dataclean.to

Generate Review Schema from B2B Feedback Data

✓ Tested Works with CSV, Excel, Google Sheets → JSON-LD Schema
By dataclean.to team · 2026-02-05

Customer feedback generates Schema.org Review markup for products and services. Automated schema generation processes ratings, review text, authors, and dates. This tool converts spreadsheet data into structured JSON-LD that qualifies for review rich results.

The Problem

B2B companies maintain review data across feedback platforms, testimonial systems, and customer databases with inconsistent formatting. Star ratings, review content, reviewer information, and timestamps vary in structure. Manual schema creation for hundreds of reviews takes excessive time. Automated generation ensures consistent structured data while maintaining relationships to reviewed items, authors, and rating scales that follow schema specifications for search visibility. Schema.org Review Specification

How to Fix It

1
Upload Review Data
Import CSV or Excel files containing review information including rating, review text, author, date, and reviewed item. System identifies review-specific fields.
2
Configure Review Mappings
Map columns to Review properties like reviewRating, reviewBody, author, datePublished, itemReviewed, and publisher for review platform.
3
Validate Review Data
System checks rating scales are consistent, validates date formats, ensures author information is complete, and verifies reviewed item references.
4
Generate Review Schemas
Tool creates JSON-LD for each review with proper nesting of Rating, author Person, and itemReviewed Product or Service entities.
5
Export and Implement
Download individual review schemas or bulk export all generated JSON-LD files ready for product page implementation.

Frequently Asked Questions

What review properties are required?
Required properties include author and reviewRating with ratingValue. Recommended additions are reviewBody, datePublished, and itemReviewed reference to product or service.
How do I calculate aggregate ratings?
System can generate AggregateRating from multiple reviews with average ratingValue, total ratingCount, and reviewCount. Attach to Product or Organization.
Can I mark verified purchases?
Yes, use additionalType or mention verification status in reviewBody. Some platforms support custom extensions for verified purchase badges.

Example: Input → Output

nameemailphonecitystatus
Alice Johnsonalice@example.com+1-555-0101New Yorkactive
alice johnsonALICE@EXAMPLE.COM5550101new yorkActive

Red rows show common data quality issues. dataclean.to normalizes and generates JSON-LD automatically.

{
  "@context": "https://schema.org",
  "@type": "Dataset",
  "name": "Cleaned Customer Data",
  "description": "Normalized customer records with standardized fields",
  "keywords": ["customer data", "CRM", "contact list"]
}
💡 How it works: Consistent data formatting reduces import errors and makes your dataset compatible with downstream tools.

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