Case study
Two-Location Restaurant Chain
Restaurants
How does a local restaurant concept become a national expansion story?
Location intelligence helped leadership prioritize markets, understand customers, and reduce underperformance risk.
Problem
What was at stake?
A local poke restaurant had a winning concept but needed a scalable, data-backed approach to identify markets where new stores would thrive.
MapZot.AI work
How the decision was modeled.
Outcome
What became clearer?
Cost of being wrong
$500K–$1.5M per store
Scaling on intuition can create uneven site performance, poor market sequencing, and expensive expansion misses.
The goal was not more data. The goal was a cleaner decision before capital, lease commitments, buildout time, and leadership attention were locked in.
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Related location decisions
Franchise & multi-unit
BIGGBY Franchisee
next locations
Where should we open the next 20 — and which stores should close or relocate?
Next-market planning plus close / relocate recommendations.
Automotive & car wash
AquaSonic Car Wash
before investment
Which locations will perform before we invest?
Sales forecasting and high-performing site prioritization before capital deployment.
Emerging restaurants
Ahi & Vegetable
right location
We may only open one store. Can we forecast it accurately?
Precision site modeling for one high-confidence opening over 12–18 months.