EDITED collects products from global retail markets across apparel, homeware and beauty. These products are sold on e-commerce platforms that employ an incredibly diverse set of practices. They are presented in their own language and local currency. Some are organised mirroring internal company structures (e.g. dress vs tops), some by material (e.g. knit vs jersey), and others might still be split by purpose, (e.g. work vs active). An outlet store, a speciality retailer and a department store may categorise and describe the exact same product in different ways in their corresponding e-commerce stores.
In order to make sense of our diverse and inherently noisy data set, EDITED develops its own comprehensive classification of products, made up of categories, subcategories, styles and details. We also normalise and augment certain product characteristics such as colour codes, patterns and **some sizes. This allows users to work with a normalised data set helping to compare like for like.
There may be instances when a product within EDITED sits in the wrong classification group, as a user you may spot this ahead of our QA team. To assist you with this EDITED allows for you to amend curations within the app.
Amending curations to be correct supports our data quality and improves the algorithms we use by strengthening their knowledge.
When you amend a curation we run it through our data pipe. If data pipe is working on a new classification we are introducing it may already have products waiting, when this is the case your curation will join a queue and could take up to 72 hours to show correctly. When traffic is less busy it may show quicker than this.