Office Products Category Taxonomy List: The 3-Level Retailer Tree

An office products category taxonomy list is the ordered set of department, category and sub-category labels a retailer uses to place every SKU, and the fullest current US-retail version comes from the Office Depot / OfficeMax product catalog. Datadory records its granularity verbatim as “One record per SKU, with a 3-level category taxonomy (department, category, sub-category)”, spanning 49,466 listings and 1,509 category nodes.

The pairing partner is the Kaggle — Superstore Sales Dataset: 9,800 US order lines across 18 columns spanning 3 January 2015 to 30 December 2018, whose Category and Sub-Category columns form a two-tier transaction-side counterpart. Those are the only two primaries Datadory catalogs for this industry. The result earns its keep when you need to normalize your own assortment against a retailer standard instead of inventing buckets nobody else reports.

The Office Depot / OfficeMax product catalog wins on every axis that matters for a taxonomy list. Datadory scores it 7 of 10 — the highest mark in this slice — and the tree shows up at three depths:

• Three ordered levels. 1,509 category nodes run department, category and sub-category across the whole assortment.
• A path on every SKU. Each of the 49,466 listings carries its full taxonomy route in the breadcrumbs field, one of 14 documented per-SKU fields.
• Cross-checkable labels. The same path appears in the category-level rollup and on the individual SKU records, so every level can be verified against an independent surface before you trust it.

One observed path shows the shape: Paper > Copy & Printer Paper > Colored Paper. The nodes organize an assortment spanning office supplies, paper, ink and toner, furniture, technology, printing services and promotional products across the United States storefront.

Read as a table, the list has five working layers — three structural, two transactional — and each is observable in the primary records rather than inferred from marketing copy.

The structural layers come from the catalog's breadcrumbs property, sitting beside 13 sibling fields documented per SKU: sku, name, brand, mpn, upc, price, priceCurrency, availability, regularPrice, uom, averageOverallRating, totalReviewCount and a category-specific specifications block. The transactional layers ride in Superstore's flat file, where Product ID values such as FUR-BO-10001798 encode category and sub-category as a prefix, keeping the label attached even when product names drift between rows.

Ranked by the relevance scores tagged to the catalog record, the heaviest users are Competitive Intelligence & Product Teams and E-commerce Operators, both scoring 3. Operators map their own assortment breadth against the 49,466-SKU tree before repricing, and a leaf that gains or loses items is an assortment signal in its own right. Developers & Data-Product Builders and Data Scientists & ML Engineers tag 2, seeding normalization pipelines built on the tree; Sales & Growth Teams and Market Researchers & Consultants tag 1, using the tree mostly to define category scope.

This page is one chapter of the industry's coverage. Start with the office-services-supplies data guide, the pillar post mapping all 22 pooled datasets — 2 primary records plus 20 related entries from neighboring verticals — and showing where a taxonomy project fits among them.

Then go deeper on the mechanics: the Office Depot / OfficeMax product catalog record carries the full per-SKU field dictionary and sample rows showing the three-level path in place, while the Kaggle — Superstore Sales Dataset record lays out the transaction file whose Category and Sub-Category columns feed the demand side of a mapping. For the pooled scorecard, the office-services-supplies data hub indexes every record in the slice, and the best office-services-supplies datasets ranking compares the two primaries directly.