USA QSR Site Selection Data Scraping Case Study — 300 New Locations Screened, 42 Approved
How a national USA QSR chain used USA QSR site selection data scraping across DoorDash and Uber Eats to screen 300 candidate locations by catchment density, cuisine mix and rating patterns — and approved 42 sites for a sequenced $46M expansion program across 25 US metros.
Client overview
Who the client is
The client is a national USA QSR chain operating 400+ existing outlets across 25 metros, with an aggressive expansion mandate to add 40+ new locations over 18 months. The chain's real estate committee had been evaluating candidate sites through a mix of broker referrals, franchisee lobbying, and mall developer pitches — an approach that historically produced a 28% new-outlet failure rate within 24 months. Each failed location represented $1.8M–3.2M in sunk lease, fit-out, and marketing costs, plus meaningful reputational drag with franchise partners. The committee wanted the next 40+ approvals to be underwritten by systematic delivery-catchment intelligence — not broker enthusiasm. They needed reliable USA QSR site selection intelligence at catchment-level resolution to screen 300 candidate locations across DoorDash and Uber Eats footprints, filter to a defensible shortlist, and sequence a $46M expansion program. Names are anonymized for confidentiality; metrics are shown exactly as delivered.
Objectives
What they wanted to achieve
- Screen 300 candidate locations across 25 US metros with delivery-catchment data
- Map merchant footprints, ratings, cuisine density from DoorDash and Uber Eats
- Identify catchments where competitive density exceeded demand signals
- Cut new-outlet failure rate from the 28% historical baseline
- Approve 40+ sites with data-anchored underwriting for the expansion program
- Replace broker-led and franchisee-lobbied site selection with catchment-led decisions
The challenge
300 sites, 12 real-estate analysts, and a 28% failure rate the committee could no longer defend
The chain's real estate committee had a 300-candidate pipeline sourced from brokers across 25 metros — and 12 analysts spent 3–4 months per cycle evaluating them using demographics, foot-traffic estimates, and site visits. But post-mortems on the 28% of new outlets that failed consistently showed one signal the committee had ignored: delivery-catchment competitive density. Locations that looked strong on demographics were structurally over-served on DoorDash and Uber Eats — with 15+ direct competitors in the same cuisine × price band already operating within a 3-mile catchment, and top incumbents already showing 6-month declining review velocity. A specific example: a suburban Dallas location approved on strong daytime foot-traffic showed 18 competitors in the same catchment on DoorDash pre-launch, with 4 of the top 5 incumbents in decline. The outlet closed within 22 months. The committee realized that delivery-catchment data was now more predictive of QSR outlet success than the traditional broker-and-foot-traffic model — and they were flying blind on the single strongest signal available.
The solution
A 300-candidate USA catchment intelligence pipeline
FoodDataScrape built a USA QSR site selection data scraping pipeline across DoorDash and Uber Eats covering 300 candidate locations across 25 US metros — with per-catchment merchant footprints, ratings distribution, cuisine density, review velocity, and 24 months of historical time-series. The 24-month history window was chosen to distinguish structural saturation from seasonal variance and to reveal whether incumbent operators were strengthening or declining before the chain committed to a lease. The build went live in six weeks; the first sequenced shortlist of 42 approved sites was delivered five weeks after go-live, and the pipeline continues to screen new candidate locations monthly as they enter the funnel.
Map 300 candidate catchments
We mapped each of the 300 candidate locations to DoorDash and Uber Eats delivery-zone polygons — producing per-location catchments with all listed merchants, cuisines, price bands, and platform coverage, reconciled across both platforms for a comparable competitive view.
Extract merchant footprint metrics
Per-catchment extractors captured merchant density, ratings distribution, cuisine mix, price-band spread, review velocity trends, and 24 months of history. Review velocity — reviews per merchant per week — surfaced as our strongest leading indicator, dropping 4–7 months before incumbent closures across the panel.
AI catchment-fit scoring
An AI catchment-fit layer scored every candidate location for site-selection risk by cuisine × price band × demand signals — ranking all 300 sites, flagging structurally saturated catchments as no-go, and surfacing the 42 highest-conviction sites for approval with straight-through underwriting support.
The AI layer
How does AI-assisted USA catchment site-selection scoring work?
AI-assisted USA catchment site-selection scoring combines USA QSR site selection data scraping with per-catchment merchant density, cuisine mix, ratings and review velocity — ranking candidate locations by data-anchored fit and flagging structurally saturated catchments before a lease is signed.
On top of the raw feed, an AI catchment-fit layer turned per-location data into USA QSR site selection intelligence: it scored every candidate location for site-selection risk by cuisine category × price band × demand signals, identified whitespace catchments with meaningful unmet demand, and produced a sequenced shortlist of 42 approved sites for the chain's next expansion phase. Review velocity emerged as the single strongest leading indicator — declining review velocity across incumbents precedes closures by 4–7 months in the US market, giving the committee real lead time before market weakness became visible in traditional real-estate signals.
- Screened 300 candidate locations across DoorDash and Uber Eats in 25 US metros
- Ranked all 300 sites by catchment-fit score with straight-through underwriting support
- Flagged 89 catchments as structurally saturated — no-go zones removed from the funnel
- Surfaced 42 highest-conviction sites for approval with data-anchored priority ordering
- Identified review velocity as the strongest leading indicator (4–7 months ahead of closures)
- Guided $46M in expansion capital allocation across the sequenced 42-site pipeline
Data captured
What data we captured
The pipeline captured a full USA QSR site selection data intelligence view. Every data point feeds directly into a catchment-fit score — merchant density measures competitive intensity, ratings distribution reveals customer standards, review velocity tracks momentum, cuisine mix identifies gap opportunities, and price-band spread surfaces the pricing corridor the incumbents are competing within:
| source | method | fields |
|---|---|---|
| DoorDash | DoorDash data scraping | catchment · merchants · ratings |
| Uber Eats | Uber Eats data extraction | catchment · velocity · cuisine mix |
| AI catchment-fit layer | Cuisine × price × demand scoring | 300 sites · ranked shortlist |
| Historical panel | 24-month time-series backfill | trend · seasonality · decline |
BEFORE VS AFTER
Before vs After Comparison
| Metric | Before | After (FoodDataScrape) |
|---|---|---|
| Site selection approach | Broker + demographics + foot traffic | Data-anchored catchment scoring |
| Sites evaluated per cycle | 12 analysts · 3–4 months | 300 sites screened in 5 weeks |
| Whitespace identification | Post-failure realisation | Pre-lease evidence-led |
| Metro coverage | Regional focus | 25 US metros in one panel |
| Time to approved shortlist | 3–4 months manual | 5 weeks automated |
| New-outlet failure rate | 28% within 24 months | Cut sharply on the sequenced 42 sites |
ROI impact
From assumption to measurable ROI
Full candidate pipeline evaluated in one systematic pass.
Data-anchored shortlist with straight-through underwriting.
Sequenced pipeline underwriting delivered to the committee.
No-go sites filtered before broker fees or lease commitments.
The data replaced the broker-and-demographics model with a defensible catchment-fit scoring layer — and gave the real estate committee an underwriteable 42-site pipeline anchored to catchments with measurable demand headroom versus incumbent supply.
Client testimonial
In the client's words
"Every failed store had catchment-level warning signs visible on DoorDash and Uber Eats before we signed the lease. The demographic reports never surfaced them. Now the catchment data is the first filter in our funnel — the failure rate on this pipeline is going to look nothing like the last one."
— VP Real Estate, national USA QSR chain (name withheld)
Why FoodDataScrape
Why they chose FoodDataScrape
- Specialists in USA food delivery data scraping
- DoorDash & Uber Eats coverage out of the box
- AI-assisted catchment-fit site-selection scoring
- 25-metro US coverage with 24-month historical time-series
- Compliance-aware sourcing and dedicated USA analyst support
- Live in six weeks with a free proof-of-concept first
Questions
Frequently Asked Questions
It combines DoorDash and Uber Eats catchment data scraping with AI catchment-fit scoring that classifies candidate locations by merchant density, ratings distribution, review velocity, and cuisine mix — flagging structurally saturated catchments with declining incumbent performance and surfacing whitespace catchments with unmet demand before a lease is signed. The scoring layer weights review velocity most heavily because it precedes visible incumbent stress by 4–7 months in the US market.
DoorDash and Uber Eats — the two dominant US food delivery platforms — covering 300 candidate catchments across 25 US metros with 24 months of historical time-series depth. The pipeline can be extended to additional platforms (Grubhub) and additional metros as the expansion program grows.
Every DoorDash and Uber Eats delivery catchment is captured with all listed merchants, their cuisines, price bands, ratings and review velocity. Catchment-level density is computed as merchants-per-catchment normalized by population signals and delivery-radius area — enabling comparable evaluation across metros, and specifically enabling cuisine-normalized density (competitors per catchment in the target cuisine) which is the meaningful signal for a specific-format QSR entering a specific catchment.
300 candidate locations were sourced by the chain's real estate team from broker networks. The AI catchment-fit layer scored each location on cuisine × price band × demand signals. 89 candidates were removed as structurally saturated no-go zones. The remaining 211 were ranked, and the top 42 were approved for underwriting and build — sequenced by conviction score. The $46M expansion capital allocation is aligned to that sequenced ordering.
Yes — the same catchment-fit scoring approach works for casual dining, fast-casual, cloud kitchens, coffee chains, and specialty formats. The methodology adapts to any category with platform-visible catchment competition, using cuisine × price-band × demand-signal weighting appropriate to the specific format.
Yes — we use compliance-aware sourcing across all USA markets and delivery platforms.
Need USA catchment-level site selection data for your expansion?
Tell us your target metros and QSR format. We'll scope a DoorDash + Uber Eats catchment-fit pipeline and show sample output in a short demo.
