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Segment SMB Prospects by Physical Footprint With Enigma

Segment SMB Prospects by Physical Footprint With Enigma

Turn a list of US SMBs into tiered outbound segments using Enigma, so “Joe’s Pizza with 1 location” and “Joe’s Pizza with 28 locations” don’t end up getting the same cold email.

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A 4-location family restaurant and a 28-location regional chain are not the same prospect. They have different buyers, different pain points, and very different budgets  but most outbound lists treat them identically, because they both come back as “restaurants” from a standard firmographic pull.

This Claybook uses Enigma’s data to close that gap. You’ll start with a list of US restaurants and end with a cleanly segmented table that tells you who’s a solo owner-operator, who’s scaling locally, who’s an emerging chain, and who’s gone regional — plus the geographic spread of their footprint, so a “4 locations all in Virginia” operator doesn’t get the same message as a “4 locations across 4 states” operator.

Each row ends up assigned to one of six messaging groups, ready to hand off to your copywriter, your AI personalization step, or your SDR routing logic.

How it works:

  • Pull a list of US SMBs with physical operations from Clay’s Company Search
  • Resolve each company cleanly against Enigma’s dataset
  • Enrich with operating location addresses and count
  • Bucket companies by location count and geographic spread
  • Auto-assign each row to one of six messaging groups for outbound routing
Information
Follow the step by steps here in Clay
Download the free template
1

If you’re already a user, you’ll be directed to your workspace. If not, you’ll be prompted to create a free Clay account.

The interactive demos on the right will walk you through each step of this Claybook. 

2

The template starts with a Company Search pre-filtered for US restaurants, 10–1,000 employees. Swap the filters for your target vertical. 

Already have a list? Swap the Company Search source for a CSV import. Just make sure you’re passing company name, domain, and state at minimum — Enigma needs those to land a clean identity match in the next step.

3

Enigma’s Resolve company identity action matches each company against its dataset using Legal Entity Name, Company Name, and Company Website.

4

This is the hero step. Get operating location addresses returns an array of physical addresses (up to 100 per company) plus a location count. A single bistro returns 1 address; a regional chain returns 28.

5

Three pre-built formula columns do the segmentation work:

  • Location Tier — Independent (1) / Local Multi-Unit (2–5) / Emerging Chain (6–20) / Regional+ (21+)
  • Unique State Location Count — parses the addresses array to count distinct US states
  • Geographical Spread — Single-state / Multi-state regional (2–3) / National (4+)

The tier thresholds map to real organizational shifts: location #2 is when owner-operators stop doing it all themselves, #6 is when a VP of Ops typically gets hired, #21 is when procurement and multi-location finance formalize. Copy the formulas as-is, or tweak the thresholds to match how your team defines tiers.

6

The final column crosses Location Tier with Geographical Spread to assign each row to one of six messaging groups:

  • A — Owner-Operator (1 loc, single-state): owner is the decision-maker
  • B — Local Scaler (2–5, single-state): just hired their first ops person
  • C — Early Regional (2–5, multi-state): already navigating multi-state complexity
  • D — Regional Chain (6–20, single-state): VP Ops territory; regional density is the moat
  • E — Multi-State Chain (6–20, multi-state regional): multi-stakeholder cycle; consistency is the pain
  • F — Scaling National (6–20 national, or 21+): infrastructure framing beats cost framing

Hand this column off to your copywriter, feed it into an AI personalization step, or use it to route leads to different SDR queues. Segmentation done.

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