Resolution Layer Case Study · Read & Diagnose
A national athletic-footwear retailer.
A footwear-catalog data-quality read — and where it leads.
This is a real EKOM catalog analysis, focused on the footwear side of the book, with the retailer's identity removed. The client is a national athletic-footwear retailer carrying sneakers, running, basketball, cleats, and kids' shoes across the major brands. Within the footwear assortment, EKOM's read surfaced 195 distinct data-quality issues — the kind standard validation misses, because it checks whether a field is filled, not whether the value is right.
Footwear has its own way of failing. A sneaker is discovered through attributes a shirt doesn't have — collar height, running surface, retro collection, cleat stud type, kids' sizing tier — and when those are blank or wrong, the shoe is in stock and simply can't be found. What follows leads with that: the shoes a shopper can't reach. 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 footwear-catalog health.
What's inside
- In-stock shoes shoppers can't find — the footwear-specific attributes that decide whether a shoe is discoverable, and where they're blank or wrong.
- Wrong shelf, wrong gender — footwear filed or gendered so it surfaces in the wrong place, or nowhere.
- The SKU layer — variant-level footwear issues the product view can't see: non-searchable sizes, broken parentage, price integrity.
- How EKOM reads a footwear catalog — why per-family passes catch what a whole-catalog sweep averages away.
- What it means, and what's next — from diagnosis to a footwear catalog that's findable and channel-ready.
In-stock shoes shoppers can't find.
The highest-impact footwear failure isn't a missing product — it's a live, buyable shoe that never surfaces where a shopper looks.
Highest-impact footwear finding · Discovery
Live shoes, invisible to search and filters.
Every size of a purchasable Jordan Luka 5 "Venom" is flagged non-searchable — in stock, correctly priced, and invisible to on-site search. A Jordan 4 Retro "Toro Bravo" is tagged to the wrong Jordan retro collection (jordan-retro-1 instead of jordan-retro-4), so it drops out of the collection filter shoppers actually browse. These are the shoes that exist in the catalog and disappear from the storefront.
The footwear attributes that drive discovery — blank or wrong
Shoes are found by attributes apparel doesn't have, and across the assortment these run heavily unpopulated: collar height (shoeHeight — low-top / mid-top / high-top / slides) blank on a large share of footwear, so shoes miss the height facet; running type & surface (runningType, runningShoeSurfaceType — road vs. trail) blank on most running shoes, so a trail runner never appears in a trail filter; cleat stud type (cleatType — molded / firm-ground / metal) blank on most cleats; and kids' sizing tier (kidsShoeSizing — grade-school / preschool / infant-toddler) blank on most kids' shoes, collapsing the single most important kids'-footwear filter.
Wrong gender — invisible in navigation
An adidas F50 League Mid-Cut Men's Soccer Cleat carries a blank gender — a clearly gendered men's cleat that never appears in gender-filtered navigation. A women's shoe labeled "Men's" despite every other field confirming the correct gender. On the variant layer, nine Nike men's SKUs are labeled "Women's Shoe." Gender is the first filter most footwear shoppers touch; a wrong value routes the shoe to the wrong shelf, or off the shelf entirely.
The SKU layer — where a size is what sells.
In footwear, the unit a shopper buys is a size. EKOM also ran a variant (SKU) analysis — one row per size a shopper can actually purchase — on a sample of the catalog. It surfaces a class of issue the product view can't see, because the product view summarizes its sizes down to a count.
- Non-searchable sizes. Every size of a purchasable Jordan Luka 5 "Venom" is flagged non-searchable — the whole size run is invisible to on-site search while remaining in stock.
- Broken product grouping. Six Sabrina 3 "Silencer" size variants point to the wrong parent product — grouped under an unrelated shoe — while some legacy variants have no parent at all, so sizes scatter instead of gathering under one product page.
- Gender mismatch within the size run. Nine Nike men's SKUs labeled "Women's Shoe" — sibling sizes disagreeing with the product's own gender.
- Price integrity. Shoes where the current price is higher than the "original" (a fake sale, factually wrong) and active items priced at $0.00 against a normal original — a free-item checkout risk.
- Barcodes / UPCs. Blank or 0 UPCs on live shoes (nothing scannable for point-of-sale or EDI), and UPCs that fail GS1 UPC-A validation in vendor routing.
- Shipping-breaking dimensions. The primary variant of nearly every product has height and width set to 0.0, which breaks shipping-rate calculation and carrier-manifest validation.
Scope. The variant pass was a sampled look at a handful of product groups — a proof of the SKU layer's value, not an exhaustive pass. A full variant analysis across the footwear catalog is a straightforward next step.
Missing fields and quiet contradictions.
Less visible than a wrong shelf, but they break fit guidance, routing, and vendor identity.
The fields fit and fulfillment depend on
Missing sizeChartId on many shoes means the size-chart widget never renders — and in footwear, a missing size chart is one of the surest drivers of a return. Several Jordan / Nike products carry a blank vendorName and vendorNumber even though every other field confirms the brand — a gap that quietly breaks vendor routing and reporting. manufacturer is blank on items with a known vendor, and vendorPartNumber formatting is inconsistent across the assortment.
Status / availability contradiction
Shoes whose status and availability disagree.
Several items are failing quality guardrails yet remain searchable and purchasable; one carries a bypassGuardrails flag set True with no documented reason. Others are marked offline for "no image" while online with several images, and one is offline but still flagged in-stock and purchasable. The status layer and the availability layer contradict each other on the same shoe.
Why a completeness check misses all of this
Every issue above passes a standard validation gate: the field is filled, the record is complete, the shoe is live. What a completeness check can't see is that the value is wrong — a cleat with no gender, a retro tagged to the wrong collection, a size run pointing at the wrong parent. Those only surface when structure and meaning are read together, footwear family by footwear family.
How EKOM reads a footwear catalog.
Why family-specific footwear problems surface here rather than averaging away in a whole-catalog scan.
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, and detecting that footwear attributes cluster by family (running, basketball, cleats, kids').
No manual setup required.
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2
Partition & pass
It ran focused passes — partitioning footwear by family and, within each, by sub-type or brand — so sparse-but-family-defining attributes like cleat type, running surface, and collar height are read in the context where they matter.
One coherent slice at a time.
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3
De-duplicate & group
Findings are de-duplicated across passes, then grouped by theme and impact — which is why a blank cleat-type or a wrong Jordan-collection tag comes through cleanly instead of washing out.
Signal, not noise.
A single whole-catalog sweep averages a large book toward its mean and loses the per-family footwear defect — the running attribute that's blank on trail shoes, the cleat with no stud type. Focused passes are how those get caught instead of buried, and it's what makes the next step precise: EKOM knows exactly which footwear fields need filling and which are already sound.
What this means — and what's next.
more than25%
More than one in four organizations facing data-quality challenges report losing over $5 million annually as a result — and in footwear that cost concentrates in discovery and returns, where every size filter, collar-height facet, running-surface tag, and size chart relies on clean structured data to function. A shoe that can't be filtered, or a size chart that won't load, doesn't announce itself; it disappears into a search with no results and a return that didn't need to happen.
Forrester Data Culture and Literacy Survey, 2023 (RES181258)
None of these are content problems. A cleat with a blank gender is invisible to gender navigation no matter how good the photography. A retro tagged to the wrong collection drops out of the filter shoppers browse. A size run pointing at the wrong parent scatters the sizes a shopper needs to convert. A missing size chart silently ends a session and seeds a return. These accumulate because standard validation checks for presence, not correctness — and footwear's most important attributes are exactly the sparse, family-specific ones a generic check never looks at.
This pass read and diagnosed. The same structural understanding powers the work that follows — turning a diagnosed footwear catalog into one that's findable on every filter and channel-ready everywhere it sells.
1 · Apply the confirmed corrections
Fix the wrong-collection tags, the blank and mislabeled genders, the broken variant parentage and non-searchable size runs, and the price/status contradictions — resolved systematically, not one shoe at a time.
2 · Enrich the footwear-specific attributes
Complete the fields that make a shoe discoverable — collar height, running type and surface, cleat stud type, kids' sizing tier, retro collection — plus sizeChartId so fit guidance renders, so every shoe shows up on the filter a buyer actually uses.
3 · Go to the SKU layer, and hold the line
Run the full variant-level pass (price integrity, UPCs, per-size searchability, shipping dimensions) as the natural next depth — and keep ongoing intelligence at intake so the footwear catalog stays findable as new drops and colorways arrive.
This is how EKOM moves a footwear catalog from insight to impact —
and keeps every shoe findable as the assortment grows.
About this analysis
This is a footwear-focused view of EKOM's second-pass catalog analysis for this retailer. 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 it through focused passes, each examining one coherent footwear family, so category-specific defects surface cleanly instead of averaging away. The findings here are the footwear slice of that read, with the retailer's identity removed and third-party brand and model names preserved.