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Tag: perplexity

  • Is CPM profitable? Should Perplexity be excluded from AI visibility tracking?

    Is CPM profitable? Should Perplexity be excluded from AI visibility tracking?

    In the world of AI search, leaders are constantly changing, and the question of which platforms to track becomes critical for SEO specialists. Recent discussions have revolved around the Perplexity platform: should we continue monitoring it or focus exclusively on giants like ChatGPT, Gemini, and Claude? We will examine how changes in market share affect visibility assessment and how much money each spent ruble will bring to understand if CPM is profitable.

    Is Perplexity really losing ground?

    Ross Hudgens, CEO of Siege Media, claims that Perplexity’s market share is shrinking. Including it in general metrics alongside ChatGPT, Gemini, Claude, and Google AI products can distort the real picture of visibility. Hudgens urges: “Everyone should remove Perplexity from their LLM trackers today.”

    However, as experience shows, hasty conclusions can be erroneous. In March 2002, at the Search Engine Strategies conference, the reduction of tracked search engines from 15 to 5 was discussed. Google was then excluded from the list. This turned out to be a serious oversight, as Google was rapidly gaining momentum. A similar situation could repeat itself today.

    Data confirms Perplexity’s decline

    • According to StatCounter, in June 2026, Perplexity held 7.91% of AI chatbot referral share, almost on par with Gemini (7.94%).
    • By August 2026, Perplexity’s share had fallen to 4.31%, while Gemini grew to 10.9%.

    “A small market share does not always mean small strategic importance. It’s like investing in stocks: you shouldn’t always look only at current capitalization, but also at growth potential.”

    The market is consolidating, but not around a single leader

    The latest Similarweb data for May 2026 shows the dominance of ChatGPT (53.9% of global web visits among seven major AI assistants). Gemini accounts for 27.9%, Claude – 9.2%, DeepSeek – 4.1%, Grok – 2.4%, and Perplexity and Copilot – 1.3% each.

    Significant changes occurred not in the disappearance of Perplexity, but in the emergence of a strong second player. ChatGPT’s share of AI chatbot web traffic decreased from 76.4% a year ago to 52.7% in May 2026. Gemini grew from 9% to 27.3%, and Claude – from 1.6% to 8.9%.

    Key players and their advantages

    • ChatGPT: over 900 million weekly active users (February), over 1 billion active users across all products (end of July). This is a huge direct consumer base.
  • Gemini: The Gemini app exceeded 1 billion monthly users (August). It has Google distribution through Search, Android, and the entire product ecosystem.
  • Claude: Has gained a strong position in the enterprise segment and among developers. In April, over 100,000 customers used Claude on Amazon Bedrock. Annual revenue exceeded $65 billion (August).

These are not three versions of the same business, but three different distribution advantages. The market looks like an emerging oligopoly, not a “winner takes all” situation.

What SEOs Should Track

Google AI Overviews and AI Mode should not be viewed as ordinary LLMs. They are much more important. In June, Google reported that AI Overviews cover more than 2.5 billion users per month, and AI Mode exceeded 1 billion monthly users. AI Mode queries have doubled every quarter since launch.

According to Similarweb, AI Overviews appeared in 43% of Google search queries in the US by May 2026, compared to 15% a year earlier. AI Mode visits grew from 126 million in June 2025 to 279 million in May 2026.

This means that Google AI is a separate layer in the world’s dominant search ecosystem. For B2B companies, Claude is also of immense importance, despite relatively low consumer traffic. PwC, TCS, and Cognizant are actively integrating Claude into their workflows, training tens of thousands of employees. Over 1,000 business customers spend more than $1 million annually on Claude.

Three-Tier AI Visibility Measurement System

  1. First Tier: Platforms with scale and strategic importance. Track ChatGPT and Gemini separately. Add Claude for B2B, corporate, and professional audiences.
  2. Second Tier: AI search embedded in existing ecosystems. Track Google AI Overviews and AI Mode separately from ChatGPT, Gemini, and Claude. The goal is to measure how AI changes the search journey. Microsoft Copilot can also be included in this tier if the company has a significant presence in Microsoft 365.
  3. Third Tier: Emerging or specialized platforms. This includes Perplexity, Grok, DeepSeek. Do not ignore them, but do not give them equal weight. Monitor them for unusual visibility, referral traffic, citations, and growth.

Don’t Let Averages Mask Real Data

An aggregated LLM visibility metric can create a false sense of accuracy. For example, if a brand has 40% citation rate in ChatGPT, 35% in Gemini, 30% in Claude, and 90% in Perplexity, a simple average would be 48.75%. This number says little if Perplexity accounts for only a small fraction of the traffic important to the business.

CPM выгодный? Стоит ли исключать Perplexity из отслеживания ИИ-видимости? — illustration 2

Instead of asking, “What is our average LLM visibility?” you should ask, “Where do the people important to this business actually encounter our brand?”

This requires combining three data sets:

  • Audience Reach: usage, visits, platform distribution.
  • Visibility: mentions, citations, referring URLs, queries that generate them.
  • Business Impact: linking AI referrals to engagement, leads, sales, and other conversions.

My Verdict on Perplexity

Ross Hudgens is right about the problem, but not entirely right about the solution. Don’t remove Perplexity from your LLM tracker, but reduce its weight. If Perplexity generates 1% of your AI traffic, giving it 25% or 33% weight in your visibility score is unjustified. However, if it accounts for more than 5% of referrals, as Hudgens himself suggests, the argument for it becomes stronger.

Perplexity’s trajectory is worth watching. Similarweb’s August 2026 analysis still describes Perplexity as an active player in the AI search market, noting its ad-free strategy and focus on subscriptions and enterprise deals. This shows why “small” and “insignificant” are not synonyms.

Frequently Asked Questions

What CPM should be considered profitable for AI platforms?

Profitable CPM in AI platforms depends on your target audience and ROI. If a platform generates high-quality leads or sales, even with a relatively high CPM, it can be profitable. The main thing is to consider not only the cost per impression but also the conversion rate.

Is it worth testing hypotheses cheaply on new AI platforms?

Yes, cheap hypothesis testing on new AI platforms is essential. This is how you can discover the next Google. You shouldn’t spend huge budgets on them, but monitoring and minimal testing will allow you not to miss a potential breakthrough.

What is cheaper: a banner or a blogger, when compared to AI platforms?

The question of what is cheaper, a banner or a blogger, is incorrect in the context of AI. AI platforms offer a different type of interaction. Price comparison should be done through efficiency metrics: how much does 1 million impressions via a banner cost versus the cost of generating 100 AI videos or the cost of publishing 500 videos via AI tools. Each channel has its own unit economics.

How to estimate the cost of uniqueizing one video using AI?

The cost of uniqueizing one video using AI depends on the complexity of the task and the tool used. It is important to evaluate not only direct costs but also the time that AI will save. The price per minute of AI video with an avatar also varies, but it often turns out to be more profitable than manual labor, especially for large volumes.

Conclusion

The AI search market is dynamic. Ignoring Perplexity or other niche platforms entirely means risking missing important signals. However, blindly giving them equal weight with giants distorts the picture. A mathematical approach to analysis, weighing platforms by their real business impact, and constant monitoring of new players are key to success in SEO and a profitable CPM strategy.

We count cash, not just the number of platforms in the tracker. Don’t overlook the outsiders who are gaining momentum. Perhaps among them lies the next market leader. Start analyzing your data today so you don’t miss new opportunities!

  • Brand mention in ChatGPT matters more than links: 92.8% of conversations without a click

    Brand mention in ChatGPT matters more than links: 92.8% of conversations without a click

    92.8% of dialogues in ChatGPT end without a transition to the website — this is Profound data that Kevin Indig analyzed in the digest on July 21. Measured on live traffic of a real product, the figure can be trusted. All search promotion relied on a single deal: you get to the top of search results, a person clicks the link to your site, and converting that visit into a lead is your concern. The link was the point where a business first touches a customer. In the AI response, this deal breaks at the very first step: the click most often simply doesn’t happen.

    Why 92.8% of dialogues in ChatGPT end without a transition to the website

    For several months, I’ve been measuring how ChatGPT, Perplexity, Alisa AQI, and GigaChat name brands in their responses: I run control prompts and see which model mentioned whom and which didn’t. Behind these runs, a figure surfaced that breaks the usual SEO logic — and made me change what I track for clients altogether. Let me break it down in order.

    Three weeks ago, we ran prompts for a client in the food delivery niche through ChatGPT. Usual routine: you type a question like “which delivery service to choose in Moscow,” and look at the response. At that point, the client had far more links from external sites than the nearest competitor. In Ahrefs — a service that calculates a site’s link weight — this is immediately visible: both the overall authority score is higher, and the number of different sites linking to it is one and a half times greater. ChatGPT named the competitor. The client — no. Didn’t mention it at all, not once in twenty prompts.

    I double-checked three times, changed the wording of the question, the date of the dialogue, and the region. The result was the same. For an hour and a half, I honestly couldn’t understand what was going on. Twenty years of website promotion taught a simple rule: the more links point to you, the higher you rank in Google and Yandex results, and therefore the more readily neural networks should name you. Here, the rule didn’t work. Only there was no search results page in the usual sense. There was a model that decided on its own whom to name and whom not to. And it decided not by a link counter.

    A brand mention cannot be bought with links — it accumulates like reputation

    At the same time, AI traffic itself is tiny in volume — Ahrefs reports that ChatGPT accounts for about 0.19% of all link transitions on the internet, compared to 42% for Google. Ridiculously small if you compare head-on. But here’s what’s more interesting: those few who do click from ChatGPT convert far more readily — conversion is about 7.1%, and the visit itself is valued 4.4 times higher than a regular one. Fewer people reach the site, but those who do — came ready to make a purchase or any other target action.

    Brand mention in ChatGPT matters more than links: 92.8% of conversations without a click

    And here’s an important twist. If the decision is made BEFORE the click, inside the model’s response itself, then you need to fight not for the click. You need to fight for how you’re named at the moment when the click hasn’t happened yet and, most likely, won’t happen at all. A mention in a ChatGPT response is not structured like a spot in search results. A spot can be earned by working on the site — links, loading speed, technical cleanup. A mention can’t be bought that way. The model gathers it from two sources: from the texts it was trained on and from pages it pulls up right during the response. And what decides is not the number of links, but how the brand is generally written about — in reviews, on forums, in industry articles, in comparisons.

    Search promotion used to be about taking top positions, because a position led to a click on the site. Promotion in AI answer engines works differently: you have to fight for the mention itself, because a click may not happen at all, and the person will still make a decision — based on how the model named the brand. A link is a one-time asset. It works as long as the page holds in the search results, as long as the site is alive, as long as the algorithm values it. A mention accumulates differently: it is reputation that has already settled into the texts on which the model was trained and which it reads now. Remove one article — the mention will not go anywhere; it is spread across dozens of sources.

    This is where my mistake with that food delivery client was. His link profile was strong, but almost all of it came from aggregators and directories, where the brand is simply listed in a list. The competitor had fewer links, but had three detailed editorial reviews describing how the service differs from others. The model was trained on them, among other things. It did not count links — it read context. The first place in Google, by the way, does not at all guarantee a mention in ChatGPT. We checked this on several clients separately: search and AI engines have different selection mechanics, and the first place in regular search results does not by itself turn into a line inside the model’s answer.

    Where a link still brings a client, and where the question is closed right in the answer

    The link has not died entirely — it is important not to confuse this. In regular Google, 68% of searches in the US by early 2026 also end without a click, according to SparkToro citing Search Engine Land. An answer without a click to the site has also come to good old search, it is just that it was visible there earlier — through blocks with ready answers, knowledge cards, and direct answers at the top of the results.

    The link still works where the decision is made AFTER the first touch: price comparison, reading reviews, technical documentation, everything that requires going into details that simply do not fit in the model’s short answer. It works in niches with a long decision-making cycle — there, a person will still reach the site, just later. The link does not work where the question is closed with one phrase in the answer. “Which delivery service to choose” — closed with a phrase. “How much does delivery from a specific restaurant in a specific area cost tomorrow evening” — not closed, a click is needed there. The same business lives on both sides of this boundary — it all depends on which specific query the user asked.

    Brand mention in ChatGPT matters more than links: 92.8% of conversations without a click

    What I now measure for clients instead of search positions

    I stopped looking only at positions in the search results and added a separate measurement: how often and in what context models mention the client’s brand directly, without a click, without a transition. We run control prompts — real audience questions — through ChatGPT, Perplexity, Alisa AI, and GigaChat, and see whether the model names the brand, in what order relative to competitors, and with what wording. ChatGPT is not the only one here. Perplexity builds its answer almost entirely from cited sources and shows them as a list — the model readily takes from articles on third-party platforms. Alisa AI mixes in fresh Yandex results, GigaChat answers in its own way. A brand that sounds in one model may be silent in another — so one ChatGPT is not enough for the measurement.

    From this, three things emerged that I now advise any marketer to track:

    • Share of mentions among top competitors for your own key questions: a specific percentage for a specific set of prompts, without general ratings.
    • Tone of phrasing: the model may mention the brand neutrally, or it may add an evaluative word like “trusted” or “expensive” — this influences choice more than the mere fact of mention.
    • Sources from which the model apparently draws this phrasing: often it’s one or two articles — these should be prioritized for strengthening, instead of spreading the budget across dozens of platforms.

    With that food delivery client, we ultimately didn’t chase new links. We wrote several detailed materials about the product on platforms that the model clearly reads and cites — and after a month and a half, the mention appeared, and visibility in AI responses grew. The client had more links, but ChatGPT named a competitor. Now we know why.

    Frequently Asked Questions

    How to measure brand mentions in ChatGPT?

    Run control prompts — real audience questions — through ChatGPT, Perplexity, Alice AI, and GigaChat. Calculate the share of mentions among top competitors, record the tone and the sources from which the model draws information.

    Brand mention in ChatGPT matters more than links: 92.8% of conversations without a click

    Can you buy a mention in neural networks with links?

    No. Links work in classic search, but neural networks don’t count them directly. A mention depends on how the brand is written about in reviews, articles, and forums — it’s reputation that accumulates in the texts on which the model is trained.

    What to do if the brand is not mentioned in ChatGPT?

    Find the platforms that the model cites most often and place detailed materials about the product there. Strengthen editorial reviews, comparisons, and expert articles — they influence mentions more than link mass.

    Should you abandon SEO in favor of neural networks?

    No. Links still work in niches with a long decision-making cycle and for transactional queries. The optimal strategy is to combine classic SEO with work on mentions in AI responses.

    Conclusion

    The bottom line is simple: 92.8% of dialogues in ChatGPT end without a click, but the decision to choose a brand has already been made. A link is a one-time asset; a mention is cumulative reputation. To win in AI search results, stop chasing links and start managing how you are written about in the texts that models read. Run control prompts, track the share of mentions and tone — and you’ll see where you’re really losing customers. Start with an audit of your mentions today.

  • AI Visibility: How to Distinguish Real Growth from Measurement Noise

    AI Visibility: How to Distinguish Real Growth from Measurement Noise

    Data on AI visibility is unstable: generative models produce different answers each time, so a single measurement can be misleading. A new study by IQRush proposes a stopping rule to determine when ratings become reliable. In this article, we will break down how to distinguish real growth from measurement noise and what practical conclusions follow for marketers.

    Why Single Measurements of AI Visibility Are Unreliable

    Search engines like SearchGPT, Gemini, and Perplexity introduce randomness into every answer. The same query can yield different sources — this is a feature of the architecture. The study showed that, for example, when testing SearchGPT on the topic of running gear, Tom’s Guide received about 9.5% of citations, while Runner’s World received approximately 6.0%. The difference of 3.5 percentage points was within the margin of error, so claiming Tom’s Guide’s superiority was incorrect.

    How Much Data Is Needed for a Reliable Rating

    The answer consists of two conditions that must be met simultaneously. First: the order must stop changing. After collecting a sufficient number of responses, the top sites begin to stand out clearly. Second: the difference between the top sites must exceed the measurement error. If competitors are too close, the rating does not reflect real superiority.

    In 30 tests across different platforms and topics, the number of responses needed to meet both conditions ranged from 33 to 94 (only responses with citations were considered). In three out of 30 cases, this was not achieved even after 125 queries — all on SearchGPT, where the top sites were too similar.

    Practical Takeaways for Marketers

    Rand Fishkin (SparkToro) advises: before spending money on tracking AI visibility, make sure the provider “shows their math.” The IQRush study provides a simple stopping rule to avoid relying on intuition. If after updating content you see a 3 percentage point increase in citations, this could be natural variation. Measure indicators before and after multiple times — a single measurement is indistinguishable from noise.

    AI Visibility: How to Distinguish Real Growth from Measurement Noise

    Different Platforms — Different Data Requirements

    Gemini loads citations from the same sites within a single response, so many citations carry little new information. SearchGPT gives fewer citations per response but distributes them more widely — each response contains more independent data. The same number of responses on two platforms does not provide the same confidence: a budget sufficient for Gemini may leave you in the dark on SearchGPT.

    When Data Is Insufficient: Know When to Stop

    In three out of 30 tests, the top sites never clearly separated. In such cases, the right decision is to refrain from publishing a rating. A tracker that can say “insufficient data” is more valuable than one that outputs a confident order with every query.

    Only leaders can be trusted: with enough responses, they pull away from the middle and tail. But even for the top 10, the typical margin of error is about five positions, and every fifth position is more than 10. Do not publish exact rankings beyond the top of the list.

    Limitations of the Study

    This is a preprint based on 30 tests across three platforms using questions generated by ChatGPT, not real user queries. The exact numbers do not transfer to your topics — treat them as a form of the problem, not a table of values. In one test, 125 questions yielded only 104 useful responses (17% loss), so the actual number of queries should be higher.

    AI Visibility: How to Distinguish Real Growth from Measurement Noise

    The method was validated internally: the early rating was compared to the final one, not to an external benchmark. However, an independent team from the University of St. Gallen (Julius Schulte, Malte Bleeker, Philipp Kaufmann) published similar results on their dataset in April, confirming that a single reading is unreliable.

    The Future of AI Visibility: From Exact Numbers to Ranges

    Reporting on AI visibility is moving toward a format with margins of error, as in advertising and web analytics. Until Search Console reports which clicks came from AI, the task falls on you: run the check multiple times and report a range, not a single number from a dashboard.

    Frequently Asked Questions

    How many times should you query AI search to get stable data?

    From 33 to 94 responses with citations, depending on the platform and topic. There is no universal threshold.

    Can you trust AI visibility dashboards?

    Only if they show the margin of error and multiple measurements. A bare number without context is a red flag.

    AI Visibility: How to Distinguish Real Growth from Measurement Noise

    What to do if citations increase by 3% after a content update?

    Take several measurements before and after. If the difference persists — it’s real growth. If not — it’s noise.

    Which platforms require more data?

    SearchGPT — due to fewer citations per response but greater independence of each response. Gemini — conversely, gives many citations, but they often repeat the same sites.

    Conclusion

    The main takeaway: AI visibility is not a fixed metric but a range. Use the stopping rule, check multiple times, and do not publish ratings if the top sites have not separated. Only then will you get data you can rely on. Start implementing these principles today to make your AI visibility reports reliable and useful for decision-making.