Posted by: Mike Isaacs
11 November 2024
Updated 11th December 2025 by Mike Isaacs
You have solid content, a decent backlink profile and a CMS your teams know well. Yet your organic growth has slowed, and newer competitors seem to appear ahead of you in Google, in AI summaries and in tools like ChatGPT.
Often the missing layer is not “more content” but clearer signals about what that content actually is.
Schema markup sits in that layer. It is a small amount of structured code that explains your pages to engines and AI systems in precise, machine readable terms. Done well, it supports rich results, higher click through rates and better inclusion in AI generated answers.
This guide answers common questions marketers ask about schema markup, why it matters for SEO and AI , and how to make it work on .NET platforms like Umbraco, Optimizely, Kentico and Sitecore.
Business value takeaway: Schema markup gives search engines and AI a clear model of your organisation, services and content so you can compete for visibility in both classic results and AI generated answers.
Why Schema Markup matters more in the AI era
Schema has been part of SEO conversations for years, but the context has changed.
Google now shows AI summaries (AI Overviews) for roughly one in five searches, especially for longer, natural language queries and questions. A recent Pew Research Center study found that 18% of Google searches in March 2025 produced an AI summary, and that users were significantly less likely to click through to websites when an AI Overview was present. (Source: Pew Research Center) Independent coverage suggests the click rate can fall to around half of the rate on a conventional results page. (Source: [Ars /))
At the same time, AI engines like ChatGPT, Gemini, Copilot and Perplexity have become everyday research tools. They combine large language models with live web , then synthesise answers with in line citations to sources. (Source: Wikipedia – Perplexity AI)
Behind the scenes, search engines and AI systems extract structured signals from the web at huge scale. The Web Data Commons project, for example, has identified over a billion pages that contain structured data such as JSON LD, Microdata or RDFa. Organisation, Product, Person, LocalBusiness, Event and FAQPage are among the most common schema.org classes captured. (Source: GPT Insights – How LLMs Learn From Structured Data) This structured layer often feeds both features and AI training or grounding pipelines.
Put simply, if your competitors expose clear, consistent schema and you do not, their content is easier for both engines and AI models to interpret, summarise and .
Business value takeaway: As AI summaries and answer engines absorb more early stage , schema becomes a key way to keep your brand visible when users no longer click through to as many websites.
What Schema Markup is and how it relates to structured data
A common first question is “What is schema markup and why does it matter for SEO?”
Structured data is any data organised in a predictable, machine readable format. That could be a database table, an API response or a JSON object.
Schema markup is a specific type of structured data that uses the shared vocabulary at schema.org to describe things like organisations, products, articles and events in a way engines understand consistently.
Most websites implement schema using JSON LD. Google supports JSON LD, Microdata and RDFa, but recommends JSON LD in most cases because it is easier to maintain and less error prone, especially at scale. (Source: Google Search Central – Intro to Structured Data) JSON LD is typically added as a <script type="application/ld+json"> block in the <head> or <body> of a page. (Source: [Google – Structured Data ))
Here is a simple illustration of the difference between generic structured data and schema markup.
Generic structured data (JSON):
{
"name": "John Doe",
"age": 30,
"city": "New York"
}
Schema markup using schema.org vocabulary:
{ "@context": "https://schema.org",
"@type": "Person",
"name": "John Doe",
"age": 30,
"address": {
"@type": "PostalAddress",
"addressLocality": "New York"
}
}
Both are structured data, but the schema version tells engines that this is a Person with an address, not just an arbitrary JSON object. That extra meaning is what enables rich results and better matching to user intent.
Business value takeaway: Schema markup provides a common language between your content and engines, making it easier for them to recognise your brand, content types and offers.
How Schema Markup improves visibility, rich results and click through rates
One myth worth clearing up early is that schema markup is a direct ranking factor.
Google’s own representatives have repeatedly confirmed that using schema or structured data does not give you a ranking boost. Structured data helps Google understand content and become eligible for certain displays or enhancements, but it does not push your page to the top of the results by itself. (Source: Search Engine Roundtable)
However, schema still matters for SEO in three important ways:
Eligibility for rich results
Many of Google’s richer presentations in the SERP – such as FAQ expansions, product snippets, recipe cards, job listings and event panels – rely on structured data for eligibility.
More compelling snippets
When schema is used to show star ratings, prices, event dates or FAQ snippets, your listing can stand out visually, provide more context and attract more clicks. Independent analyses consistently find that rich snippets tend to drive higher click through rates compared with plain blue links, because users see more of what they need up front. (Source: TopPosition – The Impact of Schema Markup in SEO)
Clearer entity understanding
Schema helps Google connect your organisation, authors, products and articles into its knowledge graph with less ambiguity. That improved understanding supports better relevance decisions and can feed into features like knowledge panels and AI when other quality signals are strong.
For a marketing leader, the practical question becomes “How can schema markup increase my site’s click through rate?”
Typical wins include:
- Product or service listings that show price and availability
- Article snippets that show author, date and FAQ drop downs
- Local office pages that show address, open hours and phone
- Event pages that show date, location and registration status
Each of these adds context that reduces uncertainty and nudges the right users to click.
Business value takeaway: While schema will not move you from position seven to position one on its own, it can an ordinary result into a richer, more clickable listing that wins a greater share of traffic from the rankings you already have.
How Schema Markup supports voice and AI assistants
Voice and conversational interfaces depend on structured, unambiguous data. When a user asks a device a full question, it needs a short, precise answer and a trustworthy source.
Google’s FAQPage structured data, for example, can make FAQs eligible for rich results in Search and for Actions in Google Assistant, which are used to read answers aloud. (Source: Google – FAQ Structured Data) The Speakable property, still in beta, lets publishers highlight parts of an article that are suitable for text to speech playback via Assistant.
For AI engines like ChatGPT, Gemini, Perplexity and Copilot, schema also plays a supporting role:
- AI tools blend model reasoning with live crawling and citation of web pages. (Source: Wikipedia – Perplexity AI)
- Independent audits have found a strong correlation between complete schema on product and content pages, and how often those pages are or surfaced in AI shopping modules and answer boxes. (Source: Erlin AI – Schema And AI Visibility)
- on large language models shows that structured data from common crawl corpora (particularly schema.org JSON LD, Microdata and RDFa) is frequently extracted and used as part of training and grounding datasets, especially for entities like organisations, products and FAQ pages. (Source: GPT Insights – How LLMs Learn From Structured Data)
There is still healthy debate about how much schema directly influences AI rankings, and search engines are clear that they look at content quality and authority first. (Source: [Search Engine Journal – SEOs Are Recommending Structured Data For AI /)) But for many AI systems, structured data reduces ambiguity around who you are, what you offer and which facts on a page are most important.
So if you are asking “Does schema markup help with voice and AI answers?” the realistic answer is:
- It does not guarantee inclusion
- It gives AI engines fewer reasons to misinterpret your content
- It raises the chance that, when you are already a relevant source, you are clearly and accurately
Business value takeaway: Schema gives voice assistants and AI engines ready made, high confidence answers they can read, summarise and attribute to your brand.
Best schema types for B2B and SME websites
Not every schema type is equally useful for B2B and SME organisations. A practical question is “Which schema types work best for B2B and SME websites?”
Here are the high value types we most often recommend for .NET based sites:
| Content type | Recommended schema types | Typical use |
|---|---|---|
| Global brand identity | Organization, WebSite, Logo, ContactPoint, AboutPage |
Describe your company, logo, main site and key contact routes |
| Offices and locations | LocalBusiness, PostalAddress, GeoCoordinates |
Show address, phone, opening hours and map friendly data |
| Services and products | Product, Service, Offer, AggregateRating |
Describe B2B services, software plans or product SKUs |
| Articles and insights | Article, BlogPosting, NewsArticle, Author |
Structure long form insight content and attribute it to experts |
| FAQs and support | FAQPage, Question, Answer |
Surface common questions in rich snippets and voice answers |
| Guides and documentation | HowTo |
Mark up step based implementation, onboarding or configuration guides |
| Events and webinars | Event, Organization |
Promote in person or virtual events, with dates and registration data |
| Careers | JobPosting, Organization |
Structure vacancies, salary ranges and locations |
| Search and navigation | BreadcrumbList, SiteNavigationElement, SearchAction |
Help search engines understand your site structure and box |
For a typical B2B SME site, a good minimum standard is:
Organizationon your global layoutWebSiteand `` on your homepageLocalBusinessor address markup on any office pagesArticleplusAuthoron blog and insight templatesFAQPagewhere questions and answers already exist on the pageProductorServiceon core offering pages
Business value takeaway: A focused set of schema types aligned to your content model makes it easier to scale structured data as your site grows, without trying to mark up everything.
Implementing schema on .NET platforms like Umbraco, Optimizely and Kentico
If you run a .NET estate, schema needs to work across multiple sites, languages and templates. That means thinking in terms of architecture, not just one off tags.
At a high level, technical teams usually:
Define schema models in code
Create C# models or models that represent your key schema types (Organization, Article, Product, FAQPage and so on). These pull data from your CMS content types.
Centralise JSON LD generation
Add helper methods or services to those models into serialised JSON LD strings using a library such as Newtonsoft.Json.
Wire into shared or components
In MVC , Razor components or block level partials, render the JSON LD into a <script type="application/ld+json"> tag.
Handle multi site and multi language
Use site specific configuration for fields like name, url, inLanguage and @id, and ensure your schema respects hreflang and canonical rules.
Here is a simplified example of adding Article schema in an Umbraco Razor:
@using Newtonsoft.Json
@{
var schemaData = new
{
@context = "https://schema.org",
@type = "Article",
headline = Model.Value("pageTitle"),
author = new
{
@type = "Person",
name = Model.Value("authorName")
},
datePublished = Model.Value("publishDate"),
mainEntityOfPage = new
{
@type = "WebPage",
@id = Model.Url()
}
};
}
<script type="application/ld+json"> @Html.Raw(JsonConvert.SerializeObject(schemaData))
</script>
In a component based .NET build (for example a block grid in Umbraco or a component in Optimizely), you can attach schema generation to each block type so that, for example, a “webinar card” always outputs an Event schema object.
Business value takeaway: Treat schema as part of your .NET component library so it is generated automatically and consistently, rather than added manually on a page by page basis.
Editor friendly ways to manage schema in Umbraco
A frequent practical question is “How do I add JSON LD schema in Umbraco without a developer?”
As a content editor or marketer, you should not need to write JSON yourself. Instead, your development team can provide editor friendly patterns such as:
Global site settings
A settings section where you maintain organisation name, logo, social profiles and contact details. The system then outputs global Organization and WebSite schema automatically.
Schema aware document types
Content types for articles, products, case studies or events can include fields that map straight into schema properties (for example, event date, location, price, speaker). When you fill in those fields, Umbraco generates valid JSON LD.
Block level schema
Block list or block grid editors can be configured so that certain blocks emit schema. For example, an FAQ block that stores a list of questions and answers can output FAQPage markup.
SEO or schema plugins
Packages like SEO Checker give editors a user interface for managing metadata and structured data without touching templates, which is helpful for teams that are not writing code.
Growcreate’s own AI Toolkit for Umbraco is moving in this direction too, with planned features for generating schema markup for products and articles directly from content, alongside existing AI assisted editing and metadata features. (Source: Growcreate – Smarter Editing With AI In Umbraco)
If you do not see these options in your Umbraco back office today, it is a sign to speak with your development partner about adding schema aware components and settings.
Business value takeaway: When schema is wired into Umbraco’s content types and blocks, editors can improve and AI visibility simply by filling in structured fields, without touching JSON or HTML.
Tools to generate, test and monitor schema
Even with good patterns, it is important to validate and measure structured data.
Key tools include:
- Google Rich Results Test – Checks whether your structured data is valid for supported rich result types and shows where available.
- Schema Markup Validator – A general purpose validator maintained by schema.org for checking JSON LD, Microdata and RDFa. (Source: Schema Markup Validator)
- Google Search Console – Provides reports on structured data issues and performance reports to see impressions, clicks and click through rates for pages with rich results. (Source: Google – Structured Data Guidelines)
When you add or improve schema, a simple way to measure impact is:
- Pick a small set of representative pages (for example, 5 service pages and 5 articles) with stable traffic.
- Implement and validate schema on those pages.
- Monitor impressions, average position, CTR and rich result coverage in Search Console over several weeks, comparing before and after.
For AI , measurement is still emerging. Some teams periodically run key queries in ChatGPT, Gemini and Perplexity to see which brands are , then track change over time as they improve structured data and content quality. Industry tools are starting to appear that focus on “AI visibility” and share of voice in generative engines. (Source: Wikipedia – Generative Engine Optimization)
Business value takeaway: Treat schema like any other optimisation – validate it, measure it and prioritise the patterns that clearly improve impressions and CTR.
Governance compliance and scalable schema architecture
Structured data is still data. It needs governance.
Consider the following when you expand schema across an enterprise .NET estate:
-
Data minimisation - Do not include personal data you would not otherwise expose on the page. Focus on business entities, public contact routes and content level facts.
-
Accuracy and freshness - Out of date pricing, event dates or contact details in schema can cause confusion in both SERPs and AI summaries. Align schema generation with the same workflows that keep content up to date.
-
Central patterns - Maintain shared schema templates and helpers in your codebase so every site and component uses the same approach. This reduces developer overhead and prevents drift.
-
Compliance - Ensure your implementation aligns with GDPR and information security controls. Growcreate’s Umbraco solutions, for example, are designed to be secure and GDPR compliant when hosted on Azure, combining platform controls with ISO 27001 aligned processes. (Source: Growcreate – Security And Compliance)
Business value takeaway: A governed schema architecture reduces risk, keeps implementation costs under control and prevents fragmented markup across your .NET platforms.
How Growcreate helps with schema technical SEO and AI visibility
Many generic SEO blogs stop at “here is what schema is.” The hard part is that knowledge into a maintainable, enterprise ready implementation across your .NET stack.
Growcreate sits at the intersection of:
- Technical SEO audits and performance optimisation (Source: Growcreate – Technical SEO Services)
- .NET and Umbraco, Kentico and Optimizely engineering, including multi site, multi language and complex content models (Source: Umbraco – Growcreate Platinum Partner Listing)
- AI adoption and AI search visibility for SMEs, including advisory services focused on strengthening your presence in AI and branding channels. (Source: Growcreate.ai – AI Consultancy For SMEs)
For schema and structured data, we typically help clients by:
- Auditing existing schema coverage and rich result eligibility across your .NET estate
- Designing a schema architecture that fits your content models, platforms and governance
- Implementing JSON LD generation in Umbraco, Optimizely, Kentico, Sitecore or custom .NET builds
- Wiring schema into component libraries and CI/CD pipelines, so it ships with every release
- Adding editor friendly workflows in Umbraco so schema becomes part of everyday content work
- Training marketing and content teams on how schema ties into SEO, AI and answer engines
Business value takeaway: With Growcreate, schema stops being an afterthought and becomes a stable part of how your .NET platforms support visibility in both classic and AI driven experiences.
Next steps for marketers and digital leaders
If you are responsible for growth on a .NET platform and want to raise your and AI visibility, a few practical next steps are:
Request a schema audit
Ask us to your current implementation, rich result eligibility and AI presence, then prioritise quick wins.
Talk to our technical SEO engineering team
Explore how schema can be integrated into your Umbraco, Optimizely, Kentico or custom .NET build as part of a wider technical SEO roadmap. (Source: Growcreate – Technical SEO Services)
Automate structured data across your .NET platform
Plan how schema fits into upcoming upgrades or modernisation work so it is baked into new templates and component libraries, not added as a separate project. (Source: Growcreate – Platform Modernisation)
Explore AI visibility opportunities
If AI search is becoming a key channel for your buyers, speak to our AI team about practical steps to improve how your brand appears inside AI answers and summaries. (Source: Growcreate.ai – AI Visibility For And Branding)
When schema is designed and implemented with your .NET architecture in mind, it becomes a quiet but powerful contributor to organic growth, brand authority and AI readiness.
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