Resolution Layer Case Study · Read & Diagnose
A sleepwear and loungewear brand.
A multi-lens data-quality read of the catalog — and where it leads.
This is a real EKOM catalog analysis, with the brand's identity removed. The client is a sleepwear and loungewear brand — pajama sets, robes, sleep shirts, and a large print-driven seasonal range, sold direct through its own storefront. EKOM ran a full-catalog quality read with no schema supplied and surfaced eleven analytical findings, ten of which are real, actionable catalog issues. The eleventh is on page six: it was checked, disproved, and removed.
This is a tidier catalog than most. Content is present, prices are clean, sizes and colors are structured. Almost everything found here sits in the small set of fields that decide where a product goes rather than what it says — which is precisely why the defects survive. Nothing looks broken. The product page is perfect; the product is just not where a shopper would go looking for it.
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
- What a shopper runs into first — garments missing from their own collections, a filter showing half the range, a permanent URL that contradicts its product.
- Where it traces back — conventions that moved over time and were never backfilled, leaving old and new records speaking different languages.
- Confirming against source — the highest-severity pattern the analysis produced turned out not to be a defect at all, and was removed. Shown in full.
- How EKOM reads a catalog like this — why a full read catches what a sampled check misses.
- What it means, and what's next — from diagnosis to a channel-ready catalog.
Where the ten verified findings concentrate, by theme.
By theme
| Theme | Count | Who feels it |
|---|
| Product-type drift & miscategorization | 3 | Shoppers + ops |
| Permanent URL errors | 2 | Shoppers + ops |
| Option-field misuse | 2 | Shoppers + ops |
| Facet fragmentation | 1 | Shoppers |
| Cross-sell grouping errors | 1 | Shoppers |
| Pricing display | 1 | Shoppers |
The count isn't the point. Every one of these passes a completeness check — the field is filled, so a validator waves it through — and every one of them is wrong in a way that only surfaces when structure and meaning are read together. Seven are defined changes that can be prepared and applied from data already in the catalog. Three need a merchandising judgment only the brand can make, and are marked as such rather than guessed at.
What a shopper runs into first.
Each of these is invisible on the product page and fatal to the collection page.
Lead finding · discovery
A hoodie and a cardigan, both filed as generic long-sleeve tops.
Two garments carry Long Sleeve Top as their product type while their own titles and internal grouping tags say plainly hoodie and cardigan — and the correct values already exist elsewhere in the catalog, in active use. A shopper filtering to hoodies will not see the hoodie. Nothing about the listing looks wrong; it simply never appears in the place a shopper would look for it.
Pajama sets missing from the pajama-set collection
Several sets are typed with a singular where the catalog standard is plural — the difference between one letter and none. Storefront collection rules match exactly, so each of those products drops silently out of the collection it belongs to, and out of every filter built on that collection. This is the single most-affected field in the catalog.
A color filter that shows half the range
Newer items publish color values in capitals; legacy and sale items use mixed case. The same hue therefore appears twice in a color filter, as two separate entries each holding roughly half the inventory. A shopper who picks one sees half of what exists and reasonably concludes that is all there is. Nobody complains about this, because a shopper who can't find something doesn't file a ticket — she just buys less.
A permanent URL that contradicts its own product
One garment is published at a handle describing a different garment type entirely, while its title, product type, and description all agree on what it actually is. Handles are permanent, indexed, linked, and shared — the one field that cannot be quietly corrected later without a redirect, and the longer it sits indexed under the wrong garment the more it costs to change.
A full-price item with a $0.00 strike-through
One lounge set carries a compare-at price of zero rather than blank, while every other full-price item in the catalog leaves that field empty. Zero is not the same as empty: depending on the template it renders a "was $0.00" badge beside the price, or divides by zero in a discount calculation. Exactly one item, and a single-field fix — it is here because of what a shopper sees, not because of how much it touches.
Where it traces back: conventions that moved.
Not carelessness — a catalog that has been maintained for years, by people who improved their conventions and never went back to update what came before.
The convention changed; the old records didn't
The capitalization split in the color facet is not a typo. It is a dividing line in time: items published recently follow one convention, older and sale items follow the one it replaced. Both are internally consistent. Neither is wrong on its own. Together they fragment every affected color into two half-empty filter entries — and the same shape repeats in the product-type drift, where the dominant plural form coexists with a handful of singular survivors.
Naming standards that arrived after the products did
A subset of items use internal style codes as their permanent handle, where the rest of the catalog uses descriptive slugs. This is almost certainly the older standard left in place. It is flagged at medium rather than high severity for one honest reason: the fix is easy, but the decision isn't, because rewriting a handle that already ranks can cost more than leaving it alone. That is a call worth making deliberately rather than in a bulk pass.
Grouping tags copied from a sibling garment
Two bottoms carry the grouping tag belonging to their companion top — the tag that drives the "complete the set" recommendation. The pattern used for every comparable pair in the range is correct; these two are not, so they surface incorrectly in the exact feature that exists to build the outfit. This is what convention drift looks like at the point of data entry rather than at the point of a standard changing.
Fields carrying something other than what they are for
One product stores a full legal disclaimer as a selectable option value — it renders as an unusable dropdown choice and lands in any index that reads option values. Another offers a one-size variant alongside a standard fitted size run, inconsistent with every comparable garment. Both are the kind of thing that happens once, works well enough not to be noticed, and stays.
Confirming against source — the finding that wasn't.
This is normally the part that stays inside the building. It is here because it is the most useful thing in this case study about how EKOM works.
Investigated · Not a defect · Excluded from every count
The pattern that looked like duplicate SKUs.
The analysis surfaced what appeared to be its single most serious finding: a large family of shared identifier values, each appearing across several products with genuinely different prints. Read at face value that is a warehouse and order-management problem — any system keyed on those values would mis-pick and conflate inventory. The finding came with a confident causal claim attached.
So it was checked against the live variant records before it was written down. Every real variant identifier is unique and sequential. The shared values aren't identifiers at all — they read as a style-family grouping code, same garment in a different print, almost certainly powering the "complete the set" recommendation. Nothing collides. No system is at risk.
Why this is in a public case study. A confident-sounding finding is the one that earns the extra check, precisely because it is the one a client would act on first. This one got the check and did not survive it — so it was removed, and every number in this analysis excludes it. Ten findings that hold up are worth more than eleven with one that costs a team an afternoon.
A second, structural layer of the analysis produced further observations about columns holding more than one kind of information. Those describe how the catalog was flattened for analysis rather than defects in the brand's data, and they are excluded from every number here as well. Counting them would have inflated the headline by more than half.
How EKOM reads this catalog.
Why a full read catches what a sampled check misses.
1
Profile the catalog
EKOM profiled the catalog with no schema supplied — inferring the vertical and every field's role, fill rate, and quality signals before any analysis ran.
No manual setup required.
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2
Read in full
Rather than sampling, EKOM read the live catalog end to end — so a handful of singular product types surviving among a dominant plural convention surfaces cleanly instead of washing out as noise.
Full coverage, not a sample.
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3
De-duplicate & group
Findings are de-duplicated, then grouped by theme and root cause — which is why a color convention that changed and a product type that drifted come through as one connected story rather than a flat list.
Signal, not noise.
A sampled spot-check would have caught some of this and missed most of it. Defects that live in a minority of records — five sets with the wrong plural, two garments with the wrong type, one price field with the wrong zero — are exactly what a sample averages away. Reading the full catalog is what surfaced them, and it is what makes the next step precise: EKOM knows which records need correction and which are already sound.
What this means — and what's next.
up to90%
Across a benchmark of 344 top-grossing US and European e-commerce sites, those with mediocre product-list and filtering usability saw abandonment rates of 67–90%; sites with even a slightly optimized toolset saw 17–33%. For a print-led brand where newness is the product and the range turns over every season, the browse layer does the work a salesperson would do in a store — and that gap is the difference between a shopper who finds the print she came for and one who decides you don't carry it.
Baymard Institute, E-Commerce Product List Usability benchmark
None of these are content problems. A hoodie filed as a long-sleeve top reads perfectly on its own page and never appears under hoodies. A color filed twice under two capitalizations splits its own inventory in half. A set typed with a singular is absent from the collection built for it. And none of it generates a complaint — which is the specific reason this class of defect survives for years in otherwise well-run catalogs. It also doesn't stay on the storefront: the same fields are read by marketplace and retail-partner feeds, by shopping engines, and increasingly by the AI assistants now standing between a customer and a product page.
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
Retype the miscategorized garments, align the singular product types to the catalog standard, normalize the legacy color values to the current convention, correct the cross-sell grouping tags, clear the stray compare-at price, and move the disclaimer out of the option field — resolved systematically from data already in the catalog, not one listing at a time.
2 · Bring the three judgment calls to the brand
The blank product type on the gifting range, the spurious one-size variant, and the style-code URLs each need a merchandising or SEO decision rather than a correction. EKOM brings the decision framed and evidenced, then applies whatever the brand chooses.
3 · Hold the line at intake
Keep ongoing intelligence at intake so each new season's prints and colorways arrive on the current convention — rather than adding a new layer of drift on top of the one just cleared.
This is how EKOM moves a catalog from insight to impact —
and keeps every listing reading right as the range grows.
About this analysis
This is EKOM's first read of this brand's catalog. The pipeline profiled the catalog with no schema and no manual setup — identifying the vertical and every field's role before any analysis ran — then read the full live catalog rather than a sample. One of the highest-severity patterns it produced was checked against the live variant records, disproved, and excluded; a separate structural layer describing how the catalog was flattened for analysis was excluded as well. The findings here are what remained, with the brand's identity removed.