GEO for B2B: What Changes When a Committee Does the Buying
In B2B the search volume is tiny and the deal is large, so the page that earns the citation is almost never your blog. Here is what to build instead.
TL;DR
Generative engine optimization for B2B, meaning work to get your company named inside AI assistant answers, differs from the consumer version in three ways. The buyer is a committee, so the decisive queries are comparative and narrow rather than broad. The volume is small enough that keyword tools call it noise, while a single answer can carry a contract, so volume is the wrong metric to prioritise on. And the page that earns the citation is usually not a blog post but a comparison page, technical documentation, a pricing page or a third party profile, because those answer the evaluation question the committee is actually asking. The practical move is to add a qualifier to the query you chase, such as your segment or your market, since that is what turns an unwinnable head term into a page you can rank with modest authority.
Search volume is the wrong metric here
Start with numbers from our own Search Console, for the 28 days ending 15 August 2026:
| Query | Impressions | Average position |
|---|---|---|
agencia geo b2b | 52 | 55.0 |
generative engine optimization for b2b | 19 | 81.7 |
agencia geo franquicias | 9 | 48.7 |
b2b aeo agency | 4 | 72.0 |
Eighty-four impressions in a month. Any keyword tool would tell you to ignore all four, and in consumer retail it would be right.
In B2B it is backwards. One of those readers becoming a client pays for the page a hundred times over, and the reason those positions sit in the fifties and eighties is not that the market is hard. It is that nobody wrote the page, ours included until today.
So the first adjustment is arithmetic. Prioritise by deal value times probability, not by monthly volume. A query with forty searches a month, an obvious buying intent and four competitors is a better target than one with four thousand searches where the first page belongs to HubSpot and Semrush.
The four questions a committee actually asks
Consumer GEO imagines one person asking one open question. A B2B purchase is researched by somebody who is not the person signing, and that changes the shape of every query.
In practice the assistant gets asked four things:
- Alternatives. "What are alternatives to X for a company of our size."
- Direct comparison. "X versus Y for a mid market team in our sector."
- Fit and integration. "Does X work with our stack, our region, our language."
- Risk. "Is X compliant with what our legal team will ask about."
None of those are answered by a definitional article. All four are answered by pages that state specific, attributable facts. That is the whole difference in one sentence: the earlier the question, the more a blog post helps; the closer to signature, the less.
If you sell to committees and your entire content investment is educational blogging, you are visible in the stage that does not pay and absent from the one that does.
The page that gets cited is usually not your blog
An assistant answering a comparison question needs something it can quote without inventing. In our audits the pages that do that job are consistent:
- Comparison pages that name the competitor and state real differences, including the cases where you are the wrong choice.
- Documentation covering integrations, data location, security and limits. Dry, factual, easy to attribute.
- Pricing pages that answer the question rather than deflecting to a form. If your price is genuinely variable, publish the ranges and the variables. We wrote a whole piece on what this work actually costs for exactly that reason.
- Third party surfaces: directory profiles, review sites, and analyst style listicles. You do not control these, which is precisely why the assistant trusts them.
There is a measurable irony here. When we checked 63 websites that rank for GEO and AEO agency queries, only 15 of them used FAQPage structured data and 8 had no structured data on the homepage at all. The industry recommending machine readable facts largely has not published its own.
The qualifier is what makes the query winnable
The single most useful pattern we have found this year did not come from a study. It came from comparing ourselves with an agency that has the same positioning and gets nine times our clicks.
Their commercial queries all carry a modifier, in their case a city: aeo agency sydney at position 6.3, geo agency sydney at 6.9. Ours carried none, so we were fighting the national head term from position 22 and getting impressions with no clicks.
For B2B the modifier is your segment. geo agency for b2b saas is a different SERP than geo agency, with a fraction of the competitors, and the person typing it has already told you what they are. Sector, company size, market and language all work the same way.
This matters most when your authority is low. A qualifier does not just narrow the audience, it changes which competitors you are up against, and that is the only lever that works before you have earned links.
What to measure, and the honest limits
Two things will frustrate you, so know them before you start.
Search Console's generative AI report gives impressions only. No clicks, no position, partial rollout. You can see that you were shown inside an AI feature and nothing about what it did. Everywhere else, AI feature traffic stays folded into your ordinary numbers, which is why nobody can honestly tell you what percentage of traffic came from an AI Overview.
Assistants are not consistent. Ask the same question twice and you can get different sources. That is not a bug you can optimise away, it is how sampling from a model works, and it means single observations prove nothing.
So measure like this: pick the ten questions a committee asks before choosing a supplier like you, ask each one in ChatGPT, Perplexity and Google AI Mode, and record which domains get named. Repeat monthly, same questions, same day of the month. What you are watching is share of mentions across a fixed question set, not a rank. Our AI visibility checker scores the underlying signals, and our AI crawler access checker confirms the assistants can fetch you at all, which is the boring prerequisite behind every other tactic.
The mistakes we see most in B2B sites
- Gating the page that answers the question. A PDF behind a form cannot be cited. If it is your best answer, publish a readable version.
- Never naming a competitor. Comparison queries are a large share of committee research, and a page that refuses to name anyone answers none of them.
- One page for four segments. A page that tries to speak to manufacturing, health and finance at once is the page an assistant skips when asked about any of the three.
- Treating the AI answer as the destination. It is a shortlist mechanism. The job is to get named there and to have a page worth landing on afterwards.
- Buying the file and skipping the substance. Publishing
llms.txt, a plain text summary of your site for language models, is an hour of work with no proven effect. Writing the comparison page you do not have is a week of work with an obvious one.
Where to start if this is new
Pick the one competitor you lose to most, write the honest comparison page, and make sure it says who each option is wrong for. Then take your five most common sales objections and answer each one on a page a stranger can read without talking to you.
That is unglamorous, and it is also what the assistant needs in order to name you in the only four conversations that decide a B2B deal.
If you want a second opinion on which of those pages to build first, tell us what you sell and who signs the contract. If you are still comparing providers for this work, we wrote how to choose an agency for it, and our case studies show the shape of the work we actually did rather than the shape of a promise.
Frequently asked questions
-
What is different about generative engine optimization for B2B?
Three things, and they all follow from who is asking. GEO, which is getting your business named inside AI assistant answers, assumes somebody types a question and reads a recommendation. In B2B that person is one member of a buying committee doing homework for a decision somebody else signs, so the questions are narrow and comparative rather than broad. Second, the volume is tiny: a query with forty searches a month can carry a six figure contract, which breaks every prioritisation rule built on volume. Third, the page that gets cited is rarely a blog post. It is a comparison page, a documentation page, a pricing page or a third party profile, because those are what answer an evaluation question.
-
How much search volume do I need for this to be worth it?
Ask the reverse question: how many deals a year would justify the work. In B2B the honest arithmetic is per deal, not per visit. Our own Search Console shows queries like "agencia geo b2b" bringing fewer than sixty impressions a month, and one of those readers becoming a client would pay for the page many times over. The trap is that keyword tools score these as noise and most agencies drop them for a head term you cannot win. Low volume plus high intent plus weak competition is the most winnable combination there is, especially when your domain authority is small.
-
Do blog posts help get cited in a B2B decision?
They help early and they rarely close. A blog post is a good citation source for the definitional stage, when somebody is still learning what a category is. By the time a committee is comparing two named vendors, the assistant is pulling from pages that state facts it can attribute: feature comparisons, integration and security documentation, pricing, and profiles on directories and review sites it trusts. If you have twenty blog posts and no comparison page, you are visible in the stage you do not monetise and absent from the one you do.
-
How long does this take to show up?
Assume a quarter before the signals move and longer before attribution makes sense. Two structural reasons, not sales caution. Assistants cache and re-crawl on their own schedule, so a new page is not read the day you publish it. And Search Console's generative AI report gives impressions only, with no clicks and no position, so you cannot watch a curve improve week by week the way you can with ordinary search. The practical consequence is to write down a baseline the day you start, because you cannot reconstruct it later.
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