Catalog Analysis · Case Study in BriefEKOM
A Canadian open-box appliance retailer.
A full storefront read, one link in, zero manual setup — and the condition field the entire business rests on was wrong in five distinct ways. One of a set of anonymized EKOM analyses.
The pattern · populated isn't the same as trustworthy
Every field below showed 100% complete on a standard check. Every one was wrong.
EKOM ran this retailer's live public storefront through our analysis pipeline exactly as we would a new account's catalog — one link, no hand-built schema, nothing telling the system what "condition" was supposed to mean. What came back concentrated in exactly the place a graded-resale business can least afford it: the condition field itself, plus a pricing pattern, a shipping-data gap, and listings pointing at the wrong product. None of it required access to the retailer's own systems to find, and none of it would show up on a completeness scan — every affected field was already marked "filled."
What surfaced
Condition mislabeled on graded units
Seven Open Box / Mixed Grade variants across three dryer models carry condition = NewCondition — contradicting the grade the shopper actually sees on the listing.
A TV served at another product's URL
The listing is a 75" Samsung Crystal UHD; the URL handle names a 65" LG OLED. Three fields disagree with the URL at once — caught twice, independently.
Zero shipping weight on shippable goods
grams = 0 on fridges, dryers, and a ~2,630g vacuum — produces $0 or failed freight quotes at checkout.
A $1.00 pricing pattern
Most Used / Mixed Grade items carry a compare_at_price exactly $1 above the selling price — a mechanical pattern worth a look before it reaches a shopper's cart.
Why it's the harder problem to catch. A completeness check confirms a field is filled; it can't tell that a "Good" condition value contradicts the listing next to it, or that a bare inventory number is standing in for a grade. On a resale storefront the condition, price, weight, and identity fields aren't back-office data — they're the whole of what a shopper is trusting when they can't inspect the item first. Twenty of the 65 findings here touch fields that were never blank; they were populated and wrong.
Where this leads · diagnosis to channel-ready
This pass read and diagnosed. Enrichment is the natural next phase.
For any graded-resale business, the next step after a read like this is completing the category-specific attributes that are typically thin, structuring the catalog for wherever shoppers search next — marketplaces, comparison engines, AI shopping assistants — and holding that line automatically as new inventory arrives, rather than re-auditing after the fact.