Competitor Pricing Datasets for Retail and E-commerce
Structured competitive pricing data with multi-year history — one row per product, per seller, per timestamp, on a schema that stays stable across retailers and countries.
- One row per product, per seller, per timestamp
- List price separated from promotional price
- Multi-year historical backfill on covered SKUs
- Stable schema across retailers and countries
One row, fully described
Every observation carries the context needed to trust it: what was matched, how confidently, at what price, and when.
Product Identity
Your SKU, the matched listing, GTIN/EAN where available, and a match confidence score.
Price Fields
List price, promotional price, currency, and shipping-inclusive total, kept separate.
Availability
In stock, out of stock, and Buy Box ownership on marketplace listings.
Seller Context
Storefront name, authorized status, and the country the offer was served in.
Capture Timing
A timestamp on every row plus the crawl cadence the observation came from.
History
Multi-year series per SKU so price trends can be rebuilt without re-collection.
Data that survives the analysis
Dashboards, models, and market studies all fail the same way — on inconsistent product matching.
Pricing Analytics
Build price indices, positioning reports, and elasticity estimates on a consistent grain.
- Index over any date range
- Category and brand positioning
Model Training
Feed clean, labelled price history into forecasting and repricing models, or into a RAG pipeline.
- Years of labelled series
- Stable schema for retraining
Market Research
Answer assortment and pricing questions across markets without standing up a scraping team.
- Cross-country comparison
- Assortment gap analysis
Delivered in the format you already load
The same normalized rows as JSON over an API, CSV on a scheduled drop, or Parquet straight into your warehouse. Refresh cadence is configured per category, so volatile marketplace listings and stable long-tail SKUs are not crawled at the same cost.
Request a sample file{
"sku": "SKU-4471",
"competitor": "amazon.com",
"seller": "TechDeals Outlet",
"match_confidence": 0.97,
"list_price": 219.00,
"promo_price": 204.82,
"currency": "USD",
"shipping_included": false,
"in_stock": true,
"buy_box": false,
"country": "US",
"captured_at": "2026-07-22T09:14:00Z"
}A dataset built to be queried, not cleaned
Normalization, matching, and validation happen before delivery, not in your notebook.
Normalized Schema
The same fields across every retailer and country, so one query runs everywhere.
Barcode-Free Matching
AI matching with confidence scores, so coverage does not depend on GTIN availability.
Historical Backfill
Years of price history delivered with the ongoing feed, not accumulated from day one.
Custom Coverage
You define the products, competitors, marketplaces, and countries in scope.
Warehouse-Ready
API, S3, scheduled drops, or a direct load into BigQuery, Snowflake, or Redshift.
Quality Checks
Validation pipelines catch mismatches and outliers before the data reaches you.
What Is Competitor Pricing Data?
Competitor pricing data is a structured record of what other sellers charge for a product — captured per listing, per seller, per marketplace, and per point in time. A single observation is a price; a dataset is that observation repeated across your assortment on a fixed cadence until it becomes a time series you can analyse.
The difference between a usable competitive pricing database and a pile of scraped numbers is normalization: the same product identified across retailers that name it differently, list price separated from promotional price, currency and shipping handled consistently, and a timestamp on every row.
What the Dataset Contains
Every row is one observed price for one product at one seller at one moment. That grain is what lets you rebuild a price history, compute a price index, or backtest a pricing rule without re-collecting anything.
Fields are stable across retailers and countries, so a query written against one market runs unchanged against the next.
- Product identity: your SKU, the matched competitor listing, GTIN/EAN where available, and a match confidence score
- Price: list price, promotional price, currency, and shipping-inclusive total
- Availability: in stock, out of stock, and Buy Box ownership on marketplaces
- Seller: storefront name, authorized status, and country
- Timing: capture timestamp and the crawl cadence the row came from
Historical Pricing Data and Trend Analysis
Current prices tell you where the market is. Historical pricing data tells you how it got there — which competitor moved first, how long a discount lasted, and whether a category reprices on a weekly rhythm or reacts to events.
Backfills go back years on covered assortments, which is what makes the dataset usable for elasticity work, seasonality models, and training data for pricing algorithms rather than only for a dashboard.
- Multi-year price history per SKU, seller, and marketplace
- Promotion windows reconstructed from the underlying series
- Price index and positioning computed over any date range
- Suitable as training or retrieval data for pricing models
Delivery, Formats, and Refresh
The dataset is delivered the way your stack already ingests data — a scheduled file drop, a warehouse load, or an API for systems that need to pull on demand. Refresh cadence is set per category, because a volatile marketplace listing and a stable long-tail SKU do not need the same frequency.
Coverage is defined by you: the products, competitors, marketplaces, and countries that matter, rather than a fixed catalogue you have to filter down.
- JSON, CSV, or Parquet via API, S3, or direct warehouse load
- Refresh from several times a day to weekly, set per category
- Custom coverage by product, competitor, marketplace, and country
- Historical backfill delivered alongside the ongoing feed