Product Identity
Build a consistent product foundation across retailer-specific page structures.
- Title, brand, SKU, model
- ASIN, UPC, EAN, GTIN
- Category and taxonomy
- Attributes, variants, pack size
- Images and product URLs
Home / Ecommerce & Retail
Retail performance from public web dataEcommerce Data Scraping for Smarter Retail Decisions
Collect and connect product, price, promotion, seller, availability, review, search, and digital-shelf signals across eligible retailers, marketplaces, and online stores.
✓Custom retail schemas✓Cross-platform collection✓Product-matching workflows✓Historical observations
RETAIL SIGNAL MONITORLATEST OBSERVATION
PRODUCT
Example Wireless HeadphonesBrand X · Model HX-200 · Black
MATCH GROUP 0418RetailerPriceStockSeller
Marketplace A$189.00 ↓In stockBrand Store
Retailer B$199.00In stockRetailer B
Marketplace C$184.00 ↑LimitedThird party
Observed offers3Example record
Search position#4Selected keyword
Content scoreReviewFields incomplete
ProductsIdentity and attributes
OffersPrice and promotion
SellersMarketplace presence
AvailabilityStock and fulfillment
ReviewsRatings and voice
SearchRank and visibility
ContentDigital shelf quality
From pages to retail decisions
A product page is not yet a reliable retail record.
The same item can appear under different titles, variants, sellers, pack sizes, prices, fulfillment methods, and locations. Raw page extraction alone cannot answer how one product performs across the market.
Kvetoiq separates product identity from offers and observations, normalizes the necessary fields, matches equivalent records, validates changes, and delivers a structure built around the decision.
01
Retailer and marketplace pagesProducts, searches, categories, sellers, reviews
↓02
Retail entities and offersProduct, variant, seller, location, fulfillment
↓03
Normalized and matched recordsIdentifiers, currency, units, taxonomy, match group
↓04
Historical retail signalsPrice, stock, seller, rank, content, review changes
↓05
Decision-ready deliveryFiles, API, cloud, database, alerts, analytics
✓What ecommerce data can be collected?
Structure the market around products, offers, availability, customers, and visibility.
The fields are selected for the intended workflow and confirmed against representative sources before production.
Commercial Offers
Observe how a product is presented and sold by each retailer or marketplace seller.
- Current and list price
- Discounts and promotions
- Coupons and event offers
- Currency and taxes where visible
- Seller and Buy Box context
Availability & Fulfillment
Capture public availability states with the necessary market or location context.
- In-stock and out-of-stock state
- Limited availability
- Delivery estimates
- Pickup or store availability
- Fulfillment method
Reviews & Customer Signals
Connect ratings and public feedback to products, variants, sources, and observation time.
- Rating and review count
- Review text and date
- Verified status where public
- Sentiment and topics
- Rating-distribution changes
Search & Visibility
Measure public product presence across selected search terms and category journeys.
- Search position
- Sponsored or organic state
- Category placement
- Share of search inputs
- Competitor presence
Content & Digital Shelf
Evaluate whether product pages contain the information customers and retail teams expect.
- Title and description quality
- Attribute completeness
- Image and video presence
- Enhanced content
- Brand-content consistency
Retail entity model
Separate the product from every offer and observation around it.
A dependable retail dataset preserves the relationships between products, variants, marketplace offers, sellers, locations, reviews, search results, and time.
✓A product may have multiple variants and identifiers.
✓Each retailer or seller creates a separate offer.
✓Price and availability are timestamped observations.
✓Location and fulfillment can change the visible outcome.
PRODUCT IDENTITY
VariantsColor · size · pack · model
Marketplace OffersRetailer · seller · price · promo
AvailabilityLocation · stock · fulfillment
ReviewsRating · feedback · sentiment
Search VisibilityKeyword · rank · sponsored state
Content SignalsAttributes · images · descriptions
Cross-platform product matching
Compare like with like, even when retailers describe it differently.
Matching begins with stable identifiers where available, then uses brand, model, title, attributes, pack size, images, and source-specific context. Ambiguous records can be routed for review instead of silently forcing a match.
Exact matching
SKU, ASIN, UPC, EAN, GTIN, model number, and verified identifier combinations.
Attribute-assisted matching
Brand, title, variant, pack size, dimensions, specifications, and image similarity.
Similar alternatives
Comparable products grouped using agreed category, attribute, and commercial rules.
Exception review
Ambiguous, conflicting, or low-confidence pairs retained for investigation.
Illustrative match reviewREVIEW WORKFLOW
Retailer ABrand X HX-200 Wireless Headphones - Black
EXACT
Marketplace BHX200 Bluetooth Headset, Black · Brand X
Retailer CBrand X HX-200 Pro Bundle
CHECK
Marketplace DHX200 Headphones + Travel Case
Store EBrand X HX-100 Wireless Headphones
NO
Retailer ABrand X HX-200 Wireless Headphones
Illustrative only. Actual matching rules, evidence, confidence treatment, and validation are defined for each project.
Retail data outcomes
Use connected retail signals across commercial teams.
Each workflow combines the relevant products, offers, sellers, locations, timestamps, and comparison rules.
Competitive Price Monitoring
Track selected product prices, promotions, sellers, and historical changes across relevant retailers.
Explore Price Monitoring →Digital Shelf Analytics
Measure content completeness, product presence, reviews, availability, and competitive page quality.
Explore Digital Shelf Analytics →MAP Review Workflows
Identify potential advertised-price exceptions and attach public evidence for internal review.
Explore MAP Alerts →Share of Search
Observe selected keyword positions, category visibility, sponsored presence, and competitor representation.
Explore Share of Search →Seller & Brand Monitoring
Track selected sellers, listing changes, content inconsistencies, and marketplace evidence.
Explore Brand Protection →Assortment Analysis
Compare category coverage, new launches, attributes, variants, white space, and competitor ranges.
Review Analysis
Analyze public ratings, review velocity, sentiment, recurring issues, product strengths, and category trends.
Availability Monitoring
Observe stock, delivery, pickup, fulfillment, and location-dependent availability signals.
Product Launch Tracking
Detect new products, variants, categories, content changes, assortment expansion, and market entry.
Team-to-data mapping
Give each retail team the signals behind its decisions.
| Team | Data required | Decision supported |
|---|---|---|
| Pricing | Prices, promotions, sellers, competitors, history | Price positioning and exception review |
| E-commerce | Content, availability, reviews, search position | Online product performance |
| Merchandising | Assortment, category, attributes, variants | Range and category planning |
| Brand protection | Sellers, listings, advertised prices, public evidence | Investigation and internal review |
| Supply chain | Stock, delivery, pickup, fulfillment signals | Availability and market response |
| Product | Reviews, ratings, sentiment, attributes | Product and content improvement |
| Research | Brands, categories, trends, launches, markets | Market and competitor assessment |
A retail record changes over time
Preserve the sequence, not only the latest value.
Historical observations show how price, promotion, availability, sellers, visibility, and content moved around a product.
01
First observedProduct and offer recorded02
Price changedPrevious and current value03
Promotion appearedCoupon or event context04
Stock changedAvailability state updated05
Seller changedOffer ownership shifted06
Rank changedSearch visibility moved07
Content updatedAttributes or media revisedRetail and marketplace coverage
Connect your retail strategy to every major marketplace.
Open a dedicated platform page to explore available fields, use cases, delivery options, and managed collection requirements. Each source is assessed for the selected market, location behavior, update pattern, and technical feasibility.
Amazon
Products, offers, sellers, reviews, rankings, availability, and categories.
Amazon Data Scraping → WalmartCatalogs, stores, prices, sellers, reviews, pickup, and availability.
Walmart Data Scraping → Shopify StoresProducts, variants, collections, inventory, content, and merchant catalogs.
Shopify Data Extraction → TikTok ShopProducts, shops, sellers, creators, engagement, pricing, and trends.
TikTok Shop Scraping → FlipkartListings, prices, sellers, ratings, reviews, specifications, and stock.
Flipkart Data Scraping → eBayListings, auctions, sellers, condition, pricing, shipping, and availability.
eBay Data Scraping → EtsyProducts, shops, prices, reviews, variations, shipping, and search position.
Etsy Data Scraping → Best BuyElectronics, specifications, prices, promotions, reviews, and stock.
Best Buy Data Scraping → WayfairFurniture, variants, pricing, reviews, categories, and availability.
Wayfair Data Scraping → AlibabaProducts, suppliers, minimum orders, prices, certifications, and trade details.
Alibaba Data Scraping → AliExpressProducts, stores, prices, discounts, shipping, reviews, and promotions.
AliExpress Data Scraping → WishListings, merchants, prices, discounts, ratings, and delivery information.
Wish Data Scraping → NeweggTechnology products, specifications, sellers, prices, reviews, and stock.
Newegg Data Scraping →TargetHome DepotLowe'sCostcoGoogle ShoppingOther eligible retailers
Retail data workflow01 02 03 04 05 06
Validate products, markets, locations, and fields before scaling.
Start with representative records so the schema, matching logic, quality checks, and delivery behave as expected.
Define Decisions
Confirm retailers, products, markets, teams, and intended outcomes.
Validate Sources
Test representative products, categories, searches, and locations.
Design Schema
Separate products, variants, offers, sellers, and observations.
Build & Match
Collect, normalize, link, validate, and retain exceptions.
Review Sample
Confirm fields, match evidence, quality rules, and output.
Launch & Maintain
Monitor the pipeline, refresh records, and manage source changes.
Retail data quality framework
Validate the commercial meaning, not only the HTML extraction.
Quality rules are defined for the project and aligned with how the receiving team will compare, aggregate, and act on the data.
Identifier Validation
Check SKU, ASIN, UPC, EAN, GTIN, model, and source-specific identifiers.
Price & Currency
Normalize numeric values, currencies, list prices, promotions, and visible offer context.
Pack & Variant Control
Separate sizes, colors, bundles, quantities, multipacks, and parent-child relationships.
Seller Normalization
Preserve seller identity, retailer context, fulfillment, and marketplace relationships.
Duplicate Detection
Identify exact and near-duplicate products, offers, pages, and historical observations.
Geographic Context
Retain ZIP code, store, city, market, currency, and delivery context where relevant.
Observation Time
Track when the source was observed, extracted, validated, and delivered.
Quality Alerts
Surface missing fields, unusual values, source changes, volume drift, and match exceptions.
Delivery and integration
Receive retail data where analysis and operations already happen.
Delivery can include the dataset, schema documentation, field definitions, timestamps, validation context, and known limitations.
CSVExcelJSONJSONLParquetAPIWebhookCloud StorageDatabaseData WarehouseBI Integration
Services behind the retail workflow
Combine the collection, analysis, and freshness capabilities you need.
Web Scraping Services
Managed extraction and structured retail data delivery.
Explore service →Enterprise Web Crawling
Broad recurring collection across complex retail sources.
Explore service →Web Scraping API
Programmatic product and marketplace data collection.
Explore service →AI-Powered Scraping
Matching, classification, enrichment, sentiment, and vision.
Explore service →Live Crawler Services
On-demand, scheduled, and change-driven retail signals.
Explore service →Custom Data Extraction
Purpose-built retail schemas and business rules.
Explore service →AI Training Data
Traceable product, matching, classification, and evaluation datasets.
Explore service →All Web Data Services
Compare Kvetoiq's complete collection and delivery capabilities.
View all services →Responsible retail data collection
Define the sources, markets, locations, fields, and intended use before launch.
Kvetoiq assesses public-source availability, necessary fields, source conditions, request controls, market and location behavior, privacy considerations, retention, and delivery requirements. Project-specific legal questions should be reviewed by qualified counsel.
Review Privacy Policy →✓Public-source and feasibility assessment
✓Necessary-field and purpose limitation
✓Source-aware request controls
✓Market, location, and account-state context
✓Privacy, retention, and delivery requirements
✓Documented assumptions and known limitations
Frequently asked questions
What retail teams ask about E-Commerce Data Scraping.
What is E-Commerce Data Scraping?
E-Commerce Data Scraping is the managed collection and structuring of eligible public product, offer, seller, availability, review, search, category, and content information from online retailers and marketplaces.
What ecommerce data can Kvetoiq collect?
Potential fields include product identifiers, titles, brands, categories, attributes, variants, prices, promotions, sellers, availability, fulfillment, ratings, reviews, search positions, and product-page content.
Which retailers and marketplaces can be assessed?
Kvetoiq can assess requested eligible sources such as Amazon, Walmart, Shopify stores, TikTok Shop, Flipkart, Target, eBay, Best Buy, Etsy, Home Depot, Lowe's, Costco, Wayfair, Google Shopping, and other retailers. Feasibility is source- and requirement-specific.
Can you collect product prices and historical changes?
Yes. A workflow can preserve timestamped observations of current price, list price, promotions, sellers, availability, and other selected fields so changes can be analyzed over time.
Can you track stock and availability?
Eligible sources can be assessed for public in-stock, out-of-stock, limited-availability, delivery, pickup, and fulfillment signals.
Can availability be collected by ZIP code or store location?
Where a public source exposes location-dependent information and the workflow is technically appropriate, collection can preserve ZIP code, store, city, region, delivery, or pickup context.
Can Kvetoiq match products across retailers?
Yes. Matching can use stable identifiers and supporting evidence such as brand, model, title, attributes, pack size, images, and source context. Ambiguous pairs can be routed for review.
How do you separate product variants?
Variant rules can use size, color, pack quantity, model, configuration, dimensions, identifiers, and parent-child relationships defined for the category.
Can you identify third-party sellers?
Where seller information is publicly visible, records can preserve seller name, marketplace context, fulfillment method, offer details, and observation time.
Can you monitor promotions and coupons?
Selected public promotions, coupons, discount values, event labels, list prices, and validity information can be collected where available and structured for comparison.
Can you collect product reviews and ratings?
Eligible public rating and review data can include rating value, review count, review text, date, distribution, and product or variant context.
Can you analyze customer sentiment?
AI-assisted enrichment can classify sentiment, topics, product attributes, recurring complaints, and positive themes under an agreed taxonomy and validation workflow.
Can you monitor search rankings and share of search?
Selected keyword and category journeys can be observed for product position, sponsored or organic state, competitor presence, and share-of-search inputs.
Can Kvetoiq support digital shelf analytics?
Yes. Relevant inputs may include product presence, availability, title and description quality, attribute completeness, image presence, reviews, search visibility, sellers, and competitor comparisons.
How frequently can ecommerce data be refreshed?
Cadence depends on source behavior, product volume, geography, rendering, validation, intended use, and responsible request controls. It is confirmed during feasibility testing.
Can you scrape Shopify stores?
Eligible public Shopify storefronts can be assessed for products, variants, collections, content, pricing, and availability according to the requested scope.
Can retail data be delivered through an API?
Yes. Delivery options may include API, webhook, CSV, Excel, JSON, JSONL, Parquet, cloud storage, databases, warehouses, and custom integrations.
How do you validate ecommerce data?
Validation may cover identifiers, required fields, prices, currencies, variants, pack sizes, sellers, duplicate offers, location context, timestamps, record counts, and product-match exceptions.
Can we review a sample before production?
In many cases, representative records can be prepared to confirm fields, retail schema, match evidence, location behavior, quality rules, and delivery format before scaling.
How does Kvetoiq approach responsible ecommerce data collection?
Kvetoiq scopes public sources, necessary fields, source conditions, market and location context, request controls, privacy considerations, retention, intended use, and delivery requirements. Qualified counsel should review project-specific legal questions.
Start with representative retail records
See what your retail market looks like in structured data.
Share your target retailers, products, markets, fields, locations, refresh requirements, matching rules, and delivery destination. Kvetoiq will help shape a practical collection and validation plan.