Making your landing page visible on ChatGPT and generative AI: the GEO guide

Typing a question into ChatGPT or Perplexity instead of Google has become second nature for a growing share of users, including for commercial-intent searches ("best tool for X", "how to choose Y"). Google followed suit with AI Overviews, which display a written answer above the classic results. In this new journey, a landing page is no longer just competing for a spot in a list — it's competing to be cited, or paraphrased, in the text a model generates. This is what's called GEO (Generative Engine Optimization). An academic study precisely measured what increases the odds of being cited, and the results don't fully overlap with classic SEO instincts.

Classic SEO and GEO: two different logics

Traditional SEO optimizes a page to rank in a list of results: keywords, backlinks, domain authority, Core Web Vitals (see our landing page SEO guide). GEO answers a different question: among the handful of sources a language model chooses to cite or synthesize in its answer, why this one and not another? An LLM doesn't rank pages — it extracts verifiable information and rephrases it, which favors factual, structured, clearly attributable content over content that's merely well indexed.

What a reference study found about visibility in AI answers

In 2024, a team of researchers from Princeton, Georgia Tech and the Allen Institute for AI published "GEO: Generative Engine Optimization" (Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan & Deshpande), presented at KDD 2024. Across a benchmark of several thousand real-world queries, the authors tested different ways of rephrasing the same content to measure which ones actually increased its presence in generated answers. Three levers stood out as statistically effective: citing verifiable external sources (+40% relative visibility), adding quantified statistics (+37%), and including quotes or testimonials in quotation marks (+22%). By contrast, keyword stuffing — the oldest SEO reflex — did almost nothing (+3%), confirming it no longer has any real grip on how a model selects its sources. The study also notes that each lever's effectiveness varies by domain: statistics matter more for fact-heavy topics, quotations more for people- and experience-related topics.

What this actually means for a landing page

An FAQ phrased the way real questions are asked

A well-built FAQ is one of the most "citable" blocks on a landing page: each question-answer pair is a self-contained unit, easy for a model to extract and rephrase. On LanderKit, every article and template FAQ is already exposed as FAQPage JSON-LD, a format both classic and generative engines can read directly. The real lever isn't the markup — that's already in place — it's the phrasing: writing questions the way a user would type them into ChatGPT ("how much does…", "how do I choose between…", "should I…") rather than as marketing headlines. Our article on the structure of a converting landing page covers where to place this block.

Verifiable numbers instead of superlatives

"The fastest template to deploy" isn't something a model can cite with confidence. "A Next.js template ready to customize and deploy in half a day" is: it's a verifiable, concrete, dated fact. The same logic applies to timelines, prices, guarantees — swap the adjective for the number wherever possible, including in sections that look purely persuasive.

Quoted testimonials, not just logos

A wall of client logos contains nothing a model can quote. A named testimonial with a specific sentence about a result achieved, on the other hand, is exactly the kind of content the study identifies as effective — see our overview of the types of social proof that sell for how to phrase them correctly.

Content rendered server-side, not hidden behind JavaScript

Generative engine crawlers (GPTBot, PerplexityBot, ClaudeBot, Google-Extended…) largely work like classic crawlers: they read the HTML the server returns, not whatever a script injects into the browser afterward. A landing page built with static or server rendering — what Next.js does by default, and therefore every LanderKit template — exposes all its text in the very first HTTP response. A fully client-rendered single-page app, on the other hand, can simply be invisible to these bots, no matter how good the content is.

GEO doesn't replace classic SEO — it builds on it

Google's AI Overviews largely draw from pages that already rank well in the classic organic index: load speed, heading structure, internal linking and domain authority remain the foundation, covered in our guide to load speed and our landing page SEO guide. GEO doesn't exempt a page from any of that; it adds a layer on top, specific to how a model then chooses what to cite among the pages it was able to crawl.

Pitfalls to avoid

• Stuffing the text with keywords, thinking it replays an old SEO logic — the study above shows this has almost no effect on citation by a generative model.
• Making up numbers just to "have a statistic to show" — an inaccurate figure, once cited and checked by a visitor, costs more in credibility than having no figure at all.
• Blocking AI crawlers in robots.txt out of caution, then wondering why the page is never cited: the two goals are contradictory — pick one.
• Turning the page into an encyclopedia entry to cram in more citable sentences — a landing page is still primarily a conversion tool; factual content should be added to the existing sections, not replace them.

A concrete example

On a coaching page like the Coach & Consultant template (demo), replacing "coaching that transforms your business" with "an 8-session program, 45 minutes each, with a written summary after every session" gives the model a sentence it can reuse directly when answering "how many sessions does a coaching program include". The same principle applies across all ten LanderKit templates (€89 each, €229 for the full bundle): each one already ships with an FAQ structure and server-rendered content — the only thing left is swapping superlatives for verifiable facts about your own offer.