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How to measure your brand visibility in AI Search, ChatGPT, Perplexity and AI Overviews with the right tools and KPIs.
Search is changing at a speed that is hard for an e-commerce owner to ignore. For years, the basic question has been simple: «Where does my site appear in Google? Today, however, a customer can ask ChatGPT, Perplexity, Gemini or see a summary in AI Overviews before they even get to the organic results. This means that AI Search is not just a new technology trend; it's a new environment for product discovery, brand evaluation and comparison of options. HubSpot's article on AI search analytics tools highlights exactly this transition: traditional SEO analytics are still necessary, but no longer sufficient to measure whether your brand is featured, mentioned, recommended or skipped by AI engine responses.
AI Search is the term used to describe a search in which the user does not just receive a list of links, but a synthetic answer generated by artificial intelligence models. This answer can be based on web results, citations, shopping feeds, knowledge graphs, structured data or the model's prior understanding of a topic. For an online store, this translates into a critical change: it's not enough to rank well for a keyword like «men's sneakers». You need to understand if your brand shows up when a user asks «what are the best men's sneakers for everyday wear?», «which store has reliable returns?» or «compare three brands based on price, quality and availability». This is where concepts like generative engine optimization, GEO, answer engine optimization and LLM visibility come into play.
HubSpot points out that AI search analytics tools are designed to fill a gap that traditional ranking tools can't fully capture: visibility into the responses of AI systems. This includes AI brand mentions, citations, positive or negative sentiment, share of voice AI vs. competitors, frequency of occurrence on specific prompts, and quality of sources used. If you were measuring impressions, clicks, average position and conversions until now, you now need to add another dimension: how often your brand becomes part of the response, even when there is no direct click.
This need becomes more pronounced when we look at data from Gartner, which predicted that traditional search volume will decline by 25% by 2026 as users shift some of their behavior to AI chatbots and virtual agents. As shown in the graph below, the shift is no small one and directly impacts acquisition strategy for e-commerce businesses.
Source: Gartner, forecast for 25% decline in traditional search volume by 2026
For an e-commerce owner, the key question is not theoretical. It's a commercial one: «If a customer asks an AI tool what to buy, will my brand show up?» AI search analytics tools help answer this with data, not assumptions. Tools like HubSpot AI Search Grader, Semrush AI Toolkit, Ahrefs Brand Radar, Profound, Peec AI, AthenaHQ, OtterlyAI, Scrunch AI and similar monitoring platforms try to measure different aspects of new search: which prompts a brand appears in, which competitors are mentioned most often, which sources are used by AI systems, whether products are linked to the right categories, and whether site content is clear enough to be leveraged by answer engines.
This is especially important because AI Search is associated with the phenomenon of zero-click searches. Even before AI answers fully matured, users often found what they needed without clicking on a result. SparkToro's study with Datos showed that in the US 58.5% of Google searches ended up with no clicks, while only 36% resulted in clicks to the open web. In Europe, the corresponding percentages were 59.7% for zero-click and 37.4% for clicks to the open web. This doesn't mean that SEO is over; it means that visibility, credibility and brand mentions before the click are becoming more important than ever.
Source: SparkToro & Datos, 2024 Google Search clickstream analysis
In e-commerce, this reality is changing the way you need to evaluate the performance of your content. A category page may not get the same organic traffic as it used to, but it can feed AI responses with information on pricing, availability, specs, shipping, reviews and returns. A blog post may not always result in a direct session, but it can help an LLM connect your brand to a specific need, such as «sustainable children's clothing,» «ergonomic office chairs,» or «cosmetics for sensitive skin.» That's why new KPIs need to combine SEO analytics, AI search analytics, brand visibility and commercial intent.
HubSpot's article emphasizes that the market for tools is growing rapidly and not all tools are the same. To evaluate an AI search analytics tool, you first need to get clear on what you want to measure. If your goal is general brand visibility, you need tracking on prompts, mentions and competitors. If you're interested in technical presence in AI Overviews, you need analytics on citations, SERP features and structured data. If you have a large e-shop with thousands of products, you need segmentation capabilities by category, brand, product type, market and language. The ideal tool should not only show if you are showing up, but also why you are showing up or why you are not showing up.
The key features worth looking for are five. First, monitoring across multiple AI environments, such as ChatGPT search, Perplexity analytics, Google AI Overviews and other answer engines. Second, prompt tracking capability, so you can track specific questions that resemble actual customer behavior, not just keywords. Third, competitive share of voice AI analytics, because the real issue is not just whether you show up, but whether you show up more or less than your competitors. Fourth, citation and source analysis, so you know which URLs, articles, product pages or third-party mentions are influencing your presence. Fifth, actionable recommendations for content optimization AI, i.e. clear suggestions on how to improve pages, structures, FAQs, schema markup and topic coverage.
The development of AI Overviews makes this need even more practical. According to a study by Semrush, the occurrence of AI Overviews in search results increased from 6,49% of queries in January 2025 to 13,14% in March 2025. This increase shows that AI answers are not an isolated feature, but are gradually being integrated into the everyday search experience. The graph below illustrates the trend based on Semrush's published data.
Source: Semrush AI Overviews Study, January-March 2025
The first step is to create a list of commercial prompts, not just keywords. If you sell home goods, don't just track «corner sofa». Create prompts like «which sofa is best for a small living room?», «compare affordable sofas with easy delivery in the UK» or «what features should I look for before buying a sofa online?». For each key product category, create 10 to 20 prompts that cover awareness, consideration and purchase intent stages. This will give you a more realistic picture than a classic keyword report.
The second step is to define competitors and benchmarks. In AI Search, success is measured not only by rankings but by presence within the answer. Record which brands are mentioned, in what order, in what context and with what sentiment. If your e-shop is mentioned as an «affordable option» but the competitor is mentioned as a «more reliable option», that's strategic insight. If you are not mentioned at all, you need to identify if content is missing, if the brand does not have enough credible mentions or if your pages are not structured enough to be understood by AI systems.
The third step is to map the sources that feed the answers. Check whether the AI tools are pulling information from your own site, marketplaces, review platforms, third-party articles, social content or competitors. This is critical because in many cases visibility into AI responses depends not only on on-site SEO, but also on the overall ecosystem of trust around the brand. For example, an e-shop with clear return policies, detailed product pages, rich FAQs, verified reviews and strong citations on trusted sites is more likely to appear as a trusted choice.
The fourth step is to link the findings to content improvements. This is where structured data plays a big role: Product schema, Review schema, FAQ schema, Organization schema and Breadcrumb schema help engines to better understand the content. At the same time, you need content that answers real customer questions clearly, without exaggeration and without vague descriptions. For each product category, create buying guides, comparison tables, pros and cons sections, availability information, shipping and after-sales support. GEO does not replace SEO; it extends it into an environment where the answer is the new search result.
The fifth step is to create a monthly reporting framework. Track AI visibility score, number of AI brand mentions, percentage of prompts where you appear, share of voice AI versus key competitors, citations to your site, mentions from third-party sources, sentiment and changes by product category. Combine this data with Google Search Console, GA4, conversion data and assisted revenue. This way, AI Search doesn't remain a vague concept, but becomes part of your marketing dashboard.
Data from AI search analytics tools are only valuable when they lead to decisions. If you see that your brand isn't being referred to high commercial intent prompts, start with pages related to those queries. Improve titles, descriptions, internal linking, FAQs, comparison content and schema. If AI answers mention competitors due to better reviews, invest in review acquisition and better social proof presentation. If tools show that answers are pulling information from third-party sites, consider digital PR, partnerships, expert content and references to trusted industry publications. Visibility in AI Search is built with a combination of technical purity, high utility content and external trust signals.
For e-commerce businesses, there is another important dimension: referral traffic from generative AI sources is starting to become commercially relevant. According to Adobe Analytics data for the US, traffic to retail sites from generative AI sources increased by 1,300% in the 2024 holiday season on a year-over-year basis, while in February 2025 it was up by 1,200% year-over-year. Although the starting base was small, the trend shows that AI assistants are starting to have a real impact on discovery and shopping journeys. The graph below shows the intensity of this growth.
Source: Adobe Analytics, data for U.S. retail websites
The practical approach for the next quarter is to select a core product category, create a prompt set, measure initial visibility, identify gaps and implement improvements. Don't try to improve everything at once. Start with categories with high margin, intense competition or high volume of organic demand. Then compare the before and after data: are you showing up on more prompts? Did citations increase? Are more of your products mentioned? Has sentiment improved? Is there an increase in branded searches or assisted conversions? These answers will show you if your AI Search strategy is producing real business value.
AI Search doesn't do away with SEO, but it does change the arena in which user trust is earned. Rankings still matter, but now you also need to measure whether your brand appears in the responses, whether it's associated with the right product categories, whether it's listed as a trusted option, and whether AI engines use your own pages as a source. The AI search analytics tools presented by HubSpot show the direction of the market: more focus on prompts, citations, LLM visibility, competitive share of voice and content quality. For an e-commerce brand, this is an opportunity to move early, organize your data and gain an advantage before the new environment becomes too competitive.
The right strategy starts with a simple premise: customers no longer search by keywords alone, but by questions, comparisons, intentions and expectations. The more clearly your content answers these needs, the more likely it is to become part of AI responses. And the more systematically you measure this presence, the faster you can improve it.
HubSpot: AI Search Analytics Tools
Gartner: Search engine volume will drop 25% by 2026
SparkToro & Datos: 2024 Zero-Click Search Study
Adobe Analytics: Generative AI traffic to retail websites
AI Search is search that provides synthetic answers through artificial intelligence, rather than simple lists of links. It is changing SEO as businesses need to ensure that their brand appears in these answers.
AI search analytics tools are critical for e-commerce brands because they measure brand presence and awareness in AI responses, beyond traditional SEO metrics. They help evaluate the brand's competitiveness and outreach strategy.
A reliable AI analytics tool should offer monitoring across multiple AI environments, prompt tracking, competitive analysis, citation analysis and actionable recommendations for content optimization.
An e-commerce brand can increase its visibility in AI Search by creating content that answers real customer questions, improving structured data, and building external trust through partnerships and mentions.
Success in AI Search for e-commerce businesses is measured through brand presence in AI responses, frequency of mentions, sentiment and quality of citations, as well as through an increase in branded searches and conversions.
GEO (Generative Engine Optimization) involves optimization for response engines and extends traditional SEO. It aims to improve the visibility and reliability of content in AI responses.
