EDITED is a retail market intelligence platform. It tracks millions of products across global e-commerce sites and reports on competitor assortment, pricing, discounting, newness and sell-out signals, often alongside a brand's own internal metrics. Buying, merchandising and pricing teams use it to see what the competitive shelf looks like right now.

What is the difference between assortment intelligence and demand intelligence?

Assortment intelligence tells you what is being sold: which products exist, at what price, and what is being discounted. That is supply-side data. Demand intelligence tells you what people want, why they want it and what they cannot find. A product missing from every competitor's assortment shows up as an absence in assortment data and as an explicit request in consumer conversation.

Is Hugo an alternative to EDITED?

They answer different questions and many teams would benefit from both. EDITED is the better tool for competitive pricing and assortment gap analysis against named competitors. Hugo is the better tool for understanding what customers actually want, why they switch brands, and what demand exists for products nobody in the category is currently selling.

Can competitor pricing data tell you what to launch next?

Only indirectly. It tells you where competitors have concentrated and where they have not, which is a useful map of supply. It cannot tell you whether an empty space on that map is an opportunity or a space that is empty because nobody wants what goes there. That distinction requires demand evidence.

EDITED Alternative: Hugo vs EDITED for Fashion Brands (2026)

EDITED is one of the best tools in retail for seeing what is on the shelf. Hugo is built for the person standing in front of it. Different halves of the same decision.

What EDITED is genuinely good at

EDITED tracks millions of products across global e-commerce and turns that into a live picture of the competitive shelf: what is newly listed, what is in stock, what is being marked down, how fast something sells out, how pricing architecture differs between retailers. Many teams also bring their own internal metrics in alongside it.

For pricing decisions, markdown timing, and "are we under-assorted in this category versus these five competitors", that is precise, defensible and hard to replicate manually. It is real data about real products, not an opinion about a trend.

Where supply-side data stops

An empty space on the map is ambiguous. Assortment analysis is excellent at showing you where competitors are not. It cannot tell you whether that gap is an opportunity or a graveyard. Some spaces are empty because nobody has got there yet. Most are empty because the demand is not there. Distinguishing between the two requires demand evidence, and there is none in a product feed.

Everyone is watching everyone. If your competitive set all run assortment intelligence on each other, the category converges. You end up optimising against the same shelf, in the same direction, at the same time.

It cannot explain a failure. A product sold out fast: was that demand, or was the buy too small? A product sat: was it the design, the fit, the fabric weight, the photography, the price anchor next to a competitor? The number says it failed. It never says why.

It has nothing on the customer. No reason, no language, no switching motive, no unmet request. That is not a flaw in the product, it is just outside its scope.

What Hugo does differently

Hugo is AI for fashion brands. It reads live consumer conversation rather than product listings: social posts, comments, forums, reviews. You ask a question in Slack in plain language and it returns segments, verbatims, sentiment and a written recommendation, with sources.

The questions that map directly onto a buying or merchandising decision:

What are people in this market asking for that nobody in our price tier sells?
Why do customers leave us for this specific competitor, in their own words?
What is actually wrong with this product, according to people who bought it and people who bought the competitor's version?
Which of our categories is losing consumer momentum while sell-through still looks fine?
What does this category look like in Amsterdam versus Stockholm?

None of those are answerable from a product feed, and all of them change what you buy.

Side by side

	EDITED	Hugo
Data source	Product listings across e-commerce	Consumer conversation: posts, comments, forums, reviews
Side of the market	Supply. What is being sold	Demand. What people want
Best question	What are competitors stocking and pricing	What do our customers want and why
Products nobody sells yet	Not in the data by definition	Requested out loud, in language
Explains why something failed	No	Yes, from customer verbatims
Interface	Dashboard and reports	Slack conversation, plus reports
Setup	Subscription, optional internal data integration	None. No data access needed

The combination that actually works

Assortment intelligence tells you where the shelf is thin. Demand intelligence tells you whether the thin part is worth filling. Run one without the other and you get one of two failure modes: filling gaps nobody wanted, or spotting a real customer need and having no idea what it would cost to compete on it.

If you can only fund one right now, ask which mistake is more expensive in your business. For most mid-market brands the answer is buying the wrong product, not pricing it slightly wrong.

A gap in the market and a market in the gap are not the same thing. Only one of them is in the assortment data.

Other tools in this space

WGSN for long-range macro trend direction. Heuritech for attribute momentum from social imagery. Trendalytics for cross-channel trend validation. StyleSage for the same supply-side question, embedded inside a PLM implementation. The full rundown, including where Hugo is not the answer, is in our comparison of fashion trend forecasting tools.

Hugo vs EDITED