Launch: Structured Data Audits for Ecommerce

Today, we're launching the free retailQ structured data audit tool. It's perfect for ecommerce sites who need to review their structured product data and schema markup.

Structured data has become a vital factor for product visibility in AI, yet many brands are still operating in the dark when it comes to their data quality. No tools exist today to read and suggest improvements to schema data based on the criteria and recommendations AI companies like OpenAI, Google and Microsoft have suggested.

That's why we've launched our tool. It is an entirely free resource that gives brands the power to audit their own - and their competitors' - structured product data to ensure they are fully optimised for AI shopping.

What is structured product data?

Structured product data is embedded within your website's code to explicitly define the elements on a page. It enables web crawlers for search engines and AI companies to easily read data from millions of websites. Instead of having to "guess" based on thousands of different website layouts, structured product data clearly labels relevant ecommerce data such as product name, description, attributes, price, and shipping options.

The allowed attributes are defined by Schema.org, a collaborative initiative set up by representatives from industry leaders including Google, Microsoft and Yahoo. It has since become the global standard for data interoperability on the web, and is used by millions of websites.

Ecommerce data relies on a specific subset of schemas:

• Product and ProductGroup: the foundational definitions for products or a collection of variants. These contain attributes to describe the physical (or digital) product, and contain other schemas to define its price, popularity and ratings.
• Offer: a sub-schema of Product, these include all information relevant to the transaction: price, currency, shipping fees, expected delivery, terms and conditions, refund policy and warranty information.
• Review & AggregateRating: Structured snippets to highlight customer feedback and star ratings. These are particularly important in AI, because reviews often contain detailed information on product features and suitability to different types of customer.
• FAQ Pages: Only recently relevant for ecommerce, this data in question and answer format helps AI answer potential buyers' questions.

Why does structured product data matter?

While structured data has always been part of search engine optimisation (SEO), its usefulness has been limited until recently. It originated as a way to format rich results, like the product's star rating, directly within search engine results. Its role has evolved into a critical component of Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) - in simple terms, how visible your products are in AI search results.

The shift to conversational commerce

Consumers' shopping habits are shifting from static keyword searches to dynamic, natural language conversations. Shoppers on ChatGPT or Gemini ask more nuanced questions, query specific product features, and seek out recommendations on suitability from the AI models.

Furthermore, in-chat checkout capabilities being integrated into platforms like ChatGPT and Gemini mean that buyers can purchase entirely within the AI interface, bypassing the merchant's website altogether. In this environment, the AI model serves as the only interface; it relies on your structured data to communicate sizes, colors and technical specifications to the customer.

Data is a competitive advantage

To provide accurate recommendations, AI models require high-fidelity data. For example, they need to know the specific strap type to find products when asked about a dress, or the coverage of a can of paint to make recommendations to someone renovating their home.

By specifying these attributes on your products, you gain an edge over competitors. If the model is confident that your product meets the user's requirements, they are more likely to rank it as the most relevant solution for a user. Think of it as "long-tail" optimisation; your product shows up for highly specific customer needs that competitors with thin data profiles will miss.

Ultimately, in an AI retail landscape, the completeness of your structured data directly correlates with your brand's digital visibility and conversion potential.

How to audit your structured data?

Using our free tool to audit your structured data is simple.

1. Navigate to the retailQ structured data audit tool
2. Enter a Product URL. Paste a link to a product page from your site. For the best results, use a product which has multiple variants (such as different sizes and colors) and has existing customer reviews. This allows the tool to verify whether your schema contains the different product variants and imports review data - two areas where many ecommerce sites currently underperform.
3. (Optional) Select a crawling region. Choose the geographic location from which you would like our tool to access your page. We strongly suggest using the United States for your initial audit. Because most AI and search engine crawlers operate from the US, this setting provides the most accurate representation of how they "see" your page. It will help you identify if your site's regional redirect settings are inadvertently blocking or altering data for international crawlers. Once you have run an audit from the US, you can run another from a different location to see how your data differs.
4. Review your results. After submitting, our system analyses the page's structured data. Your audit results, including score and suggestions, will be generated and in your inbox within a few minutes.

Interpreting your audit

Once the audit is complete, our tool gives your data an overall score, and prioritises improvements into three key areas.

• Score: Your data is assigned a total health score out of 100. This provides a high-level assessment of how "AI-ready" your product data is, based on the completeness and accuracy of the schema detected. We use criteria and recommendations issued by AI companies to build our score.
• Data: This section displays the structured data our crawler successfully identified. We're looking for Products, Reviews, Aggregate Ratings, and Product Groups. Within each one, you can see sub-fields, like Offers.
• Errors, Warnings and Suggestions:
  • Errors: These are important issues - such as missing price or product name - that significantly harm your structured data. These should be fixed immediately, as they may prevent your products from appearing in search results or AI recommendations entirely.
  • Warnings: These highlight missing but recommended fields, such as size and color. While they won't "break" your data, they may limit your visibility and the richness of your AI results.
  • Suggestions: These are a sample of AI-powered structured data enrichment. Our AI crawls your web page and reads your on-page product data. We compare it to your structured data to find gaps. For example, we can detect different size or color options, or suggest a product category.

Start fixing your data today

With the rise of shopping in AI search, structured data is no longer a nice-to-have. It is a foundational part of your marketing and AEO strategy.

By ensuring your data is clean, standardised, and comprehensive, you ensure that when an AI model is asked for a recommendation, your product has the necessary attributes to be the top choice.

Ready to see how your products stack up? Try the retailQ audit tool today and start improving your data quality.