Amazon Household Appliances Product Listings Data

The US Appliances department of Amazon.com resolved into structured retail rows: listing rows from category browses at 28 to 60 results per page, and product records one level deeper per ASIN carrying the buybox, ratings and specification layer.

Both shapes were verified live during the August 2026 research pass. A first-page response to a portable washing machine browse returned 28 distinct ASINs, every one with title, star rating, review count and price. A representative product record carried the buybox price ($219.99), an aggregate 4.3 stars across 743 ratings, a two-node Best Sellers Rank (#750 in Appliances; #94 in Portable Clothes Washing Machines) and a structured specification table: 1.04 cubic feet, top-load access, drain and spin cycles, 120 volts.

The facet vocabulary is what turns a storefront into a walkable catalog: the same brand, price-band, capacity, feature and department filters shoppers use to narrow a purchase are the screening axes a delivered feed uses to scope an extract. Get a sample of this dataset scoped to the departments and brands you care about.

One row per ASIN, flat exactly as it ships:

# One record per ASIN, flat exactly as it ships

asin             : B08QFW4ZG5
title            : Giantex Full Automatic Washing Machine, 2 in 1 Portable Laundry Washer, 8.8lbs
price            : $219.99
rating           : 4.3   ratings_count: 743
best_sellers_rank: #750 in Appliances; #94 in Portable Clothes Washing Machines
capacity         : 1.04 cubic feet   access_location: Top Load
cycle_options    : Drain, Spin        voltage: 120 volts

# An unavailable listing keeps its row - the buybox price simply goes null

asin             : B0799Q45TT
title            : BLACK+DECKER 0.9 Cu. Ft. Portable Washer, 6.6 lb. Capacity, BPWM09W, White
price            : (null - Currently unavailable)
rating           : 4.4   ratings_count: 8505
best_sellers_rank: #36 in Appliances; #1 in Portable Clothes Washing Machines
capacity         : 0.9 cubic feet     access_location: Top Load

Two conventions decide whether an appliance pricing pipeline survives contact with the full assortment. The ASIN is the spine: every rating, rank and price point resolves back to it, so a re-listing under a new ASIN reads as a new row rather than silently overwriting history. A missing buybox is a fact, not a gap: the BLACK+DECKER record above has lost its price yet still holds 8,505 ratings and the #1 rank in its browse node, so availability analysis comes free with the same extract that powers price work. Live rows scoped to your named departments ship with the sample.

Ten fields anchor the dictionary, all verified against live records rather than inferred from column names. Identity and merchandising (asin, title, price) say what is for sale and at what number; social proof (star_rating, ratings_count) carries the volume behind the stars; best_sellers_rank places the SKU inside its browse node; and the appliance attributes (capacity, access_location, cycle_options, voltage) are the specification layer separating a 0.9-cubic-foot portable from a full-size drum.

Every definition holds on ordinary priced listings and on the null-price unavailable rows alike, which is the practical test of a dictionary built from a real observation window. Detail pages expose further specification attributes - noise level in decibels appeared on washer records during verification - and those fold in as additional fields on request once confirmed against live pages.

Three chips summarize the footprint:

• Geography - the Amazon.com US marketplace, the largest single appliance assortment in the country. Other country storefronts are separate scopes and can join the same delivery when a cross-market view matters.
• Time - a live assortment under continuous merchandising with no archive behind it. Price and rank curves accrue from repeated captures on the cadence you set: weekly produces a workable positioning curve inside a quarter, daily catches the short promotional windows weekly snapshots miss.
• Granularity - one record per ASIN at SKU level, specifications nested on the product record. Best Sellers Rank is defined per browse node, so the rank always travels with its node string - #1 in Portable Clothes Washing Machines and #36 in Appliances described the same washer on the same day.

One comparability note belongs in the open: a capture reflects one currency-and-market configuration at a time, so a price series is only as consistent as the configuration held fixed across it. State the market you want pinned and the feed pins it.

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

You choose the channel and the cadence; keeping pace with an assortment this size is our problem, not your cron job. Extracts land normalized - same columns, same types, same ASIN keys - so a nightly warehouse load and an on-demand lookup agree row for row, and every row carries its capture timestamp so rank movements read as a time series instead of a mystery.

Screening axes come standard: department, brand, price band, capacity range, browse node. A sample pre-cut along exactly those axes ships first either way, sized to test in your own pipelines the day it lands.

Four workloads lean on the appliance assortment hardest:

1. E-commerce and pricing analysts benchmark their own price ladders against the reference assortment, SKU for SKU, with review volume attached to every comparable; see household appliances data for e-commerce operators.
2. Competitive-intel and product teams track brand mix, launch velocity and feature spread - which capacities, load types and voltages gain shelf share month over month; see competitive intel product teams use cases.
3. Data scientists train price-elasticity, recommendation and review models on a consistently shaped, ASIN-keyed corpus; see data scientists use cases and the ML model training use case.
4. Market researchers cite concrete assortment facts - SKU counts per category, price bands, rating distributions - when sizing appliance segments; see the price monitoring use case.

One boundary worth knowing upfront: this is the commerce layer, not a consumption layer. Kilowatt-hour profiles and measured loads live in the metering datasets on this shelf - pair them when the model needs watts rather than prices.

Provenance note - the source is Amazon's US retail marketplace. Datadory keeps the source name on the record and maps field definitions against live pages during each research pass.

Completeness note - figures quoted here were measured at research time, not advertised: 28 ASINs on a first-page response, 28 to 60 results per browse page, hundreds of thousands of appliance ASINs department-wide. Treat any count in a delivered extract as measured.

Dictionary note - all 10 core fields are verified. Specification attributes beyond the four appliance columns fold under additional fields on request and get confirmed against live records when the sample is cut.

Where to go next - cards worth reading: Topten.eu vs Amazon appliance listings for the efficiency-label counterpart, ENERGY STAR certified products and EU EPREL for regulated efficiency data, the DOE appliance standards program, and the rest of the shelf in best household-appliances datasets.