Location Data Scraping: AutoZone vs O’Reilly Footprint Decoded
                     Location Data Scraping in Action: How AutoZone and O’Reilly Win With Opposite 
Strategies
Location data scraping reveals how AutoZone and O’Reilly, near-identical in size, win with opposite 
strategies: national reach versus deep city penetration.
6,720
AUTOZONE STORES
6,500
O’REILLY STORES
3,480
AUTOZONE CITIES
3,238
Who This Case Study Is For
Good location data scraping does more than count stores — it reveals the strategy behind a footprint. 
We rebuilt the U.S. store networks of two auto-parts giants from public data to show how two near-
identical chains compete in completely different ways. This teardown speaks to:
• Retail strategists deciding between broad national coverage and deeper penetration in fewer 
markets.
• Real-estate and expansion teams benchmarking a competitor's state and city coverage.
• Market analysts who need to know where two chains truly overlap versus operate uncontested.
• Operations leaders comparing store hours and in-store service models across a category.
• Investors pressure-testing a growth story against verifiable location and service data.
Executive Summary
AutoZone and O’Reilly are two of the largest auto-parts retailers in the United States, and at first 
glance their footprints look almost identical: 6,720 stores versus 6,500, present in 53 and 49 states and 
territories, open roughly 13 hours a day each. On scale alone, it is hard to say who dominates. The 
interesting story only appears once you read the location data closely.
Using location data scraping, we reconstructed both networks from public sources: store counts, state 
and city coverage, the Texas concentration, average operating hours, and the in-store services each 
chain advertises. Two very different strategies emerge from near-identical totals. AutoZone pushes 
broad national coverage, reaching more states and over 200 more cities, and positions itself for do-it-
yourself customers. O’Reilly concentrates more deeply — Texas is clearly its strongest market — and 
leans into hands-on, in-store service.
The pattern most comparisons miss is that store count is a vanity metric. Our unique read uses 
geocoded location data to separate where the two chains actually collide from where each operates 
uncontested — and, combined with their opposite service models, shows that two stores on the same 
street may not even be competing for the same shopper. This report walks through six findings and 
what the same approach reveals about any retail network you study.
THE CHALLENGE
Why Location Data Is Hard to Get
A store locator page looks simple, but turning two national networks into one clean, comparable 
dataset is not. Each chain's locator is built for a single shopper checking one ZIP code, not for bulk 
collection — so coverage requires sweeping thousands of locations without tripping rate limits, bot 
detection, or rotating page layouts. Addresses arrive in inconsistent formats, hours are written a 
dozen different ways, and duplicate or recently closed stores quietly inflate the totals if they are not 
cleaned out.
Comparing two brands multiplies the difficulty. The fields have to be normalized to the same schema, 
addresses geocoded to real coordinates, and hours parsed into comparable numbers before any city-
versus-city analysis is evNenu mpboesrs iobfle . ReliabPloep luolcaatitioonn  data sSctroarpei nTgy pies  therefore less abState / Territory Served Growth Ra
oteu t pulling a 
page and more about a Srteosrileiesnt pipeline (pAlupps rao xv.e) rificationD loamyeinr atnhtat turns (m2e0s2s3y– 2lo0c2a5t)ors into an 
analysis-ready map.
New South Wales 88 7.8 million Urban & Drive- +11%
thru
DIY Scraping vs iWeb Data Scraping
Victoria 70 6.6 million Mall & CBD +9%
Here is how a do-it-yourself stack compares with a managed sOeurtvleictse across the dimensions that decide 
whetheQru leoecnastilaonnd data is u55sable. 5.5 million Suburban Cafes +13%
Western Australia 34 2.8 million Standalone Stores +10%
DIMENSION DIY SCRAPING IWEB DATA SCRAPING
South Australia 22 1.9 million Mall Cafes +7%
Tasmania 8 Weeks of5 e4n1g,i0n0ee0ring before cleRaeng ionalL Sivteo rweisthin da+ys6,% scoped to your Setup ti e
data brands
Australian Capital 
Territory 9 462,000 CBD Cafes +5%
Anti-bot handling Constant firefighting with blocks and Managed proxy and detection layer
CAPTCHAs
Northern Territory 5 247,000 Airport Outlets +4%
Inconsistent formats break 
Address normalization Standardized, geocoded coordinates
comparisons
Hours parsing A dozen formats, no clean averages Parsed into comparable numbers
Duplicate / closed stores Silently inflate the totals Deduplicated and validated
Maintenance Breaks on every locator change Pipeline upkeep included
Predictable, decision-ready 
Total cost Hidden in engineering hours
deliverable
FOCUS
The Brand in Focus
AutoZone and O’Reilly Auto Parts are direct rivals in U.S. auto-parts retail, and both have spent 
decades building dense physical networks. They sell to overlapping audiences — everyday drivers, 
weekend mechanics, and professional repair shops — and on paper they are remarkably evenly 
matched in store count, geographic spread, and even daily operating hours.
What separates them is intent. AutoZone has historically chased broad reach and built its brand 
around the do-it-yourself customer. O’Reilly has grown with apparent selectivity, going deep in its 
strongest regions and building a reputation for hands-on, in-store help. Everything that distinguishes 
the two is observable in public data — addresses, city and state coverage, hours, and the services 
each location advertises — which makes the pair an ideal case for turning a tie on paper into a clear 
strategic contrast.
OUR APPROACH
How iWeb Data Scraping Built the Dataset
We approached both networks as a structured collection problem with a verification layer on top. For 
every store, we captured brand, full address, city, state, and operating hours, then geocoded each 
location to real coordinates so the two networks could be compared at the state, city, and ZIP level 
rather than as raw counts.
Every record then passed through validation — addresses normalized to one schema, hours parsed 
into comparable numbers, and duplicate or closed locations removed so the totals reflect live stores 
only. We also catalogued the in-store services each chain advertises, then analyzed footprint, state 
and city coverage, the Texas concentration, hours, service strategy, and the overlap mapping that 
closes this report.
FINDING 01
A Near-Identical Footprint
Side by side, the headline numbers are almost a tie. AutoZone runs 6,720 stores to O’Reilly’s 6,500 — 
a gap of barely 3 percent. Both keep similar hours and serve overlapping audiences, which is exactly 
why a surface comparison concludes they are interchangeable.
METRIC AUTOZONE O’REILLY
Total stores 6,720 6,500
States & territories 53 49
Cities reached 3,480 3,238
Texas stores 758 880
Avg daily hours 13.14 13.11
The totals are close, but the strategy behind them is not. Every row in that table hides a different 
choice — about how wide to spread, where to go deep, and which customer to serve. The findings 
that follow unpack each one.
FINDING 02
Broad Reach vs Selective Expansion
The first real divergence is geographic spread. AutoZone is present in 53 states and territories to 
O’Reilly’s 49 — a clear sign that AutoZone is pushing for broader national coverage, planting a flag in 
as many markets as possible.
O’Reilly’s narrower spread reads as deliberate selectivity rather than weakness. Covering fewer states 
while running nearly as many total stores means it concentrates its locations more tightly, going 
deeper where it chooses to compete instead of stretching thin to claim a national map. Two near-
equal store counts, two opposite philosophies about how widely to spread them.
MAP YOUR CATEGORY THIS CLEARLY
Picture this same store-by-store, city-by-city view for you and your top rivals. iWeb Data Scraping can 
deliver a clean, geocoded location dataset for any retail network in days. Email 
[email protected] to scope it.
FINDING 03
Deeper Into the Long Tail of Cities
City coverage sharpens the contrast. AutoZone reaches 3,480 cities to O’Reilly’s 3,238 — more than 
200 additional cities. Paired with its wider state footprint, that tells a consistent story: AutoZone is 
pushing into smaller and mid-sized markets that a more selective rival might pass over.
Reaching 200-plus more cities with only about 220 more stores is itself revealing: AutoZone is 
spreading its locations across a wider set of towns rather than stacking several stores into the same 
dense metros. That long-tail coverage builds broad national visibility, but also stretches each market 
thinner — a trade-off only visible when location data is analyzed city by city instead of as a national 
total.
FINDING 04
The Texas Battle and AutoZone’s Balance
Zoom into a single state and the strategies collide. Texas is one of the biggest automotive markets in 
the country, and it is clearly O’Reilly’s stronghold: O’Reilly runs 880 Texas locations to AutoZone’s 758. 
O’Reilly is heavily concentrated in the market that matters most to it.
AutoZone, by contrast, spreads its strength across several large states rather than betting on one. Its 
top states show a more balanced distribution, which reduces dependence on any single market.
TOP AUTOZONE STATES STORES
Texas 758
California 686
Florida 464
The read is clear: O’Reilly goes deep where it is strongest, while AutoZone diversifies across multiple 
high-population states. Each approach carries a different risk profile, and only a state-by-state view 
reveals it.
FINDING 05
Same Hours, Opposite Service Models
On operating hours the two are almost indistinguishable: AutoZone averages 13.14 hours a day, 
O’Reilly 13.11. Both understand that car problems do not follow business hours. But the services 
inside those hours point in opposite directions.
AUTOZONE — DIY FOCUS O’REILLY — IN-STORE 
SUPPORT
Loan-A-Tool program Battery testing
Fix Finder diagnostics Alternator & starter testing
Hydraulic hose making Wiper & headlight installation
Rewards program Recycling services
AutoZone is built for customers who want to fix problems themselves — lending tools, helping 
diagnose, and rewarding repeat buyers. O’Reilly leans toward hands-on convenience, doing more for 
the customer in the store. Same shelf space, same hours, two very different promises about who 
walks in.
THE COMPETITIVE REALITY
Store count alone tells you almost nothing about who you are really up against. Two chains can match 
on size and still serve different customers in different places. The truth lives in the geocoded, service-
level detail — not the headline total.
Sample Data
Below is a representative slice of the geocoded location dataset behind this report — the kind of 
clean, comparable rows our pipeline delivers for an entire store network.
STORE ID BRAND CITY STATE DAILY HOURS LEAD SERVICE
AZ-00214 AutoZone Houston TX 13.5 Loan-A-Tool
OR-01188 O’Reilly Houston TX 13.0 Battery testing
AZ-03391 AutoZone Los Angeles CA 13.0 Fix Finder
Alternator 
OR-02077 O’Reilly Springfield MO 13.2
testing
AZ-04510 AutoZone Miami FL 13.0 Rewards 
program
Wiper 
OR-03642 O’Reilly Dallas TX 13.1
installation
AZ-05883 AutoZone Tulsa OK 13.5 Hydraulic hoses
BUSINESS IMPACT
Turning Data Into Decisions
The point of an analysis like this is the moves it makes possible. The six findings translate directly into 
actions a retailer or analyst can take this quarter.
• Expansion strategy: state and city coverage shows whether broad reach or deep penetration fits 
your category better.
• Overlap mapping: a geocoded view reveals where you face a rival head-to-head versus where you 
operate uncontested.
• Market prioritization: a competitor's concentration, like O’Reilly’s in Texas, flags where the fiercest 
density already sits.
• Service benchmarking: comparing in-store offerings shows whether you are courting the same 
customer as a nearby rival.
• Site selection: long-tail city data points to under-served towns worth entering before a competitor 
does.
Why iWeb Data Scraping
We exist to remove the hardest part of this work: the pipeline. iWeb Data Scraping handles the 
collection, cleaning, and structure so our clients do not maintain scrapers, fight CAPTCHAs, or wonder 
whether a blank field is a real gap or a silent failure. They receive clean, validated, decision-ready data 
on a schedule that fits their planning cycle.
That means addresses normalized and geocoded so networks compare like-for-like, hours parsed into 
usable numbers, and duplicate or closed stores removed so totals reflect live locations only. Whether 
you need a one-time competitive teardown like this one or continuous coverage across an entire 
category, the infrastructure headache is ours, and the insights are yours.