What GEO Is, and Where It Parts Ways With SEO

When someone searches for "best B2B performance marketing agencies," two different things can happen. They see ten blue links on Google and click one; or they ask ChatGPT, receive a list of four names, and decide without visiting a single website. In the second scenario your ranking is irrelevant — what matters is whether you appear in that list. GEO, generative engine optimization, is the name for work aimed squarely at that second scenario.

The term is new, the mechanics are familiar: answer engines also pull information from somewhere. The difference lies in how they process what they pull. A classic search engine ranks the page as a whole; a generative model lifts a sentence, a table row or a paragraph out of the page and drops it into its own answer. The unit of optimisation has changed.

SEO optimises rank, GEO optimises citability

In classic SEO the scoreboard is clear: keyword, position, click-through rate, organic traffic. In GEO the middle of that chain breaks. An answer engine can cite you and the user still never clicks. Your brand appears inside the answer, no session is recorded, but you have entered the decision. That is why GEO's core metric is not traffic but share of visibility: across a defined set of questions, in how many answers are you mentioned, and in what context.

In practice this produces five concrete differences:

• Unit: SEO optimises a page, GEO optimises a paragraph. The model takes the section that best matches the question, not the whole document.
• Competition: in SEO the top three results take the game. Generative answers usually blend three to eight sources, so being the fourth-best source still pays.
• Signals: backlinks still matter but no longer suffice. Unlinked brand mentions — your name in running text with no hyperlink — feed the associations the model builds.
• Timing: Perplexity and Gemini run live retrieval, so fresh content enters quickly. Knowledge baked into a model's training data shifts slowly, and there you need accumulated presence.
• Measurement: there is no Search Console equivalent. You build question sets and re-query them at intervals, manually or with tooling.

What kind of content do answer engines like to cite?

A pattern repeats in the field: models prefer text that stands on its own. If a paragraph loses its meaning once separated from its context, it does not get quoted. "As mentioned above, this method delivers a similar result" is fine for a human reader and useless as data for a model.

The content implication is simple: the first paragraph under every subheading should answer that heading's question directly. Forty to eighty words, one idea, an explicit subject. Expand, illustrate and qualify afterwards. The inverted pyramid — journalism's oldest rule — has become relevant again, this time for machines.

The second pattern is that numeric and comparative content gets cited far more often. Compare "campaign budget optimisation is generally more efficient" with "consolidating budgets at campaign level brought cost per acquisition from $38 to $26 over 30 days." For citation purposes the gap is enormous. Models skip vague phrasing and carry measurable claims. If you do not have real data, do not invent it; if you do, write it into the body text rather than leaving it inside a chart image.

The third is table and list structure. Pricing, feature comparisons, ordered procedures — when these sit in an HTML table or a numbered list, the model's inference burden drops. Burying the same information in flowing prose reads nicely for a human and costs the engine effort it may not spend.

Why source authority is built off your own site

This is the uncomfortable part of GEO: the decisive surfaces are ones you do not control. Ask an answer engine for "the best Y service in X city" and the names it produces usually come not from brand websites but from industry roundups, comparison sites, Reddit and Quora threads, press coverage and video descriptions. Saying "we are the best" on your own domain carries little weight. A third party mentioning you in a specific context carries a lot.

So GEO work resembles mention management more than content production. A workable sequence:

1. Write the target question set. Thirty to sixty real questions: purchase-intent, comparative, problem-led. Start with decision-moment questions, not definitional ones.
2. Ask each question in ChatGPT, Gemini and Perplexity. Log every brand mentioned and every cited domain in a spreadsheet. That table is your GEO baseline.
3. Mark the domains that keep reappearing in the citation column: directories, comparison pages, trade publications. Those are your priority targets — the places you need to be present.
4. Write the missing answers on your own site. Give each question its own heading and its own answer block. Rather than skimming ten questions on one page, answer the five that matter in real depth.
5. Complete your structured data: Organization, Product/Service, FAQPage, Article, Breadcrumb. Use sameAs to connect LinkedIn, Crunchbase and Wikidata entries to a single identity.
6. Re-run the same questions after four to eight weeks and record the change. GEO measurement is a time series, not a snapshot.

What structured data actually does for GEO

There is no confirmed evidence that Schema.org markup is a direct "AI ranking signal." Its indirect contribution is clear, though: structured data makes the entities on your page unambiguous to a machine. Who is who, which service is offered at what price, who wrote a piece and when, where the answer to a question ends — these are far more reliable inputs than inference.

Three markup types make a tangible difference. Organization schema defines your brand name as an entity and, through sameAs, gathers a scattered digital footprint into one identity. The author and datePublished fields in Article schema feed the model's assessment of freshness and authorship. FAQPage locks the question-to-answer mapping, removing the guesswork of "which question does this paragraph answer?"

Add two technical details. First, your content must be visible without JavaScript: many crawling agents do not execute JS, and text that is not server-rendered effectively does not exist. Second, manage robots.txt and llms.txt deliberately — GPTBot, PerplexityBot, Google-Extended and their peers. Some brands block these without realising it, then wonder why they are invisible.

Should you drop SEO and move to GEO?

No, and the two already share the same foundation. Most answer engines still draw on search indexes; a page Google cannot find will not reach Gemini either. Technical health, crawl budget, internal linking, page speed — all still apply. GEO adds a layer on top: content that is chunkable, quotable and defined as an entity.

When deciding budget, look at the composition of your traffic. If organic traffic from informational queries fell over the past year while your conversion rate held steady, the loss is probably going to answer engines — users get the answer in the answer box and only the decision-ready reach your site. In that case GEO's share should rise. If brand searches and bottom-of-funnel queries are still healthy, plan GEO as an extension of classic SEO rather than a replacement.

The most durable practice: write for both humans and models, but put the human first. A piece that leads with the answer, puts its numbers in the text and sources its claims wins on both sides. A text written for models and unreadable for people gets filtered out at the next index update.