{"id":1555897,"date":"2025-08-26T09:00:26","date_gmt":"2025-08-26T13:00:26","guid":{"rendered":"https:\/\/www.practicalecommerce.com\/?p=1555897"},"modified":"2025-08-27T09:30:57","modified_gmt":"2025-08-27T13:30:57","slug":"more-fan-out-seo-tactics","status":"publish","type":"post","link":"https:\/\/www.practicalecommerce.com\/more-fan-out-seo-tactics","title":{"rendered":"More Fan-Out SEO Tactics"},"content":{"rendered":"

Large language models such as Google’s AI Mode, ChatGPT, and Perplexity anticipate likely follow-ups to an initial query and provide the combined info in a single answer. Google called it “fan out” results when announcing the practice in a March 2025 blog post. Hence we now use the term for all generative AI platforms.<\/p>\n

In “SEO for Google\u2019s AI Fan-Out Results<\/a>,” I addressed the basics. The platforms typically explain their fan-out reasoning, or we can deduce it from the response. For example, answers often include safety precautions, selection criteria, and additional sources for informed decisions.<\/p>\n

Search optimizers increasingly target fan-out queries for on-page optimization tactics, such as publishing an FAQ section for likely fan-out answers to increase the site’s chances of being cited.<\/p>\n

It’s a valid fan-out strategy, but there is much more.<\/p>\n

Position Products<\/h3>\n

Fan-out responses are unpredictable. Gemini can fan out differently<\/a> for the same prompt. Yet all fan-out results identify areas to target.<\/p>\n

For a prompt of “best organic skincare brands to try,” Gemini would likely fan out to include prominent brands, “brands for sensitive Skin,” most affordable brands, and “unique specialties,” such as “plant-powered formulas,” cruelty-free brands, and brands that use natural ingredients for makeup colors.<\/p>\n

Prompting Gemini again with the same query could produce fan-out results for official certifications for organic products. Another might include user ratings and reviews.<\/p>\n

Collectively, all fan-out angles can help position products.<\/p>\n

Target Sources<\/h3>\n

Generative AI platforms vary in topic expertise. For example, prompting “best running shoes” in ChatGPT typically includes fan-out results from Runner’s World, owing to its thorough comparisons and lists for runner-related products.<\/p>\n

Knowing these oft-cited publications can impact merchants’ product positioning. Prompt multiple platforms, such as ChatGPT, Perplexity, and Gemini, and note the citations.<\/p>\n

Third-party tools<\/a> can help by running the prompts and generating citation reports. For example, Otterly.ai<\/a> creates a consolidated report of “most cited domains” based on your tracked prompts. The report shows results from ChatGPT, Perplexity, Microsoft Copilot, and Google’s AI Overviews, revealing the overlapping citations.<\/p>\n

From there, merchants can solicit those editors and writers on social media and elsewhere to explore visibility tactics such as contributing a column or becoming an editorial source.<\/p>\n

\"Screenshot<\/a>

Otterly.ai’s report lists the most cited URLs on leading large language models for a given prompt. Click image to enlarge. <\/em><\/p><\/div>\n

Reveal Journeys<\/h3>\n

Shoppers start buying journeys differently. Some search niche terms, while others seek solutions to specific needs. All require unique landing pages and content to engage with a brand.<\/p>\n

Knowing potential fan-out questions can refine customer acquisition<\/a>. Start with a Gemini prompt, for example:<\/p>\n

What fan-out queries does Gemini or AI Mode use for “best organic skincare brands to try”?<\/em><\/p>\n

In my testing, Gemini responded with options:<\/p>\n