Every attribute, every variant, mapped to your schema
Go beyond title and price. Extract size, color, material, weight, dimensions, technical specs, and any custom attribute from any e‑commerce site — even when it loads on click. Delivered as a clean, normalised feed that plugs directly into your PIM, pricing engine, or data warehouse.
{
"sku": "TS-882-BLU-M",
"title": "Classic Tee",
"color": "Navy Blue",
"size": "M",
"material": "100% Organic Cotton",
"weight_g": 180,
"care_instructions": "Machine wash cold"
}
14 fields per product
6 combinations per SKU
Attribute extraction pipelines powering
When “color” and “size” are only the start
Product attribute extraction goes deep into the product detail page, capturing every specification, technical detail, and variant option that matters for your catalog. Whether it's a garment's material composition, a laptop's processor generation, or a piece of furniture's assembly instructions, our pipelines interact with dropdowns, tabs, and dynamic panels to extract the full attribute set.
We normalise everything into your internal schema — mapping source‑site field names to your canonical attributes, converting units, and handling regional variations — so the data lands in your warehouse ready for analysis, not requiring weeks of manual cleaning.
- Extracts size, color, material, weight, dimensions, specs
- Handles dynamic attributes loaded via JavaScript on click
- Normalises units, sizes, and field names to your taxonomy
- Captures region‑specific attributes with local IPs & language
- Delivered as a structured feed (JSON, CSV, Parquet)
Why surface‑level scraping leaves your catalog incomplete
If you're only scraping the product title and price, you're missing the details that drive buying decisions — and internal decisions too.
Attributes hide behind interactions
Size charts, tech specs, and material breakdowns often sit in collapsed tabs or require selecting a variant. A static scraper sees none of this — leaving half the product data unfilled.
Attribute names change per brand
One store calls it “Colour”, another calls it “Color/Finish”. Without a mapping layer, your dataset becomes a mess of inconsistent field names that can't be used for automated filtering or comparison.
Regional attributes break the schema
A US site lists sizes in S/M/L; the same product on a German site uses 36/38/40. Without regional awareness and unit normalisation, your data engineers spend hours cleaning before the data is usable.
Attribute extraction that thinks like a product manager
Every feature is designed to capture the full richness of product data — not just what's above the fold.
Full attribute discovery
We automatically scan the page for all visible attributes — text specs, tables, dropdowns, and interactive tabs — capturing every data point without manual selectors.
Custom schema mapping
During discovery, we align source attributes with your internal field names. “Colour” becomes “color”; US sizes are normalised to your standard. The output is a drop‑in replacement for your PIM feed.
Regional attribute adaptation
For global brands, we deploy local IPs and capture region‑specific attributes — EU shoe sizes, Japanese textile certifications, or UK safety labels — all normalised to your master schema.
Attribute completeness monitoring
We track which fields are populated across the catalog and flag products with missing attributes. Weekly reports show completeness trends — so you can fix gaps before they reach downstream systems.
{
"source": "retailer‑a.com",
"mappings": {
"Colour": "color",
"Material": "material",
"Weight (g)": "weight_grams"
}
}
From product page to structured attribute feed
Attribute audit
We catalogue every attribute available on the target site — visible and hidden behind interactions — and map them to your desired output schema.
Pipeline & interaction build
Our engineers build the extraction logic, including variant selections, tab clicks, and dynamic content waits — all validated against a staging sample.
Sample delivery & validation
A representative product feed (500–2,000 products) is delivered in your schema. You validate attribute accuracy, unit normalisation, and completeness.
Production deployment
Full catalog extraction goes live on your schedule. Ongoing monitoring ensures attributes stay complete and new product pages are captured automatically.
Deliverables at each stage of attribute extraction
Attribute map & schema alignment
A document listing every source attribute, its expected canonical field, and any normalisation rules — signed off before extraction begins.
Sample attribute feed (1,000 products)
A fully attributed dataset delivered in your schema. You check accuracy, completeness, and unit conversion before full‑scale rollout.
Full attribute pipeline + runbook
All products, all attributes, all variants — delivered on your schedule. A runbook documents the mapping logic and monitoring setup.
Weekly completeness report
Summary of attribute fill rates, new attributes detected, and any target‑site changes managed — proactively shared with your team.
Flexible options for any attribute depth
Attribute Profile (Fixed Scope)
Best for extracting a defined set of attributes from a single retailer — one‑time or periodic delivery.
- Up to 20 attributes per SKU
- Fixed scope & price
- Schema mapping included
Managed Attribute Feed
Ongoing extraction with schema management, attribute normalisation, and completeness monitoring — hands‑off for your team.
- Unlimited attributes per SKU
- Weekly or daily refreshes
- 99.9% field completeness SLA
Enterprise Attribute Program
For multiple retailers, complex attribute taxonomies, and dedicated engineering support with custom SLAs.
- Unlimited retailers & attributes
- Custom attribute taxonomy
- SSO, audit logs, quarterly reviews
Teams that build products around rich catalog data
Product Information Management
Enrich your PIM with competitor attributes to benchmark completeness and guide your own catalog enrichment.
Apparel & Fashion
Capture size charts, fabric details, fit information, and care instructions — all critical for customer decision‑making.
Electronics & Technology
Extract technical specs, processor details, RAM, storage, battery life, and compatibility — often hidden in tabs and dropdowns.
Home & Furniture
Get dimensions, materials, assembly requirements, and weight limits — essential for filtering and comparison shopping.
Attribute extraction vs. generic product scraping
| Capability | Managed Attribute Extraction | Generic Product Scraping | Manual Data Entry |
|---|---|---|---|
| Dynamic attribute loading (tabs, dropdowns) | |||
| Custom schema mapping & normalisation | |||
| Regional attribute handling | |||
| Completeness monitoring & alerts | |||
| Ongoing maintenance | Included | Your team | N/A |
| Time to enriched catalog | 2–3 weeks | Weeks of scripting | Months |
“Our PIM is finally fed with complete, normalised competitor data”
"We needed every tech spec from a competitor's electronics catalog — which meant clicking through 12 tabs per product. ScraperScoop's pipeline handled the entire interaction flow and delivered 46 normalised attributes per SKU. Our product team called it 'magic'."
"The schema mapping is what makes this service. We sent our internal taxonomy and they mapped every source field to it — including converting UK shoe sizes to EU sizes. The data dropped straight into our PIM without a single manual edit."
"The weekly completeness report shows us exactly which attributes are missing from which products. We've used it to prioritise our own catalog enrichment — and our attribute coverage has gone from 62% to 98% in six months."
Attribute data lands exactly where your product teams work
Schema‑matched and normalised — load directly into your PIM, warehouse, or merchandising tool.
Get a scoped quote for your attribute extraction
Share the retailer URL and the attributes you need — we'll provide a scope, price, and timeline within two business days.
Common questions about attribute extraction
Your competitor's rich product details — normalised for your systems
Tell us which retailer and attributes you need, and we'll deliver a sample feed within two business days. No commitment — just proof that complete attribute extraction is possible.
Most attribute pipelines deliver the full dataset within 2–3 weeks of kickoff.
