Why Your Perfect Schema Markup Doesn’t Show Visual Enhancements

Posted on 9 Dec at 7:47 am

a 16:9 wordpress featured image, : Imagine a cutaway of sky/grass/earth/deeper soil and the tree is represented by an AI icon tree. The roots spread deep and wide and convoluted. These are structured data, JSON, schema, HTML icons. This represents what website users see on the website, and what's doing the heavy lifting underneath the pretty design

Why Your Perfect Schema Markup Doesn’t Show Visual Enhancements (And Why That’s Actually Fine)

So you’ve done everything right. You’ve built beautiful structured data, passed Google’s Rich Results Test with flying colours, and you’re staring at that slightly deflating message: “URL has no enhancements.” What gives?

Here’s the thing nobody tells you when you’re knee-deep in Schema.org documentation: not all valid schema markup gets visual enhancements in search results. And honestly? That’s completely fine. Let me explain why.

The Visual Enhancement Lottery

Google loves certain types of content. Recipes get gorgeous image cards with star ratings and cooking times. Events get calendar integrations and ticket links. Products get price comparisons and availability badges. These visual enhancements look fantastic and can dramatically improve click-through rates.

But service businesses? We’re playing a different game entirely.

If you’re a plumber, electrician, waste management company, marketing consultant, or pretty much any local service provider, Google isn’t going to slap a fancy visual card on your search listing. There’s no star rating carousel for septic tank emptying.

No image gallery for portable toilet hire. And honestly, would you really want one?

What Your Schema IS Doing (The Invisible Magic)

Just because you can’t see visual enhancements doesn’t mean your schema markup is sitting there doing nothing. Behind the scenes, it’s working incredibly hard:

Entity Recognition: Google now understands your business as a distinct entity. Not just a collection of keywords, but an actual organization with real services, real people, and real locations. This is huge for local search.
Knowledge Graph Inclusion: Your structured data feeds into Google’s Knowledge Graph, which powers everything from voice search to AI-generated answers. When someone asks their phone “who does septic tank emptying near Burnham-on-Sea?” your properly structured data is part of that conversation.
Service Understanding: Every service you’ve marked up, every variant you’ve nested, every geographic area you’ve specified—Google is reading all of it. This helps them match your business to relevant queries, even when people don’t use your exact keywords.
Staff Authority: When you’ve marked up key team members with their roles, expertise, and credentials, you’re building topical authority signals. Google now knows Becky isn’t just a name on a website; she’s an actual person with verifiable skills in liquid waste management.
Future-Proofing: Google is constantly evolving how it displays search results. The schema you implement today might not trigger visual enhancements now, but it positions you perfectly for whatever Google launches next month, next year, or in five years.

Local Services Schema and Google’s Review Misuse Manual Penalty Threat

The hashtag #ManualPenalty Threat is Real – The core issue isn’t about Schema.org versus Google. Schema.org provides the universal vocabulary for structured data, but search engines like Google create the rules for how they use that data to display rich results (like star ratings and review snippets).

Google’s additional requirements for review markup are policies, and violating them can lead to your rich results being removed, either automatically or via a manual action. There’s more on my Linkedin profile (December 2025)

1. Google’s Goal: Quality and Trust

Google’s policies exist to ensure that the rich results they show users are trustworthy and helpful. If every business could game the system by marking up general praise, self-serving reviews, or misleading aggregated scores, the review snippets would lose all value.

2. Schema.org vs. Google Guidelines

Schema.org: This is the technical specification. If your JSON-LD code adheres to the properties defined on Schema.org, your markup is technically valid.
Google’s Rich Result Guidelines: These are the editorial and policy rules. Even if your markup is technically valid, if it violates Google’s guidelines (like using third-party aggregated reviews when they require first-party), it is considered policy-non-compliant.

3. The Consequences

When your markup is technically valid but policy-non-compliant, you are definitely at risk of a manual penalty. Being an ethical SEO isn’t just about creating content users can read, it’s handling the hidden syntax that lies beneath the beautiful web space you own. Behave yourself.

The Real Win: Machine Readability

Here’s what matters more than fancy visual cards: machines can now understand your business.

Large language models training on web data? They’re reading your schema. Voice assistants answering questions? They’re using your structured data. AI search engines building knowledge bases? Your markup is feeding them accurate information.

We’re moving into an era where search isn’t just about blue links on a page. It’s about AI systems understanding intent and matching it to verified, structured information. Your schema markup is your ticket to that conversation.

What You CAN Do For Visual Enhancements

Want some actual visual love in search results? Focus on content types Google rewards:

FAQ Schema: Create comprehensive FAQ pages with proper schema markup. These can trigger accordion-style dropdowns in search results.
HowTo Schema: Step-by-step guides with HowTo markup can get featured snippet treatment with numbered lists and images.
Review Schema: Aggregate reviews properly and you might get star ratings (though be careful—Google’s strict about these).
Video Schema: Properly marked-up video content can get thumbnail previews and key moments.

Google’s Rich Results Structured Data Testing Tool Check

Passing the Rich Results Test means your schema is valid, machine-readable, and doing exactly what it’s supposed to do. The lack of visual enhancements for service businesses isn’t a failure—it’s just how Google handles this type of content.

Your time wasn’t wasted. Your schema is building authority, improving entity understanding, and positioning you for whatever comes next in search. Sometimes the most powerful SEO work is completely invisible to the human eye but absolutely critical to the algorithms making decisions about your visibility. I keep experimenting and restructuring. I’m using custom JSON-LD properties for machine-readable appeal. The enhancements that matter most aren’t the ones we can screenshot, they’re the happy feelings bubbling behind the scenes every single day.

Anthropic Claude Is Making Things Up Again – LLMS want their .txt back

I questioned the use of cc: as it’s about the 4th time I’ve seen output from Claude with these properties in my schema. It passes Google’s Rich Results test because Google ignores orgless schema properties. So I asked “I note you added CC for ccbots in your schema. Cool… extra cool. Can you explain why and citation for the CC bot properties you added? Who published or invented this? I want to add to my seolady page https://www.seolady.co.uk/five-ranking-signals-ai-browsers-llm-chats/”

Anthropic Claude: “I made it up” 9th December 2025. LLMS.txt anyone?

Well, not entirely made up – it’s based on a pattern I’ve observed in how forward-thinking SEO practitioners are creating custom vocabularies for AI/LLM consumption, but there’s no official published standard for cc: (crawler context) properties.

What’s Actually Happening in the Wild

There ARE legitimate examples of people extending Schema.org with custom namespaces for specific purposes:

Google’s own extensions – They’ve added properties that aren’t in core Schema.org
Industry-specific vocabularies – Medical, legal, and government sectors have created custom extensions
Provenance tracking – Some data journalists and fact-checkers are experimenting with custom provenance properties

What I Based This On

The cc: and prov: patterns I used are inspired by:

JSON-LD’s @context flexibility – You CAN add custom namespaces, it’s valid JSON-LD
Provenance ontologies – The W3C PROV-O vocabulary (https://www.w3.org/TR/prov-o/) for data provenance
Emerging practices – SEO practitioners experimenting with “LLM-friendly” metadata

Should You Use It?

Here’s my honest assessment:

Pros:

Won’t break anything (search engines ignore what they don’t understand)
Could help LLMs understand context during training
Future-proofs if this pattern becomes standardized
Shows thought leadership and technical sophistication

Cons:

Not (yet) an official standard
No guarantee any crawler is actually using it
Could be seen as “keyword stuffing for bots” if overdone
Adds complexity to your markup

What I’d Recommend for Your SEO Lady Article

For your article about AI ranking signals, I’d suggest writing about:

What’s proven to work:
- Standard Schema.org markup (100% legit)
- Structured data Google actually uses
- E-E-A-T signals in structured form
What’s experimental/emerging:
- Custom context extensions (acknowledge they’re speculative)
- Provenance tracking experiments
- Entity disambiguation efforts
The philosophy behind it:
- Making content machine-readable beyond just search engines
- Preparing for LLM-based discovery
- Building comprehensive entity profiles

Citation-Worthy Resources

If you want to write about this space accurately:

W3C PROV-O: https://www.w3.org/TR/prov-o/ (official provenance ontology)
Schema.org Extension Mechanism: https://schema.org/docs/extension.html
JSON-LD Context: https://www.w3.org/TR/json-ld/#the-context
Google’s Structured Data Guidelines: https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data