AI search behaves differently at each funnel stage, so your GEO tactics should too. The data is clear on the shape of it: Google-vs-AI overlap rises toward purchase, from 16% at segment and 22% at discovery to 33% at comparison and 54% at pricing, while AI Overviews are near universal except on pricing (discovery 91%, comparison 97%, segment 100%, pricing 75%). The sources the engines reach for shift just as much. So a single, flat GEO program leaves leverage on the table. This playbook turns that map into concrete actions, one stage at a time.
I’m Andrii Byzov, a fractional CMO for B2B tech, and most of my GEO work now starts by asking which stage a query sits in before deciding what to do about it. The numbers above come from the companion data study, the full-funnel AI search map for B2B SaaS. That post is the map. This one is the playbook derived from it, so if you want the underlying dataset, start there and come back here for the actions.
Key takeaways
- Overlap rises toward purchase. Segment 16%, discovery 22%, comparison 33%, pricing 54%, so tactics should differ by stage.
- AI Overviews are near universal except on pricing. Discovery 91%, comparison 97%, segment 100%, pricing 75%.
- The source mix shifts: discovery leans on tech media, review sites, and community; comparison on vendor blogs and Reddit; pricing on your own page.
- Match the action to the stage. Best-of lists for discovery, segment-specific content for segment, comparison pages for comparison, a crawlable pricing page for pricing.
- GEO improves the odds. It does not guarantee a citation, because you do not control the model.
Discovery: build a broad, credible presence
At discovery, a buyer is asking who is even in the category. AI Overviews show on 91% of these queries, so an AI answer is almost always in play, but Google-vs-AI overlap is low at 22%. That low overlap matters: ranking in Google does not carry over to being named by AI here, so you have to earn presence in both places separately. The sources that surface skew toward tech media, review sites like G2, and community discussion, not your own marketing pages.
So the actions are off-site and credibility-led. Earn placements on best-of and category lists, because a model treats a curated list as a shortcut to a vetted shortlist. Get covered in the tech media that the engines already trust. Build a steady flow of recent, specific reviews on G2 and its peers, since review sites feed AI answers heavily at this stage. The goal is broad, credible presence across the sources discovery queries pull from, so that when a model assembles its first list of options, you are on it. None of this guarantees inclusion, but it stacks the deck. The foundation under all of it is being citable, not just rankable, so your core facts stay consistent everywhere a model might read them.
Segment: be the obvious fit for a specific buyer
Segment queries ask who is best for a particular buyer: best tool for startups, for enterprise, for a regulated industry. AI Overviews hit 100% of these in the data, so an AI answer is guaranteed, yet overlap with Google is the lowest of any stage at 16%. That gap is the headline. Whatever wins in Google here is largely not what AI names, so you cannot lean on rankings. And the winners differ by segment: the startup answer and the enterprise answer pull from different sources, with community and peer discussion weighing more for startups and SMB, and formal authority and analyst-style proof weighing more for enterprise.
So the action is segment-specific content and positioning, not one generic pitch. Write pages that name the segment and make the case for that exact buyer, in their words, with their use cases and constraints. For startup and SMB segments, invest in authentic community presence where those buyers compare notes, since that is what surfaces. For enterprise, build the authority signals that segment trusts: case studies, security and compliance proof, and credible third-party validation. The point is to be the obvious fit when a model answers a segment-shaped question, rather than a generic option it has to stretch to recommend.
Comparison: own the X vs Y conversation
Comparison is where buyers shortlist: X vs Y, alternatives to Z. AI Overviews appear on 97% of these queries, and overlap climbs to 33%, so Google and AI agree more here than earlier, which means the same assets can work in both. The source mix is distinctive: vendor blogs are surfaced heavily, and on the Google side Reddit is dominant for comparison queries. So two channels matter most, and you can influence both.
The first action is to own your own comparison and alternatives pages. Publish honest X vs Y and alternatives-to pages that lay out where each option fits, because vendor blogs get surfaced at this stage and a clear, fair comparison is exactly the kind of text a model lifts. The mechanics are their own discipline, covered in how to create comparison and alternatives pages. The second action is an authentic Reddit presence, since Reddit dominates the Google side of comparison and feeds AI answers too. Show up as a genuine, helpful participant in the threads where your category gets compared, not as a drive-by promoter, because the honest signal is the whole value. Together these put your framing into the two sources comparison queries pull from most.
Pricing: make your pricing page a crawlable GEO asset
Pricing intent is the exception to almost every rule above. AI Overviews show on only 75% of pricing queries, the lowest of any stage, so a quarter of the time there is no AI answer at all. But overlap is the highest at 54%, and the source that gets surfaced is your own page: AI surfaces the vendor’s own domain 79% of the time and Google 94% for branded pricing queries, alongside procurement and software-listing sites. So unlike every other stage, the leverage is on a page you fully control.
So the action is to make your pricing page a clear, crawlable, answer-shaped GEO asset. Serve the substance in the HTML rather than behind a form or JavaScript, structure tiers with names and who each suits, add answer-shaped FAQ about what drives the price, and keep it current so it does not contradict third-party listings. You can keep a contact-sales motion and still give the engines something real to read. The full checklist is in your pricing page is your GEO asset. Because your own page is what gets surfaced most here, a thin or gated page wastes the one stage where the engines already favor you.
The playbook at a glance
| Funnel stage | Priority GEO actions |
|---|---|
| Discovery (overlap 22%, AIO 91%) | Earn best-of and category list placements, get tech-media coverage, build recent reviews on G2 and peers, keep entity facts consistent. |
| Segment (overlap 16%, AIO 100%) | Publish segment-specific content and positioning; community presence for startups and SMB; case studies, compliance, and authority for enterprise. |
| Comparison (overlap 33%, AIO 97%) | Own honest X vs Y and alternatives pages; build an authentic Reddit presence in the threads where your category is compared. |
| Pricing (overlap 54%, AIO 75%) | Make your pricing page crawlable, structured, answer-shaped, and current; keep contact sales but give the engines real substance to surface. |
A house rule keeps all of this honest: matching tactics to stage improves the odds, it does not guarantee a citation. You do not control what a model says, outputs vary by query, and they shift over time. What a stage-aware playbook buys you is leverage, spending effort where the engines actually pull sources for that intent, so you are the clearest and best-corroborated option at each step. Be skeptical of anyone selling certainty here.
FAQ
Why should GEO tactics change by funnel stage? Because AI search behaves differently at each stage. Google-vs-AI overlap rises toward purchase, from 16% at segment and 22% at discovery to 33% at comparison and 54% at pricing, and AI Overviews are near universal except on pricing. The source mix shifts too: discovery leans on tech media, review sites, and community, while pricing surfaces your own page. So the same effort pays off differently depending on intent, which means the tactics should differ. It tends to improve the odds, not guarantee them.
Which stage should a B2B SaaS team start with? It depends on where the gap is, but most teams under-invest in comparison and pricing, which sit closest to purchase. Comparison has 33% overlap and leans on vendor blogs and Reddit, so owning X vs Y and alternatives pages and an authentic community presence pays off. Pricing has the highest overlap at 54% and surfaces your own page most, AI 79% and Google 94%, so a clear, crawlable pricing page is high leverage. Discovery and segment matter too, just earlier in the journey.
Do AI Overviews show up at every funnel stage? Almost. AI Overviews appeared on 91% of discovery queries, 97% of comparison queries, and 100% of segment queries, but only 75% of pricing queries. So for discovery, segment, and comparison you should assume an AI answer is in play and write to be the source it lifts. Pricing is the partial exception, where a quarter of queries showed no AI Overview, though your own page still tends to be surfaced when one appears.
Can a GEO playbook guarantee AI cites my company at any stage? No. A stage-aware playbook improves the odds that you are the source AI reaches for at discovery, segment, comparison, or pricing, but you do not control what a model says, and outputs vary by query and shift over time. You are matching your tactics to how AI search behaves at each stage so you are the clearest, best-corroborated option. Anyone promising guaranteed citations at any stage is overselling it.
If you want this stage-by-stage playbook built and measured for your company, that is core to what I do as a fractional CMO. Start with my GEO offer or GEO consulting, and find me on LinkedIn.
Andrii Byzov is a fractional CMO for B2B tech, focused on AI-native marketing and AI search visibility.