Bloomingdale's runs on Macy's, Inc. plumbing but sells a different assortment: designer houses, exclusive lines, and offers that land in the bag instead of on the price tag.
We build the Bloomingdale's scraper, run it on your cadence and hand back the result as CSV, JSON, XLSX or an endpoint, with the schema agreed before the first run.
The site is closed to automated clients at the edge, so anti-bot handling, proxy rotation and CAPTCHA solving are part of the service rather than something you arrange on the side.
Scope is usually a list of product IDs, a set of designer shops, or a sweep of department grids with the facets you care about. Parent rows key on the seven-digit product ID and child rows on the UPC, so price history and size-level stock stay joinable when a slug or a product name is rewritten. We work only with pages any shopper can open: nothing behind a sign-in, no member-only price we cannot see, and no personal data.
The item page holds the house apart from the product: brand is a name with its own numeric id and a /buy/ path, the product name sits beneath it, and under both runs a merchandising spine the shopper never sees - a department such as BETTER COLLECTIONS or HUDSON PARK, a division such as Beauty, and a typeName such as SKIRT or BEDDING_SETS.
Structured markup is published too: a schema.org Product block lists one offer per colour-and-size pair, with a price, an availability value and a SKU that appends USA to the UPC. Collections are a master with member products, so a bedding collection fans out into duvet covers and pillowcase pairs, each with a product id of its own.
An offer on Bloomingdale's is an object, not a sentence. Each badge carries a promotion id, a short header such as With 20% offer, a checkout description, a promotionType such as Percent Off Order, and a threshold trigger saying whether it fires on quantity or on basket subtotal and at which tier. Gift-with-purchase offers attach the gift as a product with an id of its own, as with a three-piece gift on any $125 beauty purchase. The legal text travels with the badge, including the dates it runs and the exclusions - among them that the offer does not apply in Bloomingdale's The Outlet Store, which is a store locator on the site rather than a second online catalogue.
The same layer is exposed as filter values: Gift with Purchase, Buy More, Save More, Loyallist Points Offers and Offers, while Percent Off buckets reductions at 20, 30, 40, 50 and 60 percent and more. Grid tiles add their own eyebrow lines - Best seller, New markdown, Loyallist Triple Points, Fashion director find, and SELECT ITEMS ON SALE where only part of a colour range is cut.
Fulfilment is a filter too. Pickup and Delivery narrows a grid to same-day delivery or to store pickup, and every UPC reports pickup, ship-from-store and in-store stock separately once a store is chosen. Wedding and baby registries live at /registry/{names}/{id} with a sitemap of their own, and the Available for registry facet marks the items they draw on; those pages are built around private individuals, so we skip them by default, along with the shopper names attached to reviews.
Bloomingdale's is the premium contemporary-to-luxury nameplate inside Macy's, Inc., sitting beside Macy's and Bluemercury. As of the end of January 2026 the group counted 665 store locations in total, 61 of them Bloomingdale's: full-line stores, the smaller format called Bloomie's, and the off-price concept Bloomingdale's The Outlet. Dubai and Al Zahra in Kuwait are run by a licensed partner on a separate Salesforce Commerce Cloud storefront, so bloomingdales.ae shares the name and none of the plumbing. A scrape of Bloomingdale's means the US site.
Item addresses take the shape /shop/product/{slug}?ID={product id}, where the id is normally seven digits and outlives slug rewrites. A trailing CategoryID only records the shelf the shopper arrived from, so one item answers on many addresses and the ID is the value worth keying on. Category pages are /shop/{path}?id={category id}, and the tree carries around 670 entry points, with designer shops such as /shop/chanel, /shop/ralph-lauren and /shop/david-yurman standing at the same level as /shop/womens-apparel.
Filters extend the path instead of the query string: /shop/{path}/{Facet}/{Value}?id={id}, as in /shop/home/weekender-duffel-bags/Brand/Tumi. Several values on one facet join with a pipe, and grids page through /Pageindex/{n} at 60 products a page, with Sortby taking ORIGINAL, NEW_ITEMS, BEST_SELLERS, TOP_RATED, PRICE_HIGH_TO_LOW or PRICE_LOW_TO_HIGH. Site search is shut to crawlers, so coverage has to be built from the department tree and the designer shops rather than from a query box.
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Price monitoring on Bloomingdale's is not a strikethrough count. Value moves in three places at once - the ticket, the offer that applies in the bag, and the Loyallist points multiplier - so a row holding only Now and Orig. reports a coat as full price while a Take $50 Off Every $200 badge quietly takes a quarter off at checkout.
Because assortment is the difference here, the cuts worth pulling are:
For a label that sells into the store, the same feed doubles as a compliance check: whether the retail price matches the agreed one, whether an item was swept into an offer it was excluded from, and how its size run holds up elsewhere.
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Learn MoreLearn how to use web scraping to solve data problems for your organization
If you sell online, run a marketplace, or advise e-commerce clients, you already know why eBay matters: it’s one of the few places where big retailers compete side by side with thousands of small merchants and private sellers.
E-commerce teams do not just need “some” competitor data anymore. They need a continuous stream of real prices, discounts, stock levels, reviews, and seller behavior from the platforms that actually shape their markets.
Amazon provides valuable information gathered in one place: products, reviews, ratings, exclusive offers, news, etc. So scraping data from Amazon will help solve the problems of the time-consuming process of extracting data from e-commerce.
ScrapeIt is a managed web scraping agency. We write the crawlers, host them, watch them and repair them when a site moves, so what reaches you is a dataset and not a codebase.
Bloomingdale's is rebuilding its item pages in stages, which means two page shapes can be live at the same time, and its offer calendar turns over every few weeks. Holding a feed correct through that is the work we take on. Tell us the fields and the cadence, and we will confirm what is really available before the first run.
No. Bloomingdale's publishes no product API and runs no developer programme, and the JSON layer its own pages call is closed to crawlers in the site's robots file. What is public is the sitemap index, the department tree, the item pages and a schema.org Product block on each item that lists one offer per colour and size with price and availability. We read those. If you want the result as an endpoint rather than files, we host the Bloomingdale's API on our side and keep it fed on your schedule.
Brand with its numeric id, product name and product ID, department, division and typeName, colour ids with trade and normalised names, size ids with the size chart reference, and one UPC record per colour-and-size pair carrying class, subclass, vendor and mark-style codes. On top of that: tiered price rows labelled Now and Orig. with percent off, the final price with offer, promotion badges, detail bullets, materials and care, fit notes, review statistics and image paths. Availability is per UPC, split into shipping, store pickup and ship from store.
As often as the assortment justifies. Sale events turn over on published date ranges and markdowns land in waves, so a daily pass over the categories you follow catches most movement, while a watchlist of product IDs can be checked several times a day when an event is running. Full sweeps of every department are heavier and usually run weekly. We agree the cadence per segment rather than for the whole catalogue, and we pace requests so the crawl stays polite.
Both. The sitemap index lists item, category, facet and registry files, and about 175,000 product addresses sat in the item files in September 2026, which makes a full pass practical. In practice most clients want a slice: one designer shop, one department such as handbags or beauty, everything on sale, or the exclusive lines. Because filters are path segments, a slice can be defined exactly - a designer plus a colour plus a discount band - and repeated run after run without drift.
We collect only pages any visitor can open without signing in, and only facts about merchandise: prices, sizes, stock, offers, images and review statistics. We do not touch accounts, baskets or checkout, we do not take shopper names from reviews, and we leave the wedding and baby registries alone unless you ask for them, since those pages are about private individuals. If a field would need a login or a personal profile to reach, we say so instead of collecting it.
Step 1 - Make a Request
You share your needs, expectations, and desired timeframe. We’ll suggest the best solution based on your request and budget.
Step 2 - Configuring Custom Web Crawlers
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
Step 3 - Collect and Deliver
Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.
Step 4 - Maintain and Support
Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.
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