Structured Data

Structured data is essentially extra code that communicates the meaning of your content to search engines in a standardized way. Instead of hoping Google or Bing figure out that your page’s random numbers are a recipe’s cook time or a product’s rating, you explicitly tell them via structured data. Think of it as filling out a form for your webpage that search engines can easily read. Google itself says that adding structured data gives its systems “explicit clues about the meaning of a page” . In other words, you’re helping the crawlers help you, by providing clear labels for your content.

In practice, this often means using schema.org vocabulary (a set of common definitions for things like “Article,” “Recipe,” “Product,” etc.) embedded in your HTML. By doing so, you make your page’s facts and figures machine-readable. The payoff? Search engines can then reward you with “rich” search results (think star ratings, FAQs, knowledge panels) and, increasingly, citations in AI-generated answers. Not bad for some snippets of code, right?

A Few Types of Structured Data

Structured data comes in a few flavors, all serving the same purpose: to serialize your content’s details in a format crawlers understand. The main types you’ll encounter on the modern web include:

• JSON-LD (JavaScript Object Notation for Linked Data) – The favored format nowadays. You add a <script type="application/ld+json"> block in your page containing JSON that describes your content. It’s neat, separated from your HTML design, and Google officially recommends JSON-LD for implementing schema markup. JSON-LD is great because you can drop it into the page without altering HTML elements.
• Microdata – An HTML5 spec that involves adding special itemprop, itemscope, and itemtype attributes directly into your HTML tags. For example, you wrap a <div> in an itemscope and specify a schema.org type with itemtype="https://schema.org/Recipe", then label text within using itemprop="ingredient", itemprop="cookTime", etc. It intermixes with your HTML structure. This was popular in the early 2010s, but can make your HTML markup messy and harder to maintain.
• RDFa (Resource Description Framework in Attributes) – Another HTML attribute-based approach, which extends HTML tags with properties like about, property, and typeof. RDFa is more often used in the semantic web community and in contexts like embedding data in XML or SVG, but it can be used on webpages too. It’s very flexible (it isn’t limited to schema.org vocabulary), but that power comes with complexity.

All three of the above can express the same schema.org info – you could describe, say, a “Restaurant” with opening hours and menu items in any of these formats. Schema.org’s site notes that its vocabulary is encoding-agnostic and “can be used with many different encodings, including RDFa, Microdata and JSON-LD.”

Beyond these web page markup formats, structured data in a broader sense can include things like CSV data feeds, XML sitemaps, or even APIs that search engines use (for example, Google Merchant Center feeds for products, or schema.org’s data dumps). But when SEOs talk about structured data, they’re usually referring to the on-page markup (JSON-LD/Microdata/RDFa) that uses schema.org vocabulary. So our focus here is on that on-page stuff.

How Crawlers and Generative Search Engines Consume Structured Data

Traditional search engine crawlers (like the classic Googlebot) and the new wave of generative AI search engines both gobble up structured data, but in somewhat different ways and for different ends. Let’s break it down:

1. Traditional Crawlers (Googlebot, Bingbot, etc.): When a standard crawler visits your page, it reads the HTML and looks for structured data markup. If it finds, say, a JSON-LD script declaring that “this page is an Article written by Alice on 2025-07-31”, it can feed that info into multiple systems:

• Search index understanding: Structured data gives extra context which can improve how the index classifies your page. Google has stated this provides explicit clues about your page’s meaning . It’s like handing in a cheat sheet about your content. This doesn’t instantly rank you higher for keywords (structured data isn’t a direct ranking factor in the traditional algorithm), but it helps ensure the search engine actually knows what your page is about. That can indirectly help your SEO by making you eligible for relevant queries and features.
• Rich results and SERP features: Perhaps the most visible impact — structured data powers those eye-catching search results. If you’ve implemented FAQ schema, Google can show a drop-down FAQ on your snippet. If you have Product schema with reviews and price, you might get those ⭐ ratings and price info right on the results page. These enhancements can dramatically improve click-through rates. (For example, Rotten Tomatoes saw a 25% higher CTR on pages with schema markup, and Nestlé observed an 82% higher CTR on rich result pages, according to Google’s case studies .) In short, structured data can make your search listings more attractive, which can lead to more traffic, even if your “blue link” ranking itself is unchanged.
• Knowledge Graph and Entity Extraction: Structured data, especially for things like Organization, Person, Book, Recipe, etc., often gets ingested into knowledge bases. Google’s Knowledge Graph – the giant fact database of “people, places, and things” – is fueled by many sources, one of which is structured markup. If you mark up that “ACME Inc.” is a Corporation with a certain CEO, founded date, and HQ address, you’re basically feeding Google’s fact vault. Later, that could result in a Knowledge Panel for your brand, or your information being deemed authoritative. The Knowledge Graph contains “billions of facts” about entities and is referenced by many search features and now even AI answers . In the context of Google’s new SGE (Search Generative Experience), early analyses indicate the AI summaries often pull data from the Knowledge Graph and other structured databases behind the scenes . So, structured data is like ensuring you’re in the room when these AI systems are having their “let’s compile an answer” meeting.

2. Generative Search Engines (LLM-based systems like Bing Chat, Google SGE, etc.): This is where it gets really interesting for SEO folks. Generative search engines use large language models (LLMs) to synthesize answers on the fly, instead of just showing a list of links. How do they use structured data? It’s an evolving area, but a few things are happening:

• Direct parsing by LLM crawlers: The LLMs need information to generate answers, and that still comes from crawling web content (plus other data sources). It turns out at least one major player is definitely using your schema markup. In early 2025, Microsoft’s Fabrice Canel (Principal PM at Bing) confirmed that Bing’s GPT-4 based search (copilot) does look at schema markup to help its AI understand your content . In his words, schema helps the AI “understand your content.” This makes sense: Bing’s approach (codenamed “Prometheus”) combines the Bing index with GPT-style reasoning. So any clues from schema.org likely feed into that indexing layer or the retrieval step before the AI starts writing an answer. While Google hasn’t officially said “we use schema in our AI overviews,” it’s widely suspected they do, given that SGE cites the Knowledge Graph and uses Google’s existing index heavily. We can infer that if something helps Google Search understand your page (like schema does), it will help their generative AI too – they’re drawing from the same well of data.
• Knowledge Graph & databases as foundation: As mentioned, generative AI results often lean on structured knowledge repositories. Google’s SGE, for example, is “built on Google’s Shopping Graph” for product queries and also taps the Knowledge Graph for general info . So if you’re an e-commerce site, having complete Product structured data (price, availability, reviews, etc.) not only gets you rich snippets, but also feeds into Google’s Shopping Graph. SGE can then confidently say “The Acme SuperWidget costs $49 and has 4.5 stars” in its summary, because your schema markup provided those facts. For local businesses, having your LocalBusiness schema in place (plus Google Business Profile) might mean the AI knows your exact address, hours, etc., and might include those in an answer about “best coffee shops near me open now.”
• Citations in AI answers: One of the big fears with generative search is that it produces answers with no credit to content creators. To their credit, Bing and Google are trying to cite sources for facts in AI answers. How do they choose what to cite? Relevance and authority of content is key, but structured data can influence this. If your page clearly indicates “I am about X topic, here is the specific Y detail”, an AI may more easily pick it up and attribute it. Additionally, certain types of schema (FAQ, HowTo) almost format your content as a Q&A or step-by-step answer – potentially making it easier for an LLM to directly lift and credit those parts. For instance, if you have FAQ schema, Google’s SGE might show one of your Q&As as a conversational prompt or follow-up. If you have Recipe schema, SGE might enumerate recipe steps right from your structured data (with a citation). So structured data can act like a signpost saying “this bit of text answers a common question” or “here’s a fact you might use,” which is gold for an AI trying to justify its response.
• Machine understanding vs. pure NLP: Large language models are pretty good at reading unstructured text, but they’re not perfect, and they can hallucinate or mix things up. Feeding them well-structured facts reduces ambiguity. For example, if your article says in passing “Our CEO, John Doe, started the company 10 years ago,” an LLM might not easily realize that means founding year 2015. But if you have structured data that says "founder": "John Doe", "foundingDate": "2015", that is unambiguous. In fact, we’re seeing a convergence of SEO and AI here: savvy content creators are including more structured, factual snippets in content (e.g., summary boxes, fact lists) that double as good training data for AI. Schema markup accentuates that by explicitly labeling the facts. As one SEO expert noted, “schema markup helps LLMs to understand your content” — it’s basically serving the facts to the model on a silver platter .

To sum up, structured data is consumed by crawlers to better index and feature your site in traditional search, and consumed by generative AI as a reliable knowledge source to draw on when formulating answers. It’s not a stretch to say that if you want your content to be featured, quoted, or at least accurately represented by AI search, providing structured data increases your odds. It’s like speaking in the native language of the search engine – you’re making it really easy for them to know who you are and what you have to offer.

One caveat: structured data alone won’t overcome weak content. You still need to be relevant and authoritative on your topic. But all else being equal, a page that’s well-structured is far more AI-friendly than one that isn’t. In fact, Google’s own documentation flat-out says they use structured data to “understand the content of the page, as well as to gather information about the web and the world” . – your little JSON-LD block about your recipe’s calories might actually be contributing to Google’s broader understanding of “the world.” It underscores why ignoring schema markup is missing an opportunity.

Tactics to Rank Better with Structured Data (in the Age of GEO)

By now, hopefully it’s clear that structured data can improve your visibility – whether in the classic blue-link world or the new generative AI world. But what specific tactics can you apply to leverage this and rank better or get more AI love? Here are some strategies, tailored for the GEO (Generative Engine Optimization) era:

1. Implement JSON-LD for Everything Relevant – If you have content that fits a schema type, mark it up. Don’t be shy. Articles, blog posts, products, recipes, events, FAQs, how-tos, job postings – they all have schema vocabularies. Using JSON-LD, add those to your pages. This ensures no matter how an AI or crawler slices your page, the key info is explicitly noted. Google and Bing both parse JSON-LD very well. Plus, JSON-LD is painless to update. If you have a templated site or CMS, you can often generate the JSON dynamically. This is foundational: a bit like the new on-page SEO checklist item. If two sites tie on content quality, but one has schema marking up author, date, pricing, etc., that one will likely get the edge in being understood by search engines.

2. Cover Your Entity Basics (Organization/Person Schema) – One low-hanging fruit many forget: include Organization or Person schema for your website (often in the footer or about page). This is basically your site’s business card to the search engine. Define who you are, what your website is about, sameAs links to your social profiles, your logo, contact info, etc. Not only can this help get you a Knowledge Panel (which indirectly boosts credibility), it also establishes you as a known entity that the AI can recognize. For example, if SGE knows “Acme.com = ACME Inc, a software company in NY”, then when someone searches the AI for “ACME Inc revenue,” it has a much easier time connecting the dots to your site or at least not confusing your brand for something else. This tactic aligns with the concept of Entity SEO – basically helping search engines (and AIs) understand the entities (people, companies, products) behind websites. It’s very much structured data-driven. As a bonus, doing this is relatively easy: a simple JSON-LD block on your homepage can do it.

3. Use Specific Schema Types to Match Search Features – This is more of a traditional SEO tactic but still applies in GEO. If you have content that could be eligible for rich results or special treatment, use the schema for it. For example:

   • FAQPage schema on pages with Q&A content can get you that accordion in Google results, and those same Q&As might get directly pulled into an AI answer (with your site credited as the source).
   • HowTo schema on instructional content can make you show up in step-by-step carousels, and potentially, an AI might use those steps in a summarized form.
   • Recipe schema we mentioned – helpful for appearing in recipe results and getting voice assistants (which are a kind of generative search) to read out your recipe.
   • Product and Review schema – crucial for e-commerce to feed not just search snippets but also the Shopping Graph (which SGE relies on for product comparisons). If your product data is structured, an AI can more easily incorporate your product in an answer (e.g., “Which blender is best under $100?” might trigger SGE to list a few, including yours if it has rich data).
   • Event schema – could help your events get picked up in things like Google’s event search or an AI query about local happenings.

   Essentially, think about search verticals and features. Each has some schema that influences it. To optimize for generative results, you still want to be present in the underlying index and features – because that’s where the AI draws from. If you’re completely missing out on, say, the “featured snippet” game for a query (which often correlates with having clear structured info), you’re likely also missing out on the AI answer for that query.

4. Ensure Your Structured Data is Error-Free and Complete – It sounds obvious, but implement schema correctly. Use Google’s Rich Results Test and Schema.org validators. A missing comma in JSON-LD can mean Google ignores the whole thing. Also, provide all recommended properties, not just the minimum. The more complete the picture, the better. If you’re marking up a “Product”, include price, availability, brand, SKU, review ratings, etc., not just the name. This thoroughness can set you apart. It’s like giving the AI a full dossier on your product rather than a sketch. Moreover, correct schema is trusted more. Search engines might ignore or down-rank schema that looks automatically generated but empty, or incorrect.

5. Make Your Content (and Data) Accessible to AI Crawlers – This is more of a technical SEO point: many LLM-based crawlers aren’t as advanced as Googlebot in rendering JavaScript. They may fetch just the raw HTML. If your structured data is injected client-side (e.g., via Google Tag Manager or after page load with JS), some bots might not see it. It’s safest to output the JSON-LD in the initial HTML source. Same goes for important content pieces – don’t hide critical info behind login walls or heavy scripts if you want it considered. One GEO expert put it bluntly: “Most LLM crawlers cannot render JavaScript. If your main content is hidden behind JS, you are out.” So for GEO, prioritize server-side rendering and direct HTML content delivery. The structured data needs to be there without requiring a second pass or execution. In short: simplify the crawler’s job. This tactic aligns with general good SEO (fast, accessible content), but it’s even more crucial when an AI is trying to ingest your page quickly to answer a live query.

6. Keep Structured Data Updated & Use Indexing APIs – Freshness matters a lot for generative AI because they don’t like citing stale info. Fabrice Canel noted that “Gen AIs value fresh content in particular, partly as a reference check of their LLM training data” . If your structured data (and page content) says “Price: $100” but in reality you changed it to $90 and forgot to update the markup, an AI answer might quote the wrong price – not a good look. Or if your article’s schema still has an old date or missing newly added sections, you might be overlooked for more up-to-date sources. Tactically, this means: whenever content changes, update your schema markup too. And consider using tools like IndexNow (for Bing) or the Google Indexing API (for job postings and livestreams, or potentially more in the future) to ping search engines about updates. Bing’s team explicitly encourages using IndexNow to push fresh content . The faster your updated structured data is ingested, the better chance an AI uses the latest and greatest info from your site.

7. Embed Facts, Stats, and Sources in Your Content (and Mark Them Up) – Generative engines love concrete facts, especially ones that are supported by sources. A recent research paper on GEO (Generative Engine Optimization) showed that including citations, quotation snippets, and statistics in your content can significantly boost your visibility in AI-generated answers. In their experiments, simple additions like quoting a authoritative source or adding a compelling statistic improved content visibility metrics by 30–40% (position-adjusted word count) and 15–30% (subjective usefulness) on average . The takeaway for us: become the source that the AI wants to quote. If you have a notable stat, format it clearly (even mark it up with <q> or in a schema property if possible). If you mention another study or source, consider using citation markup or at least clear references. Structure your important statements in a way that’s easy to extract. For example, instead of burying “80% of marketers saw ROI from schema within 6 months” in a paragraph, you might pull it out as a bolded sentence or an item in a list (and you could even use something like the Question schema with “What percentage of marketers saw ROI from schema?” -> 80%). This overlaps with good content writing, but it’s about structured presentation of information. The better structured your knowledge, the more likely an LLM will incorporate it (and maybe even cite you as the source of that juicy stat).

8. Leverage Structured Data for Internal Consistency and Linking – This is a pro tip. If your site has multiple pages about an entity (say, individual product pages and a company page), use structured data to tie them together. For instance, on each Product page, you might link the Organization or brand to your Organization schema entry (via the brand property or manufacturer property with @id linking to your Organization JSON-LD block). This creates a web of linked data that search engines can traverse. You’re basically creating your own mini knowledge graph. While the immediate ranking impact of this is hard to measure, it contributes to the overall data completeness. And it can help ensure that when an AI talks about your brand or products, it has the full context (because you provided those connections explicitly). It’s like giving the AI a map of how your content pieces relate to each other.

9. Monitor How Your Structured Data is Used in AI Results – Finally, keep an eye on emerging tools and reports. Bing’s webmaster tools and Google Search Console have started giving some insights (e.g., Bing’s Content Safety Report in webmaster tools might show if your content was used in Bing Chat). There are third-party platforms now that attempt to track where your content gets cited in AI answers. By monitoring this, you can learn which pages of yours are getting picked up and why. Maybe your FAQ page is frequently cited – great, double down on that format. Maybe none of your content is being cited – that could be a schema or content quality issue. Treat AI visibility like a new analytics dimension you need to optimize for. It’s early days, but being proactive here can put you ahead of competitors.

10. Stay Ethical and Accurate – A quick word of caution: just as with SEO, there might be temptations to abuse structured data (e.g., marking up content that isn’t actually visible or misusing schema types) to game AI. Don’t. Not only can that lead to penalties (manual actions for schema spam are a thing in Google), but it also undermines the trust that AI might place in your site. Remember, generative AI will likely have some quality filters – if your structured data contradicts known facts or is consistently misleading, you could be flagged as an unreliable source. So focus on accurate, quality information in your markup. This builds your credibility with the machines (and with users, ultimately).

In implementing these tactics, you’re essentially aligning with what search engines (and their AI counterparts) want: clear, truthful, well-structured information. It’s a win-win: users get better results, and you get more visibility.

Conclusion

Structured data may not be the flashiest topic in marketing meetings, but hopefully you see it’s incredibly powerful. It’s the bridge between human content and machine understanding. In an older era, that meant your site could get a cool rich snippet; in today’s era, it might mean your site becomes the cited authority in a conversational answer from an AI. The playing field is shifting, but structured data remains a key way to influence how your content is interpreted.

To succeed in generative search, think beyond traditional SEO checkboxes. Consider how an AI sees your page. Does it find a neatly packaged set of facts with schema markup? Are you openly declaring the entities and relationships on your page? Are you providing evidence and context that an AI would feel safe using in its answer? These are the new questions we SEO-savvy marketers must ask.

The great news is that all the structured data work you do pays dividends across the board – it’s not solely for AI or solely for traditional search. It’s for both. You improve your classic SEO, and you future-proof for AI at the same time. Given how search is evolving, that’s a pretty efficient use of your time and resources.

So, roll up your sleeves and audit your site’s structured data. Fill in the gaps, add markup where it’s missing, and update anything outdated. Treat your schema like a living part of your content strategy, not a one-and-done IT task. And keep an eye on new schema types or guidelines that search engines roll out (Google constantly updates supported schemas in their Search Central documentation).

Finally, don’t be afraid to be a bit opinionated and creative with it. Schema.org allows extensions and custom types – if you have a use case, explore it. Early adopters often reap rewards.

At the end of the day, structured data is about communicating better with search engines. In a world where those search engines are becoming more like AI chat assistants, you want to be sure your voice is heard and your content is understood. Think of structured data as your content’s PR agent – making sure the algorithms get your story straight.

And when the algorithms get it right, everyone (you, your audience, and the search platform) wins. So go forth and markup your world! In the age of generative AI, a little extra structure can go a long way in ensuring you rank, you shine, and you stay ahead of the curve.

References:

1. Aggarwal et al. (2024). “Generative Engine Optimization.” Princeton University (Research report on optimizing content for generative AI search).
2. Berry, S. (2025). “SGE Ranking Factors: What Determines AI Overview Rankings?” SEO.com. (Article discussing how Google’s Search Generative Experience ranks content, including the role of structured data).
3. Schwartz, B. (2025). “Microsoft Bing/Copilot use schema for its LLMs.” Search Engine Land. (News article confirming that Bing’s generative AI uses schema markup to understand content).
4. Google (2023). “Introduction to structured data markup.” Google Search Central Documentation. (Official documentation explaining how structured data provides explicit clues about page content and enables rich results).
5. Schema.org (2024). “About Schema.org – Usage Statistics & Supported Formats.” Schema.org Project. (Web page noting that schema.org vocabulary is used across tens of millions of sites in formats like JSON-LD, Microdata, and RDFa, with adoption numbers as of 2024).
6. Landwehr, M. (2025). “How To Win in Generative Engine Optimization (GEO).” Search Engine Journal. (Expert commentary on optimizing content for LLM-based search, including tips like using schema markup, consistent branding, and factual content for better AI visibility).