The Datasource described on this page is an illustrative example. The actual fields, structure, and naming will vary based on your brand’s specific data model and integration. Use this as a reference for the type of data needed, not as an exact specification.
Overview
The Product Reviews Datasource contains customer-generated ratings and reviews for products. This data enables personalisation based on satisfaction signals — recommending highly-rated products, collecting reviews post-purchase, and adapting messaging based on individual review behaviour.
Key Fields
| Field | Type | Description | Example values |
|---|
review_id | String | Unique review identifier | "REV-847291" |
customer_id | String | Customer who left the review | "C-2847391" |
product_id | String | Reviewed product | "SKU-12345" |
rating | Integer | Star rating (1–5) | 4 |
review_text | String | Review content | "Great quality, runs slightly large" |
review_date | Date | Date of review submission | 2024-10-20 |
verified_purchase | Boolean | Whether reviewer purchased the product | true |
helpful_votes | Integer | Number of “helpful” votes | 12 |
Connected Use Cases
| Use Case | How Product Reviews is used |
|---|
| Enriched Product Recommendation | Prioritise highly-rated products in recommendations |
| Live Polling | Post-purchase review collection, NPS follow-up |
Data Requirements
| Requirement | Details |
|---|
| Update frequency | Daily or on review submission |
| Minimum fields | review_id, customer_id, product_id, rating |
| Format | JSON via Datasources API |
| Volume | One record per review |
Integration Notes
Product Reviews are typically sourced from the brand’s review platform (Bazaarvoice, Trustpilot, Verified Reviews, or proprietary). The data enriches recommendation quality and enables satisfaction-based personalisation.
