Co-Citation and Co-Occurrence

Co-citation is when two entities, such as two brands, are mentioned or cited together by third-party sources; co-occurrence is when a brand repeatedly appears near particular topics or terms in the same text. In AI search both act as association signals: a brand that is consistently written about next to a category and its leading competitors is more likely to be named when someone asks a model about that category.

The term comes from bibliometrics. In 1973 Henry Small of the Institute for Scientific Information defined co-citation as the frequency with which two documents are cited together, counted from the Science Citation Index, and showed that clusters of co-cited papers map the structure of scientific fields (Small, 1973). SEO borrowed the idea in November 2012, when Rand Fishkin argued in a Moz Whiteboard Friday that search engines were learning associations from brands mentioned near relevant phrases without a link (his example was 'cell phone ratings' and Consumer Reports). He later clarified that he meant co-occurrence, which is why the two terms are often used loosely and interchangeably in marketing.

The mechanism matters more for language models than it ever did for link-based ranking. Models learn associations from how often things appear together in text. A 2022 study by Kandpal et al. (Large Language Models Struggle to Learn Long-Tail Knowledge) counted pre-training documents in which a question's entities and its answer co-occur, and found that BLOOM-176B's accuracy rose from 25% to above 55% as the number of such documents grew from 10 to 10,000. A brand that rarely co-occurs with its category in public text is the long tail the model has not learned. In retrieval-based answers the same pattern appears one step earlier: the listicles, roundups and comparison pages that query fan-out sub-queries retrieve are exactly the pages where brands are grouped with their peers. Ahrefs' May 2025 study of 75,000 brands found branded web mentions had the strongest correlation with AI Overview brand visibility (0.664) of any factor tested, against 0.218 for backlinks, though the authors stress the data is correlational.

You can see your co-citation set in AI answers themselves. The competitors named alongside you in AI share of voice data are the peers models associate you with. If an enterprise product keeps appearing next to free tools, or a specialist brand is grouped with generalists, the sources models read are pairing it with the wrong set. Working on co-occurrence usually looks like this:

• Map which brands and terms appear with yours in AI answers and in the sources those answers cite.
• Earn inclusion in the roundups, comparisons and trade articles that already get cited for your target category, through digital PR and outreach for third-party mentions.
• Describe your category in the same words across your site, profiles and press materials, so independent sources repeat consistent associations (entity consensus).
• Avoid manufacturing the signal with self-promotional listicles that rank your own brand first. In early 2026, Amsive's Lily Ray linked steep Google visibility losses at several SaaS brands to large numbers of these pages.

Co-occurrence does not require a link, and a linked mention in an unrelated context adds little association. What counts is where and next to what a brand is mentioned, and whether many independent sources agree.