What changed.
What overlaps.
Across the stores you observe.
Analyze observed catalog coverage, assortment changes, category mix, prices, and availability with the source evidence attached.
Extralt turns the public ecommerce sources you choose into an observed catalog dataset. Compare stores, brands, categories, Products, Variants, Listings, Offers, and changes without pretending that catalog observations reveal sales, demand, or market share.
assortment and catalog analysis · three angles
Inspect catalog change, category mix, and brand expansion
analysis over observed catalogs
Make the scope as explicit as the result
The dataset connects source Listings to normalized Product and Variant context, observed Offers, countries, and timestamps. That structure makes downstream analysis repeatable. Aggregate by category and date for observed movement, group by brand for catalog mix, or compare observation windows to see what first appeared or disappeared.
The data is not limited to one chart. Use the available dashboard and API views, SQL analysis, or Capture exports where their current scopes fit, and keep a path back to the source URL and observation time.
- Time-series rollupsObserved Listing counts, price-tier mix, attribute mix, and brand mix per period, using the cadence of the underlying extraction runs.
- Assortment mix at any cutBreak the observed dataset down by Product, Variant, Listing, brand, category, attribute, or price tier and report the scoped denominator.
- Cross-category comparisonsCompare a brand's footprint across categories, or one category's structure across countries. Same fields, no translation step in the middle.
- First-observed productsFilter on first-observed timestamps inside a category and brand window. Treat the result as first observed in this dataset, not a confirmed launch date.
deliverables
What you get
Granularity
Variant-level
Roll up to category, brand, country
History
Observed history
From the first recorded observation
Coverage
Selected sources
Stores and markets you extracted
Access
Views · API · SQL
Plus Capture exports
why extralt
Evidence for your analysis, not invented market signals
Observed sources, named clearly
Every result is scoped to the stores, countries, URLs, and observation times in your dataset. Coverage is not presented as the whole market.
Exact configurations where matching supports it
Extend connects exact Variants to their source Listings and Offers. Matching coverage and unmatched records remain part of the result.
Data access beyond one dashboard
Use the available views and API, work through SQL where its scope fits, or export Captures for your own analysis and applications.
Current inspection starts in Explore. Its beta views cover markets, changes, Variants, Offers, and history. Deeper custom analysis uses the available API or SQL surface. Pre-built price, availability, and assortment Analyses are planned but not yet available.
who it's for
For teams that need their observed catalog data to stay usable
- Brand strategists & market researchersCompare the categories, brands, prices, and availability you observed across selected stores and countries, with the scope visible.
- Category managers & merchandisersUse observed assortment overlap, catalog changes, price tiers, and availability as inputs to category management and assortment planning workflows.
- Product & data teamsBuild catalog comparison, monitoring, and research workflows on source-backed observations without treating traffic, demand, or revenue as observed facts.
related reading
If competitor prices are the first market signal you need, use the pricing intelligence software guide to compare finished applications with an owned ecommerce data layer.
Frequently asked questions
Start with the catalogs you need to compare
Extract the selected stores and markets, then add Enrich and Extend when the analysis needs normalized or matched data.