Client Build · Entertainment Franchise
AI platform that scores franchise locations in 2 minutes.
A national entertainment franchise needed a repeatable, data-driven way to evaluate new sites before committing $500K–$2M per location.
A national entertainment franchise needed a repeatable, data-driven way to evaluate new sites before committing $500K–$2M per location.
The problem
Opening a new franchise location is one of the most capital-intensive bets a leadership team makes. For this client, the process was almost entirely manual — market research handed off to consultants, spreadsheets, and institutional instinct.
$500K–$2M
Average new location cost
Each site commitment carries construction, fit-out, lease obligations, and staffing. A single bad location decision can take years to unwind. The risk of getting it wrong is enormous.
Gut feel
Previous evaluation method
Site selection was driven by experience and instinct rather than a repeatable scoring model. There was no standardized framework for comparing one market against another, and no way to benchmark a new address against what the brand's best locations had in common.
Weeks
Time spent per site manually
Evaluating a single candidate address required pulling demographic reports, cross-referencing competitor data, and coordinating across teams. The process didn't scale, and leadership had no way to quickly triage a large shortlist.
The solution
We built a custom scoring engine trained on 20 existing locations, then wrapped it in two tools leadership could actually use — without any paid data subscriptions or third-party research vendors.
Enter any US street address. The platform geocodes it to a Census tract, pulls real American Community Survey demographic data for that tract and its neighbors, then runs the address through the brand's scoring model. Output: a HIGH / MEDIUM / LOW classification, a projected revenue range, and a confidence score. No paid data subscriptions. No analyst required. Under 2 minutes from address to result.
Stack: US Census Bureau Geocoder API · ACS 5-year estimates · Custom ML scoring engine · Static HTML frontend.
All 94 major US metros scored and ranked by expansion potential against the brand's location fingerprint. Leadership can open the dashboard and immediately see which markets are Tier 1 priorities, which are worth monitoring, and which to skip — without commissioning a custom research project for each city they're curious about.
Stack: ACS metro-level data · Brand fingerprint scoring · Ranked HTML dashboard · No server required.
How the model works
We analyzed Census tract data for every existing franchise location and built a weighted scoring model around the demographic signals that most strongly correlated with revenue performance. Five variables. All public data. No black box.
30%
Median household income
25%
25–34 age cohort
20%
College-educated population
15%
Tract population density
10%
Employment base
The results
From weeks of manual research to a 2-minute scored output — and a ranked pipeline of every major US market ready to go on day one.
80.65%
Model accuracy
Predictions fell within ±15% of actual revenue for locations in the validation set. High confidence threshold established for production use.
94
Markets ranked
Every major US metro scored against the brand fingerprint. Leadership now has a prioritized expansion pipeline without commissioning a single research project.
2 min
Per site evaluation
Down from weeks of manual research. Any team member can score a candidate address in real time, on any device, with no technical knowledge required.
The stack
The entire platform runs on public Census data and a static frontend. That means zero recurring data costs and nothing to maintain beyond the scoring model itself.
Got a high-stakes location decision?
If your business makes location decisions — franchises, retail chains, service area expansion — we can build you the same kind of data-driven scoring engine. Book a free 30-minute call to see if it's a fit.
Book a Free Audit Call