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Unveiling GEO: How AI Chatbots Shape Product Recommendations

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Written by

shivamcrushpressai

in

,

Last updated: March 12, 2026

As I dive into the intriguing world of Generative Engine Optimization (GEO), I find myself exploring how we can fine-tune a company’s online presence to have their products or services recommended by generative AI chatbots. Although still a budding marketing avenue, GEO’s potential reminds me of the early days of SEO, ripe for exploration and growth. I’m convinced that the deep insights from this research will pave the way for much-needed best practices in the market.

My team and I embarked on an extensive study from March 2024 through December 2025, focusing on the recommendation algorithms of the four most popular generative AI chatbots in the United States. We meticulously conducted 11,128 commercial queries across various sectors, seeking to unravel the factors these chatbots use to recommend products and services. We’ve continued to update our insights, the latest being on March 12, 2026.

The table below gives a detailed breakdown of our research findings, listing the factors influencing chatbot recommendations in terms of weight. Following the table, I delve into each factor, elucidating how each chatbot incorporates them into their recommendation process.

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Generative AI EngineU.S. Market Share*Algorithm
ChatGPT61.3%
  • Authoritative list mentions: 41%
  • Awards, accreditations, & affiliations: 18%
  • Online reviews: 16%
  • Customer examples & usage data: 14%
  • Social sentiment: 11%
Google Gemini13.3%

General Searches

  • Authoritative list mentions: 49%
  • Google website authority: 23%
  • Awards, accreditations, & affiliations: 15%
  • Online reviews: 13%

Local Searches

  • Local business reviews: 38% 
  • Authoritative list mentions: 29%
  • Online reviews: 19%
  • GBP website authority: 14%
Perplexity3.1%

General Searches

  • Authoritative list mentions: 64%
  • Online reviews: 31%
  • Award, accreditations, & affiliations: 5%

Local Searches

  • Local business reviews: 39%
  • Authoritative list mentions: 34%
  • Online reviews: 27%
Claude AI2.5%
  • Traditional databases & directories: 68%
  • Awards, accreditations, & affiliations: 19%
  • Customer examples & usage data: 13%
*Source: Generative AI Chatbots by Market Share

Generative Engine Ranking Factors

Allow me to take you through the key factors that guide commercial recommendations across these generative engines. Although they share common factors, each employs a unique weighting system to determine recommendations.

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NOTE: The more advanced versions of these AI chatbots may personalize their suggestions as more personal data is provided, potentially altering factor weightings.

Authoritative List Mentions

When it comes to predicting content, generative AI engines draw information from multiple authoritative sources. They echo the voices of experts, offering recommendations rooted in well-regarded lists and rankings. I find it fascinating how they lean heavily on top-ranking Google searches to refine their recommendations, which are potently informed by these highly authoritative sources.

```json { "alt": "Donut chart of ChatGPT's Recommendation Algorithm showing five components: Authoritative List Mentions, Awards, Online Reviews, Customer Data, Social Sentiment.", "caption": "Discover the key components that fuel ChatGPT's Recommendation Algorithm, from Authoritative List Mentions to Social Sentiment, each playing a pivotal role.", "description": "This image features a donut chart illustrating ChatGPT's Recommendation Algorithm. The largest segment, at 41%, is Authoritative List Mentions, followed by Awards, Accreditations & Affiliations at 18%, Online Reviews at 16%, Customer Examples & Usage Data at 14%, and Social Sentiment at 11%. Each section represents a different data source that contributes to the algorithm's overall functionality, highlighting the diverse inputs needed for accuracy. Key terms: recommendation algorithm, data sources, AI inputs, chart visualization." } ```

Claude stands apart, favoring traditional compendiums over Google-reliant lists, perhaps embracing a more traditional approach.

Awards, accreditations, and affiliations

Mentioning an award or accreditation on a trustworthy website signals authority, nudging AI to recommend the associated company or product more frequently. It’s quite interesting to see this recognition elevated in the virtual world.

```json { "alt": "Text detailing top generative engine optimization agencies for 2025, highlighting First Page Sage.", "caption": "Discover the leading GEO agencies of 2025, like First Page Sage, leveraging AI to revolutionize SEO and maximize visibility in generative engines.", "description": "An image listing top generative engine optimization (GEO) agencies as of mid-2025, highlighting the integration of traditional SEO with AI tools like ChatGPT and Gemini. It emphasizes agencies like First Page Sage, noted for their strong content strategy and leadership in 'thought leadership SEO.' Based in San Francisco, CA, First Page Sage is recognized for its high-editorial standards and AI integration, showcasing their commitment to excellence in the evolving landscape of generative engines." } ```

Online Reviews

Online reviews hold substantial sway for ChatGPT, Gemini, and Perplexity, especially reviews from platforms like Amazon, Better Business Bureau, and Glassdoor. I see how a confluence of positive reviews can significantly boost recommendation weight.

Social Sentiment

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It’s intriguing to see how the way a company is discussed online, including on news sites and social platforms like Reddit, subtly shapes ChatGPT’s recommendations. Its current influence is modest but poised for growth as trust builds in digital communities.

Customer Examples & Usage Data

Recognized endorsements and partnerships visibly boost a product’s credibility. This factor, used by ChatGPT and Claude, reinforces the reputational weight of significant customer associations or user data.

```json { "alt": "Two doughnut charts comparing Gemini's general and local recommendation algorithms with different factors like online reviews and website authority.", "caption": "Exploring Gemini's Algorithms: A visual breakdown of Gemini's general and local recommendation algorithms, highlighting the role of reviews, website authority, and list mentions.", "description": "This image features two doughnut charts illustrating the differences between Gemini's General and Local Recommendation Algorithms. The General Algorithm chart includes factors like Authoritative List Mentions (49%), Google Website Authority (23%), Awards (15%), and Online Reviews (13%). The Local Algorithm chart shows Local Business Reviews (38%), Authoritative List Mentions (29%), Online Reviews (19%), and GBP Website Authority (14%). Keywords: recommendation algorithms, online reviews, website authority, local business." } ```

Google Website Authority

Google attributes site authority based on factors like consistent content publication. Gemini values this significantly, drawing from Google’s well-established credibility measures.

Local Business Reviews

```json { "alt": "List of top custom software development firms ranked by expertise and industry specialization.", "caption": "Discover leading custom software development firms excelling in innovation, technology, and industry-specific solutions, recognized by DesignRush and Sphinx Solutions.", "description": "This image presents a list of top custom software development firms segmented into general top-ranked and industry-specific experts. Instinctools and Andersen are noted for their broad and comprehensive capabilities across technologies and FinTech, respectively. Instinctools has over 1000 successful projects, while Andersen boasts global reach with over 11 offices. Firms with industry expertise, such as Fingent Global Solutions and Onesoft Technologies, are highlighted for their specialized services in sectors like accounting, HR, and e-commerce, with significant presence in markets like India. Mentioned recognitions include DesignRush and Sphinx Solutions." } ```

For local queries, Gemini and Perplexity lean on reviews from Google Business Profiles and Yelp. This localized trust mechanism brings a nuanced understanding to the recommendation landscape.

Traditional Databases & Directories

Generative AI chatbots like Claude often delve into established resources like encyclopedias and business directories. This approach weights well-established data heavily in crafting precise business recommendations.

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ChatGPT’s Recommendation Algorithm

In my exploration of ChatGPT’s algorithm, I’ve noticed its reliance on Bing to gather authoritative lists, reviews, and rankings. It aggregates and refines recommendations through a blend of sources, ensuring a comprehensive outcome.

Often, top Bing search results heavily guide its recommendations, but in their absence, ChatGPT factors in alternative data like awards, reviews, and social sentiment. An illuminating example involved its interpretation of lawnmower choices guided largely by trusted reviews from notable publications.

```json { "alt": "Two pie charts compare Perplexity's General and Local Recommendation Algorithms showing shares of authoritative mentions, online reviews, and more.", "caption": "Compare the influences on Perplexity’s General vs. Local Recommendation Algorithms. Explore the impact of online reviews, authoritative list mentions, and more.", "description": "This image presents a comparison between Perplexity's General and Local Recommendation Algorithms through two pie charts. The left chart highlights the General Algorithm with 64% authoritative list mentions, 31% online reviews, and 5% awards and affiliations. The right chart for the Local Algorithm showcases 34% authoritative mentions, 27% online reviews, and 39% local business reviews. Each segment is color-coded for easier distinction, offering a visual insight into the factors affecting recommendation algorithms." } ```

Google Gemini’s Recommendation Algorithm

Gemini’s algorithm intrigues me with its Google-centric approach, harnessing authority and reviews together from search results to guide recommendations. Its unique method prioritizes recognized achievements, often steering clear of poorly reviewed companies.

In practical application, Gemini reinterprets product searches by balancing authority with popularity, evidenced by its moisturizer recommendations, aligning sales volume with positive reviews.

```json { "alt": "Donut chart showing distribution of ClaudeAI's recommendation algorithm with three segments: Traditional Databases, Customer Examples, and Awards.", "caption": "Explore ClaudeAI's Recommendation Algorithm: A visual breakdown showing the dominance of traditional databases, complemented by customer examples and accredited awards.", "description": "This donut chart illustrates the components of ClaudeAI's recommendation algorithm. The largest segment, at 68%, is Traditional Databases & Directories, shown in purple. Customer Examples & Usage Data represent 13% in green, while Awards, Accreditations, and Affiliations make up 19% in red. The chart provides insights into the diverse data sources informing ClaudeAI's recommendations." } ```

Perplexity’s Recommendation Algorithm

What strikes me about Perplexity is its straightforward algorithm, largely favoring search lists and reviews. It often taps into the most readily available online viewpoints to construct its recommendations.

For local business queries, its focus on high-ranking lists underscores a strategy based on easily established credibility from popular review sites.

```json { "alt": "Screenshot of a text providing names of well-known travel agencies in the US.", "caption": "Discover the top US travel agencies as recommended by an AI, featuring American Express Travel, AAA Travel, and Liberty Travel.", "description": "This image is a screenshot of text listing well-known travel agencies in the United States, including American Express Travel, AAA Travel, and Liberty Travel. The text clarifies that these names are based on the AI model's knowledge as of August 2023 and not current rankings. Useful for travelers seeking trusted agencies, this content is informative about travel planning options in the US." } ```

Claude AI’s Recommendation Algorithm

Unique in its approach, Claude AI depends on traditional databases, often highlighting historically established companies in its recommendations. This somewhat conservative method gives it a distinct identity in the generative AI landscape.

Focused purely on national businesses, it bypasses local recommendations altogether, streamlining its efforts towards broader-scale authority.

Downloading This Report & Inquiries

If you’re curious to learn more or desire a PDF copy of this report, please reach out via our contact page.

First Page Sage is also at the forefront of GEO services. Interested in knowing more? Don’t hesitate to contact us.

Inspired by this post on First Page Sage Blog.

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FAQs

What is GEO?

GEO stands for Generative Engine Optimization. It is the practice of fine-tuning a company’s online presence to influence product recommendations by generative AI chatbots; described in the article as a budding marketing avenue akin to early SEO.

Which AI chatbots are studied in the post?

The post examines four popular US chatbots: ChatGPT, Google Gemini, Perplexity, and Claude AI. It notes these engines were used in an extensive study of recommendation algorithms.

What are the main factors influencing chatbot recommendations?

The article lists factors such as Authoritative List Mentions, Awards, Accreditations & Affiliations, Online Reviews, Customer Examples & Usage Data, and Social Sentiment, with weights that vary by engine.

How does Gemini differ in weighting for general vs local searches?

Gemini uses a Google-centric approach, weighing authority and reviews from search results to guide recommendations. It differentiates between general and local algorithms, with local signals like Local Business Reviews receiving strong weighting.

What are the study's scope details (timeframe and sample)?

The study spanned March 2024 through December 2025 and analyzed four popular generative AI chatbots with 11,128 commercial queries. The researchers continued updating insights, latest as of March 12, 2026.

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