Location Data Scraping in Action: How AutoZone and O’Reilly Win With Opposite Strategies

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.

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 even possible. Reliable location data scraping is therefore less about pulling a page and more about a resilient pipeline plus a verification layer that turns messy locators into an analysis-ready map.

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.

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.

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.