Structured Data: Method, Formats and Prioritisation for SEO and GEO

Structured Data: Definition, SEO Benefits and GEO Impact (AI Search Engines)

On a website, structured data is used to describe your content in a standardised format (often via schema.org) so that search engines can interpret it without ambiguity. It does not replace HTML; it complements it by translating key information (product, price, author, organisation, FAQ, event, and so on) into a more rigorous, machine-readable language.

In a landscape where Google remains highly dominant (89.9% global market share according to Webnyxt, 2026) and where SERPs are increasingly "no-click" (60% of searches are zero-click according to Semrush, 2025), providing normalised signals is a practical lever for improving how your pages are displayed, understood and cited by both search engines and AI systems.

Why Structuring Your Information Changes Understanding and Display in the SERP

To structure information is to represent it using a predefined format: a type, properties and expected values (dates, amounts, URLs, identifiers). This reduces ambiguity when search engines need to interpret what a page means and decide how to present it.

Without a normalised layer, a search engine must infer meaning from clues (layout, CSS classes, DOM position, adjacent text). With semantic markup, you explicitly declare what constitutes a price, availability status, author, address, breadcrumb trail or FAQ, which makes (1) extraction easier, (2) entity linking more reliable and (3) certain enhancements possible.

The Link Between Structured Data and SEO: Relevance, Trust and Eligibility for Rich Results

Semantic markup helps define what information represents, not simply how it looks. A person immediately understands that "£49.90" next to an item name is a price. A crawler must infer that meaning from signals (HTML structure, CSS classes, surrounding context, page templates, and so on).

By adding a standardised vocabulary (schema.org), you can clarify essential elements such as:

• the page's main entity (a product, a service, an article, an organisation, etc.);
• its attributes (price, availability, reviews, author, dates, address, coverage area, etc.);
• relationships between entities (organisation ↔ brand, product ↔ offer, article ↔ author, etc.).

Impact on Visibility in AI Search Engines (GEO): Extraction, Citations and Disambiguation

Visibility is no longer limited to ten blue links. Google reports 2 billion queries per month featuring AI Overviews (Google, 2025). In this context, the need for structured signals grows: they help systems extract reliable facts (prices, availability, contact details, definitions, steps, FAQs), attribute information correctly and avoid misinterpretations.

To explore the logic and specifics in greater depth, see our dedicated article on structured data for GEO, which explains how normalised formats help LLM-based engines interpret content and strengthen AI visibility.

On the on-page side, Incremys data from State of AI Search (2025) indicates that pages structured with an H1-H2-H3 hierarchy are 2.8 times more likely to be cited by AI systems, and that 80% of cited pages use lists. This does not replace schema.org markup, but reinforces the same principle: the more explicit and standardised information is, the more usable it becomes.

What Crawlers Actually Interpret: Visible Content, HTML Signals and the Schema.org Vocabulary

Crawlers combine multiple signals: visible content, HTML, internal linking, metadata, sitemaps and semantic markup. Search engines then use models to interpret and aggregate that information.

The schema.org vocabulary acts as a shared contract: the same type (Product, Organization, FAQPage, LocalBusiness, etc.) and standard properties (name, description, offers, author, datePublished, etc.) can be understood consistently across sites. That is precisely why this format is valuable for GEO and LLM-style engines, which can exploit normalised objects far more reliably than highly variable HTML structures.

HTML primarily describes presentation structure (headings, paragraphs, sections, lists). It is relatively permissive and subject to significant variation across templates, frameworks and implementations. Historically, browsers have not always applied the standard in a perfectly consistent way, and many sites have relied on workarounds for stable rendering, often at the expense of semantics.

By contrast, semantic markup requires a strict, machine-readable format (types, properties, expected values). This reduces ambiguity and supports reliable automated processing. In practice, you keep HTML for users and add a normalised representation of key information for crawlers.

Rich Results, CTR and AI Overviews: What Markup Can (and Cannot) Trigger

An enhanced display (review stars, price, availability, breadcrumb trail, expandable FAQ, etc.) can improve visibility and perceived relevance in a competitive SERP. Available data confirms that appearance matters: position 1 attracts a significant share of clicks (27.6% according to Backlinko, 2026). In no-click SERPs, the goal is also to occupy more space and deliver immediately usable information.

This ties in with efforts to optimise CTR: better snippets, clearer topical focus, consistency between the promise and the page content, and strong semantic signals.

Even when everything is correctly implemented, two structural limits remain: (1) Google never guarantees a rich result, and (2) being cited in an AI Overview also depends on the page's relevance to the query, its perceived reliability and whether it provides a reusable answer.

How to Ensure Your Data Is Structured: A Step-by-Step Method

Step 1: Inventory Entities and High-Value Pages (Products, Services, Authors, FAQs)

Start by listing the entities your site actually exposes: organisation, products, services, locations, authors, content (guides, articles, FAQs), events and videos. Then identify the highest-value templates: high-intent pages (transactional, local), high-volume pages (catalogue), pillar pages and pages that provide strong proof (case studies, reviews).

Step 2: Choose the Right Schema.org Type and Define the Essential Properties

Select one main type per page (e.g. Product for a product detail page, BlogPosting for an article), then define the essential properties: those describing the core (name, description, image) and those that affect display and extraction (offers, price, availability, author, dates, contact details), depending on the use case.

To understand the concepts and the overall logic, our guide on schema SEO is a helpful companion to this article.

Step 3: Ensure It Matches the Visible Content (Consistency, Trust and Anti-Spam)

A key point, and one too often overlooked: markup must be a faithful extract of what is genuinely present on the page. In other words, it is not a means of inventing information or artificially enriching a page. Google also states that marking up content that is not visible, is misleading or is non-compliant can prevent rich results from being shown.

This rule is equally important for GEO: if an AI system detects contradictions between what is marked up and what is readable on the page, perceived reliability falls and the page becomes less citeable.

Step 4: Generate Markup Without Overcomplicating Your Templates (CMS, Components, Rules)

The most robust approach is to treat markup as a "machine output" of your templates, in the same way as a meta description or analytics data:

1. Identify high-stakes pages (products, services, local pages, pillar content, FAQs).
2. Define one rule per template (which fields always exist and which are optional).
3. Map CMS fields (title, summary, price, stock, author, date, address, etc.) to schema.org properties.
4. Validate on a sample of URLs, then roll out at scale.

To limit technical debt, document your mapping rules (property → CMS source → expected format) and align them with your design system. This prevents discrepancies during a front-end redesign or component changes.

Full Example: Turning a Page Section into JSON-LD

Consider a very simple product page in HTML.

<article>
<h1>Ergonomic office chair</h1>
<p>Adjustable chair with lumbar support.</p>
<p>Price: £199.90</p> <p>Availability: in stock</p>
</article>

The crawler must guess what constitutes the name, description, price and stock status. With JSON-LD, you make those expected fields explicit.

<script type='application/ld+json'>
{
"@context": "https://schema.org",
"@type": "Product",
"name": "Ergonomic office chair",
"description": "Adjustable chair with lumbar support.",
"offers":
{
"@type": "Offer",
"price": "199.90",
"priceCurrency": "GBP",
"availability":
"https://schema.org/InStock"
}
}
</script>

Notice the difference in rigour: a coded currency, a normalised availability value and explicit types. That standardisation is precisely what makes the format "portable" from one site to another.

Where to Place the JSON-LD Script, What to Include and What to Avoid

• Where to place it: generally in the <head> or <body>. For Google, the position does not matter as long as it is in the rendered HTML. What matters is that it loads on the relevant page.
• What to include: key information that users can verify (name, description, price, stock, author, date, contact details, etc.).
• What to avoid: invented fields, reviews that are not shown, a price that differs from what is visible, or an entity that does not match the page's main topic.

Structured Data Formats: JSON-LD, Microdata and RDFa

What Are the Different Structured Data Formats and How Do They Differ?

Three formats coexist: JSON-LD, Microdata and RDFa. Your choice depends largely on your production constraints:

• if you want to minimise impact on HTML, JSON-LD is usually the easiest to maintain;
• if your templates lend themselves to it and you prefer annotating elements directly, Microdata can work;
• if your organisation already uses RDFa attributes or a broader semantic web approach, RDFa may be the coherent choice.

JSON-LD: The Most Robust and Easiest Format to Maintain at Scale

Google generally recommends JSON-LD because it cleanly separates the data layer from HTML, reducing errors during front-end redesigns. Since 2023, Google has stated it can understand different syntaxes, but JSON-LD remains the most practical option for high-volume sites with many templates.

Microdata: Benefits, Limitations and the Risk of Template Technical Debt

Microdata annotates HTML with attributes (itemscope, itemtype, itemprop). The benefit is that structured information is tightly coupled to what is displayed, which can reduce discrepancies. The limitation is maintenance: it becomes more fragile when components evolve (design systems, A/B tests, redesigns).

In practice, Microdata can be suitable when teams have strong control over templates and want markup to remain close to the DOM. Otherwise, JSON-LD often makes governance considerably easier.

RDFa: Use Cases, Constraints and Decision Criteria

RDFa relies on HTML attributes (typically vocab, typeof, property). It can be useful in environments where vocabularies and knowledge graphs are already part of the stack. In SEO, it is less common than JSON-LD, but can be appropriate if you are pursuing a broader semantic web strategy.

Schema.org in Practice: Types, Properties, Recommended Fields and Minimum Requirements

Schema.org includes many types, but Google only uses a subset for rich results. It is therefore helpful to distinguish:

• Google eligibility: the types and fields expected to enable rich results;
• GEO usefulness: types that clarify entities, attributes and relationships, even when Google does not show a specific enhancement.

As a rule, it is better to provide fewer fields that are accurate than to populate properties with approximate data.

Which Schemas to Prioritise by Page Type: SEO, GEO and Editorial Performance

Prioritisation Principles: Intent, Evidence, Entities and Cross-Page Consistency

To prioritise, start with pages that combine (1) business impact, (2) stable demand and (3) the ability to provide verifiable information. Then secure cross-page consistency: the same entities, the same identifiers and the same conventions, to reduce contradictions (particularly on multi-domain, multi-language or multi-template sites).

This work should align with your SEO content strategy and, increasingly, your GEO content strategy.

High-Impact Schemas: Organization, WebSite, BreadcrumbList, Article, Product, FAQ, LocalBusiness

Prioritise the entities that remove the most ambiguity: Organization, WebSite, BreadcrumbList, Article/BlogPosting, Product, LocalBusiness, FAQPage, VideoObject. They act as a disambiguation layer and help keep your site coherent.

Note: Google never guarantees rich results, even when implementation is correct. Markup makes a feature possible, not automatic (Google documentation cited in the sources).

Rich Results vs Actual Display: Why Google May Ignore Valid Markup

Three key points to bear in mind:

• Eligible does not mean displayed: Google may choose not to show a rich result depending on the query, context and historical performance.
• Markup must match the main visible content; otherwise you lose the enhancement and potentially trust.
• For AI engines, the value is often less about visuals and more about citeability: coherent, verifiable structured information is more likely to be reused correctly.

Structuring for LLMs: Entity Relationships, Context and Trust Signals

To improve correct reuse by AI models, focus on connecting objects properly: a product to an offer, an article to an author, an organisation to its contact points, a local page to its address and opening hours. At site scale, that consistency complements editorial structure (headings, sections, lists) which, according to Incremys / State of AI Search (2025), correlates strongly with citations.

Quality, Errors and Governance: Staying Compliant and Avoiding Missed Opportunities

Blocking Errors vs Warnings: How to Sort, Fix and Prioritise

Not all issues carry the same impact. In practice:

• Critical errors: invalid syntax, inconsistent type, missing mandatory fields for a feature, non-compliant values (e.g. a URL expected but text provided). These can prevent any use of the markup.
• Warnings: missing recommended fields, incomplete coverage. Typically not "serious" in a penalty sense, but they represent lost potential for display, understanding or extractability.

In short: fix critical issues first, then progressively improve recommended fields on high-impact pages.

Contradictory Signals: Canonicals, Duplication, Stock/Price/Reviews and Inconsistencies

Inconsistencies undermine machine trust:

• duplicate pages with different markup (even when the content is identical);
• marked-up prices that differ from what users see (or updated on the page but not in the markup);
• a misidentified main entity (e.g. marking up a category page as a product).

Where duplication exists, a good practice is to apply the same semantic representation to all genuinely identical pages, not only the canonical URL (as highlighted in the sources).

Scaling Best Practice: Templates, Documentation, QA and Versioning

To avoid fragile layering:

• standardise by template type (product, category, article, service, local, etc.);
• document mandatory, recommended and prohibited fields;
• run recurring checks after CMS updates, redesigns, module changes or AI deployments;
• version your mapping rules: when your model changes (prices, variants, authors), update markup in a controlled way and re-test.

Testing, Validating and Monitoring Structured Data

Pre- and Post-Deployment Testing: Syntax, Rendering, Compliance and Regressions

Before release, test on a representative sample (desktop/mobile, high-traffic pages, long pages, pages with variants). After rollout, re-test on real indexed URLs. For a step-by-step approach, see our guide on how to test structured data and interpret the results.

Also refer to the official tools:

• the schema.org validator (validator.schema.org) for pure format validity;
• Google's Rich Results Test (search.google.com/test/rich-results) for eligibility.

Interpreting Errors and Warnings: Missing Fields, Incompatible Types and Invalid Values

A schema.org validator answers a primary question: "Is this JSON-LD (or Microdata/RDFa) valid and coherent with the vocabulary?" It does not guarantee that Google will show an enhancement, but it prevents structural errors (impossible types, misused properties, invalid formats).

Beyond validity, check alignment with the page: does the main entity match the topic? Do the fields reflect exactly what is visible (price, stock, author, opening hours)? Are URLs, images and identifiers accessible and stable?

At an advanced level, verify relationships: a Product should link to a coherent Offer; a BlogPosting should point to a Person or an Organization as author; a local page should declare contact details that match what is displayed.

Monitoring in Google Search Console: Enhancement Reports, Coverage and Performance

In Google Search Console, monitor:

• the "Enhancements" reports (detected types, errors, warnings);
• valid vs invalid URLs and the specific items to fix;
• performance (impressions, clicks, CTR) for the affected pages.

To set expectations with benchmarks (CTR, zero-click, AI Overviews), you can also consult our SEO statistics, SEA statistics and GEO statistics.

When to Re-Test: Redesigns, CMS Changes, Modules, Automations and Generated Content

Always re-test after a front-end redesign, CMS migration, product or service template changes, the addition of a reviews module, changes to pricing or stock systems, internationalisation, or AI automations that modify visible fields (and therefore what markup should reflect).

E-Commerce Use Case: Markup by Page Type to Maximise SEO and GEO

Goals: Improve Product Snippets and Machine Understanding

In e-commerce, the main lever is twofold: (1) enhance snippets (price, stock, reviews, breadcrumbs), and (2) clarify the catalogue for search engines and AI systems (products, variants, offers, brand, conditions). According to SEO.com (2026), 43% of e-commerce traffic comes from organic search (Google), making signal quality particularly strategic.

Markup Plan: Homepage, Categories, Pagination, Product Pages, Reviews, Local Pages, Blog

Common Pitfalls: Variants, Stock, Prices, Facets, Pagination and Outdated Data

• Variants: if your product model changes (size/colour), maintain a clear approach (parent product vs offers) and avoid conflicting fields.
• Stock and price: mismatches between visible content and marked-up fields are among the most common causes of losing enhancements.
• Facets: filtered pages can multiply quickly and create inconsistencies when they resemble category pages.
• Reviews: only declare real, visible reviews with coherent aggregation.

B2B and Service Sites: Structuring Offers, Proof and Contact Points

Goals: Clarify Services, Expertise, Entities, Proof (E-E-A-T) and Contacts

For a B2B or service site, the focus is often on making explicit: who you are (organisation), what you offer (services/offers), where you operate (locations/coverage) and why you are credible (proof, content, FAQs, case studies).

Markup Plan: Homepage, Services, Offers, Case Studies, Local Pages, Blog, FAQ

Frequent Errors: Non-Visible Claims, Unclear Entities and Inconsistent Contact Details

• Declaring information (service, coverage, commitment) that does not actually appear on the page.
• Showing different contact details across pages (or different from those on a local page).
• Not defining the entity behind the content (author/organisation), which reduces attribution and trust.

Using Structured Data Within a Content Strategy: SEO, GEO and ROI

Aligning Intent, Content and Snippets: Improving CTR and Search Engine Understanding

Markup works best when the page is already aligned with a clear search intent (informational, comparative, transactional or local). A structured page that is thin or ambiguous will still be difficult to surface effectively.

Work in parallel on:

• editorial structure (definitions, sections, proof, FAQs);
• consistency between the promise (title/snippet) and the content;
• machine usability (hierarchy, entities, fields).

Rollout Plan: Where to Start, How to Prioritise and Which KPIs to Track

A simple prioritisation framework:

1. Start with high-volume, high-ROI templates (products/services, local pages, pillar content).
2. Fix critical errors first, then improve completeness on high-traffic pages.
3. Measure in Search Console (impressions, CTR, eligible pages) and in your analytics platform (traffic, conversions).

In a zero-click SERP context (Semrush, 2025) and with the rise of AI search (+527% traffic from AI search year-on-year according to Semrush, 2025), it also makes sense to track KPIs beyond rankings: changes in impression share on enhanced pages, performance of cited pages and the stability of enhancements after redesigns.

Internal Linking and Semantic Consistency: Consolidating Entities Across the Site

Governance is about maintaining consistency over time: the same template rules, field conventions and testing practices. This is also a core topic for a technical SEO audit: inconsistent markup is often a symptom of heterogeneous templates, duplication or unreliable CMS data.

Auditing and Prioritising at Scale with Incremys

Connecting Technical Audit and Performance: Identifying What Limits Rich Result Eligibility

At site scale, issues are rarely isolated. A technical audit often uncovers broken schemas following a redesign, missing fields on a specific template or inconsistencies between mobile and desktop versions. The consequence is typically not a "penalty" but rather missed opportunities for enhanced displays and improved machine understanding.

Automating Markup Error Detection and Speeding Up Fixes Across Templates

If you need to industrialise these checks, the SEO 360° Audit module in Incremys supports an operational workflow: map the site, detect issues, then prioritise fixes. For markup specifically, the value lies in quickly identifying problematic templates, grouping impacted pages and producing a prioritised list that engineering teams can act on directly.

Steering: Consolidating Search Console and Analytics to Measure Impact and ROI

To steer effectively, you need to consolidate technical signals (errors, eligibility) and outcomes (impressions, CTR, traffic, conversions). Incremys integrates with Google Analytics and Search Console via API, bringing these sources together within an SEO 360° approach and supporting performance-led tracking.

FAQ About Structured Data

What Is Structured Data on a Web Page (and What Is It Used for in SEO)?

It is a normalised representation of information present on the page (e.g. product, price, author, address, FAQs) in a strict format that search engines can interpret consistently via schema.org. In SEO, this semantic markup reduces ambiguity for crawlers, improves understanding of the main entity and can make a page eligible for certain rich results, when Google considers it relevant.

Does Structured Data Directly Improve Rankings?

Generally, it does not act as a guaranteed direct ranking boost. However, it can improve appearance (and therefore CTR), understanding and signal quality, which can indirectly support overall performance.

JSON-LD, Microdata or RDFa: How Should You Choose?

The three main formats are JSON-LD, Microdata and RDFa. They all express the same schema.org vocabulary but are implemented differently. JSON-LD is usually the easiest to roll out at scale and is recommended by Google, particularly for high-volume sites with many templates and frequent redesigns, as it keeps the data layer separate from HTML. Microdata and RDFa annotate HTML directly and can work well when templates and governance are stable (documented mapping rules and regular QA), but they are more exposed to technical debt when the front end evolves.

How Does Structured Data Affect Visibility in AI Search Engines (GEO)?

It helps extract facts (prices, dates, contact details, definitions), disambiguate entities and keep relationships consistent. Combined with clear editorial structure (headings, lists), it increases the likelihood that AI engines reuse your information correctly and cite your pages.

How Do You Get a Rich Result and Optimise the Displayed Snippet?

Ensure the page maps to an eligible type, that required fields are present, that marked-up content is visible and compliant, then refine the snippet (title, meta description, proof, clarity) to optimise CTR. Display is never guaranteed.

What Should You Do if There Are Markup Errors: What Blocks vs What Only Reduces Performance?

Fix anything that breaks validity first (syntax, type, required fields). Then address warnings (missing recommended fields) on high-impact pages: these are typically opportunities for better display and understanding, rather than hard blockers.

Does Structured Data Work on Multilingual Sites?

Yes, provided you maintain strict consistency between each language version and its markup. JSON-LD should reflect the page's visible content in the relevant language (titles, descriptions, prices, availability, contact details). Also ensure you use the correct URLs for each language (and stable identifiers where relevant) to avoid contradictions across versions.

Do You Need to Mark Up Every Page? How Do You Prioritise for SEO and GEO Impact?

No. Prioritise high-impact pages (products, services, local pages, pillar content) and high-volume templates. Then expand progressively based on gains observed in Search Console and your business priorities.

To explore further topics in SEO, GEO and digital marketing, browse the analysis and guides on the Incremys blog, covering SEO, GEO and digital marketing.