Office Depot / OfficeMax Product Catalog (Structured)

It is the shelf of America's office-supplies incumbent, turned into rows. Office Depot operates the OfficeMax brand alongside its own, and its US storefront lists roughly 49,000 products across office supplies, paper, ink and toner, furniture, technology, printing services and promotional products - arranged down a three-level merchandising tree that runs from department to category to sub-category. The Paper branch alone carried 2,802 items when Datadory's research pass ran in August 2026.

What earns the record its keep is identity depth. Most retail catalogs hand you a title and a price; this one hands you the retailer item number, the manufacturer part number, the UPC, the brand and the manufacturer name on the same row, plus category-specific specification rows underneath. That is enough to join against supplier feeds and product registries instead of fuzzy-matching on title strings. Get a sample of this dataset scoped to the departments or categories you care about before anything else.

Five listings captured during the August 2026 research pass, flat exactly as they land in a delivery:

sku           : 6039454
name          : Astrobrights Color Paper, 8-1/2" x 11",
                Bright Assortment, Total Qty 100
brand         : Astrobrights       mpn : 98768-01RFID
price         : 7.49
taxonomy      : Paper > Copy & Printer Paper > Colored Paper
manufacturer  : NEENAH PAPER INC

sku           : 196517
name          : Boise X-9 Multi-Use Printer & Copy Paper, 10 Reams,
                White, Letter, 5000 Sheets Per Case, 20 Lb, 92 Brightness
brand         : BOISE              uom : case
regular_price : 69.99              sell_price : 48.99

sku           : 348037
name          : Office Depot Multi-Use Printer & Copy Paper, 10 Reams,
                White, Letter, 5000 Sheets Per Case, 20 Lb, 92 Brightness
brand         : Office Depot       uom : case
regular_price : 60.49              sell_price : 48.99

sku           : 336977
name          : Post-it Super Sticky Notes, 3 x 3 in, 24 Bulk Sticky
                Note Pads, 70 Sheets/Pad
brand         : Post-it            uom : pack
regular_price : 29.99              sell_price : 23.99

sku           : 589483
name          : Office Depot Brand Notebook Filler Paper, Wide Ruled,
                8" x 10-1/2"
brand         : Office Depot       uom : (none recorded)
regular_price : 3.09               sell_price : 1.00

Read rows two and three together and you have the whole private-label argument on one screen. A Boise X-9 ten-ream case and the Office Depot brand ten-ream case share an identical specification string - white, letter, 5,000 sheets, 20 lb, 92 brightness - yet carry different regular prices, $69.99 against $60.49, and sell at precisely the same $48.99. Same factory paper, two price ladders, one street price.

The other rows cover the states a price monitor has to survive. The Astrobrights row shows the identity triple doing its job: item number 6039454, manufacturer part 98768-01RFID, Neenah named as the manufacturer. The Post-it row shows a bulk pack discounted from $29.99 to $23.99 with its unit of measure stated as a pack, not a pad. The filler-paper row is the cheapest thing on the page and still carries a $3.09-to-$1.00 markdown - promotion runs all the way down the ladder.

Fourteen documented fields per row, in three groups. Identity: the retailer item number (sku), manufacturer part number (mpn), UPC, brand and manufacturer-side naming, which together make the row joinable against supplier catalogs rather than merely readable. Commerce: current price and currency, the pre-discount regular price beside it, the unit of measure naming what the price actually buys, and the stock state. Placement and reputation: the department > category > sub-category path, average star rating, review count, and the specifications block holding whatever attributes the category demands - sheet size and paper weight for paper, dimensions and material for furniture, compatibility for technology.

All fourteen are verified against live product pages rather than inferred from markup patterns, which is why the field confidence on this record reads verified. The variable part is inside specifications, not around it: attribute vocabularies differ by category, so they arrive as key-value pairs keyed consistently instead of a sparse grid of hundreds of mostly-empty columns.

Additional fields on request cover the edges: per-category listing counts (the honest answer to "how big is the catalog", which is only knowable category by category), an explicit list-versus-street-price flag where a category view reports reference price, and the US store-location layer.

Geography - the United States retail assortment is the scope, with a US store-location layer available as a separate scoping decision rather than mixed into the product extract.

Temporal - the live state of the shelf at capture time. No historical archive exists anywhere for this assortment, so a price series exists only where someone captured it - which is the practical argument for starting a cadence before the analysis is specified. Weekly snapshots build a usable positioning panel within a quarter; daily catches the markdown windows that weekly sampling walks past.

Granularity - one record per SKU, placed on the three-level merchandising tree. No rollups, no category averages: every downstream aggregation builds from the same atomic row, which is what keeps a shelf comparison reproducible instead of anecdotal. Set against Datadory's wider catalog - 1,744 records averaging 7.81 - this slice scores 7/10, credited for verified fields and identity depth, docked for the absent archive and category-variable attribute vocabulary.

API, files, or your warehouse. Daily, weekly, or hourly.

Pick the channel your team already works in: REST endpoints for live lookups against a SKU or category slice, flat files sized for overnight loads, or a direct pipe into Snowflake, BigQuery or Redshift. Cadence is yours to set - and to change when a back-to-school or tax-season calendar demands it.

Every delivery ships with the field dictionary above and sample rows for validation, and the work of keeping resolution stable stays our problem, not yours.

• Competitive-intelligence and product teams track the reference price ladder for office products: current-versus-regular spreads by category, markdown depth on private label against national brands, and assortment changes quarter over quarter - see these competitive intel product teams use cases.
• E-commerce operators benchmark their own office-products assortment against the incumbent's, using the unit of measure field to normalise cases against packs before declaring anything a deal - mapped in e commerce operators use cases.
• Data scientists and ML engineers build SKU-level price panels from repeated captures for elasticity and promotion-response work, where the explicit regular-price column beats reconstructing reference prices from history - workflows in data scientists use cases.
• Market researchers and consultants size the US office-products market bottom-up from the assortment itself, citing street prices rather than list-price press releases; method notes at market researchers use cases.
• Investors and quants read promotional intensity and private-label share as signals in the office-retail and paper-value chains, where markdown cadence leads margin commentary.
• Developers and data-product builders wire stable SKU, MPN and UPC identifiers into procurement and catalogue-matching pipelines without fuzzy joins; see developers builders use cases.

The natural companion in this industry is the Kaggle Superstore Sales Dataset: 9,800 US order lines from 2015 to 2018, of which 5,909 sit in Office Supplies. The two records answer opposite questions - Superstore is transaction history, frozen and packaged for forecasting exercises; this catalog is shelf state, live and priced. Paired, they give you what sold four years ago and what the shelf costs today, which is the shortest route between a demand model and a procurement decision. Both live in the office services & supplies data hub.

For breadth rather than depth, the broadline-retail feeds resolve Walmart and Target at comparable SKU granularity - useful when office products are one aisle among many in your question rather than the whole question. Neither carries the office-vertical identity depth here: manufacturer part numbers and specification tables are thinner on general-merchandise shelves. Browse the ranking at best office-services-supplies datasets.

Three things, stated plainly.

First, this is live commerce, not an archive. Every delivery reflects the assortment as it stands when cut; longitudinal pricing exists only where someone captured it, which is why cadence belongs in the first conversation rather than the last.

Second, price semantics have one wrinkle: some category views report reference price where a street price is expected. The extract therefore carries regular price and selling price as separate columns and validates the split against live records when your sample is cut - so a "$69.99" that is really a sticker, not a street price, never silently contaminates a spread calculation.

Third, the specifications block varies by category by design. A ream of paper and an ergonomic chair do not share attributes, and forcing them into one wide grid produces a wall of nulls. Key-value pairs keep both honest. None of this dents why the record scores 7/10: fourteen verified fields, identity deep enough to join on, and five sample rows visible before you commit.