More Fan-Out SEO Tactics

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.

In “SEO for Google’s AI Fan-Out Results,” 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.

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.

It’s a valid strategy, but there is much more to fan-out optimization.

Position Products

Fan-out responses are unpredictable. Gemini can fan out differently for the same prompt. Yet all fan-out results identify areas to target.

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.

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.

Collectively, all fan-out angles can help position products.

Target Sources

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.

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

Third-party tools can help by running the prompts and generating citation reports. For example, Otterly.ai 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.

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.

Otterly.ai’s report lists the most cited URLs on leading large language models for a given prompt. Click image to enlarge.

Reveal Journeys

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.

Knowing potential fan-out questions can refine customer acquisition. Start with a Gemini prompt, for example:

What fan-out queries does Gemini or AI Mode use for “best organic skincare brands to try”?

In my testing, Gemini responded with options:

• What are the benefits of organic skincare? You can start with different well-known pain points of non-organic skincare to attract people researching those to your brand, for example: What are parabens? What skincare ingredients can cause breakouts? What ingredients should I avoid for sensitive skin? Do common skincare preservatives cause allergies?

• What is the difference between “natural” and “organic” skincare? (You can target audiences researching “natural” skincare by explaining how organic skincare may be what they are looking for)

• What common ingredients in non-organic skincare should be avoided?

Addressing these questions on-site can drive citations in fan-out responses and thus attract new prospects, such as those unaware of an organic option.