Brand and review intelligence
Listen to how your brand changes across channels.
Crawlr brings seller, listing, rating, review, and brand-presence observations into one normalized layer so teams can identify meaningful changes without treating every channel as a separate dataset.
Signals with provenance
Brand and review changes remain connected to their product and channel context.
Ratings, review volume, review text, seller identity, listing representation, and exception evidence can be retained together for investigation.
- Product context
- MATCHED
- Review history
- TRACKED
- Exceptions
- EXPLAINABLE
How brand intelligence works
From channel observations to reviewable brand signals.
- 01
Define the watchlist
Agree brands, products, sellers, channels, review fields, and exceptions that matter to the team.
- 02
Collect with identity
Connect ratings, reviews, sellers, and listing representation to normalized products and channels.
- 03
Surface investigation
Deliver histories, theme summaries, and evidence-backed exceptions for business or legal review.
Operating detail
What goes into the workflow—and what comes out.
Capabilities
- Ratings, review counts, and review-text collection
- Theme and change analysis on agreed review datasets
- Seller, listing, and brand-presence monitoring
- Configurable exception rules and evidence retention
Inputs
- Brands and products
- Channels and sellers
- Review scope
- Exception rules
Outputs
- Rating histories
- Review datasets
- Theme summaries
- Seller and listing exceptions
Use cases
- Voice-of-customer analysis
- Brand monitoring
- Seller oversight
- Listing-quality review
Delivery modes
- API
- Scheduled review feeds
- Exception reports
- Private client web app
Brand and review intelligence FAQ
Answers about reviews, sellers, and investigation boundaries.
Connected commerce intelligence
Continue through the Crawlr data layer.
Digital shelf and content
Monitor search placement, category visibility, titles, images, descriptions, attributes, seller context, and content completeness.
Product matching
Connect retailer listings to the same underlying product with normalized attributes, explainable evidence, and reviewable confidence.
Multi-source data collection
Collect dependable structured data from websites, mobile apps, APIs, authenticated platforms, files, and other digital sources.