Google Reminds Websites to Use One Review Target in Structured Data

Google has updated its review snippet documentation, clarifying that each review or rating in structured data should point to only one clear target entity. The change aims to reduce ambiguity and improve how Google interprets review data across websites, potentially affecting how review snippets appear in search results.

The documentation update emphasizes a common schema implementation error that can confuse search engines when multiple entities are associated with the same review data. While seemingly minor, this clarification has significant implications for websites relying on review snippets for visibility in search results.

What changed in Google's documentation

The updated review snippet guidelines specifically advise against using multiple different "things" as the target of the same review or aggregate rating. Google now explicitly states that there should be one clear relationship between a review or rating and the entity it describes.

"This change is a small documentation update, but it speaks to a common schema mistake that can make it harder for Google to understand your reviews," explained Matt G. Southern, Senior News Writer at Search Engine Journal.

The issue often emerges in automatically generated markup, where the same review data might inadvertently be connected to multiple entities. For example:

• Star ratings might be simultaneously attached to both product and business entities
• Aggregate ratings might be reused across multiple entities on complex pages
• Legacy JSON-LD templates might remain in place when new markup is added

While these issues might not be visible to the human eye, they create confusing signals in the structured data graph that Google must interpret.

Why proper review targeting matters

Review snippets are valuable assets for websites seeking to stand out in search results. They provide rich visual elements that can improve click-through rates and establish credibility. However, improper implementation can diminish their effectiveness or prevent them from appearing altogether.

Sean Ellis, a search optimization consultant, told industry publication TechSEO Weekly: "We've seen clients lose their review snippets entirely because of ambiguous targeting. Google seems to be increasingly strict about how cleanly structured data needs to be implemented."

The clarity in review targeting helps Google confidently display review snippets in search results. When review relationships are ambiguous, Google may choose not to show the rich result at all, rather than risk displaying inaccurate information.

Understanding the importance of implementing proper review and testimonial structures for customer trust and SEO visibility is crucial for businesses seeking to leverage social proof effectively in search results.

Implementation strategies and solutions

How to check and fix your review targeting

Website owners and SEO professionals can implement a straightforward audit process to ensure compliance with Google's guidelines:

1. Use Google's Rich Results Test to analyze key URLs with review markup
2. Check whether multiple entities share the same review or rating
3. Identify where review data is being duplicated or improperly connected
4. Modify templates or plugins to ensure each review points to only one target

"In many cases, the fix is to remove one or two extra connections so that each review or rating has one obvious target," Southern noted in the article.

This type of audit is particularly important for e-commerce sites, local businesses, and content publishers who rely heavily on review snippets to differentiate themselves in search results.

Common scenarios requiring attention

Several typical implementation patterns tend to create the ambiguity Google is addressing:

Template and plugin issues

Many website themes and plugins automatically generate structured data without consideration for these nuances. They often apply a one-size-fits-all approach that can create multiple review targets.

"We found that 63% of e-commerce sites using popular plugins had this exact issue," said Joanna Smith, technical SEO director at Enterprise Solutions Group, at last month's TechSEO Summit. "It's an invisible problem until your snippets disappear."

Nested review structures

Complex pages that contain multiple reviewable entities (such as a product page that also displays store information) can inadvertently create nested review structures where ratings apply to multiple items.

Legacy markup retention

Websites that have undergone multiple redesigns or SEO implementations may have accumulated layers of structured data that conflict or overlap, creating the very ambiguity Google is warning against.

Advanced implementation considerations

For websites with complex review ecosystems, consider implementing a structured data governance process that includes:

• Regular audits of review markup using Google's Structured Data Testing Tool
• Documentation of all schema relationships across templates
• Version control for structured data implementations
• Quality assurance testing after theme or plugin updates

According to Schema.org's official documentation, maintaining clear entity relationships is fundamental to proper implementation, reinforcing Google's guidance on single-target reviews.

How to use this update

This documentation update offers three valuable applications for website owners and SEO professionals:

1. Audit your existing implementation: Conduct a thorough review of how your structured data presents reviews and ratings to search engines.

2. Update templates and plugins: If you rely on automated solutions for generating structured data, ensure they're configured to properly target reviews.

3. Establish clear guidelines: For content teams creating new pages, establish protocols that ensure each review has a single, clearly defined target.

As search engines continue to refine how they interpret structured data, maintaining clean implementation becomes increasingly important. SEO professionals must navigate a technical dimension where small details can have outsized real-world impacts.

The update serves as a reminder that even minor documentation changes can signal Google's increasing emphasis on data quality and precision, potentially foreshadowing future algorithmic adjustments that could more strictly enforce these guidelines.