Web Data
Collection, normalization and delivery of public-web data your systems can build on — with quality controls and full lineage.
ExploreSolution
Product Intelligence
Understand products the way the market sees them
From catalog noise to product understanding
Product decisions live at the attribute level — features, variants, specs, bundling — but public catalogs are messy, inconsistent and structured for merchants, not analysts. Heroku's product intelligence normalizes that surface into attribute-level datasets: what exists, how it's described, how it's priced, and how all of that changes.
The result supports assortment planning, feature benchmarking, gap analysis and launch tracking with a specificity that category-level data can't reach.
What's included
The moving parts
Catalog structuring
Public catalogs normalized into consistent product hierarchies with resolved variants and deduplicated entities.
Attribute extraction
Specifications, materials, claims and feature language extracted and standardized for comparison across brands and retailers.
Lifecycle tracking
New-product introductions, delistings and refresh cycles observed over time — your competitors' roadmaps, as visible publicly.
Gap analysis
Coverage matrices that show where the market is crowded and where your assortment could differentiate.
Practice notes
How the discipline is applied
Attribute depth
Hundreds of standardized attributes per category, extended per client need.
Cross-retailer view
The same product compared across channels with match provenance.
Launch detection
New-listing signals surfaced within your chosen monitoring cadence.
Live monitoring view — category price index
Observed signal
48htypical pilot setup
3gate outcomes: pass / degrade / quarantine
1canonical schema contract
Program defaults from current enterprise engagements.
FAQ
Common questions about product intelligence
Can you track our own listings too?
Yes — many clients monitor their own catalog alongside competitors for parity checks: content completeness, imagery, pricing consistency and availability.
How do you handle unstructured specs?
A combination of extraction rules, entity normalization and analyst curation. Attribute schemas are versioned, so definitions stay stable as coverage grows.
Is this just for retail?
Retail is the deepest coverage, but the same approach applies to software products, travel inventory and financial products — anywhere products are publicly described.
Related
Pairs well with
Market Intelligence
Continuous market monitoring that turns competitor moves, category trends and public signals into decision-ready intelligence.
ExploreCompetitive Intelligence
Professional competitor monitoring built on publicly available business information, with the rigor your strategy reviews expect.
ExploreSee product intelligence applied to your market
A 45-minute discovery call and a written feasibility note will tell you more than any brochure. Sample scopes included.
Samuel Osei
Attribute coverage depth question: in apparel, how granular does the schema get — fabric composition and fit labels, or just category and price?
2 comments
Bundle deconstruction is mentioned almost nowhere else in the industry. Anyone who’s compared bundle prices across retailers knows why it matters.