CatalogClarity

Knowledge Base

Reference material on product content and the systems that carry it. No opinions here, no news, no takes. Practices that held up across eight platform implementations and four retailers.

Each section is a pillar: a single page that explains the subject end to end, with the detailed articles clustered underneath it. Opinions and commentary live on the blog.

Foundations — the data itself

Taxonomy

Classification structures: the master product taxonomy, the web navigation taxonomy, the merchant reporting taxonomy, and the rules that connect them.

1 articles

Product Data

Attribution models, SKU lifecycle, enrichment workflows, coverage audits and the governance that keeps a catalog from decaying.

0 articles

Digital Assets

DAM selection and rollout, asset intake QA, image SOPs and naming, sequenced image slots, and getting assets off a CDN into managed storage.

0 articles

Metadata

Metadata schemas, controlled vocabularies, tag taxonomies, and where metadata should live when three systems all think they own it.

0 articles

Discovery — how customers find it

Site Search Optimization

Relevance tuning, synonyms, dedicated search attributes, null-result recovery, regression testing, and reading query logs like a merchandiser.

0 articles

Site Merchandising

Category page structure, PDP templates, filters that match how people actually shop, boost and bury rules, and merchandising associations.

0 articles

SEO / AEO / GEO

Structured data that matches the page, crawlable architecture, category and PDP indexation, and an honest read of what AI search changes.

0 articles

Operations — running it at scale

Audits & Reporting

Catalog health defect reporting, attribute coverage by category, sell-through and lifecycle analysis, and vendor quality scoring.

0 articles

System Implementations

PIM, DAM, OMS and commerce platform selection and rollout, migrating off homegrown systems, and managing an implementation partner.

0 articles

Automation

PIM-to-WMS and PIM-to-commerce feeds, ETL pipelines, validation at the point of entry, and the failure modes that make people distrust automation.

0 articles

Using AI with Product Data

Computer vision for attribute extraction, accuracy evaluation loops, machine translation with human review, and where AI still gets catalogs wrong.

0 articles