EDITED

EDITED is a retail intelligence platform used by buyers, merchandisers and planners at some of the world’s largest fashion brands and retailers. It tracks competitor assortments, pricing, promotions and trends across hundreds of thousands of retail sites in real time, giving teams the market context they need to make faster, more confident commercial decisions. Clients include names like Puma, Mango, Harrods and PVH.

The product I worked on sat at the intersection of market data and machine learning. EDITED had built an ML model capable of matching the same or similar products across retailers and regions, but it had no customer-facing surface worth using. My work over four years turned that into a suite of three product-matching tools, each one building on what the last had taught us about how customers actually compared products. Lists was the foundation: a spreadsheet-like working surface where customers could build small comparison sets and run the model against them. Product Matching and EDITED Match followed, each scaling the ambition as the model and the customer base matured.

I joined EDITED as Lead Product Designer in May 2020, when the product-matching capability existed as an ML model with nowhere useful to live. Over four years I designed Lists, Product Matching v1, and EDITED Match, the three tools that gave customers access to that model and turned product-level comparison into a core part of the platform. The work involved customer discovery, prototyping, close collaboration with engineering and data science, and navigating the design constraints of a third-party table library that shaped as many decisions as the research did. I also contributed to the broader product design practice during my time there.