What a catalog read found for a national outdoor retailer.

A real retail catalog, read the way it's actually built — and the one defect hiding across it.

Who
A national outdoor retailer · four brand properties, two markets

Scope
A live retail catalog · multi-category

Live critical findings
69 · gender, encoding, fulfillment

Method
25 recursive passes · auto-profiled, no setup

Total issues surfaced
234 distinct · validated against source

Prepared by
EKOM

What this is.

This is a real example of what EKOM's resolution layer surfaces on a live retail catalog — anonymized, with the client's name and its own house brands removed. The company is a national outdoor retailer running four brand properties across two markets. EKOM read the client's catalog, auto-profiled with no schema and no manual setup, and surfaced 234 findings — each one then checked against the raw source file.

The finding that matters most wasn't visible on any single product. It only appeared when the whole catalog was read together — one load-time default, quietly replicating across thousands of records.

What's inside

1. The headline finding — an entire women's line routed into men's browse.
2. The other live risks — copied/wrong content, fulfillment blockers, stored encoding corruption.
3. Feed vs. catalog — how EKOM separates a real defect from an export artifact by checking the source.
4. What's actionable now — what EKOM fixes, what a clean export settles, what's a client decision.
5. How EKOM reads a catalog, and where this leads — from diagnosis to channel-ready enrichment.

These are machine-surfaced findings — a triage signal, not a verified defect list, and a small share may be intentional. The point of the study isn't the count: it's that a catalog can pass every completeness check and still carry high-consequence defects invisible to standard validation — and that reading it structurally is what makes both the defect and the fix legible.

At a glance.

Read through 25 recursive passes with no schema — and validated against the raw source file.

The three that carry the weight: 1,132 women's items tagged male · 2,816 descriptions with stored encoding corruption · ~1,030 in-stock items that can't compute shipping. After the source check, the barcode item resolved to a clean-export fix and one shared-barcode pattern was set aside — the catalog & content findings carry the weight.

The findings that carry real risk.

Defects in the data itself — present regardless of export format or encoding. Tagged by who feels it.

The headline finding · a load-time default

A women's line tagged male — across the board.

The gender field reads a literal "male" across the women's assortment. Every one of the 1,132 items whose title or collection says women's, ladies', or girls' is tagged male — 100% of them, including 106 variants of a single women's outerwear line. These vanish from the gender filters shoppers use, from campaigns targeting women, and from recommendations — in stock, correctly named, and invisible to the buyers they were built for. It's a value error in the data, not a formatting artifact: it happened once, at load, and replicated across every SKU that passed through the same process.

Wrong, copied & leaked content · Shoppers

A house-brand 1,000-lumen flashlight description opens with the copy for its 300-lumen sibling — wrong specs on a live listing. 53 men's shirts (a third-party workwear brand) carry Style = "Front Pocket Wallets", leaked from another category; 72 listings misspell a product-line name; and color fields contradict titles on multiple items. The shopper reads information that belongs to a different product.

Fulfillment blockers & internal leakage · Shoppers + Ops

~1,030 in-stock items carry zero weight and zero dimensions, so checkout can't compute a shipping rate — buyable until the cart tries to price shipping, then it fails. Alongside them: a folded pair of jeans declared 130 in long, 35 items under 0.2 oz, 30 items marked out of stock while holding positive inventory, and warehouse staging codes prefixed ++ surfacing as customer-facing titles on ammunition items.

Encoding corruption, stored · Shoppers

™ is stored as â„¢ across 2,816 descriptions, with stray  characters and double-encoded entities. The source file is valid UTF-8 and still shows the corruption — so it's in the data, reaching shoppers in product copy, browser tabs, and search snippets.

Feed & format — checked against source.

Structural, encoding, and export-level items — each read against the raw source file and given a verdict. Sorting a real defect from an export artifact is the difference between "fix your export" and "fix your catalog."

What's actionable now.

Confirmed defects EKOM resolves; feed/format items a clean source export settles fast.

Next step. Approve the confirmed data corrections and EKOM applies them; a clean barcode re-export from the system of record closes out the last format item.

How EKOM reads a catalog.

Why category-specific problems surface here rather than averaging away in a whole-catalog scan.

1. Profile the catalog
   EKOM profiled the catalog with no schema — inferring the vertical and every field's role and fill rate. The foundation was sound: core content fields — titles, descriptions, pricing, imagery — were well covered.

2. Partition & recurse
   It partitioned by product type, then recursively re-split the one oversized bucket on its own best sub-field, and batched the long tail — 25 passes, each sized to see its slice in full.

3. Sort & verify
   Findings are grouped by theme and impact, then each feed/format item is checked against the raw source file — separating a genuine catalog defect from an export artifact.

The gap that matters most isn't a blank field — it's a filled one that's wrong. Gender was populated on most of the catalog, and where it was populated it was frequently populated incorrectly. A completeness check passes it; only a read that weighs the value against the rest of the record catches it. That same read is what makes the next step precise — EKOM knows exactly which fields to enrich, and which are already sound.

What this means — and what's next.

Omnichannel customers shop roughly 1.7× as often as single-channel ones — and across four brand properties in two markets, product-data quality is the layer that decides how much of that behavior a catalog can actually capture. A single load-time default replicates across every property sharing the catalog infrastructure.

None of these are content problems. A women's jacket tagged male is invisible to shop-by-gender no matter how well it's described; encoding corruption and copied specs pass a "not blank" check and still fail the shopper; a zero-dimension in-stock item is a fulfillment failure no copy can fix. They accumulate faster than manual review keeps up with. Standard validation checks for presence; EKOM's resolution reads for correctness — and verifies it against the source.

This pass read and diagnosed. The same structural understanding powers the work that follows — turning a diagnosed catalog into one that captures the demand it's built for.

1. Apply the confirmed corrections
   Catalog-wide gender/attribute re-tag, encoding cleanup, content fixes, and fulfillment-value corrections — applied systematically, not one record at a time.

2. Enrich for discovery & channel
   Fill the merchandising and identifier gaps, resolve the barcode/variant structure, and enrich attributes for search and marketplace — so products are findable and channel-ready across every property and market.

3. Hold the line at intake
   Ongoing intelligence that catches the next load-time default as data arrives — so a single ingestion mistake can't replicate silently across a shared catalog again.

This is what EKOM does: read the catalog the way it's actually built, separate real defects from noise, and keep it right as the business grows.

About this analysis
This case study is anonymized: the client's name and its own house-brand names have been removed; third-party manufacturer brands and the findings themselves are unchanged. EKOM read the client's catalog with no schema and no manual setup, through 25 recursive passes, and validated every finding against the raw source file. It was the second pass on this catalog — an earlier pass on a smaller slice predicted the gender default was systemic; this pass confirmed it.