(1)}%` },
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icon: Target
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},
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{
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id: 'benchmark',
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name: 'benchmark',
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title: 'Contexto Competitivo',
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score: 75,
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percentile: 65,
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summary: 'Métricas alineadas con benchmarks de la industria.',
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kpi: { label: 'Benchmark', value: 'P65' },
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icon: Globe
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}
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];
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}
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/**
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* Calcular Agentic Readiness desde datos reales
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*/
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function calculateAgenticReadinessFromRealData(metrics: SkillMetrics[]): AgenticReadinessResult {
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const totalVolume = metrics.reduce((sum, m) => sum + m.volume, 0);
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const avgCV = metrics.reduce((sum, m) => sum + m.cv_aht, 0) / metrics.length;
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const avgTransferRate = metrics.reduce((sum, m) => sum + m.transfer_rate, 0) / metrics.length;
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// Predictibilidad
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const predictability = Math.max(0, Math.min(10, 10 - ((avgCV - 0.3) / 1.2 * 10)));
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// Complejidad Inversa
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const complexity_inverse = Math.max(0, Math.min(10, 10 - (avgTransferRate / 10)));
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// ROI (simplificado)
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const roi = Math.min(10, totalVolume / 1000);
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// Repetitividad (basada en volumen)
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const repetitiveness = Math.min(10, totalVolume / 500);
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// Score final
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const score = Math.round((predictability * 0.4 + complexity_inverse * 0.35 + repetitiveness * 0.25) * 10) / 10;
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// Tier basado en score
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let tier: TierKey;
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if (score >= 8) tier = 'gold';
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else if (score >= 5) tier = 'silver';
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else tier = 'bronze';
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// Sub-factors
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const sub_factors: SubFactor[] = [
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{
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name: 'predictibilidad',
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displayName: 'Predictibilidad',
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score: Math.round(predictability * 10) / 10,
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weight: 0.4,
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description: `CV AHT promedio: ${Math.round(avgCV * 100)}%`
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},
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{
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name: 'complejidad_inversa',
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displayName: 'Complejidad Inversa',
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score: Math.round(complexity_inverse * 10) / 10,
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weight: 0.35,
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description: `Tasa de transferencia promedio: ${Math.round(avgTransferRate)}%`
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},
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{
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name: 'repetitividad',
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displayName: 'Repetitividad',
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score: Math.round(repetitiveness * 10) / 10,
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weight: 0.25,
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description: `Volumen total: ${totalVolume.toLocaleString('es-ES')} interacciones`
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}
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];
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// Interpretation
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let interpretation: string;
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if (score >= 8) {
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interpretation = 'Excelente candidato para automatización. Alta predictibilidad, baja complejidad y volumen significativo.';
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} else if (score >= 5) {
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interpretation = 'Buen candidato para asistencia con IA. Considere implementar copilots o asistentes virtuales.';
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} else {
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interpretation = 'Requiere optimización previa. Enfóquese en estandarizar procesos y reducir variabilidad antes de automatizar.';
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}
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return {
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score,
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sub_factors,
|
||||
tier,
|
||||
confidence: totalVolume > 1000 ? 'high' as const : totalVolume > 500 ? 'medium' as const : 'low' as const,
|
||||
interpretation
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Generar findings desde datos reales
|
||||
*/
|
||||
function generateFindingsFromRealData(metrics: SkillMetrics[], interactions: RawInteraction[]): Finding[] {
|
||||
const findings: Finding[] = [];
|
||||
|
||||
// Finding 1: Variabilidad
|
||||
const highVariabilitySkills = metrics.filter(m => m.cv_aht > 0.45);
|
||||
if (highVariabilitySkills.length > 0) {
|
||||
findings.push({
|
||||
type: 'warning',
|
||||
title: 'Alta Variabilidad de AHT',
|
||||
description: `${highVariabilitySkills.length} skills muestran CV > 45%, sugiriendo procesos poco estandarizados.`
|
||||
});
|
||||
}
|
||||
|
||||
// Finding 2: Transferencias
|
||||
const highTransferSkills = metrics.filter(m => m.transfer_rate > 20);
|
||||
if (highTransferSkills.length > 0) {
|
||||
findings.push({
|
||||
type: 'warning',
|
||||
title: 'Alta Tasa de Transferencia',
|
||||
description: `${highTransferSkills.length} skills con transfer rate > 20%.`
|
||||
});
|
||||
}
|
||||
|
||||
// Finding 3: Volumen
|
||||
const topSkill = metrics[0];
|
||||
findings.push({
|
||||
type: 'info',
|
||||
title: 'Skill de Mayor Volumen',
|
||||
description: `"${topSkill.skill}" representa el ${Math.round(topSkill.volume / interactions.length * 100)}% del volumen total.`
|
||||
});
|
||||
|
||||
return findings;
|
||||
}
|
||||
|
||||
/**
|
||||
* Generar recomendaciones desde datos reales
|
||||
*/
|
||||
function generateRecommendationsFromRealData(metrics: SkillMetrics[]): Recommendation[] {
|
||||
const recommendations: Recommendation[] = [];
|
||||
|
||||
const highVariabilitySkills = metrics.filter(m => m.cv_aht > 0.45);
|
||||
if (highVariabilitySkills.length > 0) {
|
||||
recommendations.push({
|
||||
priority: 'high',
|
||||
title: 'Estandarizar Procesos',
|
||||
description: `Crear guías y scripts para los ${highVariabilitySkills.length} skills con alta variabilidad.`,
|
||||
impact: 'Reducción del 20-30% en AHT'
|
||||
});
|
||||
}
|
||||
|
||||
const highVolumeSkills = metrics.filter(m => m.volume > 500);
|
||||
if (highVolumeSkills.length > 0) {
|
||||
recommendations.push({
|
||||
priority: 'high',
|
||||
title: 'Automatizar Skills de Alto Volumen',
|
||||
description: `Implementar bots para los ${highVolumeSkills.length} skills con > 500 interacciones.`,
|
||||
impact: 'Ahorro estimado del 40-60%'
|
||||
});
|
||||
}
|
||||
|
||||
return recommendations;
|
||||
}
|
||||
|
||||
/**
|
||||
* Generar opportunities desde datos reales
|
||||
*/
|
||||
function generateOpportunitiesFromRealData(metrics: SkillMetrics[], costPerHour: number): Opportunity[] {
|
||||
return metrics.slice(0, 10).map((m, index) => {
|
||||
const potentialSavings = m.total_cost * 0.4; // 40% de ahorro potencial
|
||||
|
||||
return {
|
||||
id: `opp-${index + 1}`,
|
||||
skill: m.skill,
|
||||
currentVolume: m.volume,
|
||||
currentAHT: Math.round(m.aht_mean),
|
||||
currentCost: Math.round(m.total_cost),
|
||||
potentialSavings: Math.round(potentialSavings),
|
||||
automationPotential: m.cv_aht < 0.3 && m.transfer_rate < 15 ? 'high' : m.cv_aht < 0.5 ? 'medium' : 'low',
|
||||
priority: index < 3 ? 'high' : index < 7 ? 'medium' : 'low'
|
||||
};
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Generar roadmap desde opportunities
|
||||
*/
|
||||
function generateRoadmapFromRealData(opportunities: Opportunity[]): RoadmapInitiative[] {
|
||||
const highPriority = opportunities.filter(o => o.priority === 'high');
|
||||
|
||||
return highPriority.slice(0, 5).map((opp, index) => ({
|
||||
id: `init-${index + 1}`,
|
||||
title: `Automatizar ${opp.skill}`,
|
||||
description: `Implementar bot para reducir AHT y coste`,
|
||||
phase: index < 2 ? RoadmapPhase.QUICK_WINS : RoadmapPhase.STRATEGIC,
|
||||
effort: opp.currentVolume > 1000 ? 'high' : 'medium',
|
||||
impact: opp.potentialSavings > 10000 ? 'high' : 'medium',
|
||||
timeline: `${index * 2 + 1}-${index * 2 + 3} meses`
|
||||
}));
|
||||
}
|
||||
|
||||
/**
|
||||
* Generar economic model desde datos reales
|
||||
*/
|
||||
function generateEconomicModelFromRealData(metrics: SkillMetrics[], costPerHour: number): EconomicModelData {
|
||||
const totalCost = metrics.reduce((sum, m) => sum + m.total_cost, 0);
|
||||
const annualSavings = Math.round(totalCost * 0.35);
|
||||
const initialInvestment = Math.round(totalCost * 0.1);
|
||||
const paybackMonths = Math.ceil((initialInvestment / annualSavings) * 12);
|
||||
const roi3yr = (((annualSavings * 3) - initialInvestment) / initialInvestment) * 100;
|
||||
|
||||
// NPV con tasa de descuento 10%
|
||||
const discountRate = 0.10;
|
||||
const npv = -initialInvestment +
|
||||
(annualSavings / (1 + discountRate)) +
|
||||
(annualSavings / Math.pow(1 + discountRate, 2)) +
|
||||
(annualSavings / Math.pow(1 + discountRate, 3));
|
||||
|
||||
const savingsBreakdown = [
|
||||
{ category: 'Automatización de tareas', amount: annualSavings * 0.45, percentage: 45 },
|
||||
{ category: 'Eficiencia operativa', amount: annualSavings * 0.30, percentage: 30 },
|
||||
{ category: 'Mejora FCR', amount: annualSavings * 0.15, percentage: 15 },
|
||||
{ category: 'Reducción attrition', amount: annualSavings * 0.075, percentage: 7.5 },
|
||||
{ category: 'Otros', amount: annualSavings * 0.025, percentage: 2.5 },
|
||||
];
|
||||
|
||||
const costBreakdown = [
|
||||
{ category: 'Software y licencias', amount: initialInvestment * 0.43, percentage: 43 },
|
||||
{ category: 'Implementación', amount: initialInvestment * 0.29, percentage: 29 },
|
||||
{ category: 'Training y change mgmt', amount: initialInvestment * 0.18, percentage: 18 },
|
||||
{ category: 'Contingencia', amount: initialInvestment * 0.10, percentage: 10 },
|
||||
];
|
||||
|
||||
return {
|
||||
currentAnnualCost: Math.round(totalCost),
|
||||
futureAnnualCost: Math.round(totalCost - annualSavings),
|
||||
annualSavings,
|
||||
initialInvestment,
|
||||
paybackMonths,
|
||||
roi3yr: parseFloat(roi3yr.toFixed(1)),
|
||||
npv: Math.round(npv),
|
||||
savingsBreakdown,
|
||||
costBreakdown
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Generar benchmark desde datos reales
|
||||
*/
|
||||
function generateBenchmarkFromRealData(metrics: SkillMetrics[]): BenchmarkDataPoint[] {
|
||||
const avgAHT = metrics.reduce((sum, m) => sum + m.aht_mean, 0) / (metrics.length || 1);
|
||||
const avgFCR = 100 - (metrics.reduce((sum, m) => sum + m.transfer_rate, 0) / (metrics.length || 1));
|
||||
const avgCSAT = 4.3; // Default CSAT
|
||||
const avgCPI = 3.5; // Default CPI
|
||||
|
||||
return [
|
||||
{
|
||||
kpi: 'AHT Promedio',
|
||||
userValue: Math.round(avgAHT),
|
||||
userDisplay: `${Math.round(avgAHT)}s`,
|
||||
industryValue: 420,
|
||||
industryDisplay: `420s`,
|
||||
percentile: Math.max(10, Math.min(90, Math.round(100 - (avgAHT / 420) * 100))),
|
||||
p25: 380,
|
||||
p50: 420,
|
||||
p75: 460,
|
||||
p90: 510
|
||||
},
|
||||
{
|
||||
kpi: 'Tasa FCR',
|
||||
userValue: avgFCR / 100,
|
||||
userDisplay: `${Math.round(avgFCR)}%`,
|
||||
industryValue: 0.72,
|
||||
industryDisplay: `72%`,
|
||||
percentile: Math.max(10, Math.min(90, Math.round((avgFCR / 100) * 100))),
|
||||
p25: 0.65,
|
||||
p50: 0.72,
|
||||
p75: 0.82,
|
||||
p90: 0.88
|
||||
},
|
||||
{
|
||||
kpi: 'CSAT',
|
||||
userValue: avgCSAT,
|
||||
userDisplay: `${avgCSAT}/5`,
|
||||
industryValue: 4.3,
|
||||
industryDisplay: `4.3/5`,
|
||||
percentile: 65,
|
||||
p25: 3.8,
|
||||
p50: 4.3,
|
||||
p75: 4.6,
|
||||
p90: 4.8
|
||||
},
|
||||
{
|
||||
kpi: 'Coste por Interacción',
|
||||
userValue: avgCPI,
|
||||
userDisplay: `€${avgCPI.toFixed(2)}`,
|
||||
industryValue: 3.5,
|
||||
industryDisplay: `€3.50`,
|
||||
percentile: 55,
|
||||
p25: 2.8,
|
||||
p50: 3.5,
|
||||
p75: 4.2,
|
||||
p90: 4.8
|
||||
}
|
||||
];
|
||||
}
|
||||
Reference in New Issue
Block a user