Structured data has quietly become one of the most influential elements of modern technical SEO, particularly for large ecommerce brands competing for position in increasingly crowded search engine results pages (SERPs).

As search engines move further towards entity-based understanding and richer search experiences, the way your content is structured and interpreted matters just as much as the content itself. This is becoming even more important as AI-powered search tools and large language models increasingly rely on structured signals to determine which brands and products to reference.

Schema markup helps search engines quickly understand and process a wide range of information about your website or page. For ecommerce, this includes products, prices, availability, reviews, business locations, and brand relationships.

When implemented effectively, it can enhance visibility, improve click-through rates, and support stronger organic performance. But when neglected or implemented incorrectly, it can create search engine blind spots that limit your site’s ability to compete on SERPs.

In this article, we analyse how structured data is being used across the top ecommerce websites in the UK and US, highlight common implementation issues, and explain where the biggest opportunities lie for brands looking to strengthen their technical SEO foundations.

What are structured data and schema markup?

Structured data is a standardised way of describing the content on your website to search engines. It helps them understand what your site is about more clearly. Rather than forcing Google or other search engines to infer meaning from raw HTML alone, structured data explicitly labels what a page contains, including elements like products, reviews, business locations, or recipes.

Schema markup is the language most commonly used to create structured data. It’s defined by Schema.org, a collaborative project founded by Google, Microsoft, Yahoo and Yandex. When we talk about “implementing schema” in SEO, we are really talking about adding structured data using Schema.org definitions so search engines can reliably interpret your website’s content.

When search engines confidently understand what a page is about, it enables them to provide richer results in SERPs, such as star ratings, pricing, availability, FAQs, breadcrumbs, and knowledge panels.

How structured data works

Structured data translates on-page content into a machine-readable format. Each schema type defines the:

  • entity being described, such as a Product or LocalBusiness.
  • properties of that entity, such as price, availability, rating, or opening hours.
  • relationships between entities, such as a Product having an Offer or AggregateRating.

However, it’s important to note that structured data alone doesn’t guarantee rich SERP results – it simply makes your content eligible for them. Accuracy, relevance, and quality of your content all still matter when it comes to earning rich SERP placements.

Methods of implementing structured data

There are three supported formats for implementing structured data. Let’s look at each and the uses they’re best suited to.

JSON-LD

JSON-LD is Google’s recommended implementation method. It uses JavaScript embedded within a script tag in the head or body of a page. JSON-LD is separate from the visible HTML, which makes it easier to deploy, maintain, and debug at scale.

For most websites, especially large ecommerce platforms, JSON-LD is the safest and most scalable option.

Microdata

Microdata is an HTML specification that uses tag attributes to label content directly within the HTML. It is usually implemented within the body of a page.

While Microdata is valid, it tightly couples structured data to front-end markup. This often makes maintenance more complex, especially when templates change.

RDFa

RDFa is an HTML5 extension that adds attributes to HTML tags to describe structured data. It can be used in both the head and body of a page.

RDFa is flexible but less commonly used for SEO-focused implementations, particularly in ecommerce.

Why schema is important for SEO

Schema and structured data markup vastly improves how search engines understand your website’s content, which can have a significant impact on organic visibility. When implemented correctly, schema can:

  • Enable eligibility for rich results and enhanced SERP features.
  • Increase click-through rate by making listings more informative and visually compelling.
  • Future-proof your website as search moves further towards entity-based and AI-driven results.

For ecommerce brands, strong schema markup often translates into more qualified traffic and stronger commercial performance from organic search.

How to implement structured data correctly

Structured data can be created manually or by using a schema generator tool, depending on the scale and technical demands of your website. Whichever approach you take, it’s essential to keep these tips in mind for best practice.

Only mark up content that’s visible to users on the page

This ensures your structured data reflects real, user-facing content and complies with Google’s guidelines, reducing the risk of manual actions or ignored markup.

Use one structured data format per page

A single, consistent format (ideally JSON-LD) is easier to maintain, less prone to errors, and more reliably interpreted by search engines.

Avoid duplication across multiple formats

Duplicated schema can confuse search engines, trigger validation errors, and lead to inconsistent or suppressed rich results.

Follow Google’s structured data guidelines

Adhering to Google’s requirements helps ensure your markup is trusted and processed correctly.

Test structured data before deployment

Before deployment, structured data should always be tested using Google’s Rich Results Test or the Schema.org validator.
Monitor post-deployment

Once live, Google Search Console should be used to regularly monitor structured data for any errors, warnings, and coverage issues.

Essential schema types for ecommerce websites

Google recommends several schema types that are particularly relevant for ecommerce businesses:

  • LocalBusiness: identifies a physical business location, supporting visibility in local search and Google Maps.
  • Product: describes individual products and their key details for enhanced product listings in search results.
  • AggregateRating: summarises overall user ratings for a product or business, enabling star ratings to appear in SERPs.
  • HowTo: marks up step-by-step instructional content, helping eligible pages appear as rich HowTo results.
  • FAQPage: defines question-and-answer content on a page, increasing eligibility for expandable FAQ rich results.
  • BreadcrumbList: clarifies a page’s position within a site’s hierarchy, improving navigation signals for search engines.
  • WebSite: provides high-level information about the website as a whole, supporting better understanding of site ownership and structure.
  • VideoObject: supplies metadata about video content, helping videos appear in rich search results.

Additional schema types can further aid search engine understanding, depending on site structure and content:

  • Organization: defines the business entity behind a website, supporting knowledge panel and logo eligibility.
  • Offer: provides commercial details such as price, currency, availability, and promotions.
  • Article or BlogPosting: describes editorial content, helping search engines interpret blog posts and articles correctly.
  • ShippingDetails: communicates delivery information such as shipping costs, regions, and delivery times.
  • OnlineStore: identifies the site as an ecommerce business.
  • Industry-specific store types such as ClothingStore, ElectronicsStore, or ToyStore: provides more context about the type of products sold on the site.

Schema usage across top ecommerce websites

We reviewed a sample of URLs from the top 100 UK and US ecommerce websites, based on AfterShip data (excluding auction-based marketplaces such as eBay and Etsy).

The audit covered a range of page templates, including:

  • Home pages
  • Category pages
  • Product pages
  • FAQ pages
  • Location pages
  • Recipe pages.

Together, these templates give a representative view of how structured data is being implemented in practice.

Our analysis uncovered several implementation issues that limit the effectiveness of structured data and, in some cases, prevent search engines from reliably interpreting it.

Using alternative formats with limited success

Across all templates reviewed, JSON-LD emerged as the dominant implementation method.

However, our research found that 33.45% of audited URLs didn’t contain structured data in JSON-LD, with some brands relying on Microdata alone, which can reduce reliability in rich result detection.

For example, Lululemon implements schema using Microdata. While technically valid, Google’s rich result testing tools failed to consistently detect this markup, reducing its practical SEO value.

Duplicated schema formats

Product pages on the Claire’s website use both JSON-LD and Microdata simultaneously, resulting in duplicated entities being detected.

This creates several risks, including increased maintenance complexity, skewed data for reporting, and inconsistent SERP results.

The side-to-side comparison of the rich result test for Claire’s PDP page shows discrepancies between structured data implemented with JSON-LD and Microdata.

The JSON-LD markup doesn’t include AggregateRating or Review schema, even though this information is visible on the page and marked up using Microdata. While these properties aren’t mandatory, including product ratings and reviews in JSON-LD can significantly improve SERP appearance and help attract more clicks.

The examples below illustrate the difference between search results with and without AggregateRating and Review schema implemented. The Microdata mark-up doesn’t include product description, product image, or available offer details.

Using a single format, ideally JSON-LD, avoids these issues and ensures clarity for search engines.

This mock-up Product structured data example that includes Offer, AggregateRating, and Review in JSON-LD passes the rich results test and returns fully valid items.

Incorrect type values

Coach’s website was affected by an Incorrect Type Value issue for Organization schema implemented site-wide.

rich result test - parse errors

Removing the duplicated string <script type=”application/ld+json”> resolved the parse errors and ensured the markup was validated correctly.

resolved parse error

Underutilising structured data

Several well-known brands were found to be missing core schema types entirely, including Organization, WebSite, BlogPosting, FAQPage, and BreadcrumbList, despite having eligible content.

BreadcrumbList schema, for example, wasn’t implemented across 12% of audited URLs. This represents a significant missed opportunity to strengthen entity understanding and SERP visibility.

On the other hand, some brands went beyond basic Organization schema and implemented business-specific types to provide more granular context, including MobilePhoneStore, LiquorStore, JewelryStore, ClothingStore, and DepartmentStore.

For example, Kohl’s ensures that each of its departments is marked up with relevant LocalBusiness types.

additional types of local businesses types within schema mark-up

Target also uses department types to increase visibility for specific searches.

rich result test for https://www.target.com/sl/cedar-rapids-south/1771

Missing schema

Many of the URLs reviewed were missing schema entirely. 31% of Category pages included no schema, as well as 21% of FAQ pages and 15% of Location pages.

missing schema by page type

Summary of findings (JSON-LD only)

  • 45% of URLs contained no structured data.
  • 27% of URLs contained structured data with errors.
  • Parse errors were the most common issue.
  • Category pages were the least likely to include schema.
  • Organization schema was frequently overused outside of recommended page types, with 41% of URLs containing Organization schema not on a homepage or about us page.

Let SALT help you with your schema strategy

Schema markup is one of the most underutilised yet impactful features in any technical SEO strategy for ecommerce brands. When implemented correctly, structured data helps search engines understand content, which in turn enhances SERP features, improves click-through rates, and supports revenue growth.

Need support with implementing structured data for your ecommerce website? SALT.agency can help. Get in touch today to speak to our expert team.