What customers say, organised by topic and tone.
Reviews are the most candid product feedback available, but they are scattered across retailers and written in many languages. We collect them, remove duplicates and tag each review by topic and sentiment, so product and customer experience teams can act on patterns rather than anecdotes.
Four steps from sources to signal.
Collect reviews at source
We gather review text, star rating, date, verified-purchase status and helpful votes from each retailer and marketplace, including historical reviews on first run.
De-duplicate and clean
Syndicated reviews that appear on several retailers are identified and counted once. Reviewer identities are dropped or pseudonymised to minimise personal data.
Tag topics and sentiment
Each review is tagged against a topic taxonomy built for your category, such as fit, durability or taste, with sentiment scored per topic. Arabic, Spanish, Portuguese, Bahasa and European languages are handled natively.
Report and alert
Tagged reviews feed dashboards or your warehouse, with alerts when negative mentions of a topic rise above the usual level.
What we capture.
Every field is validated, normalised and documented in a data dictionary you can share with your analysts.
Star rating
The rating given, alongside the product's running average.
Review text and language
Full text in the original language, with optional translation.
Topic tags
Category-specific themes mentioned in the review.
Topic sentiment
Positive, neutral or negative per topic, not only per review.
Verified purchase flag
Whether the retailer marks the review as a verified purchase.
Syndication flag
Whether the review also appears on another retailer.
What you receive.
- Monthly topic and sentiment report by product
- Rating trend versus named competitor products
- Alert when negative mentions of a topic spike
- Tagged review dataset for your analytics tools
- Launch impact report comparing before and after
An illustrative extract.
| Product | Market | Source | Rating | Language | Topic | Sentiment |
|---|---|---|---|---|---|---|
| Road runner 3 | United States | Retailer A | 2 | English | Sizing | Negative |
| Road runner 3 | Brazil | Marketplace B | 5 | Portuguese | Comfort | Positive |
| Trail pro waterproof | Germany | Retailer C | 4 | German | Grip | Positive |
| Trail pro waterproof | UAE | Marketplace D | 3 | Arabic | Durability | Neutral |
| Road runner 3 | Colombia | Marketplace E | 1 | Spanish | Delivery | Negative |
Illustrative rows. Sources, markets and fields are agreed with you during scoping.
More in digital shelf
Relevant industries
Underlying services
Do you collect reviewer names or profiles?
No, not by default. We minimise personal data by dropping or pseudonymising reviewer identifiers and keeping only what the analysis needs, such as rating, text, date and verified status.
How is the topic taxonomy created?
We start from a standard set for your category and refine it with your team using a sample of real reviews. The taxonomy is then held stable so that trends over time remain comparable.
Can you separate delivery complaints from product complaints?
Yes. Delivery, packaging and seller service are tagged as distinct topics, so product teams can filter them out and focus on issues within their control.