What customers say, collected in every language.
Reviews show why products win or fail in ways sales data cannot. We collect star ratings and review text from marketplaces, retailer sites and local listing platforms, handling Arabic, Portuguese, Spanish and other languages, and structure them for sentiment and topic analysis.
Reviews are scattered across dozens of sites, each with its own rating scale, date format and language. Reading a sample by hand is slow and biased towards whatever appears first.
You get a consolidated, de-duplicated review dataset with consistent ratings and dates. Product and CX teams can track sentiment and recurring issues across markets and competitors.
Everything needed to run it in production.
Rating and text capture
Star ratings, review titles, full text, helpful votes and verified-purchase flags where shown.
Rating normalisation
Different scales are converted to a common 1–5 scale, with the original value retained.
Multilingual handling
Reviews are captured in their original language with a language tag; translation to English is optional.
Syndication de-duplication
Reviews syndicated across several retailers are detected and flagged so they are not counted twice.
Personal data minimisation
Reviewer names and profile links are excluded or pseudonymised by default.
Incremental collection
After an initial back-fill, only new reviews are collected on each run to keep volumes efficient.
What lands in your systems.
| review_date | source | market | product | rating | language | review_excerpt | verified |
|---|---|---|---|---|---|---|---|
| 2026-05-03 | Marketplace A | UAE | Air fryer 5.5L | 5 | ar | سهل الاستخدام والتنظيف | true |
| 2026-05-04 | Marketplace C | Saudi Arabia | Air fryer 5.5L | 2 | ar | الحجم أصغر من المتوقع | true |
| 2026-05-04 | Retailer S | United States | Robot vacuum 2-in-1 | 4 | en | Good suction, app is slow to connect | true |
| 2026-05-06 | Marketplace B | Brazil | Smartwatch GPS 46mm | 3 | pt-BR | Bateria dura menos do que o anunciado | false |
| 2026-05-07 | Retailer V | France | Espresso machine 15 bar | 5 | fr | Très bon café, mousse parfaite | true |
Illustrative rows. Your schema, field names and formats are agreed during scoping.
Who uses it, and for what.
Category teams compare ratings for their products with competitor equivalents. Low-rated lines are reviewed with suppliers using specific customer complaints.
Brand teams identify the product benefits customers mention most often. Messaging and listing content are updated to reflect the language customers actually use.
Analysts build sentiment and topic models on a clean, multilingual review set. Normalised ratings make cross-source trends reliable.
Analysts track rating trends and review volumes as early signals of product success or quality problems. Changes often appear before they show in reported results.
- Scope. Tell us the sources, fields and frequency. We confirm feasibility within a day.
- Free sample. A real sample from your own target source, in your format.
- Build. Engineers build extractors tuned to each source. No generic templates.
- Validate. Automated and manual QA on every run before anything ships.
- Deliver and monitor. Scheduled delivery, monitored pipelines, fast fixes when sites change.
Related services
Do you collect reviewer names?
Not by default. Reviewer names and profile links are excluded or pseudonymised, in line with data minimisation. We keep only what is needed for analysis, such as rating, text, date and verified status.
Can you translate reviews?
Reviews are always captured in the original language with a language tag. Machine translation to English can be added as an extra field for teams that need a single-language view.
How far back can you collect?
Most sources allow a back-fill of historical reviews, although some limit how many are displayed. We report the earliest date reached per product and source.
How do you handle syndicated reviews?
The same review can appear on several retailer sites. We detect duplicates by text and date similarity and flag them, so counts and averages are not inflated.