As I delved into the intricacies of search engine dynamics, I realized that achieving top rankings on Google doesn’t necessarily translate to visibility in the realm of artificial intelligence. This understanding opened my eyes to the potential of strategies beyond traditional SEO.
Excitingly, I discovered the power of Answer Engine Optimization (AEO), a strategy that enables brands to secure mentions and citations across AI platforms like ChatGPT, Gemini, and Perplexity. This revelation reshaped my perspective on optimizing content for the future.
Embracing AEO allows me to steer my brand into the evolving landscape of AI-driven interactions. It’s about crafting content that these advanced engines recognize and prioritize, ultimately driving engagement in ways traditional SEO could never promise.
Have you ever felt puzzled by audience targeting in Google Ads? You’re not alone. I’ve often struggled with it too, particularly when it comes to custom segments. Yet, when understood correctly, these custom segments can become a powerful tool in our advertising arsenal.
Custom segments allow me to craft audiences using Google’s vast data pool. It’s like taking audience targeting to the next level by making it personal and tailored to individual user behaviors.
So, what exactly is a custom segment in Google Ads? Simply put, it lets us create an audience based on recent user interactions with content. Instead of targeting a generic website category, I’m aiming directly at users who have shown interest in specific topics, like running shoes, for instance.
I see content targeting transforming into a more focused audience targeting. It’s exciting and effective.
Building a custom segment is straightforward. From Audience Manager, I name my segment and choose up to four input types: interests, search terms, websites, and apps. This flexibility allows me to strategically target exactly who I want.
If I’m using multiple inputs, I prefer creating multiple custom segments. This way, I can track which segment performs better – whether it’s search terms or websites.
In my experience, search term-based custom segments often yield the best results. They specifically reach users who have searched for related terms, functioning much like Exact Match close variants.
Starting with search terms, I dive into Search, Shopping, or PMax campaigns to extract my top non-branded search terms. I use these to shape a new custom segment, ensuring to target people who searched for these terms on Google.
When applying this segment to a Demand Gen campaign, it’s crucial to stick to Google-owned networks like YouTube, Discover, Gmail, and Maps, where Google accurately knows user search behaviors.
This strategy shines because it targets the same audience from Search or Shopping at a drastically reduced cost, often around 95% less per click!
For website and app-focused segments, I focus on people who visit similar sites or use similar apps rather than exact ones. It’s a subtle but important tactic in my overall strategy.
Despite the various names for custom segments across different campaign types, their versatility makes them invaluable in my Google Ads strategy. They offer a seamless entry into advanced audience targeting beyond traditional search methods.
This article is part of an ongoing Search Engine Land series, designed to help you grasp key Google Ads features in just 3 minutes.
I’ve learned that brand visibility in AI search acts as an early indicator of influence—it shows if potential buyers are seeing and considering my brand before even visiting my site. Higher visibility means greater trust.
To calculate the brand visibility score, I observe my brand’s presence in AI-generated answers:
Brand visibility score = (Answers mentioning my brand ÷ Total answers in my space) × 100.
For example, if my brand appears in 22 out of 100 responses from ChatGPT or Google AI, my visibility score would be 22%.
This isn’t just about numbers but also about trust and authority.
The 3-Step Framework I Use to Measure Brand Visibility
To boost my brand visibility, I’ve adjusted how I measure organic search growth. This framework involves manual tracking and automation.
Step 1: Monitoring My Visibility Footprint
First, I identify where AI answers appear for my crucial queries. I frequently perform high-intent searches, like “best project management tools,” to see if an AI Overview shows up in the results.
Step 2: Benchmarking My Brand Mentions
I calculate my visibility score and compare it against competitors. This includes an audit of where my brand is cited and its sentiment.
Step 3: Tracking Changes Over Time
Brand visibility can shift with updates from competitors or LLM evolutions. I need to tie visibility shifts directly to business outcomes.
Actionable insights, such as keeping content fresh and structured, help me maintain a competitive edge.
Proving the Impact of My Brand’s Visibility
By tracking citations in AI, I can demonstrate my brand’s role in the conversation before prospects visit my site. Visibility metrics allow me to showcase gains or losses across key decision stages.
Regular checks and benchmarking against competitors ensure I stay ahead in shaping buyer perception and linking visibility to revenue growth.
Tools for Automating Brand Visibility in AI Search
A solid understanding of AI prompts and LLM changes are vital. Once I’m comfortable with the basics, tools like Semrush’s AI SEO Toolkit or AirOps are excellent for automating visibility benchmarking.
Armed with both fundamentals and the right tools, I’m set to scale my brand’s visibility in AI search.
Brand visibility is the new ranking in the SEO world.
With AI search engines now providing direct answers, my brand needs to be mentioned in those responses to truly make an impact.
It’s no longer about being the top blue link; it’s about being the brand that ChatGPT recommends or the company cited by Perplexity. But how do I measure and track this vital presence?
Here’s a straightforward three-step framework I use to enhance brand visibility, starting with assessing my brand’s visibility score.
I’ve learned that brand visibility in AI search acts as an early indicator of influence—it shows if potential buyers are seeing and considering my brand before even visiting my site. Higher visibility means greater trust.
To calculate the brand visibility score, I observe my brand’s presence in AI-generated answers:
Brand visibility score = (Answers mentioning my brand ÷ Total answers in my space) × 100.
For example, if my brand appears in 22 out of 100 responses from ChatGPT or Google AI, my visibility score would be 22%.
This isn’t just about numbers but also about trust and authority.
The 3-Step Framework I Use to Measure Brand Visibility
To boost my brand visibility, I’ve adjusted how I measure organic search growth. This framework involves manual tracking and automation.
Step 1: Monitoring My Visibility Footprint
First, I identify where AI answers appear for my crucial queries. I frequently perform high-intent searches, like “best project management tools,” to see if an AI Overview shows up in the results.
Step 2: Benchmarking My Brand Mentions
I calculate my visibility score and compare it against competitors. This includes an audit of where my brand is cited and its sentiment.
Step 3: Tracking Changes Over Time
Brand visibility can shift with updates from competitors or LLM evolutions. I need to tie visibility shifts directly to business outcomes.
Actionable insights, such as keeping content fresh and structured, help me maintain a competitive edge.
Proving the Impact of My Brand’s Visibility
By tracking citations in AI, I can demonstrate my brand’s role in the conversation before prospects visit my site. Visibility metrics allow me to showcase gains or losses across key decision stages.
Regular checks and benchmarking against competitors ensure I stay ahead in shaping buyer perception and linking visibility to revenue growth.
Tools for Automating Brand Visibility in AI Search
A solid understanding of AI prompts and LLM changes are vital. Once I’m comfortable with the basics, tools like Semrush’s AI SEO Toolkit or AirOps are excellent for automating visibility benchmarking.
Armed with both fundamentals and the right tools, I’m set to scale my brand’s visibility in AI search.
Brand visibility is the new ranking in the SEO world.
With AI search engines now providing direct answers, my brand needs to be mentioned in those responses to truly make an impact.
It’s no longer about being the top blue link; it’s about being the brand that ChatGPT recommends or the company cited by Perplexity. But how do I measure and track this vital presence?
Here’s a straightforward three-step framework I use to enhance brand visibility, starting with assessing my brand’s visibility score.
I’ve learned that brand visibility in AI search acts as an early indicator of influence—it shows if potential buyers are seeing and considering my brand before even visiting my site. Higher visibility means greater trust.
To calculate the brand visibility score, I observe my brand’s presence in AI-generated answers:
Brand visibility score = (Answers mentioning my brand ÷ Total answers in my space) × 100.
For example, if my brand appears in 22 out of 100 responses from ChatGPT or Google AI, my visibility score would be 22%.
This isn’t just about numbers but also about trust and authority.
The 3-Step Framework I Use to Measure Brand Visibility
To boost my brand visibility, I’ve adjusted how I measure organic search growth. This framework involves manual tracking and automation.
Step 1: Monitoring My Visibility Footprint
First, I identify where AI answers appear for my crucial queries. I frequently perform high-intent searches, like “best project management tools,” to see if an AI Overview shows up in the results.
Step 2: Benchmarking My Brand Mentions
I calculate my visibility score and compare it against competitors. This includes an audit of where my brand is cited and its sentiment.
Step 3: Tracking Changes Over Time
Brand visibility can shift with updates from competitors or LLM evolutions. I need to tie visibility shifts directly to business outcomes.
Actionable insights, such as keeping content fresh and structured, help me maintain a competitive edge.
Proving the Impact of My Brand’s Visibility
By tracking citations in AI, I can demonstrate my brand’s role in the conversation before prospects visit my site. Visibility metrics allow me to showcase gains or losses across key decision stages.
Regular checks and benchmarking against competitors ensure I stay ahead in shaping buyer perception and linking visibility to revenue growth.
Tools for Automating Brand Visibility in AI Search
A solid understanding of AI prompts and LLM changes are vital. Once I’m comfortable with the basics, tools like Semrush’s AI SEO Toolkit or AirOps are excellent for automating visibility benchmarking.
Armed with both fundamentals and the right tools, I’m set to scale my brand’s visibility in AI search.
I’ve learned that brand visibility in AI search acts as an early indicator of influence—it shows if potential buyers are seeing and considering my brand before even visiting my site. Higher visibility means greater trust.
To calculate the brand visibility score, I observe my brand’s presence in AI-generated answers:
Brand visibility score = (Answers mentioning my brand ÷ Total answers in my space) × 100.
For example, if my brand appears in 22 out of 100 responses from ChatGPT or Google AI, my visibility score would be 22%.
This isn’t just about numbers but also about trust and authority.
The 3-Step Framework I Use to Measure Brand Visibility
To boost my brand visibility, I’ve adjusted how I measure organic search growth. This framework involves manual tracking and automation.
Step 1: Monitoring My Visibility Footprint
First, I identify where AI answers appear for my crucial queries. I frequently perform high-intent searches, like “best project management tools,” to see if an AI Overview shows up in the results.
Step 2: Benchmarking My Brand Mentions
I calculate my visibility score and compare it against competitors. This includes an audit of where my brand is cited and its sentiment.
Step 3: Tracking Changes Over Time
Brand visibility can shift with updates from competitors or LLM evolutions. I need to tie visibility shifts directly to business outcomes.
Actionable insights, such as keeping content fresh and structured, help me maintain a competitive edge.
Proving the Impact of My Brand’s Visibility
By tracking citations in AI, I can demonstrate my brand’s role in the conversation before prospects visit my site. Visibility metrics allow me to showcase gains or losses across key decision stages.
Regular checks and benchmarking against competitors ensure I stay ahead in shaping buyer perception and linking visibility to revenue growth.
Tools for Automating Brand Visibility in AI Search
A solid understanding of AI prompts and LLM changes are vital. Once I’m comfortable with the basics, tools like Semrush’s AI SEO Toolkit or AirOps are excellent for automating visibility benchmarking.
Armed with both fundamentals and the right tools, I’m set to scale my brand’s visibility in AI search.
Brand visibility is the new ranking in the SEO world.
With AI search engines now providing direct answers, my brand needs to be mentioned in those responses to truly make an impact.
It’s no longer about being the top blue link; it’s about being the brand that ChatGPT recommends or the company cited by Perplexity. But how do I measure and track this vital presence?
Here’s a straightforward three-step framework I use to enhance brand visibility, starting with assessing my brand’s visibility score.
I’ve learned that brand visibility in AI search acts as an early indicator of influence—it shows if potential buyers are seeing and considering my brand before even visiting my site. Higher visibility means greater trust.
To calculate the brand visibility score, I observe my brand’s presence in AI-generated answers:
Brand visibility score = (Answers mentioning my brand ÷ Total answers in my space) × 100.
For example, if my brand appears in 22 out of 100 responses from ChatGPT or Google AI, my visibility score would be 22%.
This isn’t just about numbers but also about trust and authority.
The 3-Step Framework I Use to Measure Brand Visibility
To boost my brand visibility, I’ve adjusted how I measure organic search growth. This framework involves manual tracking and automation.
Step 1: Monitoring My Visibility Footprint
First, I identify where AI answers appear for my crucial queries. I frequently perform high-intent searches, like “best project management tools,” to see if an AI Overview shows up in the results.
Step 2: Benchmarking My Brand Mentions
I calculate my visibility score and compare it against competitors. This includes an audit of where my brand is cited and its sentiment.
Step 3: Tracking Changes Over Time
Brand visibility can shift with updates from competitors or LLM evolutions. I need to tie visibility shifts directly to business outcomes.
Actionable insights, such as keeping content fresh and structured, help me maintain a competitive edge.
Proving the Impact of My Brand’s Visibility
By tracking citations in AI, I can demonstrate my brand’s role in the conversation before prospects visit my site. Visibility metrics allow me to showcase gains or losses across key decision stages.
Regular checks and benchmarking against competitors ensure I stay ahead in shaping buyer perception and linking visibility to revenue growth.
Tools for Automating Brand Visibility in AI Search
A solid understanding of AI prompts and LLM changes are vital. Once I’m comfortable with the basics, tools like Semrush’s AI SEO Toolkit or AirOps are excellent for automating visibility benchmarking.
Armed with both fundamentals and the right tools, I’m set to scale my brand’s visibility in AI search.
I’ve come to realize that my brand’s presence in AI-generated answers is crucial in today’s digital landscape. It’s not enough to simply rank on Google; I need to ensure my brand is visible in those AI responses that matter the most to customers.
To calculate the brand visibility score, I observe my brand’s presence in AI-generated answers:
Brand visibility score = (Answers mentioning my brand ÷ Total answers in my space) × 100.
For example, if my brand appears in 22 out of 100 responses from ChatGPT or Google AI, my visibility score would be 22%.
This isn’t just about numbers but also about trust and authority.
The 3-Step Framework I Use to Measure Brand Visibility
To boost my brand visibility, I’ve adjusted how I measure organic search growth. This framework involves manual tracking and automation.
Step 1: Monitoring My Visibility Footprint
First, I identify where AI answers appear for my crucial queries. I frequently perform high-intent searches, like “best project management tools,” to see if an AI Overview shows up in the results.
Step 2: Benchmarking My Brand Mentions
I calculate my visibility score and compare it against competitors. This includes an audit of where my brand is cited and its sentiment.
Step 3: Tracking Changes Over Time
Brand visibility can shift with updates from competitors or LLM evolutions. I need to tie visibility shifts directly to business outcomes.
Actionable insights, such as keeping content fresh and structured, help me maintain a competitive edge.
Proving the Impact of My Brand’s Visibility
By tracking citations in AI, I can demonstrate my brand’s role in the conversation before prospects visit my site. Visibility metrics allow me to showcase gains or losses across key decision stages.
Regular checks and benchmarking against competitors ensure I stay ahead in shaping buyer perception and linking visibility to revenue growth.
Tools for Automating Brand Visibility in AI Search
A solid understanding of AI prompts and LLM changes are vital. Once I’m comfortable with the basics, tools like Semrush’s AI SEO Toolkit or AirOps are excellent for automating visibility benchmarking.
Armed with both fundamentals and the right tools, I’m set to scale my brand’s visibility in AI search.
I’ve learned that brand visibility in AI search acts as an early indicator of influence—it shows if potential buyers are seeing and considering my brand before even visiting my site. Higher visibility means greater trust.
To calculate the brand visibility score, I observe my brand’s presence in AI-generated answers:
Brand visibility score = (Answers mentioning my brand ÷ Total answers in my space) × 100.
For example, if my brand appears in 22 out of 100 responses from ChatGPT or Google AI, my visibility score would be 22%.
This isn’t just about numbers but also about trust and authority.
The 3-Step Framework I Use to Measure Brand Visibility
To boost my brand visibility, I’ve adjusted how I measure organic search growth. This framework involves manual tracking and automation.
Step 1: Monitoring My Visibility Footprint
First, I identify where AI answers appear for my crucial queries. I frequently perform high-intent searches, like “best project management tools,” to see if an AI Overview shows up in the results.
Step 2: Benchmarking My Brand Mentions
I calculate my visibility score and compare it against competitors. This includes an audit of where my brand is cited and its sentiment.
Step 3: Tracking Changes Over Time
Brand visibility can shift with updates from competitors or LLM evolutions. I need to tie visibility shifts directly to business outcomes.
Actionable insights, such as keeping content fresh and structured, help me maintain a competitive edge.
Proving the Impact of My Brand’s Visibility
By tracking citations in AI, I can demonstrate my brand’s role in the conversation before prospects visit my site. Visibility metrics allow me to showcase gains or losses across key decision stages.
Regular checks and benchmarking against competitors ensure I stay ahead in shaping buyer perception and linking visibility to revenue growth.
Tools for Automating Brand Visibility in AI Search
A solid understanding of AI prompts and LLM changes are vital. Once I’m comfortable with the basics, tools like Semrush’s AI SEO Toolkit or AirOps are excellent for automating visibility benchmarking.
Armed with both fundamentals and the right tools, I’m set to scale my brand’s visibility in AI search.
Brand visibility is the new ranking in the SEO world.
With AI search engines now providing direct answers, my brand needs to be mentioned in those responses to truly make an impact.
It’s no longer about being the top blue link; it’s about being the brand that ChatGPT recommends or the company cited by Perplexity. But how do I measure and track this vital presence?
Here’s a straightforward three-step framework I use to enhance brand visibility, starting with assessing my brand’s visibility score.
I’ve learned that brand visibility in AI search acts as an early indicator of influence—it shows if potential buyers are seeing and considering my brand before even visiting my site. Higher visibility means greater trust.
To calculate the brand visibility score, I observe my brand’s presence in AI-generated answers:
Brand visibility score = (Answers mentioning my brand ÷ Total answers in my space) × 100.
For example, if my brand appears in 22 out of 100 responses from ChatGPT or Google AI, my visibility score would be 22%.
This isn’t just about numbers but also about trust and authority.
The 3-Step Framework I Use to Measure Brand Visibility
To boost my brand visibility, I’ve adjusted how I measure organic search growth. This framework involves manual tracking and automation.
Step 1: Monitoring My Visibility Footprint
First, I identify where AI answers appear for my crucial queries. I frequently perform high-intent searches, like “best project management tools,” to see if an AI Overview shows up in the results.
Step 2: Benchmarking My Brand Mentions
I calculate my visibility score and compare it against competitors. This includes an audit of where my brand is cited and its sentiment.
Step 3: Tracking Changes Over Time
Brand visibility can shift with updates from competitors or LLM evolutions. I need to tie visibility shifts directly to business outcomes.
Actionable insights, such as keeping content fresh and structured, help me maintain a competitive edge.
Proving the Impact of My Brand’s Visibility
By tracking citations in AI, I can demonstrate my brand’s role in the conversation before prospects visit my site. Visibility metrics allow me to showcase gains or losses across key decision stages.
Regular checks and benchmarking against competitors ensure I stay ahead in shaping buyer perception and linking visibility to revenue growth.
Tools for Automating Brand Visibility in AI Search
A solid understanding of AI prompts and LLM changes are vital. Once I’m comfortable with the basics, tools like Semrush’s AI SEO Toolkit or AirOps are excellent for automating visibility benchmarking.
Armed with both fundamentals and the right tools, I’m set to scale my brand’s visibility in AI search.
I’ve learned that brand visibility in AI search acts as an early indicator of influence—it shows if potential buyers are seeing and considering my brand before even visiting my site. Higher visibility means greater trust.
To calculate the brand visibility score, I observe my brand’s presence in AI-generated answers:
Brand visibility score = (Answers mentioning my brand ÷ Total answers in my space) × 100.
For example, if my brand appears in 22 out of 100 responses from ChatGPT or Google AI, my visibility score would be 22%.
This isn’t just about numbers but also about trust and authority.
The 3-Step Framework I Use to Measure Brand Visibility
To boost my brand visibility, I’ve adjusted how I measure organic search growth. This framework involves manual tracking and automation.
Step 1: Monitoring My Visibility Footprint
First, I identify where AI answers appear for my crucial queries. I frequently perform high-intent searches, like “best project management tools,” to see if an AI Overview shows up in the results.
Step 2: Benchmarking My Brand Mentions
I calculate my visibility score and compare it against competitors. This includes an audit of where my brand is cited and its sentiment.
Step 3: Tracking Changes Over Time
Brand visibility can shift with updates from competitors or LLM evolutions. I need to tie visibility shifts directly to business outcomes.
Actionable insights, such as keeping content fresh and structured, help me maintain a competitive edge.
Proving the Impact of My Brand’s Visibility
By tracking citations in AI, I can demonstrate my brand’s role in the conversation before prospects visit my site. Visibility metrics allow me to showcase gains or losses across key decision stages.
Regular checks and benchmarking against competitors ensure I stay ahead in shaping buyer perception and linking visibility to revenue growth.
Tools for Automating Brand Visibility in AI Search
A solid understanding of AI prompts and LLM changes are vital. Once I’m comfortable with the basics, tools like Semrush’s AI SEO Toolkit or AirOps are excellent for automating visibility benchmarking.
Armed with both fundamentals and the right tools, I’m set to scale my brand’s visibility in AI search.
Brand visibility is the new ranking in the SEO world.
With AI search engines now providing direct answers, my brand needs to be mentioned in those responses to truly make an impact.
It’s no longer about being the top blue link; it’s about being the brand that ChatGPT recommends or the company cited by Perplexity. But how do I measure and track this vital presence?
Here’s a straightforward three-step framework I use to enhance brand visibility, starting with assessing my brand’s visibility score.
I’ve learned that brand visibility in AI search acts as an early indicator of influence—it shows if potential buyers are seeing and considering my brand before even visiting my site. Higher visibility means greater trust.
To calculate the brand visibility score, I observe my brand’s presence in AI-generated answers:
Brand visibility score = (Answers mentioning my brand ÷ Total answers in my space) × 100.
For example, if my brand appears in 22 out of 100 responses from ChatGPT or Google AI, my visibility score would be 22%.
This isn’t just about numbers but also about trust and authority.
The 3-Step Framework I Use to Measure Brand Visibility
To boost my brand visibility, I’ve adjusted how I measure organic search growth. This framework involves manual tracking and automation.
Step 1: Monitoring My Visibility Footprint
First, I identify where AI answers appear for my crucial queries. I frequently perform high-intent searches, like “best project management tools,” to see if an AI Overview shows up in the results.
Step 2: Benchmarking My Brand Mentions
I calculate my visibility score and compare it against competitors. This includes an audit of where my brand is cited and its sentiment.
Step 3: Tracking Changes Over Time
Brand visibility can shift with updates from competitors or LLM evolutions. I need to tie visibility shifts directly to business outcomes.
Actionable insights, such as keeping content fresh and structured, help me maintain a competitive edge.
Proving the Impact of My Brand’s Visibility
By tracking citations in AI, I can demonstrate my brand’s role in the conversation before prospects visit my site. Visibility metrics allow me to showcase gains or losses across key decision stages.
Regular checks and benchmarking against competitors ensure I stay ahead in shaping buyer perception and linking visibility to revenue growth.
Tools for Automating Brand Visibility in AI Search
A solid understanding of AI prompts and LLM changes are vital. Once I’m comfortable with the basics, tools like Semrush’s AI SEO Toolkit or AirOps are excellent for automating visibility benchmarking.
Armed with both fundamentals and the right tools, I’m set to scale my brand’s visibility in AI search.
As someone deeply involved in the agency world, I know firsthand how crucial it is to stay ahead in the competitive landscape. With Profound’s Agency Growth plan, I’ve discovered a transformative way to expand my agency’s AEO practice, regardless of its size.
The image above captures the essence of our approach—streamlined and powerful, much like the plan itself. This plan is my gateway to developing and refining AEO strategies that drive success and help my agency scale new heights.
Today, I have the pleasure of speaking with Anuj Srivastava, Principal/Partner at NY Engineers. With experience in supervising over 350 franchises, Anuj has a proven track record of helping them open stores 50% faster than competitors. We dive into how engineering and marketing strategies can work together to successfully launch a new franchise.
First Page Sage: Thanks for joining us, Anuj. Could you share more about NY Engineers and your role there?
Anuj Srivastava: Certainly! At NY Engineers, I serve as a Principal/Partner, primarily focusing on franchises, retail, and multi-site rollouts. Our team is renowned for delivering fast, cost-effective mechanical/electrical/plumbing (MEP) and fire protection (FP) engineering services tailored to clients expanding across various locations. We’re licensed in all 50 U.S. states, have completed over 4,000 projects, and our turnaround is 50% quicker than industry norms.
First Page Sage: I know that scaling franchises presents unique challenges. What are some key engineering hurdles you encounter, and how do you tackle them?
Srivastava: When a franchise expands to multiple locations, maintaining consistency and speed becomes vital. We ensure brand standards like equipment specs, layouts, and utility loads adhere to local codes. Change orders are costly, so we focus on upfront modeling to offer zero change order assurance. We also standardize components and coordinate early procurement to mitigate supply-chain issues.
First Page Sage: How do your engineering services intertwine with marketing and lead generation during franchise design?
Srivastava: The success of a franchise relies not only on proper engineering but also on effective marketing. While we ensure the physical infrastructure is ready on time, marketing maximizes occupancy and ROI. SEO and content strategies are vital for visibility, making sure each location is easily discoverable and drawing in customer traffic. Not integrating marketing would mean missing potential opportunities.
First Page Sage: Can you highlight an often-overlooked area where engineering and marketing overlap, and how they can collaborate better?
Srivastava: A key area for collaboration is data-driven site selection and pre-opening diagnostics. Engineering and marketing teams should exchange early data, like utility loads and customer behavior, to forecast foot traffic and peak hours. Furthermore, creating a consistent brand experience through both design and messaging helps reduce friction as the brand scales.
First Page Sage: Looking to the future, how do you foresee the engineering-marketing landscape shifting in franchise expansion, and what advice would you offer to brands working with both engineering and marketing firms?
Srivastava: The integration of technology, data, and digital marketing with physical infrastructure is on the rise. Trends include greater use of BIM for efficient design and alignment of physical and digital launches. Emphasizing sustainability also complements both cost control and brand story. I advise brands to see their engineering and marketing partners as one team, ensuring that infrastructure and digital readiness align, along with consistent messaging across all platforms for a successful launch.
I can’t help but feel restless as I ponder the evolving landscapes of SEO and AI search. Treating ChatGPT like Google seems like a recipe for failure in today’s world of RAG, reranking, and probabilistic systems.
As someone engulfed in SEO for years, I’ve tried to relate each new technology to the tools I know well.
Remember the buzz around “mobile SEO” when mobile search surged or when “voice search optimization” became the new must-know with voice assistants?
In my journey, I once thought I had Google all figured out. That belief shattered after examining how ChatGPT selects citations, analyzing Perplexity’s ranking process, and digging into Google’s AI Overview criteria.
I’m not claiming that SEO is obsolete or that we’ve encountered a total paradigm shift. I want to share the lingering questions that suggest we might need to fundamentally alter our methods of understanding.
These questions have emerged from months of intense analysis of AI search systems, documented observations of ChatGPT’s behavior, and reverse-engineering Perplexity’s ranking factors.
The Questions That Won’t Let Me Sleep
The questions reflecting on AI’s complexities have dismantled much of what I once confidently believed about search optimization.
When Math Doesn’t Add Up
While I grasp PageRank and link equity, encountering Reciprocal Rank Fusion in ChatGPT’s code led to moments of realization where I comprehended my gaps:
Why does RRF prefer consistency over singular excellence in query results? Is securing the #4 spot across multiple queries superior to achieving #1 once?
How do vector embeddings alter semantic distance from conventional keyword matching? Are we striving for semantic intent or mere words?
Why does temperature=0.7 cause unpredictable rankings? Are repeated tests now mandatory?
How do cross-encoder rerankers approach query-document pairs versus PageRank? Is now the time to shift towards real-time relevance?
These questions echo traditional SEO concepts but seem rooted in entirely different mathematical frameworks when juxtaposed with LLMs. Or are they?
When Scale Feels Unbreachable
While Google indexes trillions, ChatGPT retrieves a measly 38-65 results. This stark 99.999% reduction leads to pressing inquiries that linger:
Why does ChatGPT retrieve so few results compared to Google’s billions? Is this a short-term anomaly or a foundational shift?
How do token limits imbuing rigid confines differ from traditional search’s freedom? When did search results shrink in their dimensionality?
Does the k=60 constant in RRF conceal a ceiling on visibility? Has position 61 supplanted the secondary page?
Are these mere modern-day constraints? Or do they signal a novel information retrieval ideology?
The Questions that Continue to Haunt Me
Here are 101 questions that persist, gnawing at what I believed I knew about SEO in the AI era:
Is OpenAI employing CTR for citation rankings?
Does AI perceive our page layout as Google does or focus just on text?
Should our writing gear towards shorter paragraphs for AI to digest content adeptly?
Can interaction metrics like scroll depth or mouse movement influence AI ranking signals?
What is the effect of low bounce rates on our citation potential?
Could session data like reading order prompt AI model rerankings?
How might a nascent brand integrate into offline training data to earn visibility?
What strategies optimize a web/product page for probabilistic systems?
Why do citations transform inexplicably?
Is running multiple tests necessary to gauge variance?
How can Google’s “blue links” aid in acquiring specific answers to long-form questions?
Do LLMs mirror the same reranking algorithms?
Does web_search act as a binary switch or a probabilistic trigger?
Should our focus pivot to accolades or citations?
Is reranking deterministic or stochastic?
Do Google and LLMs utilize identical embedding models, and if so, what’s the corpus variance?
Which pages garner maximal requests by LLMs and maximum visits by users?
Should we monitor drift post-model updates?
Why does EEAT manipulate seamlessly in LLMs contrary to traditional Google search?
Who among us amplified traffic tenfold post-Google algorithm revelation?
Why does the answer structure morph even within a mere day’s interval?
Could post-click engagement amplify our odds of inclusion?
Is session memory gearing citation bias towards preliminary sources?
Why inherently are LLMs more prone to bias than Google?
Does offering a downloadable dataset escalate citation potential?
Why does content in Turkish retain anachronistic data despite contemporary queries?
Are vector embeddings capturing semantic difference distinctly from keyword associations?
Should we master LLMs’ “temperature” value henceforth?
How can a modest website emerge in ChatGPT or Perplexity answers?
What events unfold if our entire site optimizes solely for LLM targeting?
Might AI agents evaluate images alongside pages at an instant, or simply focus on surrounding text?
How could we ascertain AI tools leveraging our content?
Could AI models quote a lone sentence from our blog posts?
How do we ensure AI comprehends our business purpose?
What differentiates pages showing in Perplexity or ChatGPT but absent from Google?
Does AI preference newer content over steadfast, older references?
Once retrieved, how might AI rerank content?
Could LLMs retain our brand voice enveloping their outputs?
Is there a mechanism enabling AI summaries with direct links to our pages?
Can we monitor when our content is quoted without linked acknowledgment?
Can we identify prompts or themes fostering additional citations?
What shifts when monthly client SEO reports rebrand as “AI Visibility AEO/GEO Reports”?
Is there a facility to estimate brand mentions within AI results akin to search volume metrics?
Could Cloudflare logs reveal AI bot exposure to our domain?
Could model updates reset pre-existing reranking preferences while retaining partial memory?
Why are traditional queries often more definitive sans AI hallucinations?
Who in the system dictates final citation preferences?
Can human feedback loops reshape LLM source rankings?
When might an AI initiate midpoint searches amid answers, and why are multiple continuous AI searches within a single chat window observed?
Does being once-cited predispose future citation allocation? Can LLM ranks sustain visibility likened to Google’s top 10?
Do frequent citations autonomously elevate a domain’s retrieval priority?
Are user clicks on linked sources embedded in feedback signals?
Do Google and LLMs employ identical deduplication protocols?
Might citation velocity be traced akin to SEO link velocity?
Will LLMs someday curate a lasting “citation graph” paralleling Google’s link constructs?
Do LLMs correlate brands entwined in related subjects or question clusters?
How long elapses before repeated interactions etch into durable brand memory within LLMs?
Why doesn’t Google reveal 404s while LLM responses do?
Why fabricate citations while Google directs only to accessible URLs?
Do LLM retraining phases present reset opportunities post-visibility slump?
How should we construct recovery roadmaps against AI model misinformation?
Why might some LLMs cite while others disregard?
Are ChatGPT and Perplexity leveraging identical web data repositories?
Do OpenAI and Anthropic gauge trust and freshness identically?
Do source-specific limits apply to maximum AI citations per response?
How shall we verify citation following content evolution?
What’s the simplest route to trace prompt-level visibility over extended periods?
How can we persuade LLMs to regard our assertions as factual?
Does a topic-aligned video linked to the page fortify cross-format grounding?
Could identical questions lead to divergent brand suggestions for differing users?
Might LLMs register previous brand engagements?
Can previous click histories skew subsequent LLM endorsements?
How do retrieval and reasoning converge on citation attributions?
Why does ChatGPT retrieve 38-65 outputs while Google spans billions?
How do cross-encoder rerankers diverge from PageRank in query-document evaluations?
How does a backlink-void site surpass authorities within LLM result sets?
Why impose token barriers absent in conventional search?
Why does LLM temperature determination yield erratic rankings?
Does OpenAI allocate a dedicated crawl budget to web properties?
Do Knowledge Graph recognition and LLM token embedding methods diverge?
How is crawl-index-serve distinct from retrieve-rerank-generate dynamics?
Do temperature settings in LLMs generate inconsistent rankings?
Why is tokenization integral?
How does a knowledge cutoff induce unintentional blind spots versus real-time crawling dynamics?
When Trust Turns Probabilistic
I grapple with how Google reliably links to tangible URLs while AI systems, astoundingly, can fabricate information:
Why might LLMs fabricate citations while Google anchors existing URLs?
How do hallucination rates of 3-27% stand against Google’s 404 incidence?
Why do similar queries yield conflicting “facts” in AI over search indices?
How does obsolete data prevail in Turkish content despite contemporary inquiries?
Are we orienting ourselves around systems liable to mislead users? How does one manage that eventuality?
Where We Stand
I’m not suggesting AI search optimization/AEO/GEO is utterly unlike SEO. Yet, I confront 100+ unanswered questions challenging my foundational SEO acumen at this moment.
Perhaps solutions await folks with more advanced insights. For now, I remain entwined in seeking answers but know these queries will persist, with brand new ones arising on the horizon.
The mechanisms generating these queries aren’t vanishing. We must engage, scrutinize, and potentially innovate approaches to fathom and leverage them.
The victors in this novel expanse won’t inevitably own the totality of wisdom. But they will bravely ask, probe, and identify workable solutions amid ambiguity.
Have you noticed a change in Google’s mobile image search? I have, and it’s all thanks to their latest expansion—introducing AI-driven ad carousels that now feature prominently in the Images tab on mobile. No longer confined to shopping categories, these ads are everywhere!
Why I’m excited. Google has innovatively integrated ads directly into image search results. It’s a game-changer, providing brands like yours and mine with an eye-catching new way to reach potential customers as they compare and explore visuals online. This new format offers a unique opportunity to capture attention early in the consumer journey.
The details:
The introduction of horizontally scrollable carousels that combine images, headlines, and links is worth noting.
What’s fascinating is the AI-driven ad matching technology. It ensures the visuals correspond to what users are searching for, even in non-commercial sectors like law or insurance.
All of this came to light when Anthony Higman, founder of ADSQUIRE, shared snapshots of these carousels on X.
The big picture. With ads becoming seamlessly woven into visual searches, Google is paving the way for immersive ad experiences that merge organic and paid discovery. This is a significant leap beyond traditional text ads and product listings.
When I think about the evolution of the web, I can’t help but reflect on Sir Tim Berners-Lee’s significant contributions. Recently, he expressed concerns that artificial intelligence might undermine the web’s ad-supported model.
In an enlightening conversation with Nilay Patel on Decoder, Berners-Lee shared his worries about how AI could disrupt the current flow of data that fuels ad revenue. He warned that if users stop clicking on links and visiting websites due to AI-driven changes, the very foundation of our ad-supported web could crumble.
Why this matters to us. There’s a noticeable split in our industry. On one side, it’s “just SEO,” but on the other, some foresee a future where AI platform visibility overtakes traditional search engine rankings and traffic. While SEO remains relevant, there’s no denying a shift in how we access content. According to Berners-Lee, ignoring this could lead to our ad-supported model failing while AI platforms continue to thrive.
On monopolies. Berners-Lee also spoke about the risks of having a central provider dominate the web. He reminisced about a time when multiple browsers and search engines offered more choices, contrasting with today’s monopolistic landscape.
On the semantic web. After years of working on the Semantic Web, Berners-Lee observed how AI could harness structured data. He highlighted Schema.org’s role in making data machine-readable, and how this could evolve with AI to form a sophisticated web of data.
On blocking AI crawlers. The conversation shifted to Cloudflare’s initiative to restrict AI crawler access. When asked if websites could integrate “pay me first” protocols, Berners-Lee mentioned existing micropayment systems, suggesting ways to monetize web information access in an AI-driven world.
The interview. If you’re curious about Berners-Lee’s thoughts on the future of the web and AI, check out the full interview on The Verge.
