The emergence of AI-generated search and discovery is prompting merchants to assess their products’ visibility on these platforms. Many search optimizers attempt to apply conventional metrics like traffic from generative AI and rankings within AI answers, but both approaches fall short. Traffic: Emphasizing traffic misses the core function of AI answers, which is to fulfill an on-site need rather than driving clicks. AI-generated responses usually lack links to branded websites. For instance, Google’s AI Overviews sometimes link product names to organic search listings. Therefore, visibility does not necessarily translate into traffic. A merchant’s products might appear in an AI answer but receive no clicks. Rankings: AI answers frequently include lists, leading many sellers to try to monitor rankings to rank at or near the top. However, tracking such rankings is unfeasible. AI answers are inherently unpredictable. A Sparktoro study found that AI platforms recommend different brands and in different orders every time the same question is asked by the same person. Better AI Metrics Below are improved metrics to evaluate AI visibility. Product or Brand Positioning in LLM Training Data Training data is crucial for AI visibility because large language models rely primarily on their learned knowledge. Even when querying Google or other sources, LLMs often use their training data to shape search terms. Hence, it’s vital to monitor what LLMs retain about your brand and competitors, especially any inaccuracies or outdated information. Then, prioritize supplying missing or corrected data on your website and across all owned channels. Manual prompting in ChatGPT, Claude, Gemini, and similar platforms can help identify these gaps. Useful prompts include: - “What do you know about [MY PRODUCT]?” - “Compare [MY PRODUCT] vs [MY COMPETITOR’S PRODUCT]. ” AI visibility trackers like Profound and Peec AI can automate these prompts to monitor product positioning over time. Keep in mind that: AI tracking tools submit prompts through LLM APIs.

However, humans often receive different results due to personalization and variation among AI models. API-generated results are superior for assessing training data since LLMs typically return information from their training data (rather than live searches) to conserve resources. Visibility scores in these tools depend entirely on the prompts used. Separate branded prompts into individual folders as they tend to score 100%. Also, prioritize non-branded prompts reflecting your product’s value proposition; irrelevant prompts generally score zero. Most Cited Sources LLM platforms increasingly perform live searches when generating responses, querying sources such as Google, Bing—or even communities like Reddit. Indeed, organic search affects AI visibility. Citations from these live searches, like articles and videos, influence AI answers. However, citations vary widely because LLMs explore many different (and often unrelated) queries. Consequently, aiming to feature in every cited source is unrealistic. Still, certain prompts repeatedly produce the same influential sources, which merit consideration for including your brand or product. AI visibility tools can compile the most cited URLs related to your brand, product, or industry. Brand Mentions and Branded Search Volume Use Search Console or similar traditional analytics tools to track: - Queries containing your brand name or variations. - Clicks deriving from these queries. - Impressions related to these queries. The more AI answers incorporate a brand name, the more likely users will search for it. In Search Console, apply a filter in the “Performance” section to analyze data from branded queries.