I recently discovered how AI is revolutionizing the way customers find local businesses. Tools like Google AI Overviews, Gemini, and Ask Maps are paving the way for more detailed, conversational searches.
It’s clear to me that traditional search rankings are no longer the sole factor in gaining visibility. Ensuring your business details are complete and accurate—like your Google Business Profile, reviews, and local content—can make a big difference.
I’m excited to join SOCi and Google for an exclusive webinar, Winning the Next Era of Local Visibility, on June 3. It’s a golden opportunity for anyone looking to stay ahead of the curve.
During this webinar, I look forward to learning:
How AI is transforming local search dynamics.
The types of signals that AI considers for recommendations.
Strategies to boost visibility on Search, Maps, and Gemini.
The implications of Ask Maps for your brand.
I’m convinced that AI is already shaping customer discovery, so it’s crucial to ensure your business isn’t left behind.
I’ve been diving into the world of ChatGPT lately, and it turns out there’s a fascinating bias toward commercial intent in its fan-out analysis. Let me break down what this means for our content strategy.
Over the course of my tests with 90 ChatGPT prompts, I discovered that commercial prompts lead to web searches a whopping 78.3% of the time, while informational prompts only did so 3.1% of the time.
This discrepancy got me thinking about how to craft our content to increase the chances of being featured in ChatGPT responses.
ChatGPT doesn’t source every response from the same place. Some responses are derived from its training data, while others are based on live web searches. This process, known as query fan-out, involves expanding a prompt into several background searches, and then curating a synthesized response from multiple topics. If our pages don’t fit into these subtopics, we miss out on getting pulled in.
So, our challenge now is more than just ranking well; it’s about ensuring our pages open the door to this fan-out process from the get-go.
In our samples, informational pages fell short. I encourage you to continue reading to uncover the paths the system actually favored.
I conducted tests across three industries: beauty, legaltech/regtech, and IT. The analysis explored prompt intents, the resulting query expansions, and the intents portrayed by those expansions.
The main takeaway is that most queries are aligned with commercial, rather than purely informational, intent.
Why This Question Matters Now and the Role of Query Fan-Outs
Understanding query fan-outs is crucial because it shifts the content creation approach. The system expands a prompt into several background searches, retrieving and synthesizing information from these subtopics.
This behavior triggers parallel web searches connected to the initial prompt, providing opportunities for mentions and citations.
Multi-query expansion is a fundamental design element in today’s search systems. As Google describes AI Mode, it breaks a question into subtopics, searches them simultaneously across sources, and combines the results into one coherent response.
This raises a key strategic question: should we focus more on top-of-funnel educational content or on lower-funnel comparison, shortlist, and recommendation content?
I designed this experiment to address that problem.
We aimed to see where fan-out occurs by intent category across selected industries: informational, commercial, transactional, or branded.
The hypothesis was clear: while informational prompts wouldn’t trigger fan-out, commercial ones would, and would either remain at the same level or move further down the funnel.
ChatGPT fan-outs were observed to align predominantly with commercial intent.
Disclaimer: This analysis reflects observed prompt expansion behavior in ChatGPT. Although Google AI Mode is cited for context to illustrate multi-query expansion as a pattern, it is not evidence of ChatGPT’s architecture.
The Setup: What We Tested
The experiment sampled 90 prompts, focusing heavily on informational intent.
Prompt intent
Prompts
Share of sample
Prompts with fan-out
Fan-out rate
Informational
65
72.2%
2
3.1%
Commercial
23
25.6%
18
78.3%
Branded
1
1.1%
0
0.0%
Transactional
1
1.1%
0
0.0%
Our sample primarily featured informational prompts, with some commercial and very few branded and transactional ones.
The test was structured around three industries: beauty/personal care, legaltech/regtech, and IT/tech.
The Result: Commercial Prompts Dominated
The findings were clear and conclusive.
Of the 90 prompts, 20 triggered a fan-out. Remarkably, 18 were of commercial intent and only 2 were informational.
Informational prompts accounted for a mere 10% of fan-out triggers (2 out of 20). When they triggered expansion, they were reframed into more evaluative, solution-seeking subqueries.
This indicates that, in this sample, commercial intent overwhelmingly influenced fan-out.
These 20 prompts resulted in 42 fan-out queries, averaging 2.1 per triggered prompt.
Here’s a breakdown of those fan-out queries:
39 were commercial.
2 were branded.
1 was informational.
Even if a prompt led to expansion, it typically transformed into a focus on comparison, feature filtering, shortlist creation, or brand-specific exploration, not broad educational discovery.
Methodology: Our Analytical Approach
Our experiment involved 90 prompts across three industries, mainly informational with fewer commercial prompts, and minimal branded and transactional queries.
The analysis involved:
Choosing a representative set of prompts.
Identifying fan-outs.
Classifying each fan-out by intent.
Analyzing distribution by prompt metadata.
Our approach followed three key steps:
Classifying prompts by intent labels.
Counting prompts that triggered any fan-out.
Reviewing expansion queries and their intent labels.
This process revealed two distinct perspectives:
A prompt-level view to determine which prompts instigated fan-out.
A fan-out-query view to assess the intent of downstream expansions.
This distinction is important: the first identifies prompts that initiate the fan-out path, while the second examines where the system proceeds once engaged.
Interpreting the Results: Fan-Outs Trend Down-Funnel
The clearest takeaway is that, in this instance, fan-outs behave more like decision support rather than topic exploration.
Commercial prompts frequently opened new discovery paths.
Once open, these paths typically remained commercially focused.
The system often expanded into comparisons, feature-based analyses, product listings, and pricing inquiries.
Here are some illustrative examples:
“Suggest the best accounting software for small business and explain why” expanded to a commercial comparison query on features.
“What are the top AI document management systems for lawyers?” led to multiple product-centered legaltech queries.
“What are the best products for skin care?” grew into a shortlist-style inquiry around product categories and reviews.
The rare informational examples expose more about the system’s tendencies than the rules themselves.
“I need an open-source document management system. What can you suggest?” initially coded informational, shifted to solution recommendations.
“AI tools for legal research and document automation” also redirected into clearly commercial/evaluative queries.
Ultimately, even broad prompts frequently translate into more focused, commercially driven retrieval paths.
Implications for Our Content Strategy
Let’s not abandon informational content; however, we should recognize that informational content alone doesn’t consistently align with fan-out expansions, at least in this dataset.
If our goal is to shine in AI responses tied to product selection or vendor discovery, we need to strengthen our coverage with content that lines up with these downstream commercial intents.
Consider the following:
Creating “best-of” and shortlist pages
Developing thorough comparison pages
Writing “which tool should I choose” guides
Feature-led category explainers
Alternative option pages
Evaluation-focused FAQs
Incorporating recommendation passages in broader educational pieces
In practical terms, our content model should integrate both top- and bottom-of-funnel strategies, with strong commercial bridges.
A comprehensive piece can still be beneficial, provided it contains elements that the system can readily transform into decision-support inquiries.
An educational piece that lacks direct references to products, tradeoffs, features, use cases, or selection criteria is less likely to match the system’s fan-out paths.
In short, consider not only answering the obvious inquiries but also forecasting the subsequent evaluative step the system might generate behind the scenes.
Understanding Our Limitations
These results offer direction rather than universal truths.
90 prompts highlight a pattern, but don’t establish AI retrieval behavior as a law.
The prompt mix skews heavily towards informational content, with few branded or transactional samples. The findings don’t signify absence.
While diverse, the dataset isn’t normalized for brand, style, or use case. Some sectors lean easily into product-discovery language.
This analysis observed recorded fan-outs rather than controlling for platform-level testing. It reflects what occurred within this set rather than guarantees of ChatGPT’s constant behavior.
Google’s fan-out description provides context; however, this isn’t a Google AI Mode test. It’s ChatGPT-centric, with strategic—not architectural—takeaways.
Next Steps for Testing
Future versions of this test should further isolate the question while widening the dataset.
A follow-up should map fan-outs to specific content formats.
The aim isn’t solely to affirm that commercial intent triumphs, but to pinpoint which page templates and structures proficiently capture AI-preferred fan-out paths.
I’ve recently learned that YouTube is testing an innovative search feature called “Ask YouTube”. This aims to make searching on YouTube more conversational and interactive, just like Dave from YouTube explained. It deepens our interaction with content, allowing us to explore topics with more depth.
What it looks like. I had the chance to see it in action through a captivating GIF:
How can I try it? If, like me, you’re curious to test this feature, visit youtube.com/new. There, you can opt-in to experience this new way of interacting with YouTube.
Currently, this experiment is only open to Premium users in the US who are 18 and older. However, Google has plans to expand access soon, which is promising for non-Premium users.
What it does. Here’s an example shared by Dave from YouTube:
“If you’re in the experiment, you can try it out by selecting “Ask YouTube” in the search bar. For instance, you might ask for help planning a 3-day road trip from San Francisco to Santa Barbara. Instead of just a list of videos, you’d receive a detailed, step-by-step itinerary. The response incorporates a mix of long-form videos, Shorts, and informative text, featuring local tips and must-see stops. You can even ask follow-up questions, like “where can I find good coffee?” to discover local gems along your journey. This approach surfaces various videos and video segments, complete with titles and channel details, making it easier to find new creators and content that matches your search.”
Why we care. The integration of AI search is becoming prevalent in all Google platforms, and YouTube is joining this transformation. We should anticipate more AI-enhanced search experiences across various Google services as they evolve over time.
For more insights and updates, you can check out detailed coverage on Techmeme.
I often wonder how to adapt my content marketing strategies in today’s AI-driven world. With AI acting as the discovery layer, it’s crucial for me to rethink how my content is found and consumed.
I’ve learned that developing a robust content marketing strategy in the AI era requires integrating original insights citations in AI-generated answers. This approach is vital to enhancing the visibility and credibility of my content.
The reasoning-based discovery layer offered by AI provides an unprecedented opportunity for me to reach audiences more effectively. By leveraging these AI capabilities, I can ensure that my content not only reaches but resonates with my target audience.
As I dove into the fascinating world of ChatGPT-driven shopping, I discovered that Walmart and Target are key players. In fact, Walmart often tops the charts when it comes to rank-1 buy links. Meanwhile, Target excels in overall presence, offering a variety of options that captivate users.
What surprised me the most is the dynamic nature of the recommendation system. The carousel reshuffles with every request, ensuring that the shopping experience remains fresh and personalized. This shuffling uncovers intriguing patterns in user behavior, drawing insights from the staggering 22.5 million shopping offers analyzed.
I recently embarked on a fascinating journey to explore how ChatGPT’s Shopping feature is activated. It’s intriguing how product categories seem to play a more significant role compared to purchase intent language.
In my analysis of 1.18 million prompts, supported by a detailed review of 7,500 labeled examples, I discovered a notable pattern. Prompts that specifically mention shippable consumer goods are highly likely to trigger Shopping cards. However, prompts about software, services, travel, and financial products almost never have the same effect.
I noticed that adding specific constraints, like price, features, or intended use, boosted the chances of the Shopping trigger, though only within the confines of product categories.
The process boils down to a straightforward rule: if the primary noun in your prompt is something you could easily buy on Amazon, there’s a good chance the Shopping feature will appear. Using this logic, I developed a classifier that can replicate ChatGPT’s Shopping behavior with an impressive accuracy of around 95–97%.
I’ve learned that few searches actually lead to clicks, and discovery now occurs across AI, social media, and search engines. To keep our ecommerce brand visible, we need to make smart organic content investments.
The landscape of organic content is changing, shifting from a mindset of ‘publish more’ to ‘prove more.’ AI summaries and shopping features directly answer user questions in search results, which means visibility alone isn’t enough to resolve buyer uncertainties.
As an ecommerce brand, our goal is to achieve organic visibility that garners recognition and trust amid the SERP noise. It’s crucial to invest in organic assets that achieve three things:
– Reduce buyer uncertainty.
– Are easily readable by machines.
– Work across multiple discovery platforms.
The forces shaping organic content’s ROI in 2026
I’m observing three key forces influencing how content performs in searches today.
AI discovery is normal now
Generative AI is a regular feature in organic search results, providing direct answers to broad questions through tools like Google’s AI Overviews. These systems often use citations from web content to form their answers.
