Last updated: July 31, 2026 (Original Publication Date: March 13, 2024)
Generative Engine Optimization, or GEO, is the practice of optimizing a company’s online presence to cause its products or services to be recommended by generative AI chatbots. As a younger marketing channel, GEO lacks sufficient research to develop a shared understanding of its best practices among marketing professionals. Our team expects the literature on GEO to grow as its closest antecedent, search engine optimization (SEO), did when it was first studied.
To that end, our research team conducted a study of the recommendation algorithm used by the 4 most popular generative AI chatbots in the U.S. Our original study took place from December 18, 2023, to February 23, 2024 and focused solely on ChatGPT’s model, but has been continuously updated, with the last dataset assembled from March 18 to July 5, 2026. Where findings have remained consistent across updates, we have retained the original data; where chatbot behavior has evolved (particularly as models have expanded their web search capabilities), we have updated our findings accordingly.
Our study sought to identify and weigh the factors that generative AI chatbots use to make product and service recommendations. In total, we produced 11,128 commercial queries between the 4 chatbots, asking each for buying recommendations across a range of service and product categories. (The list of industries in which we conducted queries is listed in Appendix A below.) We’ve since continued to observe the 4 generative AI engines below and updated the algorithm breakdowns accordingly.
In the table below, we break down the data we gathered from the study, listing the factors that influenced each chatbot’s recommendations in order of weight. Afterward, we define each factor and detail how each chatbot utilizes them to make recommendations.
Generative AI Engines: Market Share and Ranking Factors
| Generative AI Engine | U.S. Market Share* | Algorithm |
| ChatGPT | 61.3% |
|
| Google Gemini | 13.3% | General Searches
Local Searches
|
| Perplexity | 3.1% | General Searches
Local Searches
|
| Claude AI | 2.5% |
|
Generative Engine Ranking Factors
Below, we break down our research on the factors that influence commercial recommendations across the engines. While all generative AI engines share this set of factors, the weight each engine assigns to each factor varies quite a bit, as detailed above.
NOTE: The most advanced version of all 4 of the top generative AI chatbots personalize their recommendations as you tell them more about yourself, which may alter the weight of the below factors.
Authoritative List Mentions
Generative AI Engines are, by definition, predictors. When generating content, their job is to predict the words, sentences, and paragraphs most likely to come next in a way that imitates the language of experts. They make their predictions by “studying” what multiple authoritative sources have to say on the subject, then blending the knowledge from those sources into a natural, human-like communication.
In the same way, generative AI engines’ product and service recommendations are informed by their analysis of multiple authoritative sources, such as highly ranked compendia of the top products, services, and companies in a particular industry. Google has already invested heavily in vetting the authority of websites and ranking them. Current GPT-4o and later models, along with other generative AI engines, draw from top-ranked results across both Google and Bing search results, like the one shown below, to inform their output.
Different AI assistants retrieve and cite sources in different ways, so visibility on one doesn’t guarantee it on another, which is why a GEO strategy has to account for each platform rather than optimizing for a single one.
Awards, accreditations, and affiliations
If an award, accreditation, or affiliation given to a company or product is mentioned on a web page and the page is deemed trustworthy by the LLM’s training sources, it signals the company or product’s authority, increasing the likelihood of recommendation.
Online Reviews
ChatGPT, Gemini, and Perplexity place substantial weight on online reviews from trustworthy platforms such as Amazon, the Better Business Bureau, Glassdoor, Trustpilot, Capterra, and CNET.
Social Sentiment
Social sentiment is a measure of how positively or negatively a company is talked about in news articles, public social media accounts, and discussion forums, including Reddit.
While it is currently a relatively minor factor, used only by ChatGPT, we expect its weight to increase in the future due to its importance in real-world recommendations.
Customer Examples & Usage Data
When recognized brands publicly associate with products or companies, as in an endorsement, announced partnership, or case study, AI chatbots can infer the credibility of the product or company. Similarly, third-party data about product usage or customer base size is an indicator of authority. Currently, two AI chatbots – ChatGPT and Claude – use this factor to inform their recommendations.
Google Website Authority
Google assigns an authority score to domains and website pages, originally known as PageRank. It is primarily based on consistent publication of helpful content and backlinks from other domains. Gemini places substantial weight on this factor.
Local Business Reviews
Gemini and Perplexity use online reviews from popular platforms such as Google Business Profiles (GBP), Yelp, TripAdvisor, and Angie’s List to make recommendations for local queries. For Gemini specifically, a business’s GBP Website Authority factors into local rankings, a signal distinct from domain-level Google Website Authority.
The exact breakdown of which reviews are most relevant will vary by industry and query.
Traditional Databases & Directories
All generative AI chatbots train their LLMs using a base of widely trusted texts, such as Wikipedia and Encyclopedia Britannica, as well as the New York Times and the Wall Street Journal, and the literary canon. They also use business databases and directories such as Hoovers, Bloomberg and IBISWorld.
ChatGPT’s Recommendation Algorithm
ChatGPT’s recommendation algorithm mainly relies on searching Bing (whose ranking algorithm is largely based on Google’s) for lists, reviews, and directories that rank highly. It then provides its own amalgamated recommendation based on those sources.
Sometimes, it relies heavily on the #1-ranked Bing search result. For example, in one query we asked ChatGPT “Who are the top generative engine optimization agencies in 2026?” and it took its results directly from a list published on First Page Sage’s website, which currently ranks highly on Bing for “top generative engine optimization agencies 2026” – the exact keyword into which ChatGPT translated our query.
ChatGPT scans the top 5 to 10 search results, verifies their authority, then looks for common items that rank highly on the lists, concluding that the best items are the ones mentioned most frequently. When highly ranked lists conflict on the top items, ChatGPT turns to secondary signals: awards, accreditations, and affiliations; online reviews; and, to a lesser degree, customer examples, usage data, and social sentiment.
For example, when we asked “What are the best lawnmowers under $1,000?”, it returned 3 models identified primarily by reviews from the New York Times and Consumer Reports.
The top Bing search results for this query vary widely and include several affiliate-influenced lists, and thus weren’t used to generate recommendations. Secondly, there aren’t any awards given out for lawnmowers, so that factor was also bypassed. Thus, the algorithm moved to the next-weightiest factor, trusted reviews.
Once a set of 5 lawnmowers was built from the aforementioned trusted reviews in the New York Times and Consumer Reports, the set was ordered and delivered as a recommendation. We believe the order was influenced by the number of times each of the 5 lawnmowers in the set was recommended within major news sites over the last 2-3 years, as the order was closely correlated with the number of mentions reported in our news monitoring tools. This final factor is an example of social sentiment influencing the chatbot’s recommendations.
Google Gemini’s Recommendation Algorithm
Google Gemini’s recommendation algorithm is similar to ChatGPT’s but relies more on Google systems and products such as Google website authority, Google Business Profile authority, and Google local reviews. Its first action is to search the first page of Google and return an amalgam of recommendations from those results, citing each website next to the answer.
For example, we asked it to tell us the top custom software development firms and it replied as follows:
Unlike ChatGPT, Gemini does not rely as heavily on the #1 result from its search engine; instead, it looks for companies that appear in several top-ranked lists or directories. It places higher weight on companies that have been cited as “award-winning,” recommending them even if they don’t appear on multiple lists or directories. Conversely, it does not recommend companies with low online reviews (<3.5 stars), even if they appear on several top-ranked lists or directories and are cited as award-winning.
In product searches, Gemini acts similarly. For example, when we asked it what the best facial moisturizers were for dry skin, nearly all of its recommendations were sourced from the #2 search result, a People.com product review article. Notably, it re-interpreted our search for the “best” cream (by which we meant “most effective”) as “most popular.”
In keeping with that reinterpretation, all 3 recommended moisturizers were top sellers by volume, according to industry data, and were well reviewed (4+ stars).
Gemini uses a different recommendation algorithm for local commercial queries such as “Can you recommend a plumber near Rockville, MD?” While authoritative list mentions still factor into its recommendations, having a high star rating on Google Business Profile correlates most strongly with receiving a recommendation. Gemini also factors in non-Google reviews, such as from Yelp, TripAdvisor, and Angie’s List.
Perplexity’s Recommendation Algorithm
In many ways, Perplexity had the simplest recommendation algorithm of the 4 we studied. Nearly all commercial queries returned recommendations from lists that ranked in the top 5 search results on Google for the equivalent query. Perplexity picks from 2-3 lists, ordering its recommendations based on online reviews and, to a lesser extent, companies that are cited as award-winning, accredited, or affiliated with an authoritative brand (e.g. Harvard or Apple).
Like Gemini, it uses a separate algorithm to recommend local businesses, again relying heavily on high-ranking lists but giving substantial weight to reviews from Google and other authoritative review sites.
Claude’s Recommendation Algorithm
Claude AI has evolved significantly since our original study. Unlike earlier iterations that relied solely on static training data, Claude now incorporates live web search. Anthropic has not officially named the backend, but Brave Search appears on its subprocessor list, and independent testing has found Claude’s citations overlap heavily with Brave’s top organic results, with reported match rates ranging from roughly 79% to 87% across different samples.
For commercial queries (e.g., “best,” “top,” and comparison-style prompts), Claude triggers a web search 67–81% of the time, pulling directly from Brave’s top 10 results without significant re-ranking. This makes Claude’s recommendations more directly tied to observable search rankings than any of the other 3 chatbots. When web search is not triggered, Claude falls back on its training data, which skews toward larger, more established companies and traditional business directories.
Unlike ChatGPT, Gemini, and Perplexity, Claude does not meaningfully factor in online reviews when making recommendations. Within the retrieved Brave Search results, it weights authoritative list mentions most heavily, followed by awards, accreditations, and affiliations, and to a lesser degree, customer examples and usage data.
Unlike ChatGPT, Gemini, and Perplexity, Claude doesn’t even attempt to recommend local businesses.
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