Most claims in SEO and AI search optimization are anecdotal — secondhand numbers from someone else’s site, in a different niche, at a different point in time. This cluster takes a different approach: rather than repeating industry-wide statistics, it provides repeatable methodologies you can run against your own content and your own data, so your conclusions are actually grounded in evidence specific to your situation.
The 10 articles here cover how to analyze a Google core update methodically instead of guessing at causes, and how to design controlled experiments for internal linking, schema markup, and Core Web Vitals changes — isolating one variable at a time so you can actually attribute results. On the AI side, you’ll find frameworks for running an AI search visibility case study, an AI citation study (tracking which sources get cited by which assistants, and why), and a GEO experiment to test whether generative-engine-optimization changes actually improve citation rates. The cluster closes with a comparison framework for AI search versus traditional SEO, a method for building your own search trends report from tracked data, and a living framework for assessing the state of AI search and SEO that’s meant to be revisited quarterly.
This cluster is the most methodology-heavy on the blog — each article gives you a structure to follow, not just a conclusion to accept. For the practical starting point most of these methodologies build on, see the SEO Audit service guide.
Featured guide
State of AI Search & SEO: A Working Framework — a framework for assessing where AI search and SEO stand today, built to be updated as the landscape shifts.
Latest in Research & Data
Popular in this cluster
- How to Analyze a Google Core Update
- How to Run an AI Citation Study
- Designing a GEO Experiment
- AI Search vs. Traditional SEO: How to Compare Them Fairly
- How to Build Your Own Search Trends Report
What this cluster covers
- Diagnostic methods — analyzing core updates without guessing
- Controlled experiments — internal linking, schema, and Core Web Vitals testing
- AI visibility research — citation studies and GEO experiments
- Frameworks — comparing AI search vs. SEO, and tracking your own trends over time
Explore other clusters
AI Search & AI SEO · GEO / AEO · SEO Fundamentals · Technical SEO · Content Strategy & Marketing · Future of Search
Want the full strategic guide, not just the blog? Read the SEO Audit service guide.
All 10 Research & Data Articles
The complete Research & Data cluster — every guide in this pillar, in one place.
| Article | What it covers |
|---|---|
| AI Search vs. Traditional SEO: How to Compare Them Fairly | A framework for comparing AI search optimization and traditional SEO without treating them as competing disciplines. |
| Designing a Core Web Vitals Experiment | How to test whether a performance fix actually moves Core Web Vitals scores and rankings, rather than assuming it does. |
| Designing a GEO Experiment | A methodology for testing whether generative engine optimization changes actually improve AI citation rates. |
| Designing an Internal Linking Experiment | How to structure a test of internal linking changes so you can actually attribute results to the change you made. |
| How to Analyze a Google Core Update | A framework for diagnosing what a Google core update actually changed for your site, instead of guessing. |
| How to Build Your Own Search Trends Report | A framework for compiling a search trends report from your own data rather than relying on secondhand industry claims. |
| How to Run a Schema Markup Case Study | A structured approach to testing whether adding or expanding schema markup changes how a page appears in search. |
| How to Run an AI Citation Study | A method for tracking which sources AI assistants cite for a given topic, and how to interpret the results. |
| How to Run an AI Search Visibility Case Study | A methodology for documenting how a page performs in AI search tools, framed as a repeatable case study process. |
| State of AI Search & SEO: A Working Framework | A framework for assessing where AI search and SEO stand today, built to be updated as the landscape shifts rather than treated as a fixed sn |
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