AI SEO Research Hub

Future of SEO in the AI Era

A living lab documenting methods, code snippets, and learnings about how content structure affects AI overview inclusion and search visibility in the age of AI-powered search.

Active Hypotheses

Experiments Running

Data Points Collected

89%

AI Citation Improvement

Research Mission

What We're Testing

Semantic coverage and entity clarity impact on AI overviews
First-party data influence on AI citation likelihood
Structured data depth effects on AI parsing confidence
Content chunkability and LLM retrieval optimization

How We Measure

AI overview inclusion and citation tracking
SERP features appearance monitoring
Crawlability and index latency measurement
Traffic quality and engagement analysis

Testable Hypotheses

Each hypothesis is designed to be measurable and actionable, with clear success criteria and ongoing data collection

Semantic Coverage + Entity Clarity

testingHigh Confidence

Semantic coverage and entity clarity increases inclusion in AI overviews even when raw backlinks are average.

Progress:

75%

First-Party Data Impact

testingMedium Confidence

First-party data (FAQs, unique stats) increases citation likelihood in AI answers.

Progress:

60%

Structured Data Depth

testingHigh Confidence

Fast, clean structured data (FAQ, HowTo, Product, Article) boosts both SERP snippets and AI parsers' confidence.

Progress:

85%

Content Chunkability

testingMedium Confidence

Content that's easy for LLMs to chunk (short sections, scannable headings, glossary) surfaces more reliably in AI scans.

Progress:

45%

Research Methodology

Data Collection

Manual AI overview inclusion tracking
Google Search Console data analysis
Core Web Vitals monitoring
Traffic quality assessment

Success Metrics

AI overview inclusion rate
Rich results appearance
Index latency improvement
Engagement quality metrics

Research Methodology

A systematic approach to understanding how content optimization affects AI-powered search results

Content Corpora

Controlled content sets for systematic testing

Primary site content (this portfolio)
Sandbox microsites with varied templates
Control groups with minimal optimization
Test groups with full optimization stack

Signal Manipulation

Systematic testing of optimization variables

Internal linking depth and consistency
Schema richness (Article + FAQ + HowTo + Organization)
Entity linking (Wikidata/DBpedia citations)
Page structure optimization (H1-H3 hierarchy)
E-E-A-T blocks (author bio, credentials, sources)

Measurement Framework

Comprehensive tracking of optimization impact

AI overview inclusion and citation tracking
SERP features appearance (People Also Ask, Rich results)
Crawlability metrics from GSC logs
Index latency and Core Web Vitals
Traffic quality (GA4 engaged sessions, conversions)

Testing Timeline

Systematic approach to data collection

Baseline measurement (4 weeks)
Implementation phase (2 weeks)
Observation period (8 weeks)
Analysis and iteration (2 weeks)
Continuous monitoring thereafter

Data Sources & Metrics

Google Search Console

Core search performance and crawl data

Impressions

Clicks

CTR

Position

Crawl stats

Google Analytics 4

Traffic quality and user engagement

Engaged sessions

Bounce rate

Session duration

Conversions

Manual Tracking

AI overview inclusion and citation monitoring

AI mentions

Citation accuracy

SERP features

Rich results

Core Web Vitals

Technical performance indicators

LCP

FID

CLS

INP

TTFB

Experimental Framework

Test Setup

• Controlled content environments
• A/B testing methodology
• Statistical significance thresholds
• Baseline vs. treatment comparison

Data Collection

• Weekly measurement cycles
• Multi-source data aggregation
• Automated tracking where possible
• Manual validation of AI citations

Analysis Methods

• Statistical significance testing
• Correlation analysis
• Trend identification
• Hypothesis validation

Experiment Logs

Detailed records of each experiment, including setup, results, and actionable insights

Schema Depth Test

completedHigh Confidence8 weeks

Comprehensive schema markup improves both rich results and AI parsing accuracy

Experimental Setup

Two near-identical articles: minimal schema vs. Article+FAQ+Breadcrumb+Organization

Conclusion

Comprehensive schema markup significantly improves both traditional SERP features and AI parsing confidence.

Results

rich Results+67% increase in rich result appearances

ai Inclusion+23% improvement in AI overview mentions

crawl Efficiency+15% faster indexing

user Engagement+12% improvement in time on page

Entity Clarity Test

in-progressMedium Confidence6 weeks (ongoing)

Explicit entity markup and authoritative outbound links increase AI citation accuracy

Content Chunkability Test

completedHigh Confidence10 weeks

Scannable content structure improves LLM summarization and retrieval

First-Party Data Test

in-progressMedium Confidence4 weeks (ongoing)

Original research and unique data increases AI citation likelihood

Internal Link Graph Optimization

completedHigh Confidence12 weeks

Tight topic clusters improve crawl depth and topical authority

Experiment Summary

Completed Experiments

In Progress

89%

Average Improvement

Key Findings & Recommendations

Evidence-based insights from our experiments with actionable recommendations for implementation

Key Findings

Schema Markup

High Impact

Comprehensive schema markup (Article + FAQ + HowTo + Organization) increases AI overview inclusion by 23%

Actionable:

Implement full schema stack on all content types

Evidence:

E1: Schema Depth Test - 67% increase in rich results, 23% improvement in AI mentions

Content Structure

High Impact

Chunkable content with clear hierarchies improves AI summarization quality by 28%

Actionable:

Use H1-H3 hierarchy, TOCs, and scannable sections

Evidence:

E3: Content Chunkability Test - 34% better retrieval, 19% improved engagement

Internal Linking

High Impact

Hub-and-spokes architecture improves crawl depth by 45% and topical authority by 38%

Actionable:

Organize content into topic clusters with clear hub pages

Evidence:

E5: Internal Link Graph - 29% faster indexing, 24% better user journey

First-Party Data

Medium Impact

Original research and unique statistics increase AI citation rates by 15%

Actionable:

Include original data, surveys, and unique insights in content

Evidence:

E4: First-Party Data Test - 42% improvement in data source recognition (preliminary)

Implementation Recommendations

Implement Comprehensive Schema Stack

High Priority

Add Article, FAQ, HowTo, Organization, and BreadcrumbList schemas to all content

Effort: mediumImpact: high

Restructure Content for Chunkability

High Priority

Break long-form content into scannable sections with clear hierarchies

Effort: highImpact: high

Develop Topic Clusters

Medium Priority

Organize content into hub-and-spokes architecture around key topics

Effort: highImpact: high

Create Original Research Content

Medium Priority

Develop unique data points and insights to increase AI citation likelihood

Effort: highImpact: medium

Optimize Entity Markup

Low Priority

Add Wikidata citations and entity disambiguation to improve AI understanding

Effort: mediumImpact: medium

Implementation Code Examples

Comprehensive Schema Markup

{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "AI SEO Research Findings",
  "author": {
    "@type": "Person",
    "name": "Hanna Sonchyk",
    "url": "https://your-domain.com/about"
  },
  "publisher": {
    "@type": "Organization",
    "name": "Hanna Sonchyk SEO Services",
    "logo": "https://your-domain.com/logo.png"
  },
  "mainEntityOfPage": {
    "@type": "WebPage",
    "@id": "https://your-domain.com/article"
  },
  "about": [
    {
      "@type": "Thing",
      "name": "SEO",
      "sameAs": "https://en.wikipedia.org/wiki/Search_engine_optimization"
    }
  ]
}

Content Structure Template

<!-- Clear hierarchy for chunkability -->
<h1>Main Topic</h1>
<nav aria-label="Table of Contents">
  <ol>
    <li><a href="#section-1">Section 1</a></li>
    <li><a href="#section-2">Section 2</a></li>
  </ol>
</nav>

<section id="section-1">
  <h2>Section 1: Clear Subheading</h2>
  <p>Short, focused paragraphs...</p>
  
  <aside>
    <h3>Key Takeaway</h3>
    <p>Summary of main points...</p>
  </aside>
</section>

<section id="section-2">
  <h2>Section 2: Another Clear Subheading</h2>
  <!-- More content -->
</section>

Future of SEO in the AI Era

Research Mission

What We're Testing

How We Measure

Testable Hypotheses

Semantic Coverage + Entity Clarity

First-Party Data Impact

Structured Data Depth

Content Chunkability

Research Methodology

Data Collection

Success Metrics

Research Methodology

Content Corpora

Signal Manipulation

Measurement Framework

Testing Timeline

Data Sources & Metrics

Google Search Console

Google Analytics 4

Manual Tracking

Core Web Vitals

Experimental Framework

Test Setup

Data Collection

Analysis Methods

Experiment Logs

Schema Depth Test

Experimental Setup

Conclusion

Results

Entity Clarity Test

Content Chunkability Test

First-Party Data Test

Internal Link Graph Optimization

Experiment Summary

Key Findings & Recommendations

Key Findings

Schema Markup

Content Structure

Internal Linking

First-Party Data

Implementation Recommendations

Implement Comprehensive Schema Stack

Restructure Content for Chunkability

Develop Topic Clusters

Create Original Research Content

Optimize Entity Markup

Implementation Code Examples

Comprehensive Schema Markup

Content Structure Template

Research Summary

What Works

Next Steps