Retail pricing intelligence
Competitor pricing, assortment and availability. Every SKU, every day.
Retail pricing intelligence built on the web data infrastructure that runs the largest marketplaces at scale. Competitor price monitoring, AI product matching, store-level pricing and stock tracking, delivered clean into your pricing engine or in Aperture, our market view for retail teams.
Retail data for pricing teams, category managers and commerce-intelligence platformsDaily competitor pricing, assortment and availability, matched to your catalog and ready to act on.
What retail pricing teams use it for.
One dataset, many decisions. Every use case below runs on the same matched catalog, the same daily capture and the same QA.
Competitor price monitoring
Shelf and promotional prices for every matched SKU across the retailers you compete with, captured daily or hourly, with history.
pricepromohistoryDynamic pricing data feeds
Clean, matched competitor prices delivered straight into your pricing engine on a schedule, with a manifest and a QA pass on every drop.
feedapis3Assortment gap analysis
What competitors range that you don't, by category, brand and price band. New listings flagged the day they appear.
assortmentnew skuAvailability and stock tracking
In stock, out of stock, low stock, backorder and lead time, by store or zip code, so you can price into competitor stock-outs.
availabilitystorePromotion and markdown tracking
Rollbacks, clearance, coupons, bundles and loyalty prices captured as structured fields, not screenshots.
promo typedepthMarketplace and seller monitoring
Third-party sellers, Buy Box ownership, seller counts and price dispersion on the marketplaces that set consumer expectations.
sellersbuy boxProduct matching that holds up in a pricing meeting.
A price comparison is only as good as the match behind it. Import.io matches your catalog to competitors in layers, scores every match, and shows the evidence.
- 01Exact identifiers. GTIN, UPC, EAN, MPN and model number, normalised across retailer formats.
- 02Attribute matching. Brand, size, pack count, colour, capacity and specification, extracted by AI from titles, bullets and spec tables.
- 03Visual matching. Product images compared where identifiers are missing or listings are ambiguous.
- 04Confidence and review. Every match carries a score. Low-confidence pairs go to a human review queue, and your corrections train the matcher.
Match once, monitor forever. Matched pairs persist across runs, so your price index does not drift because a retailer renamed a listing.
Stand Mixer, 5 qt tilt-head, 10 speeds, Empire Red
- gtin
- 0 84190 53207 4
- model
- SM150-ER
- capacity
- 5 qt
- colour
- Empire Red
- price
- $449.99
5 Quart Tilt-Head Stand Mixer with Pouring Shield, Red (SM150-ER)
- gtin
- 0 84190 53207 4
- model
- SM150-ER
- capacity
- 5 qt
- colour
- Red
- price
- $399.00 was $449.99
Built for retail websites as they actually are.
Retail sites are the hardest on the web: aggressive anti-bot, store-specific pricing, JavaScript everywhere, and prices that change while you read them. This infrastructure has run them since 2012.
Store and zip-level pricing
Prices and availability as a shopper in a given store or zip code sees them, not a national default.
store 45202 · pickup todayMarketplace scale
Sustained capture on the largest marketplaces in the world without degrading.
450M queries a year, one feedAnti-bot handled
Fingerprinting, rate limits, challenges and geo-blocks handled inside the run, not by your team.
challenge → handled inside SLAJavaScript-heavy storefronts
Client-rendered prices, lazy-loaded variants and dynamic promotions resolved before extraction.
rendered 23 requests in 1.3 sVariants and pack sizes
Every size, colour and pack count as its own row, with parent linkage, so unit prices compare correctly.
per-unit price normalisedPromotions as data
Rollback, clearance, coupon, BOGO, loyalty and bundle prices captured as typed fields with depth and dates.
promo BOGO · ends 09/21International retail
Any locale, currency and script, fetched from the right region with the right store context.
de fr ja pt-BR 190 localesDaily QA on every drop
Row counts, fill rates, price sanity and match stability checked against baseline before delivery.
7 checks → released 06:41Retail data types we capture.
Structured, typed and matched. Each field documented in a contracted schema before the first delivery.
Product details
Title, brand, GTIN, MPN, category, attributes, images, variants and descriptions.
Prices and promotions
Shelf price, sale price, unit price, promotion type and depth, coupon and loyalty price, price history.
Availability
In stock, out of stock, low stock, backorder, ship and pickup options by store and zip.
Rankings and search position
Position by keyword and category, sponsored versus organic, share of shelf.
Reviews and Q&A
Ratings, review text, dates, verified purchase flags and question and answer threads for sentiment analysis.
Sellers and Buy Box
Seller name, count, fulfilment method, Buy Box winner and price dispersion across third-party sellers.
Retail price monitoring, explained.
Retail price monitoring is one part of digital shelf analytics, using the broader eCommerce product-data layer to collect competitor prices, promotions and availability for the products you sell, matched to your own catalog so that every comparison is like for like. Pricing teams use it to set and adjust prices, protect margin, respond to competitor moves and spot assortment gaps.
The hard parts are not the dashboards. They are getting the data reliably from retail websites that actively resist automated access, matching products correctly across retailers that describe the same item differently, capturing store-level and promotional prices rather than a national default, and checking every delivery so a broken page does not become a broken price decision.
Import.io handles those four problems as infrastructure: capture on the hardest retail sites in production since 2012, layered AI product matching with evidence, store and zip-level context, and daily QA against a contracted schema. The result is available as Aperture, a pricing intelligence application, or as a managed web data extraction into your own pricing engine.
- FrequencyDaily as standard. Hourly or intraday for fast-moving categories and key value items.
- CoverageNational and marketplace retailers, regional chains, DTC brand stores and international sites.
- MatchingGTIN and model first, then AI attribute and image matching with a confidence score on every pair.
- DeliveryAperture dashboards, CSV and BI exports, or feeds to S3, SFTP, API and webhook.
- Time to valueFirst matched competitor dataset typically within two to three weeks of scoping.
Retail data at a scale most vendors quote, and we run.
The largest US retail sites and marketplaces run in production here every day, for the platforms that sell competitive intelligence to everyone else. If they trust the capture, your pricing team can.
Ask for a referenceIn production continuously.
Queries a year on one production feed.
Sources under one enterprise SLA.
Leading platforms run their capture here.
Identifier-backed match confidence.
Managed production operations.
72. Sources under one enterprise SLA for a single customer, with query rollover.
Questions retailers ask.
Short answers. Longer ones on the call.
How often are competitor prices updated?
Daily as standard. Hourly or intraday for key value items and fast-moving categories. Cadence is set per source and per category, and reported against in the monthly report.
How accurate is the product matching?
Identifier-backed matches (GTIN, UPC, model) score 0.99. Attribute and image matches carry their own confidence score, and anything below your threshold goes to a review queue where your team can confirm or reject with the evidence on screen. Confirmed matches persist across runs.
Can you capture store-level or zip-level pricing?
Yes. Prices, promotions and availability are captured in the store or zip context you specify, so you see what a shopper in that location sees rather than a national default.
Which retailers and marketplaces can you cover?
National retailers, marketplaces, regional chains, DTC brand stores and international sites. Feasibility per source is confirmed in days, including sites with aggressive anti-bot measures.
How is the data delivered?
In Aperture as dashboards, alerts and exports, or as a managed feed to S3, SFTP, API or webhook in CSV, JSON or Parquet, with a manifest and a QA pass on every delivery.
Is competitor price monitoring legal?
Collecting publicly available pricing information is standard practice across retail. Import.io operates rate-aware collection, respects robots and terms, and works under data processing agreements. Specific requirements are handled in scoping.
How quickly can we start?
Feasibility per competitor site in days. A first matched competitor dataset is typically delivered within two to three weeks of scoping.
Build what comes next