Landing Page Schema for LLM Crawling: Technical Setup Guide

Landing page schema is structured data markup that tells LLMs and AI search engines exactly what your content contains. Schema.org currently offers 797 types to label entities, FAQs, and offers, making pages instantly parseable for AI systems. Companies implementing schema have seen 344% growth in AI Overview citations, as structured data bridges human-readable content with machine-parseable information.

At a Glance

• Essential schema types: FAQPage, HowTo, Product, and Speakable schemas help LLMs and Google Overviews understand your content structure

• JSON-LD dominates: Used by 50.6% of all websites, JSON-LD is Google's preferred format over microdata (23.7%)

• Not a ranking factor: Schema markup isn't a direct SEO signal but enables rich results and improves AI comprehension

• Proven impact: Seer Interactive achieved 57% growth in Featured Snippets through schema optimization

• Deploy via GTM: Google Tag Manager enables schema deployment at scale without touching website code

• Test before launch: Use Google's Rich Results Test and Schema.org Validator to catch errors before deployment

Landing page schema is the foundation that lets LLMs instantly parse your offers, FAQs, and calls-to-action. In this guide, we show B2B marketers how to structure every page so large language models and Google AI Overviews can't miss you.

Why Does Landing Page Schema Matter in the LLM Era?

Schema markup tells search engines and AI systems exactly what your landing page contains. As one guide puts it:

"Schema for AI is a way of telling Google AI and LLMs (such as ChatGPT and Perplexity) what your website contains. It's a language that AI models can understand, as it uses structured data to provide necessary information, such as NLP understanding."
— Click Intelligence

Without structured data, crawlers must guess what your page is about. With it, they can reliably extract facts, steps, and offers.

The Shift from SEO to GEO

The digital marketing landscape is transforming. According to IDC, success now "demands internal retooling to focus on schema markup, improved/augmented content, and diversified omni-channel engagement" (IDC). Generative AI makes search more conversational and agentic, so pages that speak the language of LLMs gain a clear advantage.

What Schema Actually Does

Schema.org is a collaborative, community activity with a mission to create and promote schemas for structured data on the Internet. The vocabulary currently consists of 797 types, 1,454 properties, and 14 datatypes and is sponsored by Google, Microsoft, Yahoo, and Yandex.

Important caveat: schema markup is not a direct SEO ranking factor. Instead, it helps engines read your content, creating richer results and improving AI comprehension.

Key takeaway: Structured data bridges the gap between human-readable content and machine-parseable information, making your landing pages eligible for rich results and AI Overview citations.

Which Schema Types Should Every Landing Page Include?

Not all schema types carry equal weight for LLM crawling. Here are the essentials.

FAQPage Schema

A FAQPage is a WebPage presenting one or more frequently asked questions. This type is powerful because:

• Clear question-answer pairs align with how LLMs decompose queries

• It signals intent to both Google and AI systems without bloating the page

• It increases the chance of becoming a cited source in AI-generated answers

BreadcrumbList Schema

A BreadcrumbList is an ItemList consisting of a chain of linked Web pages, typically ending with the current page. It helps crawlers understand your site hierarchy and navigation structure.

The position property reconstructs the order of items, with the convention that:

• Lower values are listed first

• The first items correspond to the top of the breadcrumb trail

• Values should be integers, beginning with "1"

Speakable Schema

The speakable property indicates sections of a Web page that are particularly appropriate for text-to-speech conversion. For landing pages, this means:

• Mark your value proposition and key benefits as speakable

• Use CSS selectors or XPaths to specify sections

• Keep each speakable section to roughly 20-30 seconds of audio (two to three sentences)

Essential Schema Checklist

Google maintains specific criteria about how schema should be used. Misusing the markup, whether intentionally or not, can result in a structured data manual action.

JSON-LD or Microdata—Which Markup Do LLM Crawlers Prefer?

JSON-LD has become the dominant format for structured data implementation.

Adoption Statistics

According to W3Techs:

• JSON-LD is used by 50.6% of all websites

• Microdata is used by 23.7% of all websites

• Microformats is used by only 0.4%

Among the top 1,000,000 websites, JSON-LD maintains a similar lead at 50.3% versus Microdata's 25.9%.

Why Google Prefers JSON-LD

Google's John Mueller has stated:

"We currently prefer JSON-LD markup. I think most of the new structured data that are kind of come out for JSON-LD first. So that's what we prefer."
— SearchPilot

Technical Differences

With JSON-LD, a JavaScript object is inserted into the HTML of your page to define data, whereas microdata uses HTML tags and attributes to define data (SearchPilot).

Interestingly, SearchPilot found that changing from microdata to JSON-LD had no measurable impact on organic traffic. The format matters less than having structured data at all.

Key takeaway: Use JSON-LD for new implementations—it's easier to maintain, Google's preferred format, and works well with tag management systems.

Implementing JSON-LD Schema with Google Tag Manager

Google Tag Manager lets you deploy schema at scale without touching your website code.

Why Use GTM for Schema?

Google Tag Manager is a free tool that allows marketers to add or update tags without having to know any website coding. This means you can:

• Deploy schema across hundreds of pages with one piece of dynamic code

• Update markup without developer involvement

• Test changes before going live

Step-by-Step Implementation

Follow these steps from Search Engine Land:

1. Create JSON-LD markup for your target page type

2. Create a new custom HTML tag in Google Tag Manager and name it descriptively (e.g., "Organization Schema Markup")

3. Paste JSON-LD markup into the Custom HTML text box

4. Save your Custom HTML tag

5. Create new trigger for Page Views

6. Configure the trigger to fire on specific pages

7. Connect the trigger to your schema markup tag

8. Publish your container

Dynamic Schema with Variables

For pages with variable content (like blog posts), Go Fish Digital recommends a four-step process:

1. Create a schema code template

2. Insert variables into that template

3. Replace those variables with information from the page

4. Add the schema code to multiple pages via GTM

Here's an example of dynamic Article schema:

var schema = {  "@context": "https://schema.org",  "@type": "Article",  "mainEntityOfPage": {    "@type": "WebPage",    "@id": {{Page URL}}  },  "headline": {{Blog_Heading}},  "image": {{Image_URL}},  "author": {    "@type": "Person",    "name": {{Author_Name}}  },  "datePublished": {{Publish_Date}}}

Choose "Custom HTML" as the tag type and "Page View" as the trigger type. The trigger should fire on "Some Page Views" based on your URL conditions.

How to Test & Validate Your Schema for AI Readiness?

Validation catches errors before they affect your search visibility.

Google's Rich Results Test

The Rich Results Test is your first stop for validation. Key features:

• Supports JSON-LD, RDFa, and Microdata formats

• Shows which rich result types were found on the page

• Identifies errors and suggestions for your structured data

• Uses smartphone as the default user agent

The test results cover:

• URL status

• Crawling permissions

• Detected structured data

• Errors and warnings

Schema.org Validator

The Schema Markup Validator is based on Google's former Structured Data Testing Tool. It:

• Extracts JSON-LD 1.0, RDFa 1.1, and Microdata markup

• Displays a summary of the extracted structured data graph

• Identifies syntax mistakes in the markup

According to the Schema.org announcement, the new validator is simpler because it's dedicated to checking that you're using JSON-LD, RDFa, and Microdata in widely understood ways.

Validation Checklist

• Run URL through Rich Results Test

• Check Schema.org Validator for syntax errors

• Verify all required properties are present

• Ensure markup matches visible page content

• Test on both mobile and desktop user agents

• Check Google Search Console for crawling errors

Tracking Impact: From Rich Results to AI Overview Wins

Measuring schema performance validates your implementation effort.

Case Study Results

Seer Interactive achieved remarkable results through schema-driven optimization:

• 57% growth in Featured Snippet wins on optimized pages

• 344% growth in AI Overview citations in just five months

• 49% more AI Overview growth than top competitor

AI Overview Monitoring

AI-powered SERP features like Google's AI Overview and Bing's Ask AI are reshaping organic visibility. To track your performance:

1. Use Position Tracking tools to identify keywords eligible for AI SERP features

2. Filter keywords included in Google AI Overview results

3. Compare your domain's ranking versus competitors in AI Overview

Key Metrics to Track

The Current State of AI Evaluation

By December 2023, less than 6% of generative AI evaluations accounted for human-AI interactions, and less than 10% considered broader contextual factors. This means most organizations are still figuring out how to measure AI search impact.

Key Takeaways & Next Steps

Landing page schema is no longer optional for B2B marketers targeting AI search visibility.

Summary of Best Practices

1. Use JSON-LD as your primary markup format

2. Implement essential types: WebPage, FAQPage, BreadcrumbList, Speakable

3. Deploy via GTM for scalable management without developer dependencies

4. Validate before shipping using Rich Results Test and Schema.org Validator

5. Track AI Overview performance alongside traditional SEO metrics

Action Items

• Audit your current landing pages for existing schema markup

• Identify high-priority pages for implementation

• Create JSON-LD templates for each page type

• Set up GTM tags with appropriate triggers

• Establish baseline metrics before deployment

Generative AI makes search more conversational and assistive. Pages without clear schema risk invisibility as AI engines shift toward entity-driven answers.

For B2B companies looking to optimize landing pages for both SEO and GEO, Relixir provides end-to-end solutions including schema implementation, AI search monitoring, and GEO-optimized content generation. The platform helps companies like Rippling, Airwallex, and HackerRank monitor and improve their visibility across ChatGPT, Perplexity, and Google AI Overviews.

Frequently Asked Questions

What is landing page schema and why does it matter for LLM crawling?

Landing page schema is structured data (usually JSON-LD) that labels the entities, questions, steps, and offers on your page so large language models and AI search engines can parse them instantly. By clarifying page intent through types like WebPage, FAQPage, and Speakable, marketers boost eligibility for rich results and AI Overview citations. As Google and chat-based engines shift toward entity-driven answers, pages without clear schema risk invisibility.

Which schema types most often earn citations in AI Overviews?

FAQPage, HowTo, and Product schema repeatedly surface in Google AI Overviews because they give LLMs neatly structured answers, steps, and specs. Seer Interactive saw a 344% jump in AI Overview citations after restructuring articles around these types. Prioritizing entity-rich types makes your content skimmable for AI engines and increases citation odds.

Frequently Asked Questions

What is landing page schema and why does it matter for LLM crawling?

Landing page schema is structured data (usually JSON-LD) that labels the entities, questions, steps, and offers on your page so large language models and AI search engines can parse them instantly. By clarifying page intent through types like WebPage, FAQPage, and Speakable, marketers boost eligibility for rich results and AI Overview citations. As Google and chat-based engines shift toward entity-driven answers, pages without clear schema risk invisibility.

Which schema types most often earn citations in AI Overviews?

FAQPage, HowTo, and Product schema repeatedly surface in Google AI Overviews because they give LLMs neatly structured answers, steps, and specs. Seer Interactive saw a 344% jump in AI Overview citations after restructuring articles around these types. Prioritizing entity-rich types makes your content skimmable for AI engines and increases citation odds.

How does JSON-LD compare to Microdata for schema implementation?

JSON-LD is preferred over Microdata for schema implementation because it is easier to maintain and Google's preferred format. JSON-LD allows for easy addition and removal without altering HTML, whereas Microdata requires editing page content. Despite the format, having structured data is crucial for AI comprehension.

Why use Google Tag Manager for deploying schema?

Google Tag Manager allows marketers to deploy schema across multiple pages without needing to modify website code. It enables dynamic updates, testing before going live, and scalable management, making it an efficient tool for implementing structured data like JSON-LD.

How can Relixir help with landing page schema optimization?

Relixir provides end-to-end solutions for optimizing landing pages for both SEO and GEO, including schema implementation, AI search monitoring, and GEO-optimized content generation. This helps companies improve visibility across AI search engines like ChatGPT, Perplexity, and Google AI Overviews.

Sources

1. https://schema.org/docs/gs.html

2. https://www.seerinteractive.com/work/case-studies/ai-driven-optimization-boosted-ai-overview-citations-and-serp-feature-wins-for-an-outdoor-living-leader

3. https://clickintelligence.co.uk/guides/ai-seo/google-ai-llm/schema-markup-essentials-for-google-ai-chat-based-search

4. https://w3techs.com/technologies/comparison/da-jsonld,da-microdata,da-microformats

5. https://my.idc.com/getdoc.jsp?containerId=US53300525

6. https://www.forrester.com/report/genai-forever-changes-all-forms-of-search/RES182189?ref_search=3520107_1743024617707

7. https://schema.org/FAQPage

8. https://schema.org/BreadcrumbList

9. https://schema.org/speakable

10. https://moz.com/blog/new-schema-types-to-create-interactive-rich-results

11. https://www.searchpilot.com/resources/case-studies/seo-split-test-lessons-changing-microdata-to-jsonld

12. https://searchengineland.com/add-schema-markup-site-using-google-tag-manager-272516

13. https://gofishdigital.com/blog/how-to-create-dynamic-schema-with-google-tag-manager/

14. https://support.google.com/webmasters/answer/7445569?hl=en

15. https://schema.org/docs/validator.html

16. https://blog.schema.org/2021/05/21/announcing-schema-markup-validator-validator-schema-org-beta/

17. https://developer.semrush.com/api/basics/api-tutorials/projects-api

18. https://arxiv.org/abs/2502.11357