Luxury marketplace & e-tail

SSENSE Product Data

Luxury womenswear, Menswear, Streetwear, Shoes from ssense.com, collected into one SSENSE dataset you can query, download, or have delivered.

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source specification

Source: ssense.com
Segment: Luxury marketplace & e-tail
Origin: Canada — founded 2003 in Montreal
Categories: Luxury womenswear, Menswear, Streetwear, Shoes
Records: 2,400
Formats: CSV · JSONL · API
Refresh: Daily

about the source

What SSENSE sells, and how its catalogue behaves

SSENSE is the Montreal retailer that merged luxury and streetwear into one credible assortment before most of the industry accepted the two belonged together. Its buy is younger and more design-forward than traditional luxury e-tail, and its editorial voice drives real demand for the brands it backs.

why teams track it

What SSENSE shows about luxury retail

SSENSE is where emerging designers get validated, so its assortment functions as a leading indicator for which labels are about to matter. For anyone tracking the luxury-streetwear intersection it is the single most informative source in the market.

Visit ssense.com

collection method

How we read ssense.com

The SSENSE collector uses SSENSE's JSON product endpoints, read per category so the assortment is enumerated completely.

What it captures from SSENSE

Designer attribution across an unusually wide brand roster
Coverage of emerging labels absent from traditional luxury e-tail
Both womenswear and menswear on one schema
Markdown timing across seasonal sale windows

live dataset

2,400 SSENSE products on record

Womenswear catalogue from SSENSE — names, prices, colours, materials and images.

2,400

Product records

Fields observed

Daily

Re-crawled

11 Oct 2026

Last crawl

Columns in the current SSENSE crawl

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

Run inputs

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

SSENSE scraper API referenceShopping data overview Use it as training data

related sources

Next to SSENSE

Cettire

Australia — founded 2017

Discounted luxury · Womenswear · Menswear

Mytheresa

Germany — Munich, online since 2006

Luxury womenswear · Menswear · Shoes

LuisaViaRoma

Italy — Florence, retailing since 1930

Luxury womenswear · Menswear · Shoes

THE OUTNET

United Kingdom — off-season luxury, launched 2009

Off-season luxury · Womenswear · Shoes

Italist

Italy — direct from Italian boutiques

Luxury womenswear · Menswear · Shoes

NET-A-PORTER

United Kingdom — founded 2000 in London

Luxury womenswear · Shoes · Bags

Browse all 49 fashion sources

questions

SSENSE data, answered

Why is SSENSE useful for spotting emerging designers?

Its buy skews younger and more experimental than traditional luxury e-tail, so labels appear on SSENSE well before they reach the established retailers. Tracking new designer entries over time gives you a watchlist that consistently leads the rest of the market.

Are womenswear and menswear both included?

Yes, and menswear is genuinely significant here rather than an afterthought — it is a large part of why SSENSE matters. Both departments are collected by default on the same schema.

How many SSENSE products are in the dataset?

2,400 product records at the latest count, from a crawl on 11 Oct 2026. The dataset is re-crawled daily.

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

SSENSE 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 SSENSE's mix across designers.

How do I get SSENSE data?

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

Try SSENSE data first

Download 100 rows from the latest ssense.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.