whyrating-engine-legacy/urt-taxonomy/track-c-analytics/C2-KPI-Mapping-Guide.md

# 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*