veripath/docs/business-model/value-creation-model.md

# 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.

The evidence-tier rules above govern how a finding is *reported*. The section below governs a separate question: which clients are worth prioritizing in the first place.

## Client Targeting: Visibility × Capacity × Ticket Size

**Status:** Accepted — 2026-08-06
**Origin:** Derived from Layer 3 conversation design (Capacity question) and cross-checked against the economics capture tools

### The problem with "target high-volume businesses"

Volume is a useful proxy for value, but it's not the same variable, and treating it as one leads to mistargeting. Three things actually determine how much a DOP engagement is worth to a given business, and they can point in different directions:

| Variable        | What it measures                                                              | Why it matters                                                                                                                                                                                           |
| --------------- | ------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Visibility**  | How many people already see this business in search/Maps/AI answers per month | A discovery failure only costs money if there was traffic to lose. Fixing a booking button on a 5-review business with no search presence recovers close to nothing.                                    |
| **Capacity**    | Whether the business could actually absorb more clients right now             | If a business is already turning people away, more discovered leads don't convert to more revenue — they convert to a longer waitlist. The value proposition has to shift from "more customers" to "better customers" or "less operational friction." |
| **Ticket size** | Revenue per transaction or per client relationship                            | A lower-volume, high-ticket business can have more dollars at stake per lost lead than a high-volume, low-ticket one. Volume alone undercounts value here.                                              |

**The claim this section replaces:** "higher volume = higher value." **The corrected claim:** high value requires high visibility *and* either open capacity or high ticket size. Volume alone, without one of those two, does not reliably predict what the engagement is worth.

### Practical segmentation

|                     | **Open capacity**                                                                                                                                                                                        | **At/near capacity**                                                                                                                                                                                     |
| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **High visibility** | Best-fit segment. Discovery fixes convert directly into new revenue — the standard "plug the leak" pitch applies cleanly.                                                                              | High-value but different pitch. Frame around protecting reputation, raising ticket size, and filtering for higher-value clients — not lead volume. See Capacity branch in the rapid economics capture tool. |
| **Low visibility**  | Lower near-term value regardless of capacity — there isn't much traffic to recover yet. May still be worth pursuing on ticket size alone, or as a longer-term visibility-building engagement rather than a quick-win pitch. | Lowest-priority segment for this service as currently scoped — little to recover and nowhere to put it if recovered.                                                                                   |

Ticket size is a modifier across all four cells: a high-ticket business in any cell is worth more attention than the table alone suggests, since the dollar value of each recovered or protected client is larger.

### Where this data comes from

- **Visibility** — approximated from Layer 1/2 findings: review count, listing completeness, whether the business shows up for its own name and category searches.
- **Capacity** — the Layer 3 Capacity question ("if 10 extra clients called next month, could you take them or would you turn them away?"), captured verbatim in `economics-v1.md`.
- **Ticket size** — average transaction value or client relationship value, if the owner states it during Layer 3; otherwise inferred loosely from vertical and observed pricing on the GBP listing (Tier 2 only, never presented to the client as verified).

### What this is not

This is a targeting heuristic for prioritizing outreach, not a scoring formula with hard numeric thresholds. Do not assign point values or automate a go/no-go decision from it without real data across multiple clients first — per the maturity ladder, this framework should be applied manually and adjusted based on what actually predicts a good engagement, before any part of it gets automated.