Luxury marketplace & e-tail

Shopbop Product Data

Contemporary womenswear, Denim, Shoes, Bags from shopbop.com, collected into one Shopbop dataset you can query, download, or have delivered.

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source specification

Source: shopbop.com
Segment: Luxury marketplace & e-tail
Origin: United States — founded 1999, Amazon-owned
Categories: Contemporary womenswear, Denim, Shoes, Bags
Records: 32,854
Formats: CSV · JSONL · API
Refresh: Daily

about the source

What Shopbop sells, and how its catalogue behaves

Shopbop is a US contemporary retailer, owned by Amazon, carrying a broad multi-brand assortment with particular strength in denim and American contemporary labels. Its catalogue API returns clean structured records, which makes it one of the more dependable sources in this set.

why teams track it

What Shopbop shows about luxury retail

Shopbop is the US contemporary benchmark with Amazon's operational discipline behind it, and its promotional cadence is distinct from the influencer-led rhythm of the West Coast retailers. Running it against Revolve separates genuine market moves from one retailer's calendar.

Visit shopbop.com

collection method

How we read shopbop.com

The Shopbop collector uses Shopbop's public products catalogue API, which returns the multi-brand assortment as structured records.

What it captures from Shopbop

Brand attribution across the contemporary roster
Structured catalogue API records with stable identifiers
Denim fit and wash detail where published
List and promotional price captured separately

live dataset

32,854 Shopbop products on record

Product catalogue from Shopbop — names, prices, colours, materials and images.

32,854

Product records

Fields observed

Daily

Re-crawled

10 Oct 2026

Last crawl

Columns in the current Shopbop crawl

brandstring
categorystring
colorsarray
createdAtdate
currencystring
descriptionstring
imagesarray
marketplacestring
materialsarray
namestring
pricenumber
subCategorystring
updatedAtdate

Run inputs

None. The Shopbop dataset is filled by a scheduled crawl of shopbop.com, not by per-request runs.

Shopbop scraper API referenceShopping data overview Use it as training data

related sources

Next to Shopbop

FWRD

United States — luxury arm of Revolve Group

Luxury womenswear · Menswear · Shoes

Revolve

United States — founded 2003 in Los Angeles

Contemporary womenswear · Dresses · Shoes

Moda Operandi

United States — founded 2010

Runway pre-order · Luxury womenswear · Bags

Louis Vuitton

France — founded 1854 in Paris

Bags · Leather goods · Womenswear

Ralph Lauren

United States — founded 1967 in New York

Womenswear · Menswear · Home

Casadei

Italy — founded 1958 in San Mauro Pascoli

Luxury footwear · Heels · Boots

Browse all 49 fashion sources

questions

Shopbop data, answered

How reliable is the Shopbop source?

More reliable than most. It exposes a structured products catalogue API, so records come back with stable identifiers and typed fields instead of being reconstructed from markup. Storefront redesigns do not disturb it.

Does it overlap with Revolve?

There is meaningful brand overlap, and that is useful rather than redundant — the same product carried by both is a direct read on retailer-level price and markdown differences. Customers benchmarking US contemporary typically take both for exactly that reason.

How many Shopbop products are in the dataset?

32,854 product records at the latest count, from a crawl on 10 Oct 2026. The dataset is re-crawled daily.

How do Shopbop records differ from a single-brand feed?

Shopbop lists many designers under one storefront, so each record carries the designer as its own field next to the retailer it was collected from. That lets you compare one designer across retailers, or Shopbop's mix across designers.

How do I get Shopbop data?

Download the latest Shopbop crawl from the datasets catalogue as CSV or JSONL, query it through the API under the dataset id "shopbop", or ask us for a scheduled delivery into your own storage.

Try Shopbop data first

Download 100 rows from the latest shopbop.com crawl and check them against your own numbers before committing.

Talk to an engineer

Explore collected catalogue fields and discuss your data requirements — how we work.