What are the best product datasets for ecommerce analysis?

For ecommerce analysis, the most useful product datasets combine catalog metadata, pricing and promotions, availability, seller/offer information, and ratings/reviews. The right dataset depends on whether you need competitive intelligence, demand forecasting, recommendation models, or product classification.

Best commercial datasets

Marketplace product feeds

Bright Data Ecommerce/Product Datasets

• Covers major marketplaces and retailers including Amazon, Walmart, eBay, Target, Etsy, Best Buy, Home Depot, and many regional platforms.
• Typical fields: product URL, SKU/ASIN/GTIN, title, brand, description, category tree, variants, images, list/final price, discounts, availability, seller, ratings, review counts, and review text.
• Best for: large-scale competitive pricing, assortment monitoring, catalog matching, review analysis, and marketplace intelligence.

Oxylabs Ecommerce Scraper API / datasets

• Provides structured product, search-result, price, seller, and review data from marketplaces and retailer sites.
• Best for: teams that need frequent custom refreshes, live product monitoring, or API-first ingestion.

DataForSEO Merchant API

• Returns product and seller results with location/language context, including displayed/current/regular prices, ratings, labels, delivery details, and sellers.
• Best for: integrating marketplace/product signals into existing SEO, search, or pricing data pipelines.

WebDataInsights and similar multi-marketplace vendors

• Useful when you need broad international coverage, including Amazon India, Flipkart, Lazada, Mercado Libre, Alibaba/AliExpress, Zalando, and regional marketplaces.
• Best for: cross-marketplace catalog, pricing, and brand visibility analysis.

Best free/open datasets

Amazon Reviews 2023

• A large academic dataset from UC San Diego’s McAuley Lab with roughly 571 million Amazon reviews from 1996–2023, along with product metadata across 30+ categories.
• Best for: review sentiment, product recommendation, ranking, customer-behavior modeling, and LLM fine-tuning on commerce language.

OTTO Recommender System Dataset

• Session-level ecommerce interaction data used in a well-known recommendation competition.
• Best for: session-based recommendation, click/cart/order prediction, ranking, and sequential models.

M5 Walmart Sales Forecasting

• Hierarchical retail sales data used in the M5 competition.
• Best for: demand forecasting, intermittent demand, promotions/calendar effects, and supply planning.

UCI Online Retail

• Transaction-level data from a UK online retailer.
• Best for: customer segmentation, RFM analysis, basket analysis, churn proxies, and classical forecasting exercises.

Kaggle ecommerce datasets

• Good source for smaller datasets covering product classification, retail transactions, fashion products, reviews, and customer behavior.
• Best for: learning, prototypes, and portfolio work—but validate provenance, license, completeness, and update date before using any dataset commercially.

Dataset choice by use case

Goal	Best dataset types	Good starting sources
Competitive price monitoring	Time-stamped list/final price, promotions, seller, stock	Bright Data, Oxylabs, DataForSEO Merchant
Assortment and catalog analysis	SKU/ASIN/GTIN, brand, categories, attributes, variants	Bright Data, retailer/marketplace feeds
Review and sentiment analysis	Review text, rating, date, product metadata	Amazon Reviews 2023, commercial review feeds
Recommendation models	User/session interactions, clicks, carts, orders	OTTO, Amazon Reviews, internal event data
Demand forecasting	Unit sales, calendar, price/promo, inventory	M5, internal sales data, retailer feeds
Product classification	Titles, descriptions, images, category labels	Amazon metadata, retailer catalogs, Kaggle
Seller/Buy Box monitoring	Seller identity, offer price, delivery terms, availability	Amazon/marketplace APIs and commercial feeds

Fields to insist on

For a commercial dataset, require these wherever relevant:

• Stable identifiers: ASIN, SKU, GTIN/EAN/UPC, merchant product ID
• Time stamp: collection time and source page time
• Product details: title, brand, category path, description, variant attributes
• Commercial data: list price, final price, discount, currency, shipping cost
• Availability: in stock/out of stock, delivery promise, inventory signal
• Seller data: seller name/ID, Buy Box winner, offer count, condition
• Customer signals: rating, review count, review text, review date
• Source lineage: marketplace, country, URL, extraction date

For serious analysis, prioritize time-stamped feeds over one-off product snapshots. A historical price, availability, and review time series is much more valuable than a static catalog.