We know that AI search engines cite listicles as recommendation inputs. But does your position inside those listicles affect how AI engines actually present you?
To find out, we analyzed over 5.7 million data points across three distinct industries and eight AI engines:
ChatGPT
Claude Sonnet 4
Gemini
Google AI Mode
Google AI Overview
GPT-5 Search
Microsoft Copilot
Perplexity
TL;DR
When an AI engine cites a listicle, does your position inside it affect the answer? Yes, but with nuances:
Visibility: Being featured in a cited third-party listicle heavily influences whether your brand is mentioned in an AI-generated answer.
Position: Your rank in those listicles strongly correlates with your placement in the AI's response.
That matters because AI answers are often read like recommendations. A company named first or second usually feels more prominent than one introduced near the bottom.
To investigate this, Peec AI analyzed nearly 200,000 AI responses, generating over 5.7 million individual data points across B2B SaaS, Emerging MarTech, and US Finance.
The strongest practical takeaway: Quality over quantity. Don't chase every listicle. Find the few third-party listicles AI engines repeatedly cite for your tracked prompts. Then work to improve your inclusion, rank, and supporting evidence on those pages.
Why listicles are AI’s recommendation engine
Classic SEO asks: Do we rank on Google? GEO asks a second question: When AI answers, which sources does it retrieve, and how do those sources shape the answer?
In commercial categories, listicles, which are articles formatted as a numbered or ranked list, are one of the most common content types cited by AI engines. When someone asks “what's the best project management software,” AI engines frequently pull from ranked “best of” articles rather than brand websites directly.
They treat them as structured recommendation inputs. But if a third-party listicle ranks Competitor A first, Competitor B second, and your brand eighth, does the AI engine actually mirror that exact order in its own response?
That is exactly what our data set out to investigate.
The setup: What we analyzed
To understand exactly how listicle rank impacts AI visibility, we analyzed over 5.7 million data points - tracking exactly if, when, and where specific brands appeared in AI answers - collected between September 2025 and March 2026.
We looked at three distinct markets to see how category maturity affects AI behavior:
B2B SaaS: An established category with well-known vendors
Emerging MarTech: An emerging, highly fragmented category
US Finance: A regulated category driven by a smaller pool of available listicles
Instead of looking at every listicle on the internet, we focused exclusively on third-party listicles (such as independent review sites, industry blogs, and news publishers) that AI engines actually cite over and over again. We then measured how a brand's position inside those listicles impacted three specific outcomes:
Visibility: Did the brand get mentioned in the AI's answer?
Answer Position: If mentioned, at which spot was the company mentioned in the response?
Mention Count: How many times was the brand repeated?
Finding 1: The Right Third-Party Listicles Are Strongly Associated With Visibility
Does being in a listicle matter at all?
The short answer is yes. Across all three markets, brands exposed through frequently cited third-party listicles by AI engines were significantly more likely to appear in AI answers.
Simply being present in a listicle that an AI engine trusts provides a massive advantage for your brand's visibility.
Does your exact position in the listicle matter?
This is where the nuances of different markets appear. While B2B SaaS shows the highest absolute visibility lift for being #1 (+16.5 percentage points (pp)), the data reveals that the importance of your exact rank depends heavily on the maturity of your category:
Emerging MarTech (The sharpest rank drop-off): In this emerging market, exact position matters intensely. While Rank 1 provides a +13.4 pp lift, that visibility drops off significantly for lower tiers. When AI engines have weaker prior knowledge about a fragmented category, the absolute top recommendations in retrieved sources carry the most weight.
B2B SaaS (The flatter impact): Here, the exact rank matters slightly less. B2B SaaS gets a massive overall boost just from listicle inclusion. Once a brand appears in a frequently cited listicle, the AI engine (which already has strong internal associations with these established vendors) is highly likely to mention it, whether it ranks #1 or #3.
US Finance (The trusted source effect): This category shows large overall effects but less predictable rank ordering. Because there is a smaller pool of listicles in this market, a few highly cited sources potentially carry immense weight. The engine may mention multiple brands from the same frequently cited listicle, regardless of their position within them.
For brands looking to increase their AI search visibility, the message is practical: Being present in the right third-party listicles is the most critical hurdle. Being near the top is highly beneficial, especially in emerging or fragmented categories. However, do not treat any single rank tier as a universal rule.
Finding 2: Rank most clearly shapes where brands appear in the answer
Getting mentioned is only the first step.
Once a brand appears in an AI answer, its position also matters. A brand mentioned first or second usually reads like a stronger recommendation than a brand introduced near the bottom.
For brands ranked #1 in a frequently AI-cited third-party listicle, the estimated improvement in answer position was:
US Finance: 1.80 positions earlier
B2B SaaS: 1.17 positions earlier
Emerging MarTech: 0.82 positions earlier
For example, in B2B SaaS, this means that a brand historically buried at #6 in an AI response could climb to the #5 spot simply by being ranked first in the third-party listicles that the AI engine cites.
Unlike the visibility metrics in Finding 1, this effect is consistent across all three industries. This is the cleanest strategic insight from the study: AI-cited third-party listicles matter. And your rank inside them most consistently shapes where your brand appears in the final answer.
That distinction matters for your reporting. A campaign can improve a brand’s overall mention rate without improving its average rank within the AI's answer. It can also move a brand from the bottom of an answer toward the top, even if total visibility only changes modestly. You should therefore track visibility and position as two separate metrics.
Finding 3: Mention count is a different signal
Mention count measures how often your brand name is repeated inside the AI’s answer. While useful, it should not be confused with actual recommendation leadership.
A brand can appear exactly once at the very top and be the clear winner. Another brand might be repeated five times simply because the AI is comparing caveats, alternatives, or tradeoffs.
For brands with Rank 1 exposure in frequently AI-cited third-party listicles, the estimated increase in mention count was:
Emerging MarTech: +0.67 mentions
US Finance: +0.66 mentions (statistically insignificant)
B2B SaaS: +0.43 mentions
Unlike the cross-industry consistency we saw in Finding 2, mention count diverges sharply by market. In B2B SaaS and Emerging MarTech, the pattern is consistent: stronger listicle exposure generally correlates with more repeated brand mentions across an AI response. US Finance, however, breaks the mold. In this regulated market, the Rank 1 effect is not statistically significant (indistinguishable from zero), while some lower rank tiers show much larger mention-count effects.
One plausible explanation is that AI engines may name a top financial institution once as the obvious answer and move on, while using heavy repetition to explain the caveats of less obvious, lower-ranked options.
The bottom line: Don’t use mention count as your only GEO metric. Track it alongside visibility and position. Use mention count to diagnose how much space your brand receives, but use visibility to understand how often you appear in the answer, and position to understand whether you are being surfaced as a leading option.
Finding 4: More Listicles Eventually Stop Adding Much
The study also found strong signs of diminishing returns. Depending on the market and your rank in listicles, maximum impact is often reached after just a few repeated placements. These are not exact quotas, but rather directional signs that the value of another mention eventually plateaus.
This should fundamentally change your operating model.
The goal is not: Get listed in every listicle.
The goal is: Win the few listicles AI engines actually cite.
Five strong placements in frequently AI-cited third-party sources will matter more than 50 placements in articles AI engines never retrieve.
Finding 5: AI search engines don’t behave the same way
The associated visibility increase you get from a Rank 1 placement in a third-party listicle varies significantly depending on the AI search engine.
While engine behavior is complex, the data points toward a general trend: engines that cite fewer sources tend to give each individual citation more weight.
Engines with tighter source retrieval (like ChatGPT, GPT-5 Search, and Microsoft Copilot) often showed larger visibility associations for top-ranked listicles.
Engines that pull from a wider array of sources (like Google AI Overview and Google AI Mode) tended to show smaller individual lifts.
The main point is that GEO reporting should be engine-aware. You can use this pattern to loosely adjust your strategy depending on your target AI engine:
For tighter-retrieval engines (e.g., ChatGPT, Copilot): Prioritize dominating the absolute top listicles. Because these models cite fewer sources, winning a placement in the exact sources they use carries more influence.
For broader-retrieval engines (e.g., Google AI Mode, Google AI Overview): Because the individual impact of a single listicle is slightly diluted, casting a slightly wider net across multiple trusted sources can be beneficial to ensure the engine picks up your brand.
The GEO listicle playbook
Based on our research the GEO-informed approach is highly selective: Find the specific listicles AI engines actually cite for important prompts, and improve your presence inside those exact sources.
However, before you build your workflow, you need to understand your market. The same listicle strategy will not perform the same way in every industry.
Step 0: Diagnose your market structure
As our data showed, category maturity dictates how AI engines use retrieved sources.
Fragmented / emerging markets (e.g., Emerging MarTech): AI engines have weaker prior knowledge, so retrieved content carries immense weight. Improving placement in a few frequently cited listicles can have an immediate impact.
Established markets (e.g., B2B SaaS): Major brands already have strong internal associations within the AI engine. Listicle work absolutely matters. But it must be paired with broader brand-building, review generation, and category education.
Regulated / sparse markets (e.g., US Finance): Because there is a smaller pool of listicles in this market, a few highly cited sources potentially carry immense weight. Focus your efforts on securing inclusion within these key listicles rather than fighting for the exact #1 spot.
Ask yourself: Do AI engines confidently name the same brands every time in my category? Are there many independent listicles, or only a few trusted (regulatory) publishers? Your answers will dictate how aggressively you pursue the next steps.
Step 1: Start with a commercial prompt set
For this playbook, we assume you are already tracking a targeted set of prompts that influence category discovery and vendor evaluation (e.g., "Best [category] platforms for [company type]" or "[Brand A] vs [Brand B]").
If you aren't confident in your current prompt list, don't skip ahead. Check out our deep dive on how to choose the right prompts for LLM tracking before moving to Step 2.
Step 2: Track responses across target engines
For each AI response, systematically record the core data points: which URLs were cited, whether those URLs are listicles, which brands were mentioned, their position in the answer, and their mention count. Because retrieval behavior varies, ensure you track these metrics at the individual engine level, rather than as an aggregate.
Step 3: Identify the listicles AI actually uses
You must separate the listicles that merely exist from the listicles AI engines actually retrieve. Filter your data for URLs that appear across multiple prompts, chats, or engines.
How this looks in Peec AI: Instead of manually tracing URLs, teams can open the Source URL view and filter cited sources specifically to listicles. Because engine behavior varies, you can toggle your view to analyze a single AI engine, all of them at once, or any custom combination. From there, sort by retrievals and instantly trace each URL back to the exact prompts where it appeared.
Step 4: Prioritize high-opportunity gaps
Once you know which listicles are cited, prioritize them by expected impact. The strongest opportunities usually have a combination of high retrieval count, high citation frequency, commercially valuable prompts, and, most importantly, competitor presence where your brand is currently missing.
How this looks in Peec AI: Instead of asking "which listicles exist?" you can see exactly which listicles matter. Inspect cited listicles, see exact brand positions, and prioritize gaps using the dedicated Gap Analysis, where we already sort source URLs weighted by citation volume and competitor presence.
Step 5: Build the evidence publishers need
Improving listicle placement is not just about cold outreach. Publishers need concrete reasons to include you or rank you higher. Give them the assets they need to justify your placement: clear feature and comparison pages, fresh customer reviews, integration guides, and transparent pricing. In many instances, you can also pay for the placement.
Step 6: Track outcomes separately
After executing your campaign, don’t collapse everything into one vague "visibility score." As our data proved, absence and low placement are both problems, but they are not the same problem. Track your Mention Probability, your Answer Position, and your Mention Count as distinct metrics to see exactly how your influence is growing.
What this study does & doesn’t prove
This study is based on observational research, not a randomized experiment. Because we didn't randomly assign brands to listicle positions to see what would happen, we must frame our takeaways as observed associations.
Specifically, ranking higher in third-party listicles is associated with three main benefits:
Higher mention probability in AI search engines
Earlier (better) placement within the answer
More repeated brand mentions (in many cases)
We observed these trends even after controlling for prompt, engine, date, brand-level differences, and retrieval composition. While this data is useful for strategy, it is not causal proof.
Important caveats to remember:
The findings apply to frequently retrieved listicles, not every listicle on the web.
Third-party listicles provide the cleanest signal.
We only looked at third-party listicles. Your own listicles or listicles written by your competitors might be treated differently by LLMs.
Markets differ. The association is positive across the three studied markets. But the pattern and magnitude vary.
Engine behavior changes over time. Monitor source retrieval continuously.
To dig into the full technical specification and methodology, read our companion paper here. Or, if you are ready to see exactly which listicles are driving visibility in your specific market, start your free trial of Peec AI and close your high-opportunity gaps.
