Definition
Search everywhere optimization is the practice of building visibility wherever a target audience looks for information before it decides: Google and Bing, AI assistants such as ChatGPT and Gemini, YouTube, TikTok, Instagram, Reddit and other forums, marketplaces like Amazon, app stores and review sites. It keeps the SEO acronym but reads the 'E' as 'everywhere' instead of 'engine'.
The term is credited to Ashley Liddell, founder of the UK agency Deviation, who began using 'Search Everywhere Optimisation' around 2023. Neil Patel promoted the phrase on LinkedIn in August 2024, describing SEO as changing from search engine to search everywhere optimization. Rand Fishkin gave it its widest audience in a SparkToro post on May 30, 2025, where he asked the industry to stop inventing acronyms such as AIO, GEO and LLMEO and use this one instead. He credited Liddell for the original use and NP Digital's Nikki Lam for adopting it in client work.
The difference from GEO is scope. GEO targets generative engines specifically. Search everywhere optimization starts from the audience and treats AI answers as one of several places a decision gets made. It is a channel-planning frame more than a set of techniques, and its main argument is that SEO practitioners already have the skills to work across platforms.
The channels also feed each other, which makes the frame relevant to AI visibility. AI answers cite community and video content heavily, so presence on those platforms can lead to citations; see Reddit citations and YouTube transcript citations. Our data on social media citations by AI model shows how often each assistant cites social platforms. Facts that differ between your site, your marketplace listings and your social profiles can also end up as contradictions in an AI answer, since the model may draw on all of them.
A search everywhere plan usually includes:
- Audience research on where people search for the topic and what they type or ask on each platform.
- Content shaped to each platform's format, such as short video, long-form guides, marketplace listings and forum answers, built from the same core facts.
- Consistent brand and product data across all of them.
- Earned coverage from creators, reviewers and communities, which also produces third-party mentions that AI models pick up.
Measurement is per channel: rankings in Google, visibility in AI answers, YouTube and TikTok search results, marketplace rank and share of conversation on forums. For the AI part, cross-platform AI visibility compares how the brand appears across ChatGPT, AI Overviews, Perplexity and other assistants. The broadest outcome signal is branded demand, such as branded searches and direct traffic, which tends to rise when a brand shows up in more places. AI shopping and marketplace search add a product-data layer to the same plan.
Examples of Search Everywhere Optimization
- A UK pet insurer finds its audience researches breeds on TikTok, compares prices on comparison sites and asks ChatGPT what policies exclude. It plans content for each, starting with a plain-language exclusions page that ChatGPT begins citing.
- A B2B data tool publishes benchmark results as a YouTube walkthrough with a full transcript, a LinkedIn article and a docs page. AI answers about the benchmark start citing the YouTube video and the docs page.
- A furniture brand fixes inconsistent dimensions between its Amazon listings and its own product pages after an AI shopping answer quoted the wrong width for a sofa.
- A developer tools company has two engineers answer recurring questions in relevant subreddits under clearly labeled staff accounts, and the threads begin appearing as sources in AI answers about the tool.
