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Add Client Targeting section (Visibility × Capacity × Ticket Size) to value-creation-model

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2026-08-06 14:31:05 +00:00
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@@ -91,4 +91,40 @@ If an agent proposes a finding as Verified Evidence and the human reviewer rejec
- 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.
- Agent-reported findings without supporting artifacts are not sufficient for Verified Evidence.
## Client Targeting: Volume × Capacity × Ticket Size
**Status:** Proposed addition, pending review
**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 core 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.