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whyrating-engine-legacy/urt-taxonomy/track-c-analytics/C2-KPI-Mapping-Guide.md
Alejandro Gutiérrez 3eda9bdbfa Add complete URT v5.1 taxonomy framework (11 artifacts) 
Universal Review Taxonomy v5.1 implementation with:
- Track A (Training): A1 Quickstart, A2 QA Protocol, A3 Calibration Set, A4 Full Manual
- Track B (Engineering): B1 Code Registry, B2 Database Schema, B3 Owner Routing, B4 API Contract
- Track C (Analytics): C1 Issue Lifecycle, C2 KPI Mapping Guide
- Track D (Integration): D1 Dashboard Specification

Covers 7 domains, 28 categories, 138 subcodes, 16 causal codes, and 7 metadata dimensions.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-24 10:51:41 +00:00

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# C2: KPI Mapping Guide
## Universal Review Taxonomy (URT) v5.1 - Analytics Track
**Document**: C2 - KPI Mapping Guide
**Version**: 1.0
**Status**: Production Ready
**Date**: 2026-01-23
**Depends On**: URT Specification v5.1, C1-Issue-Lifecycle-Framework
---
## Purpose
This guide translates URT classifications into actionable business metrics. It provides:
- Domain and category-level KPIs with calculation formulas
- Composite indices for executive-level monitoring
- Intensity-weighted scoring methodologies
- Trend detection and anomaly identification rules
- Dashboard specifications and alert configurations
- Integration with the Issue Lifecycle Framework (C1)
---
## 1. Domain-Level KPIs
### 1.1 Overview Matrix
| Domain | Primary KPI | Unit | Target (Green) | Warning (Yellow) | Critical (Red) |
|--------|------------|------|----------------|------------------|----------------|
| **O** (Offering) | Product Quality Score | 0-100 | >= 80 | 60-79 | < 60 |
| **P** (People) | Personnel Excellence Index | 0-100 | >= 85 | 70-84 | < 70 |
| **J** (Journey) | Process Efficiency Score | 0-100 | >= 75 | 55-74 | < 55 |
| **E** (Environment) | Environment Satisfaction Index | 0-100 | >= 80 | 65-79 | < 65 |
| **A** (Access) | Accessibility Score | 0-100 | >= 85 | 70-84 | < 70 |
| **V** (Value) | Value Perception Index | 0-100 | >= 70 | 50-69 | < 50 |
| **R** (Relationship) | Trust & Loyalty Score | 0-100 | >= 80 | 60-79 | < 60 |
---
### 1.2 O - Offering Domain
**Primary KPI**: Product Quality Score (PQS)
**Definition**: Measures customer perception of core product/service quality.
**Formula**:
```
PQS = 100 * (V+ spans - V- spans * Intensity_Weight) / Total_O_spans
Where:
Intensity_Weight = {I1: 1.0, I2: 2.0, I3: 4.0}
```
**Secondary KPIs**:
| KPI | Formula | Target |
|-----|---------|--------|
| **Function Reliability Rate** | V+ spans in O1 / Total O1 spans | >= 90% |
| **Quality Consistency Index** | 1 - (StdDev of weekly O2 scores / Mean) | >= 0.85 |
| **Completeness Score** | V+ spans in O3 / Total O3 spans | >= 95% |
**Benchmark References**:
- Industry Average: 72
- Top Quartile: 85+
- Best-in-Class: 92+
---
### 1.3 P - People Domain
**Primary KPI**: Personnel Excellence Index (PEI)
**Definition**: Measures customer perception of staff behavior, competence, and communication.
**Formula**:
```
PEI = 100 * weighted_sum(category_scores) / 4
Where:
P1_score (Attitude) = weight 0.30
P2_score (Competence) = weight 0.25
P3_score (Responsiveness) = weight 0.25
P4_score (Communication) = weight 0.20
```
**Secondary KPIs**:
| KPI | Formula | Target |
|-----|---------|--------|
| **Staff Attitude Score** | Sentiment ratio of P1 spans | >= 85% positive |
| **Competence Rating** | Weighted average of P2 spans by intensity | >= 80 |
| **Response Quality Index** | (P3 positive + P4 positive) / Total P3+P4 | >= 80% |
**Benchmark References**:
- Industry Average: 78
- Top Quartile: 88+
- Best-in-Class: 94+
---
### 1.4 J - Journey Domain
**Primary KPI**: Process Efficiency Score (PES)
**Definition**: Measures smoothness, timeliness, and reliability of customer journey.
**Formula**:
```
PES = 100 * (1 - friction_index)
friction_index = (
0.35 * timing_friction + # J1 negative ratio
0.30 * ease_friction + # J2 negative ratio
0.20 * reliability_friction + # J3 negative ratio
0.15 * resolution_friction # J4 negative ratio
)
```
**Secondary KPIs**:
| KPI | Formula | Target |
|-----|---------|--------|
| **Wait Time Satisfaction** | V+ spans in J1.01 / Total J1.01 spans | >= 75% |
| **Process Simplicity Score** | Inverse of J2 negative intensity-weighted count | >= 70 |
| **Reliability Index** | V+ in J3 / Total J3 | >= 85% |
**Benchmark References**:
- Industry Average: 68
- Top Quartile: 80+
- Best-in-Class: 88+
---
### 1.5 E - Environment Domain
**Primary KPI**: Environment Satisfaction Index (ESI)
**Definition**: Measures perception of physical, digital, and ambient environments.
**Formula**:
```
ESI = 100 * weighted_sum(category_scores) / 4
Where:
E1_score (Physical) = weight 0.30
E2_score (Digital) = weight 0.30
E3_score (Ambiance) = weight 0.20
E4_score (Safety) = weight 0.20
```
**Secondary KPIs**:
| KPI | Formula | Target |
|-----|---------|--------|
| **Cleanliness Score** | V+ in E1.01 / Total E1.01 spans | >= 90% |
| **Digital Experience Score** | Average sentiment of E2 spans | >= 75 |
| **Safety Perception Index** | V+ in E4 / Total E4 (I3 weighted 3x) | >= 95% |
**Benchmark References**:
- Industry Average: 74
- Top Quartile: 84+
- Best-in-Class: 91+
---
### 1.6 A - Access Domain
**Primary KPI**: Accessibility Score (AS)
**Definition**: Measures ease of access, inclusivity, and convenience.
**Formula**:
```
AS = 100 * (1 - barrier_index)
barrier_index = (
0.25 * availability_barriers + # A1 negative ratio
0.35 * accessibility_barriers + # A2 negative ratio (weighted higher)
0.25 * inclusivity_barriers + # A3 negative ratio
0.15 * convenience_barriers # A4 negative ratio
)
```
**Secondary KPIs**:
| KPI | Formula | Target |
|-----|---------|--------|
| **Availability Rate** | V+ in A1 / Total A1 spans | >= 85% |
| **ADA Compliance Indicator** | 100 - (A2 negative spans * 10) | >= 90 |
| **Inclusivity Score** | V+ in A3 / Total A3 spans | >= 90% |
**Benchmark References**:
- Industry Average: 76
- Top Quartile: 87+
- Best-in-Class: 94+
---
### 1.7 V - Value Domain
**Primary KPI**: Value Perception Index (VPI)
**Definition**: Measures customer assessment of fairness, transparency, and overall worth.
**Formula**:
```
VPI = 100 * (
0.25 * price_sentiment + # V1 normalized score
0.30 * transparency_score + # V2 normalized score
0.15 * effort_perception + # V3 normalized score
0.30 * worth_assessment # V4 normalized score
)
```
**Secondary KPIs**:
| KPI | Formula | Target |
|-----|---------|--------|
| **Price Satisfaction** | V+ in V1 / Total V1 spans | >= 60% |
| **Transparency Score** | V+ in V2 / Total V2 spans | >= 80% |
| **Worth Ratio** | V4.01 positive mentions / Total V4.01 | >= 65% |
**Benchmark References**:
- Industry Average: 62
- Top Quartile: 75+
- Best-in-Class: 85+
---
### 1.8 R - Relationship Domain
**Primary KPI**: Trust & Loyalty Score (TLS)
**Definition**: Measures trust, dependability, recovery, and loyalty perceptions.
**Formula**:
```
TLS = 100 * weighted_sum(category_scores) / 4
Where:
R1_score (Integrity) = weight 0.35
R2_score (Dependability) = weight 0.25
R3_score (Recovery) = weight 0.20
R4_score (Loyalty) = weight 0.20
```
**Secondary KPIs**:
| KPI | Formula | Target |
|-----|---------|--------|
| **Trust Index** | V+ in R1 / Total R1 (I3 negative weighted 3x) | >= 80% |
| **Recovery Effectiveness** | V+ in R3 / Total R3 spans | >= 75% |
| **Loyalty Indicator** | V+ in R4 / Total R4 spans | >= 70% |
**Benchmark References**:
- Industry Average: 70
- Top Quartile: 82+
- Best-in-Class: 90+
---
## 2. Category-Level Metrics
### 2.1 Offering Categories (O1-O4)
| Category | Metric Name | Data Sources | Aggregation | Comparison Periods |
|----------|-------------|--------------|-------------|-------------------|
| **O1 Function** | Functional Success Rate | O1.01-O1.05 | Positive ratio | WoW, MoM |
| **O2 Quality** | Quality Perception Score | O2.01-O2.05 | Intensity-weighted avg | WoW, MoM, YoY |
| **O3 Completeness** | Completeness Index | O3.01-O3.04 | Binary success rate | MoM |
| **O4 Fit** | Customer Fit Score | O4.01-O4.04 | Weighted average | MoM, QoQ |
**Calculation Details**:
```
Functional_Success_Rate =
(Count(V+, O1.*) + 0.5 * Count(V0, O1.*)) / Total(O1.*)
Quality_Perception_Score =
SUM(sentiment * intensity_weight) / SUM(intensity_weight)
Where: V+ = +1, V- = -1, V0 = 0, V± = sentiment_ratio
```
---
### 2.2 People Categories (P1-P4)
| Category | Metric Name | Data Sources | Aggregation | Comparison Periods |
|----------|-------------|--------------|-------------|-------------------|
| **P1 Attitude** | Attitude Score | P1.01-P1.05 | Sentiment ratio | WoW, MoM |
| **P2 Competence** | Competence Rating | P2.01-P2.05 | Intensity-weighted avg | MoM, QoQ |
| **P3 Responsiveness** | Responsiveness Index | P3.01-P3.05 | Weighted average | WoW, MoM |
| **P4 Communication** | Communication Quality | P4.01-P4.05 | Sentiment ratio | WoW, MoM |
---
### 2.3 Journey Categories (J1-J4)
| Category | Metric Name | Data Sources | Aggregation | Comparison Periods |
|----------|-------------|--------------|-------------|-------------------|
| **J1 Timing** | Timing Satisfaction | J1.01-J1.05 | Inverse negative ratio | Daily, WoW |
| **J2 Ease** | Effort Score | J2.01-J2.05 | Friction index | WoW, MoM |
| **J3 Reliability** | Process Reliability | J3.01-J3.05 | Consistency measure | MoM, QoQ |
| **J4 Resolution** | Resolution Effectiveness | J4.01-J4.05 | Success rate | WoW, MoM |
---
### 2.4 Environment Categories (E1-E4)
| Category | Metric Name | Data Sources | Aggregation | Comparison Periods |
|----------|-------------|--------------|-------------|-------------------|
| **E1 Physical** | Physical Space Score | E1.01-E1.05 | Weighted average | WoW, MoM |
| **E2 Digital** | Digital Experience | E2.01-E2.05 | UX score formula | WoW, MoM |
| **E3 Ambiance** | Ambiance Rating | E3.01-E3.05 | Sentiment ratio | MoM, Seasonal |
| **E4 Safety** | Safety Index | E4.01-E4.05 | Critical-weighted avg | Daily, WoW |
---
### 2.5 Access Categories (A1-A4)
| Category | Metric Name | Data Sources | Aggregation | Comparison Periods |
|----------|-------------|--------------|-------------|-------------------|
| **A1 Availability** | Service Availability | A1.01-A1.05 | Availability ratio | Daily, WoW |
| **A2 Accessibility** | ADA Compliance Score | A2.01-A2.05 | Barrier-weighted | MoM, QoQ |
| **A3 Inclusivity** | Inclusivity Index | A3.01-A3.05 | Sensitivity-weighted | MoM, QoQ |
| **A4 Convenience** | Convenience Score | A4.01-A4.05 | Friction measure | WoW, MoM |
---
### 2.6 Value Categories (V1-V4)
| Category | Metric Name | Data Sources | Aggregation | Comparison Periods |
|----------|-------------|--------------|-------------|-------------------|
| **V1 Price** | Price Perception | V1.01-V1.05 | Sentiment ratio | MoM, YoY |
| **V2 Transparency** | Transparency Score | V2.01-V2.05 | Trust-weighted avg | MoM, QoQ |
| **V3 Effort** | Effort Perception | V3.01-V3.05 | Inverse effort index | WoW, MoM |
| **V4 Worth** | Worth Assessment | V4.01-V4.05 | Value ratio | MoM, QoQ, YoY |
---
### 2.7 Relationship Categories (R1-R4)
| Category | Metric Name | Data Sources | Aggregation | Comparison Periods |
|----------|-------------|--------------|-------------|-------------------|
| **R1 Integrity** | Integrity Score | R1.01-R1.05 | Trust-weighted avg | MoM, QoQ |
| **R2 Dependability** | Dependability Index | R2.01-R2.05 | Consistency measure | MoM, QoQ, YoY |
| **R3 Recovery** | Recovery Score | R3.01-R3.05 | Success rate | WoW, MoM |
| **R4 Loyalty** | Loyalty Index | R4.01-R4.05 | Retention signals | MoM, QoQ, YoY |
---
## 3. Composite Indices
### 3.1 Overall Experience Index (OEI)
**Definition**: Master index combining all domains into a single experience score.
**Formula**:
```
OEI = SUM(Domain_Score * Weight) / SUM(Weights)
Domain Weights:
O (Offering): 0.20
P (People): 0.18
J (Journey): 0.15
E (Environment): 0.12
A (Access): 0.10
V (Value): 0.12
R (Relationship): 0.13
------
1.00
```
**Example Calculation**:
```
Given:
O = 82, P = 88, J = 75, E = 80, A = 85, V = 68, R = 78
OEI = (82*0.20 + 88*0.18 + 75*0.15 + 80*0.12 + 85*0.10 + 68*0.12 + 78*0.13)
= (16.4 + 15.84 + 11.25 + 9.6 + 8.5 + 8.16 + 10.14)
= 79.89
OEI = 79.9 (rounded)
```
**Thresholds**:
| Level | Score | Interpretation |
|-------|-------|----------------|
| Excellent | >= 85 | Top-tier experience |
| Good | 75-84 | Above average |
| Acceptable | 65-74 | Room for improvement |
| Poor | 50-64 | Significant issues |
| Critical | < 50 | Immediate intervention required |
---
### 3.2 Service Excellence Index (SEI)
**Definition**: Combined measure of people and process quality.
**Formula**:
```
SEI = (P_domain * 0.55) + (J_domain * 0.45)
Where:
P_domain = Personnel Excellence Index
J_domain = Process Efficiency Score
```
**Use Case**: Service-oriented businesses (hospitality, healthcare, support).
**Example Calculation**:
```
Given: P = 88, J = 75
SEI = (88 * 0.55) + (75 * 0.45)
= 48.4 + 33.75
= 82.15
SEI = 82.2 (rounded)
```
---
### 3.3 Value Perception Index (VPI-C)
**Definition**: Value perception weighted by product quality reality.
**Formula**:
```
VPI-C = V_domain * quality_modifier
quality_modifier = 0.5 + (O_domain / 200)
Range: [0.5 * V, 1.0 * V] based on O score
```
**Use Case**: Prevents value scores from being inflated when quality is low.
**Example Calculation**:
```
Given: V = 68, O = 82
quality_modifier = 0.5 + (82 / 200) = 0.5 + 0.41 = 0.91
VPI-C = 68 * 0.91 = 61.88
VPI-C = 61.9 (rounded)
```
---
### 3.4 Trust & Loyalty Index (TLI)
**Definition**: Relationship quality with historical weighting for repeat customers.
**Formula**:
```
TLI = R_domain * historical_weight
historical_weight = 1.0 + (0.1 * log2(1 + repeat_reviews))
Where:
repeat_reviews = count of reviews from returning customers
```
**Example Calculation**:
```
Given: R = 78, repeat_reviews = 15
historical_weight = 1.0 + (0.1 * log2(16)) = 1.0 + 0.4 = 1.4
TLI = min(100, 78 * 1.4) = min(100, 109.2) = 100
TLI = 100 (capped)
```
**Note**: TLI caps at 100 but the historical weight can push borderline scores into higher brackets.
---
## 4. Intensity-Weighted Scoring
### 4.1 Intensity Weights
| Intensity | Weight | Impact Multiplier | Rationale |
|-----------|--------|-------------------|-----------|
| **I1** (Mild) | 1.0 | 1x | Baseline feedback |
| **I2** (Moderate) | 2.0 | 2x | Clear signal requiring attention |
| **I3** (Strong) | 4.0 | 4x | Critical feedback requiring immediate response |
### 4.2 Weighted Score Calculation
**Formula**:
```
Weighted_Score = SUM(sentiment_value * intensity_weight) / SUM(intensity_weight)
Where:
sentiment_value: V+ = +1, V- = -1, V0 = 0, V± = 0
intensity_weight: I1 = 1.0, I2 = 2.0, I3 = 4.0
```
### 4.3 Worked Examples
**Example 1: Balanced Feedback**
```
Spans:
- V+, I2 (sentiment: +1, weight: 2.0)
- V-, I1 (sentiment: -1, weight: 1.0)
- V+, I1 (sentiment: +1, weight: 1.0)
- V-, I2 (sentiment: -1, weight: 2.0)
Weighted_Score = ((+1*2) + (-1*1) + (+1*1) + (-1*2)) / (2+1+1+2)
= (2 - 1 + 1 - 2) / 6
= 0 / 6
= 0.00 (neutral)
```
**Example 2: Severe Negative Impact**
```
Spans:
- V+, I1 (sentiment: +1, weight: 1.0)
- V+, I1 (sentiment: +1, weight: 1.0)
- V+, I2 (sentiment: +1, weight: 2.0)
- V-, I3 (sentiment: -1, weight: 4.0) <- Critical negative
Weighted_Score = ((+1*1) + (+1*1) + (+1*2) + (-1*4)) / (1+1+2+4)
= (1 + 1 + 2 - 4) / 8
= 0 / 8
= 0.00 (one I3 negative cancels three positives)
```
**Example 3: Strong Positive Momentum**
```
Spans:
- V+, I3 (sentiment: +1, weight: 4.0)
- V+, I2 (sentiment: +1, weight: 2.0)
- V-, I1 (sentiment: -1, weight: 1.0)
Weighted_Score = ((+1*4) + (+1*2) + (-1*1)) / (4+2+1)
= (4 + 2 - 1) / 7
= 5 / 7
= 0.71 (strong positive)
```
### 4.4 Converting to 0-100 Scale
```
Normalized_Score = 50 + (Weighted_Score * 50)
Where:
Weighted_Score ranges from -1.0 to +1.0
Normalized_Score ranges from 0 to 100
```
---
## 5. Trend Detection Rules
### 5.1 Statistical Significance Thresholds
| Metric Change | Minimum Sample | Significance Level | Detection Method |
|---------------|----------------|-------------------|------------------|
| Domain Score | 30 spans | 95% CI | Z-test |
| Category Score | 20 spans | 90% CI | T-test |
| Subcode Count | 10 spans | 85% CI | Poisson test |
| CR-B/W Shifts | 15 instances | 90% CI | Chi-square |
### 5.2 Minimum Sample Sizes
| Analysis Level | Minimum Sample | Confidence Window |
|----------------|----------------|-------------------|
| Daily trending | 50 reviews | +/- 10% |
| Weekly trending | 150 reviews | +/- 5% |
| Monthly trending | 400 reviews | +/- 2.5% |
| Quarterly trending | 1000 reviews | +/- 1.5% |
**Reliability Formula**:
```
margin_of_error = z * sqrt(p*(1-p) / n)
Where:
z = 1.96 for 95% confidence
p = proportion (e.g., positive ratio)
n = sample size
```
### 5.3 Seasonality Adjustment
**Method**: Multiplicative seasonal decomposition
```
Adjusted_Score = Observed_Score / Seasonal_Index
Seasonal_Index = Historical_Period_Average / Annual_Average
```
**Common Seasonal Patterns**:
| Industry | High Seasons | Low Seasons | Adjustment Range |
|----------|--------------|-------------|------------------|
| Retail | Nov-Dec, Mar-Apr | Jan-Feb | 0.85 - 1.20 |
| Hospitality | Jun-Aug, Dec | Jan, Sep | 0.80 - 1.25 |
| Healthcare | Jan-Mar (flu), Oct | Jun-Jul | 0.90 - 1.15 |
| B2B Services | Q4, Q1 | Summer | 0.88 - 1.12 |
### 5.4 Anomaly Detection Parameters
**Z-Score Method**:
```
anomaly IF |z_score| > threshold
z_score = (observed - mean) / std_dev
Thresholds:
Warning: |z| > 2.0
Alert: |z| > 2.5
Critical: |z| > 3.0
```
**Moving Average Deviation**:
```
anomaly IF |deviation| > threshold * MA_std
deviation = current_value - moving_average
threshold = 2.5 (default)
MA_window = 7 days (daily), 4 weeks (weekly)
```
---
## 6. Comparative Reference (CR) Analytics
### 6.1 CR-B (Better) - Improvement Tracking
**Definition**: Signals explicit customer recognition of improvement.
**Metrics**:
| Metric | Formula | Interpretation |
|--------|---------|----------------|
| **Improvement Rate** | CR-B spans / Total CR spans | % of comparisons showing improvement |
| **Improvement Velocity** | CR-B count this period / CR-B count last period | Acceleration of improvement |
| **Domain Improvement Map** | CR-B distribution by domain | Where improvements are noticed |
**Impact on Issue Resolution**:
```
CR-B on resolved issue subcode → VERIFIED state (per C1 framework)
CR-B count >= 2 on open issue → Consider issue improving
```
### 6.2 CR-W (Worse) - Regression Indicator
**Definition**: Signals explicit customer recognition of decline.
**Metrics**:
| Metric | Formula | Interpretation |
|--------|---------|----------------|
| **Regression Rate** | CR-W spans / Total CR spans | % of comparisons showing decline |
| **Regression Severity** | Avg intensity of CR-W spans | How severe the decline is |
| **Regression Hotspots** | Top subcodes with CR-W | Where quality is declining |
**Alert Triggers**:
```
IF CR-W_count > threshold OR CR-W_rate > 15%:
ALERT("Quality Regression Detected")
IF CR-W on RESOLVED issue:
REOPEN(issue)
ESCALATE(reason="regression")
```
### 6.3 CR-S (Same) - Stagnation Detection
**Definition**: Signals persistent issues that haven't improved.
**Metrics**:
| Metric | Formula | Interpretation |
|--------|---------|----------------|
| **Stagnation Rate** | CR-S spans / Total CR spans | % of comparisons showing no change |
| **Persistent Issue Index** | CR-S count by subcode | Issues that won't go away |
| **Resolution Failure Rate** | CR-S on RESOLVED issues / Total RESOLVED | % of false resolutions |
**Stagnation Alerts**:
```
IF CR-S_count >= 3 on same subcode in 30 days:
FLAG("Persistent Issue - Investigation Required")
IF CR-S on RESOLVED issue:
REOPEN(issue)
INCREMENT(false_resolution_count)
```
### 6.4 Customer Perception Change Tracking
**Composite CR Score**:
```
Perception_Trend = (CR-B_weight * CR-B_count - CR-W_weight * CR-W_count) / Total_CR
Where:
CR-B_weight = +1.0
CR-W_weight = -1.5 (asymmetric - declines weighted more)
Total_CR = CR-B + CR-W + CR-S (excludes CR-N)
```
**Interpretation**:
| Perception_Trend | Status | Action |
|------------------|--------|--------|
| > 0.3 | Improving | Continue current initiatives |
| 0 to 0.3 | Stable | Maintain and monitor |
| -0.3 to 0 | Concerning | Investigate declining areas |
| < -0.3 | Critical | Immediate intervention required |
---
## 7. Dashboard Specifications
### 7.1 Executive Summary View (Domain-Level)
**Display Elements**:
```
┌─────────────────────────────────────────────────────────────────────────┐
│ CUSTOMER EXPERIENCE DASHBOARD │
│ Period: 2026-01 | Reviews: 2,847 │
├─────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────┐ ┌────────────────────────────┐ │
│ │ OVERALL (OEI) │ │ TREND INDICATORS │ │
│ │ 79.9 │ │ ▲ Improving: O, P, E │ │
│ │ ▲ +2.3 MoM │ │ ▼ Declining: V │ │
│ └─────────────────┘ │ → Stable: J, A, R │ │
│ └────────────────────────────┘ │
│ │
│ DOMAIN SCORES │
│ ┌───────┬───────┬───────┬───────┬───────┬───────┬───────┐ │
│ │ O │ P │ J │ E │ A │ V │ R │ │
│ │ 82 │ 88 │ 75 │ 80 │ 85 │ 68 │ 78 │ │
│ │ +3.2 │ +1.8 │ -0.5 │ +2.1 │ +0.3 │ -2.4 │ +0.8 │ │
│ └───────┴───────┴───────┴───────┴───────┴───────┴───────┘ │
│ │
│ COMPOSITE INDICES │
│ ├── Service Excellence (SEI): 82.2 ▲ │
│ ├── Value Perception (VPI-C): 61.9 ▼ │
│ └── Trust & Loyalty (TLI): 84.5 → │
│ │
│ ALERTS (3 Active) │
│ ├── [!] V domain below target (68 < 70) │
│ ├── [!] CR-W spike in J1.01 (Wait Time) │
│ └── [!] 5 open I3 issues > 24h old │
│ │
└─────────────────────────────────────────────────────────────────────────┘
```
**Visualizations**:
- Gauge charts for each domain score
- Trend arrows with MoM delta
- Alert badges for threshold breaches
- Mini sparklines for 12-week trends
---
### 7.2 Operational View (Category + Issues)
**Display Elements**:
```
┌─────────────────────────────────────────────────────────────────────────┐
│ OPERATIONAL DASHBOARD │
│ Domain: O (Offering) | Categories: O1-O4 │
├─────────────────────────────────────────────────────────────────────────┤
│ │
│ CATEGORY BREAKDOWN │
│ ┌──────────────────────────────────────────────────────────────────┐ │
│ │ O1 Function ████████████████████████░░░░░░ 85% ▲ +3.1% │ │
│ │ O2 Quality ██████████████████████░░░░░░░░ 78% ▲ +2.5% │ │
│ │ O3 Completeness████████████████████████████░░ 92% → +0.2% │ │
│ │ O4 Fit █████████████████████░░░░░░░░░ 73% ▼ -1.8% │ │
│ └──────────────────────────────────────────────────────────────────┘ │
│ │
│ ACTIVE ISSUES (O Domain) │
│ ┌─────────────────────────────────────────────────────────────────┐ │
│ │ ID │ Subcode │ State │ Priority │ Age │ Spans │ Owner │ │
│ ├─────────────┼─────────┼───────┼──────────┼──────┼───────┼───────┤ │
│ │ ISSUE-0142 │ O2.05 │ INP │ 5.60 │ 3d │ 5 │ Ops │ │
│ │ ISSUE-0156 │ O4.01 │ ACK │ 4.20 │ 1d │ 3 │ Ops │ │
│ │ ISSUE-0161 │ O1.04 │ DET │ 3.85 │ 4h │ 2 │ Prod │ │
│ └─────────────┴─────────┴───────┴──────────┴──────┴───────┴───────┘ │
│ │
│ ISSUE METRICS │
│ ├── Open Issues: 12 │
│ ├── Avg Resolution Time: 2.3 days │
│ ├── Recurrence Rate: 8.2% │
│ └── SLA Compliance: 91.5% │
│ │
└─────────────────────────────────────────────────────────────────────────┘
```
**Visualizations**:
- Horizontal bar charts for category scores
- Issue list with sortable columns
- Pie chart for issue state distribution
- Timeline for issue age distribution
---
### 7.3 Deep-Dive View (Subcodes + Trends)
**Display Elements**:
```
┌─────────────────────────────────────────────────────────────────────────┐
│ DEEP-DIVE: O2 Quality │
│ Period: 2026-01 | 347 spans │
├─────────────────────────────────────────────────────────────────────────┤
│ │
│ SUBCODE DISTRIBUTION │
│ ┌────────────────────────────────────────────────────────────────┐ │
│ │ O2.01 Materials │████████████░░░░│ 89 spans │ 72% V+ │ +5% │ │
│ │ O2.02 Craftsmanship│█████████░░░░░░░│ 67 spans │ 81% V+ │ +2% │ │
│ │ O2.03 Presentation │████████████████│ 98 spans │ 85% V+ │ +8% │ │
│ │ O2.04 Detail │██████░░░░░░░░░░│ 45 spans │ 68% V+ │ -3% │ │
│ │ O2.05 Condition │████████░░░░░░░░│ 48 spans │ 52% V+ │ -12% │ │
│ └────────────────────────────────────────────────────────────────┘ │
│ │
│ 12-WEEK TREND: O2.05 (Condition at Delivery) │
│ ┌────────────────────────────────────────────────────────────────┐ │
│ │ 80% ┤ │ │
│ │ 70% ┤ *───* │ │
│ │ 60% ┤ * *───* │ │
│ │ 50% ┤* *───*───* │ │
│ │ 40% ┤ *───*───* │ │
│ │ └──W1──W2──W3──W4──W5──W6──W7──W8──W9─W10─W11─W12────── │ │
│ └────────────────────────────────────────────────────────────────┘ │
│ │
│ COMPARATIVE REFERENCE SIGNALS │
│ ├── CR-B (Better): 3 spans (6%) │
│ ├── CR-W (Worse): 8 spans (17%) ← ALERT: Above 15% threshold │
│ ├── CR-S (Same): 5 spans (10%) │
│ └── CR-N (None): 32 spans (67%) │
│ │
│ INTENSITY DISTRIBUTION │
│ ├── I1 (Mild): 12 spans (25%) │
│ ├── I2 (Moderate): 28 spans (58%) │
│ └── I3 (Strong): 8 spans (17%) ← 4 negative I3 spans │
│ │
│ SAMPLE SPANS (Most Recent) │
│ ├── "Food arrived cold again" - V-, I2, CR-S (Jan 22) │
│ ├── "Temperature was perfect this time!" - V+, I2, CR-B (Jan 21) │
│ └── "Stone cold pizza, unacceptable" - V-, I3, CR-N (Jan 20) │
│ │
└─────────────────────────────────────────────────────────────────────────┘
```
**Visualizations**:
- Stacked bar charts for subcode distribution
- Line charts for trend analysis
- Heatmaps for intensity by subcode
- Word clouds for span text analysis
---
### 7.4 Visualization Recommendations by KPI Type
| KPI Type | Primary Viz | Secondary Viz | Interaction |
|----------|-------------|---------------|-------------|
| Domain Score | Gauge/Dial | Trend sparkline | Drill to categories |
| Category Score | Horizontal bar | Comparison to benchmark | Drill to subcodes |
| Trend | Line chart | Moving average overlay | Zoom/pan time range |
| Distribution | Pie/Donut | Treemap for hierarchy | Filter by segment |
| Comparison | Grouped bar | Bullet chart | Toggle comparison periods |
| Volume | Area chart | Stacked area for breakdown | Highlight anomalies |
| Correlation | Scatter plot | Heatmap matrix | Identify clusters |
---
## 8. Alert Rules
### 8.1 Threshold-Based Alerts
| Alert Level | Condition | Response Time | Notification |
|-------------|-----------|---------------|--------------|
| **Critical** | Any domain < Red threshold | Immediate | SMS + Email + Dashboard |
| **Warning** | Any domain < Yellow threshold | 4 hours | Email + Dashboard |
| **Info** | Any domain < target but in green | 24 hours | Dashboard only |
**Domain-Specific Thresholds**:
```yaml
alerts:
O_offering:
critical: < 60
warning: < 80
target: >= 80
P_people:
critical: < 70
warning: < 85
target: >= 85
J_journey:
critical: < 55
warning: < 75
target: >= 75
E_environment:
critical: < 65
warning: < 80
target: >= 80
A_access:
critical: < 70
warning: < 85
target: >= 85
V_value:
critical: < 50
warning: < 70
target: >= 70
R_relationship:
critical: < 60
warning: < 80
target: >= 80
```
---
### 8.2 Trend-Based Alerts
| Alert | Condition | Lookback | Action |
|-------|-----------|----------|--------|
| **Declining Domain** | Score drops > 5 points for 2+ consecutive periods | 3 periods | Investigate root cause |
| **Accelerating Decline** | Rate of decline increasing | 4 periods | Escalate to leadership |
| **Stalled Recovery** | No improvement after intervention | 6 periods | Re-evaluate strategy |
| **Regression After Fix** | Score drops after improvement | 2 periods | Review resolution quality |
**Configuration**:
```yaml
trend_alerts:
declining_domain:
threshold: -5
consecutive_periods: 2
severity: warning
accelerating_decline:
acceleration_threshold: -2 # decline rate increasing by 2+
periods: 4
severity: critical
stalled_recovery:
improvement_threshold: 3 # expecting +3 after fix
periods: 6
severity: warning
```
---
### 8.3 Volume-Based Alerts
| Alert | Condition | Window | Action |
|-------|-----------|--------|--------|
| **I3 Spike** | I3 negative count > 2x rolling average | 7 days | Immediate triage |
| **Review Surge** | Total reviews > 3x typical volume | 24 hours | Check for viral event |
| **Complaint Cluster** | Same subcode appears 5+ times in window | 48 hours | Create/prioritize issue |
| **Domain Overload** | Single domain > 50% of all feedback | 7 days | Investigate systemic cause |
**Configuration**:
```yaml
volume_alerts:
i3_spike:
multiplier: 2.0
window_days: 7
min_count: 3 # at least 3 I3s to trigger
severity: critical
review_surge:
multiplier: 3.0
window_hours: 24
severity: info
complaint_cluster:
count_threshold: 5
window_hours: 48
severity: warning
domain_overload:
percentage_threshold: 50
window_days: 7
severity: info
```
---
### 8.4 Comparative (CR) Alerts
| Alert | Condition | Action |
|-------|-----------|--------|
| **CR-W Surge** | CR-W rate > 15% of all CR spans | Flag potential regression |
| **CR-S Persistence** | CR-S count >= 3 on same subcode in 30 days | Flag unresolved issue |
| **CR-B Absence** | No CR-B on resolved issue within verification window | Question resolution |
| **Perception Decline** | Perception_Trend < -0.3 | Escalate to leadership |
**Configuration**:
```yaml
cr_alerts:
cr_w_surge:
threshold_rate: 0.15
window_days: 30
severity: critical
cr_s_persistence:
count_threshold: 3
window_days: 30
severity: warning
cr_b_absence:
resolved_issue_window_days: 60
severity: info
perception_decline:
trend_threshold: -0.3
severity: critical
```
---
## 9. Integration with Issue Lifecycle (C1)
### 9.1 Linking KPI Movements to Open Issues
**Correlation Analysis**:
```
FOR each domain D:
IF D_score decreased this period:
open_issues = GET_ISSUES(domain=D, state IN [DET, ACK, INP])
FOR issue IN open_issues:
IF issue.span_count increased this period:
FLAG(issue, "Contributing to domain decline")
IF issue.priority_score > threshold:
RECOMMEND("Prioritize " + issue.id)
```
**Issue Impact Scoring**:
```
Issue_Impact = (span_count / total_domain_spans) * intensity_weight * age_factor
Where:
age_factor = 1.0 + (days_open / 30) # older issues have higher impact
```
---
### 9.2 Resolution Impact on Metrics
**Verification Impact Model**:
```
Expected_Improvement = (
resolved_issue_span_count / total_domain_spans
) * resolution_effectiveness
resolution_effectiveness = {
VERIFIED: 1.0, # Full impact expected
RESOLVED: 0.7, # Partial impact (not yet verified)
REOPENED: -0.5 # Negative impact (false resolution)
}
```
**Tracking Resolution Effectiveness**:
| Metric | Formula | Target |
|--------|---------|--------|
| **Resolution Impact Rate** | Domain improvement post-resolution / Expected improvement | >= 80% |
| **Verification Rate** | VERIFIED issues / RESOLVED issues (within window) | >= 70% |
| **False Resolution Rate** | REOPENED issues / RESOLVED issues | < 10% |
---
### 9.3 Leading vs Lagging Indicators
**Leading Indicators** (Predictive):
| Indicator | Predicts | Lead Time |
|-----------|----------|-----------|
| CR-W spike in subcode | Domain score decline | 2-4 weeks |
| I3 negative surge | Issue escalation | 1-2 weeks |
| New issue detection rate | Operational challenges | 1-3 weeks |
| Repeat customer CR-S | Loyalty decline | 4-8 weeks |
**Lagging Indicators** (Confirmatory):
| Indicator | Confirms | Lag Time |
|-----------|----------|----------|
| Domain score change | Impact of initiatives | 4-6 weeks |
| CR-B rate increase | Resolution effectiveness | 2-4 weeks |
| Issue verification rate | Process quality | 4-8 weeks |
| Overall Experience Index | Business health | 6-12 weeks |
---
### 9.4 Predictive Signals from URT Data
**Issue Emergence Prediction**:
```
P(new_issue) = f(
i1_clustering, # Mild complaints clustering
intensity_escalation, # I1 -> I2 -> I3 pattern
cr_s_accumulation, # "Still" comments building up
specificity_trend # S1 -> S2 -> S3 (getting more specific)
)
Trigger early warning when:
P(new_issue) > 0.6 AND affected_reviews > 5
```
**Churn Risk Signal**:
```
Churn_Risk = (
0.30 * (R4.05 negative rate) + # "Never again" signals
0.25 * (V4.04 negative rate) + # "Would not recommend"
0.20 * (CR-W rate) + # Perceived decline
0.15 * (issue_recurrence_rate) + # Repeated problems
0.10 * (TH temporal_pattern) # "Always been like this"
)
Alert when Churn_Risk > 0.5
```
**Recovery Prediction**:
```
P(successful_recovery) = f(
issue_age, # Younger = better
resolution_speed, # Faster = better
r3_score, # Better recovery actions = better
cr_b_early_signals # Early CR-B = better
)
Adjust TTR targets based on P(successful_recovery)
```
---
## Document Control
| Field | Value |
|-------|-------|
| **Document** | C2 - KPI Mapping Guide |
| **Version** | 1.0 |
| **Status** | Production Ready |
| **Date** | 2026-01-23 |
| **Author** | URT Working Group |
| **Depends On** | URT Specification v5.1, C1-Issue-Lifecycle-Framework |
| **Part Of** | Track C: Analytics Layer |
---
## Related Documents
| Document | Purpose | Status |
|----------|---------|--------|
| **URT Specification v5.1** | Core taxonomy and classification rules | Frozen |
| **C1 - Issue Lifecycle Framework** | Issue tracking and resolution states | Complete |
| **C3 - Benchmark Framework** | Cross-business comparison | Planned |
| **C4 - Alert & Escalation Rules** | Automated notification logic | Planned |
---
*End of C2: KPI Mapping Guide*
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