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:

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:

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:

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:

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