As someone who’s been following OpenAI’s journey, I’m excited to share that they’re laying the groundwork for ChatGPT’s advertising business. These early steps reveal that OpenAI has more work to do to measure up against major players like Google when it comes to performance and ROI.
What’s happening. OpenAI has started testing an Ads Manager dashboard with a select group of partners, confirmed by sources at ADWEEK. This tool, aimed at marketers, allows for real-time campaign launching, monitoring, and optimization, drawing parallels with the established digital advertising management platforms.
Why it matters to me. OpenAI is building a self-serve advertising ecosystem around ChatGPT with the Ads Manager, in preparation for AI assistants becoming a significant channel. As conversational search becomes more prevalent, I believe it’s crucial for marketers like us to consider visibility in AI-driven responses, expanding beyond traditional platforms like Google Search.
Getting in on this early means we could gain unique insights into performance, formats, and optimization strategies within this fresh advertising landscape.
How it works now. For now, early testers are receiving weekly CSV performance reports, which include metrics like impressions and clicks. It’s evident that the ads product is in its initial stages, and more advanced analytics and tools are likely as the program matures.
The challenge: Initial tests indicate click-through rates for ChatGPT ads are lagging behind those of Google Search, marking a significant hurdle for OpenAI as they strive to showcase the value of advertising within conversational AI.
The cost of entry. Reports suggest that some early advertisers are being asked to commit a minimum of $200,000 in spend, significantly raising the stakes for OpenAI to deliver demonstrable performance and ROI.
Between the lines. Building an effective ad ecosystem entails more than just ad inventory. As marketers, we expect comprehensive reporting, optimization tools, and reliable performance — areas where established platforms like Google have a considerable head start.
Ever wondered what exactly Answer Engine Optimization (AEO) is? In this guide, I’ll walk you through how AEO works and share tips on getting your brand featured in AI-driven shopping responses on platforms like ChatGPT and Google.
By understanding AEO, you can enhance your brand’s presence when prospective customers ask questions related to your industry online. This guide aims to simplify the concept and provide actionable insights to get your brand noticed more efficiently across myriad digital touchpoints.
I’m thrilled to share how AI is revolutionizing content workflows. Imagine having AI-powered link suggestions seamlessly integrated into your writing process—before you even hit publish.
This innovation ensures our content is not only optimized for search engines but also rich in meaningful context for our readers.
As I delve into the world of AI-driven search, it’s clear that advice around AI is becoming way too simple. What really sets you apart are knowledge graphs, expert entities, and how you influence trusted datasets.
Recently, I came across a Harvard Business Review article that resonates with the shifts we’re noticing in SEO. AI Overviews and Google’s AI-enhanced search features are not only creating what’s known as a zero-click environment but they’re also redefining user journeys and behaviors.
User journeys that were once multi-touch are now compressed into a single, synthesized answer. The metaphor of the “Search” monolith crumbling visually captures this transformation.
In this dramatic shift, brands like mine lose many traditional touchpoints, requiring a change in marketing strategy. HBR brilliantly highlights how algorithms are reshaping first impressions. However, while pointing in the right direction, the article’s tactical advice feels too generic and superficial.
Much of the advice sounds strategic yet lacks deep operational insight. This gap is crucial for sustainable visibility and long-term success.
The challenge is deeper than what appears as simple advice to navigate at an executive level. Real structural change is essential to adapt to the evolving search landscape.
The Problem with Flock Tactics
The HBR article brings forward schema, authorship signals, and branded concepts but these suggestions risk becoming “flock tactics.” They spread because they’re easy to grasp, yet they lose their edge once widely adopted.
Schema
Schema is highly debated in LLM and AI optimization. Although Microsoft Bing uses schema for its LLMs, Google’s models have a more complex relationship with third-party LLMs.
Incorporating schema in AI and SEO activities is useful, but presenting it as a fundamental tactic neglects its diminishing returns when everyone implements it.
Another oversight is the importance of external knowledge systems such as Wikidata. LLMs often rely on these authorities more than on any single website.
There’s a significant gap in understanding how models process structured versus unstructured data signals.
E-E-A-T — Shallow Authorship Signals
Using real experts’ credentials aligns with E-E-A-T but often becomes superficial, focusing on bios and headshots without actually strengthening expertise.
There’s a profound difference between mere display of bios and nurturing an expert entity recognized in academia or industry.
Only genuine expertise creates the signals that AI models trust.
Vanity Concepts
Creating branded concepts like “The Acme Index” sounds appealing but is difficult to successfully execute. External adoption is key for them to gain traction.
These concepts must be embraced by reputable sources, which is a hurdle many brands fail to overcome.
The Structural Blind Spots
Beyond tactics, there are deeper structural issues in perceiving AI solely as an external shift rather than an opportunity to innovate internally.
Internalizing AI Infrastructure
The potential to integrate AI deeply into operations, through AI assistants or domain-specific agents, is often overlooked.
In controlled environments, fundamentals like site architecture and data structures remain crucial for success, even if they need to be reimagined for AI.
When I think about how ChatGPT retrieves information, I find it fascinating that most sources it pulls in don’t make it to the final answers. According to a report by AirOps, a whopping 85% of the sources identified by ChatGPT never appear in its final response.
Why this matters to me. If I’m aiming to have my content mentioned in AI-generated answers, it’s clear that simply being discovered by the AI isn’t sufficient. Most pages that get retrieved ultimately don’t get the exposure I’m hoping for.
Key insight. It’s interesting to note that just because a page ranks and is retrieved doesn’t mean it gets cited. My content has to align closely with the prompt or the context it supports to be chosen.
Per the report: the focus shifts to how well I can optimize my content for selection in the AI synthesis process, beyond just showing up in the search results.
By the numbers:
82,108 citations appeared in final responses, but only 15% of the retrieved pages were mentioned. That means 85% of the pages that surfaced during research didn’t make it into the answers.
Citation rates also varied based on query type:
18.3% for product discovery queries, 16.9% for how-to queries, and 11.3% for validation searches.
Fan-out queries. I noticed that when ChatGPT generates an answer, it often triggers additional internal searches, resulting in a “second citation surface.” This stood out in the dataset findings:
89.6% of prompts prompted two or more follow-up searches. Fan-out searches expanded 15,000 prompts into 43,233 queries. Interestingly, 32.9% of the cited pages were results from these fan-outs and not the original prompt.
95% of fan-out queries had zero traditional search volume.
Google ranking correlation. I’ve learned that high rankings in Google significantly improve chances of citation:
55.8% of cited pages ranked within Google’s top 20. Pages in Position 1 were cited 3.5 times more often than those outside the top 20.
About the data. AirOps examined 548,534 pages from 15,000 prompts to understand how ChatGPT expands queries and selects which citations to include.
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.
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.
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.
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.
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
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.
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
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.
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.
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.
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.
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.
I’ve always loved exploring new places, and now Google Maps is making it even more exciting with its new feature, ‘Ask Maps.’ This AI-powered addition transforms the way I interact with maps by allowing me to simply ask questions and receive personalized recommendations.
Google has introduced this conversational AI feature to assist us in navigating complex real-world queries. ‘Ask Maps’ leverages Google’s Gemini AI models to provide us with personalized, actionable answers tailored to our preferences and needs.
What’s new and exciting? Now, I can ask questions like, “Is there a public tennis court with lights that I can play at tonight?” or “My phone battery is low — where can I quickly charge it nearby?” The magic of ‘Ask Maps’ is in its ability to give me a conversational response complete with a custom map view.
Key capabilities include:
Personalized recommendations — Google Maps remembers my search and save history, which means it knows I love vegan restaurants before I even ask!
Trip planning — I can request recommended stops along my route and receive insightful details like directions, ETAs, and tips from over 500 million community contributors.
Direct action — I love how I can book reservations, save interesting places, or easily share them with friends right from the response.
Why do I care? ‘Ask Maps’ is revolutionizing the way I discover places by shifting the focus from simple keyword searches to interactive, AI-driven recommendations. Businesses wanting to be noticed need rich, accurate, and engaging Google Maps profiles as this is the data utilized by Google’s AI for recommendation making.
What to keep an eye on: ‘Ask Maps’ is already being rolled out in the U.S. and India for both Android and iOS, with desktop access coming soon. I’m excited about these advancements!
What’s next? As AI plays a bigger role in how we find places, it’s crucial for advertisers and local businesses to keep their listings accurate and review-rich to make the most of Gemini’s capabilities. I’m looking forward to how this changes the landscape for businesses.
Hey there! I’m excited to share some insights into a groundbreaking partnership between Profound and Partnerize. It’s all about using AI to turn visibility into verified revenue. Trust me, this is a game-changer for any brand eager to scale up their AI investments smartly.
AI Search is evolving at lightning speed, and as brands, we need to do more than just monitor our AI visibility. The key is figuring out how to measure its value effectively. Those who master this will be the ones leading the pack in scaling their spending efficiently.
Partnerize’s powerful payment infrastructure, which already handles billions in partner transactions, gives us a robust platform to ensure these measurements translate into real financial gains. Imagine being able to track and verify revenue directly tied to AI visibility—sounds like a win, right?
When I think about the leading agencies that help brands achieve stellar AI visibility, a few standout names come to mind. These agencies are experts in enhancing LLM citations and ensuring that brands remain discoverable across cutting-edge platforms like ChatGPT, Gemini, and Perplexity.
It’s fascinating to see how these agencies navigate the complexities of AI optimization to ensure their clients not only capture the audience’s attention but also maintain a strong presence in the digital realm. Their expertise is invaluable for brands looking to thrive in an ever-evolving technological landscape.
By leveraging their skills in areas such as AEO, AI SEO, and other digital strategies, these agencies provide comprehensive solutions to enhance online visibility and brand strength. Their innovative approaches keep brands at the forefront of AI advancements, making them essential partners in digital success.
I’m thrilled to share that Profound Agents can now seamlessly create presentations, documents, and webpages within Gamma as part of my automated workflows. No more hassle of exporting data and rebuilding it elsewhere. My Agent takes the outputs from upstream nodes and crafts them into ready-to-share assets in Gamma, streamlining the entire process.
