Key Takeaways
Abstract
Findings- Finding 1: Winning Early Predicts Losing Later
- Finding 2: The Naming Happens at the Comparison Stage
- Finding 3: Challenger Brands Get a Fair Shot Early, Then Get Squeezed
Action Items- The Playbook: Invest by Stage, Not by Habit
- Step 1: Pick the stage you actually want to be named at, and invest there.
- Step 2: Split your strategy by AI tool.
- Step 3: Build comparison-stage assets on purpose.
- Step 4: If you are a challenger, treat the comparison stage as the real battleground.
- Step 5: Measure each stage separately.
- What the Data Is Actually Saying
Research Delivered to Your Inbox
Get weekly, data-backed insights on AI visibility and search visibility optimization.
TOFU Visibility Doesn't Carry Forward
A brand that shows up on “what is X” questions is actually less likely to be named when buyers move to “best X” and “what should I buy” questions. The stages do not feed each other.
Each Funnel Stage Has Different Winners
Each stage of the buying journey pulls from a different set of brands. The brands that win the educational questions are mostly not the brands that win the comparison and purchase questions.
AI Names Brands Most at Comparison Stage
Brands get named far more often at the comparison and purchase stages than at the educational stage. Across the whole dataset, brands were named about 14% of the time on educational questions, but roughly 47% at comparison and 43% at purchase.
Challenger Brands Lose at Decision Stage
Challenger brands get a fair shot early, then get squeezed. Challenger brands (defined below) are named about as often as everyone else on educational questions, but fall far behind the established players at the comparison and purchase stages.
AI Names Brands During Comparison, Not Discovery
On Perplexity, Claude, and Gemini, the naming happens at the comparison stage, not the educational one. These tools rarely name brands on “what is X” questions, then name them several times more often on “best X” questions.
Abstract
For fifteen years, B2B content strategy rested on one assumption: build dominance at the top of the funnel and authority flows downstream. Win the “what is X” questions today, own the comparison and purchase conversation tomorrow. That was always hard to prove or disprove in classic SEO. Too many time lags, too many other things moving at once. AI search changes that. For any topic you can ask an educational question, a comparison question, and a purchase question on the same day, on the same tools, and see exactly which brands get named at each stage. So we can finally ask the question directly: do the brands that win early also win later? We ran 750 AI answers across 50 B2B SaaS topics and 5 tools to get the answer. The old model predicted that early winners would also win later. The data from our research showed the opposite. Brands named on the early “what is X” questions were less likely, not more likely, to be named later on comparison and purchase questions.
50 Topics, Three Stages, Five Tools - How We Built the Dataset
We took 50 B2B SaaS topics. For each one we asked three questions:
- Educational (top of funnel). Example: “what is customer success” or “what is product-led growth”.
- Comparison (middle of funnel). Example: “best customer success platforms for B2B SaaS”.
- Purchase (bottom of funnel). Example: “what should I look for when buying customer success software”.
We asked each question on five AI tools: Google AI Overviews, ChatGPT, Perplexity, Claude, and Gemini. For every answer we recorded which brands got named. That gave us 4,654 brand-by-stage-by-tool records to analyze.
Two definitions used throughout the study:
- Challenger brands are smaller or newer companies with low domain authority, meaning a Moz Domain Authority score of 30 or below. Domain Authority is a 0 to 100 score that estimates how strong a website is overall. Challengers are typically startups and niche players going up against established incumbents.
- “Named” here means the brand was actually named in the AI’s answer. (This is a slightly different measure than what you saw in Experiment B, which looked at how often a brand was named when its own page was cited. Both point the same direction; they just count slightly different things, so the exact percentages will not line up one to one between the two articles.)
A note on ChatGPT: on most educational questions, ChatGPT (the gpt-4o-2024-11-20 model we tested) answered from memory instead of searching the web. So its early-stage numbers reflect what it already “knew,” not what content it found. You cannot move ChatGPT’s educational answers with new content; you can only move them by being present in the data it was trained on, like Wikipedia, Reddit, and widely cited research. This again influences your content strategy.
Finding 1: Winning Early Predicts Losing Later
The old model said a brand that shows up early should be more likely to show up later. We found the reverse, and it was consistent and strong.
Compared with a brand that did not appear on the educational question, a brand that did appear was:
| How the brand showed up early | How much less likely it was to be named later |
|---|---|
| Cited through its own website | about 74% less likely |
| Mentioned inside a community or third-party page | about 86% less likely |
| Simply named in the answer | about 72% less likely |
The reason is structural, not mysterious. Each stage of the journey surfaces a different kind of page. Educational questions pull up vendor-neutral explainers and definitions. Comparison questions pull up “best tool” lists, review sites, and product pages. Purchase questions pull up pricing pages, case studies, and analyst rankings. Different pages, different brands. The overlap is small by design.
In plain terms: owning the “what is customer success” answer does not pre-position you for “best customer success software for mid-market SaaS.” That is a separate contest, and most brands have not entered it.
Note: Experiment C is ongoing and the dataset keeps growing, so treat the exact figures as directional. The stable finding is the one in the table above: showing up at the top of the funnel predicts being absent at the comparison and purchase stages.
Finding 2: The Naming Happens at the Comparison Stage
Brands get named much more often once buyers start comparing options. Across the whole dataset, brands were named about 14% of the time on educational questions, then roughly 47% at comparison and 43% at purchase.
That jump is largest on the tools that barely name anyone early on:
| AI tool | Named at educational | Named at comparison | Named at purchase |
|---|---|---|---|
| Perplexity | 11% | 46% | 50% |
| Claude | 14% | 53% | 35% |
| Gemini | 5% | 47% | 31% |
| Google AI Overviews | 26% | 42% | 44% |
| ChatGPT | 21% | 50% | 56% |
On Perplexity, Claude, and Gemini, a brand can be nearly invisible on “what is X” questions and then get named several times more often on “best X” questions. If your AI visibility dashboard only tracks educational questions, you are underreporting your real visibility by several times over.
Note: Experiment C is ongoing and the dataset keeps growing, so treat the exact figures as directional. The stable finding is that being absent on educational questions does not mean you are absent at the comparison stage, especially on Perplexity, Claude, and Gemini.
Finding 3: Challenger Brands Get a Fair Shot Early, Then Get Squeezed
Early on, brand size barely matters. Later, it decides almost everything. We sorted brands into four tiers by Domain Authority, from the biggest established leaders down to the challengers.
| Brand tier | Named at educational | Named at comparison | Named at purchase |
|---|---|---|---|
| Leaders (DA 71+) | 14% | 46% | 45% |
| Mid-large (DA 51 to 70) | 16% | 52% | 46% |
| Mid-market (DA 31 to 50) | 11% | 45% | 41% |
| Challengers (DA 30 or below) | 15% | 25% | 18% |
On educational questions, every tier lands in a tight band of roughly 11% to 16%. The AI gives challengers a fair shot. But at the comparison and purchase stages, the established tiers climb to roughly 45% to 52%, while challengers stall at 25% and then 18%. The gap is not about content quality early on; it opens up downstream, where being a known brand compounds.
The practical takeaway for a challenger: a healthy early-stage naming rate is not a sign you are winning. It is the one stage where the playing field is level. The work is downstream.
Note: Experiment C is ongoing and the dataset keeps growing, so treat the exact figures as directional. The challenger tier in particular rests on a smaller number of records, so read it as a clear direction rather than a precise number. The stable finding is that challengers start level and fall behind at the comparison and purchase stages.
ACTION ITEMS
Pick the stage you actually want to be named at, and invest there.
Split your strategy by AI tool.
Build comparison-stage assets on purpose.
If you are a challenger, treat the comparison stage as the real battleground.
Measure each stage separately.
Download & Implement Your Action Plan
Get a prioritized list of exactly what's preventing AI from finding and citing your content, with fixes ready to implement.
The Playbook: Invest by Stage, Not by Habit
Step 1: Pick the stage you actually want to be named at, and invest there.
Owning the educational answer for a topic does not carry you to the comparison or purchase answer. If you want to be named when buyers compare tools, you have to build for that stage directly.
Step 2: Split your strategy by AI tool.
For ChatGPT and Google AI Overviews, the priority is simply getting cited. For Perplexity, Claude, and Gemini, the priority is showing up at the comparison stage, because that is where they name brands.
Step 3: Build comparison-stage assets on purpose.
That means “Brand vs. competitor” pages, “best tool for [specific use case]” guides, clear feature breakdowns, and honest pricing pages. These are the pages AI pulls from on comparison and purchase questions. Better educational explainers will not fill this gap.
Step 4: If you are a challenger, treat the comparison stage as the real battleground.
You will not out-authority the incumbents overnight, but the comparison stage is where you can earn named mentions through being present in the places buyers compare options: review sites, community threads, and category guides that name you in a specific use case.
Step 5: Measure each stage separately.
A 15% naming rate on educational questions and a 47% rate on comparison questions are both normal and useful, but they are different conversations. Collapsing them into one “AI visibility” number hides the most important pattern in the data.
What the Data Is Actually Saying
The content funnel was a useful model for an era when every piece of content competed in one ranking system and authority built up over time. AI search does not work that way.
Each stage of the buying journey draws from its own pool of pages and brands. Educational content competes in one pool, comparison content in another, purchase content in a third. Winning one does not automatically win the others.
This is not a reason to stop investing in content. It is a reason to invest at the stage where you actually want to be named, not the stage that is easiest to write. Most B2B SaaS companies default to educational content because it is easy to produce and shows up in traffic reports. Comparison content is harder and less shareable, but it is the stage where AI names brands in the moments that drive buying decisions.
The funnel did not disappear. It broke into three separate markets, each with its own entrants and its own rules. The companies that learn to compete in all three, instead of assuming a win in one carries over, are the ones building for how AI search actually works.
ABOUT THE RESEARCHER
Head of SEO Research
“Pushkar leads SEO Research at VisibilityStack, driving the development of proprietary methodologies and frameworks that power our platform. His deep expertise in search algorithms and AI systems informs our technical approach. Pushkar has led SEO research initiatives at multiple technology companies, developing frameworks that have driven hundreds of millions in organic pipeline for B2B SaaS clients.”
