# ICP Definition – geolocal.io

> **Single source of truth for who we sell to.** Aligns with [Canonical Strategy](../strategy/CANONICAL_STRATEGY.md) §1a.  
> Last updated: 2026-07-18

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## Primary ICP (Tier 1 – best fit)

| Attribute | Description |
|-----------|-------------|
| **Business type** | Independent, locally owned SMBs with a **physical location** or clear local service radius. Includes **services and local retail**. |
| **Typical verticals** | **Services:** wellness, personal care, home services, auto repair, tourism activities. **Local retail:** general stores, hardware, pharmacies, gift/outfitter shops, specialty food. **Hybrid:** bike shops, marinas, bakeries that sell and serve. |
| **Employees** | 1–15 (solo operators to small teams). |
| **Revenue** | Roughly $50k–$1M annual gross (directional, not a hard gate). |
| **Tech maturity** | Has some web presence (even thin). May use booking tools, POS, or neither. No dedicated AI/engineering team. |
| **Pain point** | AI assistants cannot reliably describe them, recommend them, or complete a next step (book, visit, buy/pickup). They lose the “named recommendation” moment. |
| **Motivation** | Want AI to tell the truth about what they do or carry — without learning a new craft stack. Will pay if it drives visits, calls, bookings, or sales. |

**Illustrative retail case:** A general store in Athol, Idaho should be able to make ChatGPT-class assistants answer: we carry toothbrushes; the chicken strips are a local favorite; here are hours and how to get here — from the store’s own structured truth, not a guess.

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## Secondary ICP (Tier 2)

| Attribute | Description |
|-----------|-------------|
| **Business type** | Multi-location local brands or regional operators (a handful to ~20 locations). |
| **Typical verticals** | Multi-site auto, dental, tutoring, small retail chains with local stores. |
| **Motivation** | Standardize AI-readiness across locations with light ops burden. |

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## Intermediate ICP (distribution, not the end SMB)

| Attribute | Description |
|-----------|-------------|
| **Type** | Tourism boards, visitor bureaus, chambers of commerce. |
| **Job** | Fund and aggregate member AI-readiness; improve destination helpfulness. |
| **See** | [UC-02](../product/use-cases/uc-02-intermediate-tourism-board.md) |

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## Shared fit signals

- Place-based: consumers ask about them with a location in mind  
- Owner or store manager can decide (not only enterprise procurement)  
- Willing to put a small discovery pointer on a site or landing page  
- Some path to value: appointments **or** walk-in/pickup/retail demand **or** both  
- Often already spending something on ads, SEO, or web help (not mandatory)

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## Buying signals

1. “ChatGPT / AI doesn’t know we exist” or recommends a competitor  
2. Phone and foot traffic still matter; online presence is weak or outdated  
3. Agency or chamber asking about AI search / AI visitor experience  
4. Tourism or main-street context where “what’s here / what do they have” is the job  

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## Anti-ICP (do not target)

- **Pure e-commerce** with no meaningful local footprint (Shopify’s world)  
- Enterprise with large dedicated engineering teams building their own agent stack  
- Pure B2B with no consumer or visitor discovery motion  
- National marketplaces and travel packagers (Airbnb/Kayak class) as primary customers  

Note: “Does not take online appointments” is **not** automatic anti-ICP. Many strong retail fits will not be booking-led.

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## How to use this file

- **GTM:** lead scoring and partner briefs  
- **Product/engineering:** genre packs and data model must support services **and** local retail primitives  
- **Investors:** wedge is long-tail **local** businesses, not “appointments SaaS only”

*Update this file first when ICP changes, then propagate.*