Scrape Store Locations API

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Why Get Store Locations Data?

  • Types of stores in a particular zipcode or postal location has long been a strong descriptor about the underlying demographics, income growth and population distribution of the local residents and it’s used extensively in traditional machine learning models (naive bayes, Support vector machines etc.) as well as newer neural network based deep learning models.

    I mean its pretty intuitive too, right? wouldn’t you say that its a sign of prosperity if a local zipcode has a new Whole foods or some other expensive organic food store compared to a dollar general or a family dollar grocery store? or if an area has upmarket Steakhouse chain like Ruth’s Chris with average menu items over $50 compared to just having fast food options. As data analytics professionals, we obviously are not making any judgment on people or residents but rather trying to estimate variables such as lifetime value of a resident in a particular zip code which not only determines value of houses etc. in an area but is also a useful component in predicting if there is any space in the local market for a new entrant in a particular retail segment.

  • The other major application of store data is that you want to use it for lead generation in sale and marketing of your own product/services.

  • Lot of our users get store locations data as a first step before narrowing down their focus geographically and acquiring store footfalls data which is much more expensive dataset but also a more insightful one. I mean, having a store in a particular zipcode is one thing, but determining how many people actually go through there and also estimating the length of time they spend at a particular store will be the best indicator of whether the store will be successful or not.

    To take the Ruth’s Chris Steakhouse example a bit further, imagine they opened up a store at your zipcode, and due to novelty lots of people went there the first time so it witnessed a high store footfalls in initial couple of months, but month on month if the footfalls keep decreasing than you would probably want to know that especially if you are a competitor trying to set up shop there. I personally think Ruth’s Chris is an overpriced restaurant and I would much rather just go to a Longhorns, but thats just me!

  • You can get bulk data on locations for doctors, lawyers, restaurants, bars etc. by using our yellowpages.com scraper API

Specrom Store Locations APIs Pricing

Specrom Store Locations APIs are a collection of APIs that lets you extract geoencoded store locations by specifying a zip code. If you need a dataset with locations for all the stores in US than please contact us and we will be happy to sell you the complete dataset at a discount of 20-70% of the list price at other major retailers such as ScrapeHero, red lion data etc.

You’ll have to generate an API key on the Algorithmia page but it’s free (no credit card required) and you get free credits equivalent to our basic tier. If you need more calls, simply purchase one of the higher tiers. In case you requirement exceeds our Ultra tier than please contact us to get volume discounts.

Basic/Free Pro Ultra Custom
Pricing 100 calls free per month $5 for 1000 calls $50 for 15,000 calls Ask for a quote

One of the distinguishing features of our plans is that there are no monthly charges and you only pay according to your usage.

Demo Page with sample code in Java, JavaScript, PHP, R, Python:

Input

"30601"

Output

[
  {
    "address": "4375 Lexington Rd",
    "city": "Athens",
    "distance": 5.47,
    "name": "Athens Supercenter",
    "phone": "706-355-3966",
    "state": "GA",
    "store_id": 2811,
    "zip_code": "30605"
  },
  . . . (output truncated)

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