Why the F.A.C.T.S. Model Is the Key to Search Everywhere Optimization

For many years, Google has utilized a model for assessing the value of web content that goes by the acronym E.E.A.T., which stands for experience, expertise, authoritativeness, and trustworthiness (originally it was E.A.T.; in 2022 a second E was added for experience). Google recommends that web content creators display E.E.A.T. signals, which communicate value to searchers and which Google’s search algorithms also attempt to favor. 

Another longstanding formula from Google is Relevance, Distance, and Prominence. These three signals are included in the company’s document “Tips to Improve Your Local Ranking on Google” and represent the only official version of the factors that influence local search ranking. 

Models like these can be useful for assessing what you need to improve in order to boost rankings in organic or local search. But until now, there’s been no similar model for the factors that determine AI visibility, or that capture the priorities for multi-location marketers in the interconnected channels of search, social, reputation, and AI — that holistic strategy that we’ve begun to call Search Everywhere Optimization. 

This is why SOCi developed the F.A.C.T.S. model. F.A.C.T.S. stands for the factors we think are most important in a holistic strategy: Freshness, Authority, Consistency, Trust, and Semantic Relevance. 

In this post, we’ll explore how the F.A.C.T.S. model is grounded in research. For more information on how to use the F.A.C.T.S. model to boost your brand’s local visibility, see our F.A.C.T.S. checklist. 

Freshness

Freshness is about the recency of the content you publish both on your website and in third party profiles like Google, Yelp, Facebook, and Instagram. Fresh content is important to humans, of course; and in turn, AI platforms and search engines also strongly prefer recent content.

The research backs this up. A recent Ahrefs study found that the average URL cited by AI platforms is 25.7% more recent than in traditional search. Even more strikingly, according to AirOps more than 70% of pages cited by AI were updated in the last 12 months; SE Ranking even found that on ChatGPT, 76.4% of the top cited pages were updated in the last 30 days. 

Authority

On this topic, our model is similar to Google’s. Authority is a complex signal that includes all of the ways a brand can convey that it is recognized as a leader in its industry. If you’ve been in business since 1963, listing that fact in your Google profile is one example of signalling authority. Authority can also be conveyed by other trusted online resources, such as professional certifications, best-of lists, and online publications that speak positively about your brand. 

Because authority is such a complex signal, it can be hard to cite definitive research proving its importance. Just the fact that it’s part of E.E.A.T. shows how significant it is; and Google’s Prominence factor in local search is another way of describing authority as well. One key finding, again from AirOps, makes the point that brands who publish authoritative content in their area of expertise and are recommended by trusted online sources are 40% more likely to appear in AI answers than brands lacking one or the other. 

Consistency

In a local visibility context, the topic of consistency has evolved over time. It used to be important to have the name, address, phone number, and website (NAPW) of your brand’s locations cited consistently on as many directory sites as possible. With the contraction of the competitive landscape and the rise of Google, these long-tail citations became largely irrelevant. 

But the rise of AI, with its need to ground answers in well-known sources, has re-emphasized the need to manage your brand’s presence in several places – not on hundreds of sites, but on the short list of sources AI platforms are most likely to cite for local queries.

SOCi’s research on this topic indicates that Google Maps, your business website, Yelp, and Facebook (in that order) are the most commonly cited sources for local queries on ChatGPT, Gemini, and Perplexity. However, as we’ve also demonstrated, top sources differ somewhat by industry, and individual brands may find that their citation profiles differ from the norm. 

Our Local Visibility Index indicates that lack of consistency is one of the key factors leading to inaccuracy in AI mentions and lack of brand visibility in AI platforms. For example, whereas 98% of brand locations studied had a claimed Google profile, only 80% had claimed profiles on Yelp and only 53% were managing Facebook store pages. As a likely consequence, the overall accuracy rate of LLM citations for local brands is only about 79%.

Trust

We use the term trust to refer to signals from sources other than the brand that show you have the approval of both ordinary consumers and experts. In local search, trust is largely conveyed through ratings and reviews on sites like Google and Yelp, which are used as a primary selection factor by AI platforms when recommending local brands. 

Our Consumer Behavior Index shows that 92% of consumers consult online reviews when choosing a local business. Modeling themselves on consumer behavior, AI tools use reviews to determine if a brand is spoken of highly by its customers. The Local Visibility Index finds that the average rating of a business recommended by ChatGPT is 4.4 stars, higher than the rating of the average business on Google (4.2 stars) or Yelp (3.1 stars). AI platforms are setting a higher standard than ever for inclusion in a more selective set of results. 

Semantic Relevance

Like authority, semantic relevance is complex, but can be summarized by asking the question: does your brand — on its website, local landing pages, online profiles, and posts — offer answers to all the questions its ideal customers might have before choosing it over the competition? 

As with many of the factors in the F.A.C.T.S. model, this question is more important than ever in an AI context. Per Orbit Media, the average length of a query in traditional search is 4 words, whereas the average length of an AI query is 23 words. Consumers who use AI are asking longer, more nuanced questions, and brands must produce detailed, useful content to make sure they are not excluded from the conversation. 

In Conclusion

Hopefully this article has shown you how the F.A.C.T.S. model is grounded in empirical research. But it’s also important to note that the components of the F.A.C.T.S. model appeal directly to consumers, who need to feel that brands are offering up-to-date information; that they are authorities in their areas of expertise; that various sources speak of them consistently; that they can be trusted; and that they have answers to all of a customer’s important questions. No matter what the research shows, brands are on the right track if their online strategies are designed to meet these fundamental customer needs. It’s no accident that meeting the needs of potential customers puts you in good standing to appear prominently in search, social, and AI platforms.