Resolution Layer Case Study  ·  Read & Diagnose

A major appliance retailer.

A deep-pass data-quality read across refrigeration, cooking, and laundry listings — and where it leads.

Scope
Appliance catalog  ·  refrigeration, cooking, laundry, ventilation
Lead finding
A rival brand's copy on your own product page
Issues surfaced
33 actionable (43 flagged)
Method
Deep-pass analysis  ·  cross-referenced against syndicated content sources
Vertical
Major Appliances — refrigeration, cooking, laundry, ventilation
This pass
Read & diagnose — step one of the arc
Prepared by
EKOM
Type
Case study — client anonymized

What this is.

This is a real EKOM catalog analysis, with the retailer's identity removed. The client is a major appliance retailer carrying refrigeration, cooking, laundry, and ventilation equipment across established manufacturer brands. EKOM ran a deep pass on the appliance category — cross-referencing each listing's syndicated content against the retailer's own page and against sibling products from the same brand — and surfaced 43 flagged items, 33 of which are real, actionable catalog issues, the kind standard validation misses, because it checks whether a field is filled, not whether the value is right.

What follows leads with the findings a shopper hits first — customer-visible, live on the site today — then traces the pattern back to its structural root: a handful of catalog fields that quietly combine several kinds of information into one column, and a syndicated-content pipeline that occasionally hands one brand's copy to another. Third-party brand and model names are kept as they appeared; only the retailer's own identity has been removed.

These are machine-surfaced findings meant as a triage signal, not a verified defect list — a small share may be intentional. Even so, the concentration and specificity of what surfaced is a strong read on catalog health.

What's inside

At a glance.

Where the 33 actionable findings concentrate, by theme. Many issues span more than one theme, so theme counts are approximate.

33
Actionable
findings
10
Critical
30
High severity
or above
7
Issue
themes

By theme

Theme~CountWho feels it
Cross-source content contamination5Shoppers + brand partners
Spec, install-type & lifecycle contradictions6Shoppers
Identifier & barcode defects5Ops
Brand taxonomy fragmentation3Shoppers + brand partners
Slug, URL & category errors5Shoppers
Content completeness5Shoppers
Catalog structure (non-atomic fields)4Ops
The count isn't the point. These are issues that pass every completeness check — the field is filled, so a validator waves it through — yet are wrong in ways that only surface when structure and meaning are read together. They cluster where they hurt most: product discovery and buyer trust.

What a shopper runs into first.

Highest impact — the issues a shopper actually sees, live on the site today.

Highest-Visibility Finding  ·  Cross-Brand Content Contamination
A rival brand's copy, quoted verbatim, on your own product page.
A feature bullet on a KitchenAid combo wall oven's listing explicitly mentions Maytag® ranges — a different manufacturer's copy, contaminating the KitchenAid page. Elsewhere, a KitchenAid refrigerator's final feature bullet is a verbatim concatenation of every bullet that came before it, and a KitchenAid range's first nine "feature" bullets are variant model numbers, not descriptive copy at all. None of this is missing content — the syndicated-content pipeline delivered something to every field. It just isn't always the right something.

Contradicting itself in the same breath

A refrigerator's name states 21.51 cu ft; its own description states 31 cu ft — a 44% swing on the same listing. A range hood's name says wall-mount installation; its description says under-cabinet. A wine cooler's product name reads "Built-In Freestanding" — two mutually exclusive installation types, in one name. A separate range hood's name claims a 30-inch width while its description references a 24-inch hood. None of these require outside data to catch — the contradiction is sitting in two fields of the same record.

Barcodes that don't scan

Several GE items carry two pipe-delimited values in a single UPC field instead of one canonical code — in some cases a truncated or leading-zero-stripped duplicate of the real number. Two other items are missing UPC and GTIN-14 entirely, which is enough to break inventory receiving, POS scanning, and third-party price-comparison feeds outright.

Blank where a shopper expects an answer

A microwave, a wine cooler, and a top-freezer refrigerator each carry a completely empty description field — no specs, no copy, nothing for a shopper or a search engine to read. A range hood's description cuts off mid-sentence. Three more items are missing the brand prefix every other listing in the catalog carries in its product name.

Where it traces back.

Most of what surfaces above traces to one of two structural roots.

One brand, three names

GE's sub-lines are cataloged as PROFILE and CAFE — entirely separate brand values from GE itself, splitting one manufacturer's inventory into three unfiltered pools. The same pattern repeats for a second manufacturer's premium sub-line, cataloged separately from its parent brand. A shopper filtering by the parent brand never sees the sub-line's inventory; a brand partner running their own performance report sees a fraction of what's actually listed under their name.

A slug generator that doesn't know when to stop

A handle-kit's URL slug reads cafe-cafe-… — the brand word prepended twice, because the generator added it to a product name that already started with the brand. The same pattern shows up on at least three more items in the same product line. Elsewhere, a single misspelling — "Gemston" instead of "Gemstone" — propagated into a product's name, slug, and URL alike. Two more items are filed under the wrong category branch entirely: a freestanding range under wall ovens, and a set of wall-oven accessories under range accessories — each invisible to the shopper browsing the category it actually belongs in.

Fields built to hold one thing, doing the work of three

Four core catalog fields — the product name, the category-ancestry path, the syndicated feature list, and the English URL slug — each combine several distinct kinds of information into a single column rather than keeping them separate. That's not a defect in any one listing; it's a structural choice that makes several of the findings above harder to catch and harder to fix at scale, because correcting one instance doesn't correct the pattern.

How EKOM reads this catalog.

Why a cross-referenced pass catches what a field-by-field check misses.

1
Map the storefront
Walked the public storefront to build a complete map of the catalog's structure, categories, and live listings — no access to the retailer's own systems required.
Full public-catalog coverage, one link in.
2
Cross-reference against source
Checked each listing's syndicated content against the retailer's own page, and against sibling products from the same brand — which is what surfaces a rival brand's copy sitting where it doesn't belong.
Content-contamination and duplication patterns surfaced.
3
Structured analysis passes
Ran the catalog through structured passes for spec consistency, identifier integrity, brand taxonomy, and content completeness — then grouped findings by theme and root cause.
33 actionable findings, de-duplicated and grouped.

A field-by-field completeness check would have passed most of this catalog — every field above is populated. Reading records against each other, and against their own syndicated source, is what turns "populated" into "correct," and it's what makes the next step precise: EKOM knows exactly which records need correction and which are already sound.

What this means — and what's next.

would abandon83%
83% of shoppers say they'd abandon an e-commerce site that gave them insufficient product information — and 73% say they'd think less of the brand for it, up 11 points from the year before. Syndicated content feeds — the same kind implicated in several of the findings above — exist to solve exactly this problem. When they misfire, they don't just leave a gap; they actively hand a shopper the wrong answer.
Syndigo, "The State of Product Content," 2024
None of these are content problems in the cosmetic sense. A rival brand's name on your product page is a brand-trust problem before it's ever a data problem. A contradictory spec is something a shopper catches themselves, mid-decision, right before checkout. A missing barcode isn't a discovery issue at all — it's a fulfillment failure waiting to happen at the warehouse, not the storefront. These accumulate because standard validation checks for presence, not correctness — and a cross-referenced pass, not a field-by-field one, is what makes the pattern visible instead of averaging away.

This pass read and diagnosed. The same structural understanding powers the work that follows — turning a diagnosed catalog into one that reads right everywhere it's seen.

1  ·  Apply the confirmed corrections
Re-map the contaminated feature bullets, correct the contradictory specs, and resolve the duplicated barcode fields — resolved systematically from data already in the catalog, not one listing at a time.
2  ·  Unify the brand taxonomy, and close the slug/category leaks
Fold the fragmented sub-brands back into their parent brand for filtering purposes, and correct the mis-filed listings and duplicated slugs that trace back to the same generator logic.
3  ·  Hold the line at intake
Keep ongoing intelligence at intake so new SKUs and syndicated feed updates don't quietly reintroduce the same cross-contamination as the catalog grows.
This is how EKOM moves a catalog from insight to impact —
and keeps every listing reading right as the business grows.
EKOM
The Resolution Layer
ekom.ai
About this analysis

This is EKOM's first read of this retailer's appliance catalog. The pipeline read the live public storefront and cross-referenced each listing's syndicated content against the retailer's own page and against sibling products from the same brand, so a pattern like a rival brand's copy landing on the wrong listing surfaced cleanly rather than averaging away. The findings here are that read, with the retailer's identity removed and third-party brand and model names preserved.

Case Study  —  Client anonymized  ·  The Resolution Layer
EKOM