# Value Creation Model

## Purpose

Explain how the company creates measurable or directional value for local service businesses.

## Core Value Thesis

The company creates value by reducing Silent Customer Loss through continuous monitoring, diagnosis, prioritization, and approved low-risk optimization of digital pathways that affect discovery, trust, and customer action.

## Value Categories

### Customer Path Leakage

Customer Path Leakage occurs when a customer has interest or intent but fails to contact, book, visit, or otherwise engage the business because of digital friction.

This category often produces **verified evidence** because the issue can be tied to a concrete defect, timestamp, platform state, or measurable artifact.

### Discovery Failure

Discovery Failure occurs when a potential customer never meaningfully encounters the business because customers, search engines, or AI systems do not find, understand, surface, or accurately represent it.

This category often produces **indicative evidence** because absence or weak representation in search or AI-assisted discovery can be sampled and demonstrated, but not always proven as a direct lost customer.

## Evidence Tiers

### Tier 1: Verified Evidence

Verified Evidence is based on concrete, timestamped, observable, or instrument-backed findings.

**Examples:**
- Wrong phone number
- Incorrect business hours
- Broken booking link
- Broken contact form
- Incorrect listing data
- Indexing problem
- Metadata problem
- Canonical tag issue
- Missing or incorrect Google Business Profile information

**Reporting standard:**

Verified Evidence may be presented as a confirmed issue.

### Tier 2: Indicative Evidence

Indicative Evidence is based on sampled, directional, or probabilistic findings that suggest risk or opportunity but do not prove direct customer loss.

**Examples:**
- AI answer absence
- AI misrepresentation
- Weak entity clarity
- Inconsistent service descriptions
- Thin FAQ coverage
- Missing structured data
- Competitors appearing more consistently in AI answer samples
- Weak local discovery signals

**Reporting standard:**

Indicative Evidence must be presented as directional risk or opportunity, not as confirmed customer loss.

## Reporting Principle

Reports must distinguish between confirmed issues and directional risks.

The company should not overstate indicative findings as proven customer loss.

## Before/After Proof

Customer Path Leakage may support stronger before/after proof.

Discovery Failure may support directional before/after comparison, but should be labeled as sampled or indicative when appropriate.

# Evidence Tier Assignment Authority

Agents may propose an evidence tier for a finding, but the final evidence tier used in any client-facing report must be approved by a human reviewer during Version 1.

A finding may only be classified as Verified Evidence when it is supported by concrete, current, observable, timestamped, or instrument-backed evidence.

If a finding does not clearly meet the standard for Verified Evidence, it defaults to Indicative Evidence until a human reviewer confirms otherwise.

Ambiguous findings must not be presented as confirmed customer loss.

If an agent proposes a finding as Verified Evidence and the human reviewer rejects that classification, the finding defaults to Indicative Evidence unless the reviewer discards the finding entirely or requests additional evidence.

**Examples:**

- A currently visible wrong phone number on a verified business profile may qualify as Verified Evidence.
- A broken booking link observed and timestamped during review may qualify as Verified Evidence.
- A stale or unconfirmed citation mismatch defaults to Indicative Evidence until confirmed.
- Absence from sampled AI answers is Indicative Evidence.
- Agent-reported findings without supporting artifacts are not sufficient for Verified Evidence.