Ninety-four percent of B2B buyers now use large language models during the buying process. But the more useful finding from 6sense’s 2025 Buyer Experience Report is how they use them. LLM adoption peaks in the middle of the journey, when buyers are comparing vendor offerings, synthesizing information, analyzing customer sentiment, drafting RFPs, modeling costs and planning implementations.
In other words, buyers are not simply asking ChatGPT to choose a vendor and blindly buying whatever it recommends. They are using AI to advance, pressure-test and double-check decisions that are already taking shape. Gartner’s 2026 buyer research reinforces that distinction: 45% of B2B buyers used generative AI during a recent purchase, primarily to gather information about vendors and products, while 69% preferred to validate AI-generated insights with a sales representative.
AI is shaping what buyers believe before they ever visit your website and WAY before they think about filling out your contact us form. Generative Engine Optimization (GEO) or Answer Engine Optimization (AEO) is the work of making sure the evidence they find is accurate, differentiated and strong enough to survive that evaluation.
This matters for every B2B company, but it’s slightly different for SaaS. Software is digitally delivered, heavily documented, frequently compared and surrounded by years of public customer commentary. AI systems can evaluate a SaaS product using its website, documentation, review profiles (from various trusted peer review libraries), Reddit conversations, YouTube transcripts, analyst coverage, comparison articles, release notes and customer complaints.
That abundance of public information creates an enormous opportunity. It also means SaaS companies have less control over how their products are understood than most of them realize. After conducting dozens of GEO audits for SaaS companies and comparing the findings with non-SaaS B2B businesses, several differences show up consistently. So many in fact that we put on a webinar to talk about it. The biggest lesson is that SaaS companies should not simply bolt GEO onto an existing SEO plan. They need to rethink which surfaces they influence, which product knowledge they make public and which metrics they use to determine whether the work is accomplishing anything.
Catch the full replay here:
Does strong SEO guarantee AI recommends your SaaS product?
For most of 2025, the standard advice was to continue doing good SEO and assume GEO visibility would follow. That advice was appealing because it let companies avoid changing anything. And SEO agencies didn’t have to change their cash cow approach. It has also not held up particularly well.
SEO remains important. A technically sound website, relevant content, recognizable authority and strong organic rankings all help LLMs find and understand a company. But they do not guarantee that the company will appear in an AI-generated shortlist, especially when the buyer is asking for a comparison, a recommendation or advice tailored to a specific use case.
The reason is straightforward: ranking a webpage and recommending a solution are not the same task. A search engine may determine that a page is the most relevant result for a keyword. An LLM building a vendor shortlist may need to understand product fit, customer sentiment, implementation difficulty, UI/UX preferences, pricing, industry suitability and meaningful differences among several competing platforms. That requires a broader evidence set than a conventional SEO program is designed to manage.
SaaS companies therefore need to treat SEO as one input into GEO, not as the GEO strategy itself. If an agency says its GEO methodology is technical SEO, authoritative content and backlinks, it has not explained how it plans to influence AI-generated recommendations. It has just explained SEO and put a new acronym on it.
SaaS GEO requires putting unique knowledge on land you do not own
Traditional SEO trained marketing teams to concentrate on owned property: the company website, its pages, its technical infrastructure, its content and the links pointing toward it. GEO requires SaaS companies to influence a much larger information environment that includes G2, Capterra, Gartner Peer Insights, Reddit, YouTube, LinkedIn, industry publications and other third-party platforms.
That is rented land. The company does not control the platform, its algorithms, its moderation policies or whether the rules change next month. But the fact that the land is rented does not mean the information placed there is generic or belongs to no one. In many cases, the most valuable information on those properties is uniquely yours.
Your customers know why they selected you, which competitor they replaced, what implementation was actually like, which limitation nearly killed the deal and whether the support experience matched what sales promised (you better hope it did!). Your product team understands the decisions behind the roadmap. Your customer success team hears the same implementation problem every week. Your salespeople know which competitor appears in nearly every deal and which objection repeatedly slows the buying process.
That is proprietary knowledge. The GEO challenge is getting it out of private conversations, internal documents and employee brains and onto the public surfaces AI systems use to understand the category. And often, frankly, being brave enough to make it public.
When a customer describes their experience on G2 or a product expert explains a feature in a YouTube video, the information now exists on rented land. The company does not own the platform, but it helped create the knowledge being shared there.
GEO is not merely about earning mentions on third-party domains. And it actually isn’t about being EVERYWHERE. It is about deliberately distributing real expertise, customer evidence and product knowledge across the places LLMs already look for answers.
That is why a generic off-site SEO playbook is insufficient, but the objective is also not just “get mentioned on Reddit.” It is to make sure an honest explanation of why customers switch from your largest competitor exists in the community discussions LLMs can reference when comparing you. The objective is not “get more G2 reviews.” It is to make sure customers with your priority use cases have publicly documented the product strengths that actually influenced their decision.
What is the G2 2026 playbook?
Most SaaS review programs were built for human skimming. Companies asked customers for 5-star reviews, offered a gift card and hoped the result would protect or improve the average rating. The stars mattered. The detailed language inside thousands of individual reviews mattered much less because almost no buyer was going to read all of them.
But robots? Well robots don’t have anything else competing for their time. They’ll gladly crawl through a thousand reviews looking for clues they’re relevant to their users’ use case. They can isolate reviews written by customers with a particular company size, industry, or (and this is the biggie!) competitor history. They can extract repeated claims about implementation, support, usability and product limitations. They can use a specific three-year-old review if it contains the clearest available answer to a buyer’s question.
In our audit work, review sites appear far more often in SaaS recommendation answers than they do for other B2B categories. But as with everything in GEO, it has nuance. Mature software categories show up way more. The reason makes plain, simple sense. They have years of structured reviews written in language that directly addresses product fit, feature quality, implementation, support and competitive alternatives. That is exactly the evidence an LLM needs to compare vendors.
A review-site profile and the meat of the review themselves is therefore no longer a passive reputation asset. It is basically public product documentation. SaaS companies should assign review-site management to a real owner who is responsible for category accuracy, profile completeness, review freshness, competitive themes and the claims AI systems repeatedly extract. If the Software Advice profile was created by an intern twelve years ago and nobody has touched it since the product repositioned, the robots do not know the profile is neglected. To them, it’s evidence.
Customers who switched from a competitor are the best source of differentiated language
Most SaaS companies describe themselves as easy to use, flexible, innovative and customer-centric. Those phrases are so common that they provide almost no help to a buyer or an LLM trying to distinguish among products. Customers who switched from a named competitor are, on the other hand, absolute gold. They can, and should be encouraged to, describe the decision in comparative terms.
They switched for a reason. Getting them put that reason into text is not only smart, but actually valuable. This is the crazy thing about doing GEO work. Most of it exposes shit we should’ve just been doing smarter years ago, but there was never a compelling reason. Well, now there is.
SaaS companies should build a deliberate switcher program by identifying customers who moved from their largest competitors and documenting why. That information should inform case studies, comparison content, sales enablement and review-generation campaigns. The goal is not to manufacture testimonials or hand customers fake language to paste into a review site. It is to help happy customers articulate the parts of their experience most useful to another buyer facing the same decision.
Reddit matters because buyers use it to find the information vendors avoid publishing
Buyers do not go to Reddit because they want another product brochure. They go there to learn what the vendor is not telling them. If you want a great current example of this, go look for threads around Salesforce’s Marketing Next product. Yikes!
That’s exactly why ChatGPT in particular trusts Reddit. The platform contains the candid, comparative and experience-based information an AI system needs when answering a vendor-selection question. And in the inverse to review sites, we see an overindex on Reddit citations in new software categories, where the corpus of reviews don’t exist.
Recognizing Reddit’s importance has also caused many marketing teams to arrive at the worst possible conclusion: automate fake participation. That is not a Reddit strategy. It is pollution and it angers me as an actual Reddit user. I’d brag about my comment karma, but I’m too fake humble to do so. A credible SaaS Reddit program requires a knowledgeable human who understands the product and category and ALSO the community. AI can monitor relevant communities, surface useful conversations and help draft a response. But a real person must decide whether the company has something valuable to contribute and rewrite the response in language that does not sound like a robot wearing an employee badge.
Companies should not outsource their entire Reddit presence to an agency or automation tool. An outside partner can help with monitoring and process. It cannot replace the internal expertise required to participate credibly in a technical community. Plus, if the time comes to actually engage, you want an internal person who can officially be the face of the conversation.
Hiding product information is no longer a good strategy
B2B SaaS companies have spent years hiding information that buyers clearly want. Pricing requires a sales call, especially enterprise pricing. Anthropic recently talked about this at SaaStr and how that legacy mentality just had to go. Likewise, straight forward competitive comparisons live in an internal battlecard. Implementation details sit inside a slide deck. Data sheets require a form submission. I could go on and on about all the buyer-enablement that B2B has become used to gating.
This approach has frustrated buyers for year and now? AI makes it strategically dangerous. When a buyer asks Claude a direct question about your product, the system needs an answer. It will look for information from the company and then seek third-party evidence. If neither source provides a clear answer, the model may infer or invent one from incomplete context.
Refusing to publish information no longer prevents the buyer from getting an answer. It simply removes your company from the influencing it. SaaS companies should publish direct, accessible explanations of pricing, product fit, implementation, integrations, limitations, competitive differences and the use cases where the product performs best. This information should exist in readable webpage copy, not solely in PDFs, images, JavaScript applications or gated assets.
GEO rewards clarity because AI systems need language they can extract, understand and reuse. The company that answers the question directly has an advantage over the one that wraps the answer in seven paragraphs of positioning language and a “contact sales” button.
Honest comparison pages are now a core SaaS acquisition asset
SaaS buyers routinely ask AI systems to compare named products. They want to know which platform is better for a mid-market business, which is easier to implement, which has stronger support and which integrates with a particular technology stack. If your company does not publish useful details, the model will construct one from other sources or hallucinate.
Also. Obviously. A good comparison page should not declare that your product wins every possible scenario. That is not credible to a human, and it does not give an LLM enough information to make a nuanced recommendation. A strong comparison explains where each platform is stronger, which type of company should choose each one, meaningful differences in implementation and support, important feature gaps and the conditions under which your product is not the best fit.
SaaS companies often avoid this level of transparency because they fear giving a competitor attention. The buyer is already comparing you. The only decision is whether your perspective will be present when it happens.
Question-led YouTube videos create a second source surface for SaaS evaluation questions
YouTube is the most slept on channel in B2B. It’s not just a brand-awareness channel for SaaS companies. It is a source surface that search and AI systems (mostly AI Overviews from Google) can use when answering detailed product and category questions. This is most valuable when the video addresses a narrow middle- or bottom-of-funnel question: how two platforms compare, which product fits a specific company size, what implementation involves or how a feature works.
But it can also work at the top of the funnel. Here I am for the broad concept of “what is GTM in business?”
A focused three-to-five-minute video can give an AI system a clean, reusable explanation. The title should closely match the buyer’s question. The more exact, the better. The answer should begin quickly and name the product, category, use case and relevant competitors explicitly. The transcript should then be reviewed and corrected before the video is considered finished.
The transcript is the essential part of this strategy. AI systems are not consuming the video like a human viewer. They are only pulling the text attached to it. If the transcript mangles the company name, technical terms or competitor names, the source becomes less useful or actively misleading.
This does not require a large production budget. A clearly titled, well-structured explanation can be more useful to both buyers and LLMs than a beautifully produced webinar with a snazzy 15 second intro where the actual answer is buried 37 minutes into the recording. Webinars, sales calls and subject-matter-expert interviews can all be repurposed into focused videos built around individual buyer questions.
Visibility scores do not tell SaaS companies whether GEO is working
The GEO software market has created a lot of aggregate visibility metrics. They can be directionally useful, but are also fraught with nuances most people don’t understand. They can show whether a brand appears more often across a selected prompt set and help marketers identify competitors or citation patterns. But a visibility score is still a calculation based on prompts someone chose.
If those prompts do not reflect real buyer behavior, improving the score accomplishes very little. SaaS companies should use GEO platforms primarily to inspect the evidence behind the number: which sources are cited, which competitors dominate specific use cases, which lists influence recommendations, which claims repeatedly appear and where the company is absent.
The purpose of the platform is to help the team decide what to do. It is not to create a graph that goes into a board deck with no explanation of whether the movement produced customers.
SaaS GEO should be measured through trials, pipeline and revenue
Product-led and sales-led SaaS companies will measure GEO differently, but both should connect the work to business goals. Even not-for-profit businesses are still in the business of money – they just call them donations instead. Product-led companies can track AI-assistant traffic, free-trial registrations, product signups, conversion rate, time to activation, paid conversion and revenue generated by AI-referred users right within Google Analytics. Sales-led companies should track demo requests, self-reported AI discovery, branded search, qualified pipeline, deal size, sales-cycle length and closed-won revenue through UTMs and self-reported “where did you hear about us” fields and conversations.
To be clear here, there is an attribution complexity that is going to annoy you. But if you’ve been in B2B for any length of time, it’s just a continuation of the dark funnel. Direct referral traffic will not capture every AI-influenced journey. A buyer may ask Claude for a shortlist, Google each company separately and then submit a form after clicking an organic branded result. Analytics may credit Google even though Claude, arguably, created the consideration set. That is why self-reported attribution remains essential. Ask buyers how they heard about the company, include the question on conversion forms and have sales ask during the first call.
The objective of GEO is not visibility for its own sake. It is becoming discoverable and credible at the moments that influence revenue.
What should SaaS companies prioritize for GEO in the next 30 days?
A SaaS company does not need a giant GEO transformation plan before istarting. The first month should focus on identifying the public information already shaping buyer decisions and fixing the clearest gaps.
Fix measurement before claiming success
Confirm that GA4 is categorizing AI-assistant traffic correctly and that important conversions are firing. Review the “How did you hear about us?” field and establish current benchmarks for branded search, trials, demos, pipeline and revenue.
Audit every major review profile
Check G2, Capterra, Gartner Peer Insights and category-specific platforms. Review category placement, product descriptions, feature information, screenshots and the age and substance of existing reviews. Do not stop after looking at the star rating.
Identify customers who switched from the largest competitors
Interview them or mine existing customer-success and sales notes. Document why they switched, what improved and which use cases mattered. Use that language to improve comparison content, case studies and review requests.
Review the sources influencing priority AI answers
Choose the middle- and bottom-of-funnel questions most important to revenue. Run them across the AI platforms your buyers use. Document the cited sources, repeated claims, missing information and dominant competitors.
Publish information buyers cannot currently find
Create or improve one high-priority comparison page. Ungate one useful product resource. Answer one pricing, implementation or product-fit question directly.
Turn existing expertise into question-led video
Take a webinar, sales-call theme or subject-matter-expert interview and create several focused videos around individual buyer questions. Correct every transcript.
Give rented-land work a real owner
Assign ownership for review sites, community monitoring and third-party narrative management. Do not leave the public story of the product scattered across whoever happens to have a login.
SaaS companies have an enormous GEO advantage, but it will not last forever
SaaS is one of the categories most exposed to AI-driven buying because so much of the decision can be made using public information. That sounds threatening; it is also a huge opportunity.
A smaller SaaS company can beat a much larger competitor by explaining a niche use case more clearly, publishing more useful comparisons, earning more specific customer reviews and answering the questions the larger company keeps talking around. Large competitors may have more brand recognition and more content. They also tend to move slowly, hide information and communicate through messaging that has been approved into meaninglessness.
AI systems reward useful evidence. They need clear descriptions, explicit comparisons, credible third-party validation and accessible answers. SaaS companies that provide those things can influence how buyers understand the category before the sales conversation begins.
But they will not get there by treating GEO as a new reporting tab inside the SEO retainer. Your website and rankings matter, but so do your reviews, customer language, Reddit participation, YouTube transcripts, comparison pages and all the other public evidence teaching the robots what to believe about you.
The real SaaS GEO opportunity is not merely getting mentioned by AI. It is giving AI systems enough accurate, differentiated and credible information to understand why the right buyer should choose you.
Frequently Asked Questions
Does good SEO automatically translate into AI recommendations for a SaaS product?
No. Ranking well in search and being recommended by an AI system are different tasks: search engines evaluate page relevance, while AI systems evaluate whether a product is a credible answer to a buyer’s question. There is a LOT of evidence for this now, as detailed in the article: “Why your brand doesn’t show up in AI Answers, Even Though You Rank on Google.” SaaS companies need dedicated GEO work, including third-party evidence and clear public documentation, in addition to SEO.
Why do review sites like G2 and Capterra matter more for SaaS GEO than for other B2B categories?
AI systems can isolate and extract review language written by customers with a specific company size, industry or competitor history, then reuse that language when answering a buyer’s question. Because SaaS products are heavily reviewed online, this makes review-site language a direct input into how AI systems describe and recommend the product.
Should a SaaS company automate or outsource its Reddit presence?
No. Automated or fake participation risks community backlash and can damage the authentic, candid reputation that makes Reddit valuable to AI systems in the first place. Companies should instead monitor relevant threads and respond transparently, using outside help only for monitoring and coordination, not fake engagement.
What should a SaaS company measure to know if GEO is working?
Aggregate visibility scores are only directionally useful; the real measurement is whether AI-influenced traffic converts into trials, pipeline and revenue. Product-led and sales-led companies should track this differently, but both should tie GEO activity to business outcomes rather than a visibility score alone.
What is the fastest first step for a SaaS company starting GEO?
Start by confirming GA4 correctly categorizes AI-assistant referral traffic and that key conversions are firing, then run priority buyer questions across the AI platforms your buyers actually use. This creates a measurement baseline and reveals which sources are shaping the answers your prospects see.
