B2B buyers are increasingly turning to ChatGPT when conducting vendor research, and I’ve discovered how essential it is for B2B companies like mine to stand out in this AI-powered landscape.
As I navigate this digital revolution, I focus on building visibility in AI search to ensure my business is consistently recommended during the buying process. Here’s how I’ve approached it and why it’s vital.
In today’s world, mastering the art of AI Search Optimization is not just beneficial; it’s necessary. By integrating key strategies into my B2B marketing plan, I’ve learned how to effectively leverage AI tools to stay ahead in the competitive marketplace.
Do you want to take your Answer Engine Optimization (AEO) to the next level? Content siloing might just be the strategy you need. It’s a tactic that has transformed how I approach structuring topics to enhance authority and improve crawlability. Let’s delve into what content siloing is and how you can successfully implement it to boost AI citations.
Think of content siloing as creating a tightly knit topic network within your website, where each piece of content supports and strengthens the others. By organizing related content into isolated ‘silos,’ you not only streamline user navigation but also make it easier for search engines to index and understand the relevance of your content. This improved visibility can lead to better ranking in AI-powered search results.
Implementing content siloing involves a strategic approach to linking content. Begin by identifying your core topics and create subtopics that branch off these main areas. Each article within a silo should link to related content, reinforcing the overall theme and strengthening your site’s authority on the subject matter. This method ensures that your website becomes a trusted source of information in the eyes of both users and search algorithms.
I’ve noticed how beauty brand visibility in AI searches is increasingly influenced by social discovery and third-party validation.
Even before a user inputs a prompt, AI search visibility is shaped by conversations on social platforms. Brands featured in generative responses are typically those actively discussed and validated across these channels. By the time someone turns to AI search, the groundwork has often been laid.
Using beauty as an example, I’ve explored how social discovery impacts brand visibility and why AI search reflects these signals.
Discovery Didn’t Move to AI – It Fragmented
Brand discovery is now fragmented across many platforms. While AI tools affect the middle of the funnel, much discovery happens before someone engages with a prompt.
Social platforms significantly influence the signals determining AI visibility. By the time users reach decision points in generative search, their opinions and perceptions may already be shaped. Delaying influence until AI search might narrow the window of opportunity.
Social interactions are a major upstream influence. According to eMarketer research, about two-thirds of U.S. consumers use social platforms like search engines.
It’s not just Gen Z—this trend shows how people validate information and discover brands. These platforms are frequently cited in AI results, particularly in the beauty sector.
In a study I worked on with a beauty brand, platforms like Reddit, YouTube, and Facebook often topped the list of cited domains in AI Overviews and ChatGPT.
While Reddit might seem anti-brand, YouTube frequently appears in citation data, posing a valuable, yet often overlooked, opportunity for citation optimization.
It’s easy to be drawn to stats about AI usage, from prompt numbers to daily activity levels. Yet when you compare these figures against business goals like traffic or transactions, the reality shifts.
Social platforms are a core part of mainstream search behavior. For many, searching on TikTok or YouTube is second nature. In fact, almost 40% of TikTok users search the app multiple times a day, with 73% doing so at least once daily.
Referral data highlights the difference. In a 12-month review of 973 ecommerce sites, only about 0.2% of traffic came from ChatGPT referrals, while Google’s organic search was nearly 200 times larger.
Though AI search is growing and valuable, social platforms and traditional search still dominate in terms of behavior, sessions, and transactions.
The Validation Loop: Why AI Needs Social
Optimizing for social is akin to optimizing for AI. Large language models don’t serve as primary truth sources. Instead, they reflect human consensus from the data they process.
AI systems often regard brand-owned sites skeptically. A study showed that just 25% of sources in AI-generated responses were brand-managed.
Conversely, these engines prioritize third-party validation. Research by OtterlyAI showed up to 6.4% of AI citation links came from Reddit, surpassing many traditional publishers.
A measurable link exists between sentiment and visibility. Positive brand sentiment on social platforms correlates with higher visibility in AI results.
Seeing video solely as a social or branding channel rather than a search surface misses the mark.
On platforms like TikTok and YouTube, AI uses spoken language, text, and captions to assess trust. Within beauty, for example, Google’s daily search volume dwarfs ChatGPT’s, yet “how-to” prompts find favor with video due to its detailed advice.
Beauty has split into two realms according to Yotpo’s analysis. Brands like Paula’s Choice excel in AI for their detailed educational content, while traditional marketing brands lag.
Terms like “dermatologist recommended” rank high in AI as language models prefer expert endorsements for ranking.
Breaking the High-Production Barrier: Content at Scale
Budget is often seen as a blocker. Many assume Hollywood-level production is needed for success. This is an outdated view.
Today’s landscape rewards authenticity over perfection with viewers seeking real stories, not polished ads.
Effective video optimization doesn’t require film school. Brands can tap into internal skills without new hires.
Partner with creators: Using platforms like Billow or Social Native, brands can collaborate with creators for as little as $500. This investment can translate into tangible search visibility.
Utilize social-savvy staff: Often, your best asset is internal. Encourage team members who use social media to generate authentic content while fostering a creative culture.
Focus on strategy: Major followings aren’t essential. I’ve seen a TikTok account start modestly with a part-time creator end up generating significant views in months by targeting valuable search terms.
Starting fresh with a limited budget doesn’t mean limited reach. Businesses need clarity on their goals and a disciplined approach.
A quick five-minute video can offer more data to a large language model than many blog posts. Here’s how I can enhance my brand’s visibility for AI data retrieval.
With OpenAI’s significant deal with Disney, web scraping is undergoing a transformation. This agreement lets OpenAI employ high-fidelity, human-verified cinematic content to minimize AI inaccuracies.
These opportunities enhance my brand’s visibility and recognition, as AI models crave high-quality data. Video becomes a crucial asset for my brand in this evolving landscape.
Here’s why video is becoming the AI’s truth source and how I can leverage it to defend my brand’s identity.
Brand drift in AI occurs when an AI doesn’t have specific data about my brand, leading it to piece together my brand’s story from generalized information.
This interpolation risks creating misleading brand narratives. Imagine a situation where an AI inaccurately describes my SaaS company’s product features because it lacks precise data.
Streamer.bot faced a similar issue, with AI-generated instructions that were confidently incorrect, creating unnecessary confusion and workload.
Even local businesses are affected. A restaurant owner reported repeated inaccuracies shared by Google AI about their menu in an article.
Providing a canonical truth source, like video, prevents AI from distorting my brand’s message.
Authoritative videos carry significant semantic value, offering detailed transcripts and visual proof that establish a solid truth source, helping avoid misinformation from any other platforms.
Videos pack high data with nuance, offering multiple layers of communication through visuals, sound, and text.
Studios such as Berlin-based Impolite produce high-quality videos to help brands retain their identity, preventing brand drift by offering rich data sources for AI.
For instance, Karman’s “The Space That Makes Us Human” project showcases expert-led video that serves as an authentic truth source for brands.
Authenticity now acts as a crucial technical signal. Verification ensures that AI models can trust the provenance of a video.
Real-world footage is the ultimate high-trust data source. AI-generated videos typically lack the real-world’s dynamic intricacies.
Organizations like the Coalition for Content Provenance and Authenticity (C2PA) and the Content Authenticity Initiative (CAI) enhance digital content transparency.
These entities allow brands to digitally sign videos, establishing a trustworthy indicator for AI models versus unsigned content.
Similarly, I can understand more about media verification, establishing an unbroken chain of evidence from creation to consumption.
On LinkedIn, a “CR” mark on media indicates its origin and editing history, boosting content authority and authenticity.
Google’s integration of C2PA signals ensures AI-related policies are reflected in search and ads, maintaining accurate representation and disclosure.
In content marketing, adopting C2PA helps me safeguard against misinformation, acting as a quality assurance measure.
If necessary, I can utilize Sony’s camera authenticity solutions to embed real-time digital signatures in media, proving it’s genuine and trustworthy.
C2PA-compliant editing tools allow me to create a manifest detailing all edits and tools used, preserving the content’s integrity.
A cryptographic seal verifies the content’s integrity, alerting AI to broken data chains, ensuring only accurate information is spotlighted.
Given the content overload today, traditional verification methods struggle, but verified subject matter experts (SMEs) stand out as credible sources online.
By pairing expert insights with video evidence, brands provide AI with authentic, non-replicable authority that audiences trust.
Incorporating video as central content captures nuanced details, giving birth to high-quality content across various media platforms.
Repurposing video into text, images, audio, and social media content builds an authority loop, increasing the probability of data retrieval by AI models.
I should predict where AI might misrepresent my brand and utilize verified expert voices and video documentation to address potential misinformation.
It remains vital for me to focus on context over mere compliance in brand building through high-fidelity, cryptographically signed video, safeguarding identity and authenticity.
The mandate is simple: Record reality. Ensuring I provide a verifiable video record prevents AI from creating false narratives about my brand.
AI search has a subtle impact on trust, sales velocity, and potential client shortlists, which often isn’t reflected in analytics data. These insights came to light through a series of revealing experiments I’ve been involved in.
It was a chance encounter with a new prospect who mentioned, “I actually found you via Grok.” That was a pivotal moment for me. Not only had we not attempted to rank on Grok, but we also weren’t monitoring it. Yet, here it was, influencing potential buyers’ search and evaluation processes.
This realization permeated conversations with other clients; fascination with AI search was rampant, but there was skepticism regarding data credibility. Many wanted visibility on platforms like ChatGPT but hesitated due to unclear attribution.
So, I embarked on structured testing using resources I could control entirely—our agency website, personal experiments, e-commerce ventures, and select domains for testing purposes. The goal wasn’t to attain AI rankings but to decode which elements remain crucial once AI integrates into buying decisions.
These inquiries involved figuring out if AI search altered purchasing preferences or merely the ranking of brands. Additionally, I wanted to understand if revenue metrics could be influenced by AI visibility without hitting the analytics tracking radar and how AI-driven recommendations might affect performance across other channels.
I realized early conversations around AI search revolved around visibility metrics—think brand citations, visibility screenshots from AI tracking platforms, and more. I believed that the primary role of search remains to aid decision-making. My experiments aimed to determine if AI search retained this capability and transformed business outcomes.
Focusing on measurement was crucial. Instead of just relying on API data—which often diverges from user interactions—I observed live interfaces of ChatGPT, Perplexity, Gemini, and Google AI Overviews. Prompt tracking aided in identifying patterns but was not a definitive gauge of success.
During my first experiment, the creation of self-promotional ‘best of’ lists on my own website revealed fascinating insights. Agencies frequently leveraged a tactic where they placed themselves atop ‘best X’ lists, allowing AI systems to inadvertently amplify their prominence.
Inspired by Glen Allsopp’s extensive research, which highlighted how ‘best’ lists were frequently cited by ChatGPT, I tested the findings on my brand webpage. I was intrigued by the rapid visibility of my site, LawrenceHitches.com, across AI platforms for queries like “best SEO agency Sydney.”
However, ranking visibility alone lacked significance. Similarly, when I fabricated a landscaping site to further test self-promotional tactics, it also appeared swiftly in AI responses, reaffirming visibility alone’s limited value.
Through these experiments, it became evident that while AI simplifies appearing on search radars, building and sustaining trust remains pivotal—a sentiment ringing true from the likes of Wil Reynolds. Self-lauding across one’s platform may catalyze skepticism rather than assurance.
I’ve also seen how prompt tracking tools became popular, with demand from clients ever-increasing. Yet, reliability remained a challenge. Surfer SEO research suggested brands often appeared differently in API outputs versus real user sessions. With overlap sometimes as low as 24%, discrepancies remind us that prompt appearances could be misleading.
This is where the narrative eases away from where brands show up and involves questioning efficacy: How did AI influence sales velocity? Did consultations eliminate the need for education? Was buying speedily initiated?
I discovered that signals—where leads factored AI tools into decision-making without prompting—started appearing, shaking traditional attribution’s foundation. A telling instance was Kadi, an e-commerce brand we support, encountering a buyer who, influenced by AI, engaged in a thorough purchasing journey yet showed attribution through Instagram.
For Kadi, digital PR efforts garnered visibility spurt, but gaps in fundamentals meant traditional SEO foundation work was essential to move past quick traction and truly compete. AI played a silent role in buyer decisions, even when attribution data failed to capture its essence.
My journey with StudioHawk provided another layer of understanding. Post a rebranding and digital migration, SEO emerged as a potent channel, complemented by AI leads that became more recurrent.
Sales processes further illustrated the transformation, where AI-affected leads saw reduced education requirements and minimized objections, closing deals notably faster than traditional SEO leads. The blend of ChatGPT, Perplexity, and Grok-influenced conversions stood testament to AI’s influence, even as traditional paths remained evasive in attribution reporting.
Throughout these endeavors, I’ve realized that while AI doesn’t redefine discovery, it compresses consideration significantly. The buyer’s journey is evolving beyond static funnels. AI provides succinct answer summaries, reshaping the ‘messy middle’ where amenities like risk reduction, vendor shortlisting, and trust assurance occur.
It’s evident AI aids decision-making once foundational trust is laid. Traditional SEO confirms search engines recognize your entity, but its real value is now within supporting thoughtful content that pre-sells your services.
So, as I reflect, brands need to realign focuses. Record where AI’s footprints actually land beyond mere appearances. Prioritize intelligibility over creativeness in content. Opt for consistency in entity-driven narratives and prioritize content resonating with comparison and risk evaluations.
Every day, millions turn to ChatGPT for answers, but have you noticed your brand isn’t included in those results? I’ve been there, wondering why my brand isn’t gaining visibility and how to change that. If you’re like me and want to understand what’s happening, I’ve gathered the seven main reasons why ChatGPT might be ignoring your brand.
Understanding these reasons is the first step to making a change. You’ll learn specific steps to enhance your visibility in AI searches, and I can tell you from experience, it’s worth the effort.
Perhaps you’re wondering: what can I do to ensure my brand stands out? Don’t worry, I’m here to guide you through actionable strategies for gaining prominence in AI search results.
As I delve into the intriguing world of AI visibility, I’ve noticed an intriguing trend. While ChatGPT effectively references Reddit threads, YouTube channels, and LinkedIn profiles, it seems to bypass X/Twitter entirely. This observation piqued my curiosity: which social platforms truly matter in the spotlight of AI?
Through my exploration, I’m uncovering the essential roles these platforms play in shaping AI’s presence and influence. Reddit stands out with its vibrant discussions, YouTube captivates with visual content, and LinkedIn provides a professional touch. The absence of X/Twitter raises questions about its impact on AI’s digital journey.
By understanding these dynamics, I aim to paint a clearer picture of how AI tools, such as ChatGPT, navigate and cite social media for enhanced visibility. Join me as I dig deeper into these platforms, shedding light on the evolving landscape of AI awareness.
I’ve always been fascinated by the intersection of AI and healthcare, and every month I eagerly anticipate the newest updates. The Goodie team curates these insights, letting us peek into the dynamic shifts within the AI and medical sectors.
Imagine the transformative potential of AI in healthcare. From diagnostics to patient care, companies like OpenAI, Google, and Anthropic are leading the charge, each with unique contributions and innovations.
Agentic AI is now a hot topic among executives. I’m here to break down precisely what’s happening, what remains unchanged, and how e-commerce brands should adapt.
As an SEO leader working with e-commerce brands, I’m often in the position of clarifying the realities behind buzzwords like ‘agentic AI’. Executives frequently inquire about its implications for growth, risk, and competition.
Executives crave facts over hype. They seek concise explanations, grounded insights, and actionable advice.
My role as an SEO leader becomes essential here, not in predicting the future, but in enlightening leadership about the changes, the constants, and how to proceed pragmatically. Here’s my roadmap.
Start with Defining ‘Agentic’
First, I focus on demystifying the term. Agentic systems don’t replace customers; they work on their behalf. While the intent and preferences originate from individuals, the execution is taken over by the software.
The working dynamics shift, where tasks like discovery, comparison, and even execution are now managed by software, processing data faster than any human.
In discussions with executive teams, I emphasize simple illustrations:
“We’re not losing customers; instead, we’re incorporating a new decision-maker, which is the software acting as a customer proxy.”
Understanding this calms the conversation and steers focus away from fear towards preparation.
Manage Expectations to Avoid Hype
Another key role I play is in tempering expectations. Agentic AI won’t sweep over all at once. Its effects will be gradual and varied across different categories.
Some industries, with standardized products and organized data, will adapt faster. Others will face more challenges due to complexities and regulatory hurdles.
I often see leadership teams falling into two detrimental traps:
Panic: Hastily altering strategies and budgets without clarity.
Dismissal: Ignoring changes until it impacts performance, leading to rushed responses.
I offer a steady perspective, noting that agentic AI merely accelerates existing trends. It’s not about chasing new features but reinforcing strong fundamentals.
I encourage conversations to evolve beyond search rankings. When agents lead the journey, the critical question becomes, “Are we eligible to be chosen?”
Eligibility hinges on clear, consistent, and trustworthy data. Agents must grasp your offerings, target audience, pricing, availability, and risk factors associated with choosing your brand.
Raising thoughts about data consistency, pricing reliability, and whether policies add or reduce uncertainty positions SEO as a practical bridge between strategy and execution.
SEO Beyond Marketing
There’s a misconception that SEO is confined to marketing. Agentic behavior challenges this notion.
Selection by an agent involves variables beyond marketing, like data accuracy, technical integrity, inventory management, and payment reliability.
My explanations revolve around broadening SEO’s scope—it’s about ensuring the business is machines-readable, trustworthy, and consistent.
SEO becomes vital in helping leaders identify system or data gaps that could hinder the brand’s selection, highlighting its connection to both risk management and operational resilience.
In most e-commerce brands, agentic systems affect the top of the funnel first. Discovery shifts towards more personalized, conversational interactions.
Instead of brief search phrases, users convey needs, constraints, and preferences, which the agent then transforms into actions.
This decreases the significance of owning category head terms. If an agent has comprehensive user data, it acts like a knowledgeable repeat customer.
This presents a new reporting challenge. Not all SEO work will appear as direct demand creation, yet it still impacts outcomes. Leaders need to anticipate this shift.
Rethink Consideration
The consideration phase evolves too. Traditionally, it involves hosting reviews, comparisons, and reassurances.
With agentic intervention, consideration morphs into a filtering process, retaining only the options that align with user preferences.
This necessitates a quality over quantity strategy in content, emphasizing structural trust signals and consistent, verifiable information.
Brands might be selected without user awareness. While this could boost conversions, it also poses a risk to brand recognition if not addressed elsewhere.
Measurement often concerns executives, and agentic AI complicates this. With more processes happening inside AI, fewer interactions leave traceable or clear data.
I address this early by stressing that while this isn’t a failure of optimization, it merely highlights the analytics limits in a complex digital landscape.
The focus should shift to directional indicators and blended performance over precise attribution, acknowledging the new decision-making landscape.
Advocate Proactive, Low-risk Responses
The crux of leadership dialogue is next steps. Fortunately, most appropriate responses to agentic AI carry low risk.
Enhancing product information, eliminating inconsistencies, strengthening reliability signals, and addressing technical vulnerabilities benefit the business now and pave the way for the future.
Building brand trust outside search also plays a critical role. Trusted brands are more likely to be selected by agents performing comparisons.
This strategy reassures leaders that success doesn’t require radical change but calls for focused improvement.
Agentic AI: Focus Shifts, Fundamentals Persist
For us SEO leaders, agentic AI modifies our focus. Instead of solely optimizing for visibility, we aim to protect eligibility, reduce ambiguities, and illustrate influence.
This demands confidence and clear articulation, challenging hype with grounded perspectives. Agentic AI renders SEO more strategic and no less crucial.
Agentic AI isn’t an imminent threat or foolproof advantage. It’s a transformation in decision-making approaches.
For e-commerce brands, the winners are those who stay composed, communicate effectively, and transition their SEO approach from driving clicks to securing selections.
This transition forms the backbone of the current SEO leadership discussions.
I recently embarked on a fascinating exploration of ChatGPT’s brand recommendation patterns, and let me tell you, the findings offer a lot to chew on!
We all know that AI responses are a roll of the dice – ask the same question ten times, and you’re bound to get ten different answers. But I couldn’t help but wonder, just how varied are these responses?
Rand Fishkin’s intriguing research dives into this very question. His findings have significant repercussions for how we approach AI visibility tracking for brands.
Fishkin experimented with prompts ranging from recommendations for chef’s knives to cancer care hospitals, as well as Volvo dealerships in Los Angeles.
His results showed that AI systems like ChatGPT almost never recommend the same set of brands in the same order twice.
Moreover, when asking about something specific like running shoes, certain brands tend to appear more frequently than others.
Building on this research, I zeroed in on B2B scenarios, adding some of my own twists: does the complexity of the prompt or the competitiveness of the category make a difference to AI’s consistency?
To investigate, I crafted twelve varied prompts, half of which addressed highly competitive B2B software categories, like accounting, and the rest focused on niche categories, such as user entity behavior analytics (UEBA) software.
Further, I examined simple prompts against nuanced ones that included specific personas and use cases.
Each prompt was fed into ChatGPT 100 times using different IP addresses to mimic 1,200 unique users.
Now onto the juicy part: the findings.
Submitting a single prompt to ChatGPT 100 times revealed that, on average, 44 different brands got mentioned. However, some response sets listed as many as 95 brands, heavily dependent on the category.
Notably, competitive categories yield twice as many brand mentions per 100 responses compared to niche ones.
Simple vs. nuanced prompts? ChatGPT typically mentions fewer brands in response to nuanced requests, but this isn’t a hard and fast rule.
When diving deeper into ChatGPT’s brand consistency, I found that in a set of 100 B2B software recommendations, only about five brands (11% of the total) were mentioned 80% or more of the time.
Dominant brands in a category like accounting software were names we all recognize: QuickBooks, Xero, Wave, and the like.
If you’re not among the big guns, working within a niche offers a strategic advantage given the increased chance to be consistently recognized by AI.
For marketers, this study underscores the necessity of standing out and perhaps carving a niche if dominance in a broad category seems out of reach.
Moreover, most AI visibility tools might not give you the full picture if they’re conducting only a single spot-check. For more reliable data, multiple runs per prompt are essential.
So, if you’re tracking pivotal prompts, run each a handful of times to get a better sense of your brand’s visibility.
I’m excited to share that future reports will explore ChatGPT’s understanding of brands and whether consistent recommendations reflect deeper brand awareness.
This article was originally published on Visible and republished with permission.
