SEO

SEO Tutorials Jun 6, 2026

Auditing Your Own Site for AI: robots.txt, llms.txt, JSON-LD, and the Four Gates of Citation

Most AI-visibility advice points outward — earn citations, get on Wikipedia, court the review platforms. All worthwhile. But there's a cheaper, faster lever sitting right under you: your own website. If a model can't retrieve your pages, can't rank them, can't extract clean claims from them, or can't attribute those claims back to you, no amount of off-site work fully compensates. This is a practitioner's walkthrough of the on-site AI audit — the files and signals that matter, organized around the four gates an answer has to pass through to cite you.

#GEO #Schema Markup #AI Discoverability

SEO Strategy & ROI Jun 6, 2026

Where AI Gets Its Answers: Building a Citation Source Map and a Digital-PR Target List

Earning citations is the right goal, but most digital-PR programs aim blind — pitching whoever the team already knows, hoping it helps. There's a more precise way to work. When a model answers questions about your category, it draws on a finite, repeatable set of sources. If you can see which domains those are, classify them by whether they currently help you or your rivals, and find the ones that cite competitors but never you, your target list stops being a guess and becomes a map. This post is about building that map and reading it.

#GEO #Citations #Digital PR

SEO Tutorials Apr 20, 2026

The Wikipedia Lever: How a Well-Structured Entry Moves Your Knowledge Depth Score

Of every lever in Generative Engine Optimization, a well-formed Wikipedia entry has the most predictable payoff on how LLMs describe your brand. Wikipedia corpora are oversampled in nearly every major model's training data, cited heavily by search-augmented providers, and treated as a canonical fact source. Yet most brands either have no entry at all, a three-sentence stub, or an entry that was edited once in 2021 and left to rot. This is the playbook to fix that without getting your article deleted or your account blocked.

#For SEO Managers #Citations #Wikipedia

SEO Tutorials Apr 13, 2026

Schema Markup for LLMs: 7 Elements That Matter, 12 That Don't

Schema markup is the single most over-prescribed piece of tactical advice in GEO. Every checklist tells you to add it. Few tell you which parts actually affect how LLMs describe your brand, which parts only help Google's rich snippets, and which parts have become decorative. This post is the triage: the seven schema elements worth implementing properly in 2026 for AI visibility, the twelve you can safely deprioritize, and the one that matters more than all the rest combined.

#For SEO Managers #Schema Markup #AI Discoverability

SEO Strategy & ROI Apr 11, 2026

Why GEO Has a Lower Marginal Cost Than SEO (and Why It May Stay That Way)

SEO, by 2026, is an expensive discipline. A mid-market organic program runs six figures a year before you buy a single tool. GEO, for now, runs on a different marginal cost curve — a single authoritative citation can shift your score across five providers at once, with no content creation and no link building. This is not a permanent advantage, but it is a meaningful one, and the window to exploit it is open. This post is about the unit economics of the two disciplines, and why they look the way they do.

#For CMOs #GEO #AI Visibility

SEO Industry Insights Apr 10, 2026

GEO for E-commerce and DTC: Why Reviews + Schema Outperform Paid PR

Retail discovery is shifting, and the signals that matter for an e-commerce brand to appear correctly in a language model's answer are not the same signals that moved the needle in paid acquisition. Structured review data, clean product schema, and consistent attribute coverage across listing sites tend to outperform headline-grabbing press pushes in driving AI visibility for DTC brands. This piece unpacks why the economics of the channel invert the old playbook, what DTC and e-commerce operators should actually invest in, and what to stop funding that does not carry over.

#Schema Markup #Reviews #E-commerce

SEO Tutorials Apr 6, 2026

Earning Citations on Sources LLMs Actually Trust in 2026

For twenty years, the SEO playbook said earn backlinks from high-authority domains. The GEO playbook is narrower and more specific. LLMs do not treat all links equally. Some sources are massively overweighted in training and retrieval — Wikipedia, a handful of major news outlets, a specific set of review platforms, and certain community sites. The rest contribute marginally or not at all. This post is the ranked list of sources that actually move AI visibility in 2026, with a practical path to earning placement on each.

#For SEO Managers #Citations #Digital PR

Brand Strategy SEO Apr 2, 2026

"SEO Already Covers This" — The Rebuttal You Can Forward to Your CMO

The sentence "our SEO tool already covers this" is pronounced confidently in most CMO-level meetings when GEO comes up, and it survives scrutiny less well than it sounds. The objection collapses around a specific structural mismatch: SEO tools measure ranking in a list of results, and LLMs do not produce lists of results. Once the unit of success is different, the tooling that measures one unit cannot substitute for the tooling that measures the other — a point worth making precisely, because the underlying confusion is costing marketing leaders real budget decisions every week.

#For CMOs #For SEO Managers #Myth-Busting

SEO Tutorials Mar 30, 2026

The Reddit Citation Ladder: From Zero Mentions to Default Source

Reddit is disproportionately cited in LLM answers. Search any BrandGEO audit's per-provider citation surface and Reddit threads appear alongside Wikipedia at the top of the retrieval list. Yet most brands approach Reddit in exactly the way that makes the platform hostile: promotional posts, shallow engagement, shadowbans within a week. This post lays out the ladder that works — the one that earns genuine citations over twelve months without tripping any of Reddit's defenses.

#For Founders #Citations #Reddit

SEO Market Research Mar 24, 2026

How Google's AI Overviews Changed CTR Curves — What Published Data Tells Us

For twenty years, the SEO click-through-rate curve was stable enough to plan against. Position one got roughly 28% of clicks. Position two got 14%. Positions three through ten declined in a predictable pattern. Content and SEO teams built campaign models on top of that curve and, broadly, the curve held. Then Google launched AI Overviews, and the curve changed shape. The published research from Ahrefs, Similarweb, and several independent SEO teams lets us look at the new curve with reasonable confidence. The new curve is not a small deviation from the old one. It is a different curve.

#For SEO Managers #AI Overviews #CTR Decline

SEO Tutorials Mar 23, 2026

G2, Capterra, Trustpilot: Which Review Platform Actually Affects Your AI Visibility?

Most B2B SaaS brands try to maintain presence on G2, Capterra, Trustpilot, and a scatter of smaller review sites simultaneously. That is a mistake. For AI visibility purposes, one of those platforms almost always dominates the others in your category — and the effort spent thinly across all of them produces weaker results than the same effort concentrated on the right one. This post is the framework for picking the primary platform, setting up the review-acquisition flow, and deciding what to do about the others.

#For Founders #Citations #Reviews

SEO Tutorials Mar 16, 2026

Digital PR for LLMs: How to Get Quoted in AI Answers (Not Just Google News)

Digital PR was originally optimized for two audiences: human journalists looking for stories, and Google's news indexing system looking for fresh authoritative content. In 2026 a third audience has become the dominant one — language models building their summaries of your category. The craft of PR has to shift accordingly. This post lays out how the discipline is changing, what still matters from the old playbook, and what specifically you should write differently when the goal is to be quoted in AI answers.

#For CMOs #Citations #Digital PR