I remember when link building was the cornerstone of SEO. While it’s still relevant, its role has evolved as Google set clearer standards, focusing more on quality, relevance, and intent.
Today, in our AI-driven search world, the focus has shifted towards brand mentions, which have become a critical SEO initiative. Brand mentions provide references similar to citations, but in AI search, they explain how brands appear in LLMs (Large Language Models).
Brand mentions are now influential factors for AI search strategies and are gaining more weight in traditional SEO algorithms. Focusing on them should be a priority in 2026 to ensure lasting organic visibility.
Let me guide you on how we can prioritize and benefit from brand mentions.
How and Why to Prioritize Brand Mentions
Brand mentions have become essential in our AI search environments, moving beyond just backlinks. LLMs focus on analyzing mentions, context, and the recurring links between your brand and your target topics.
These mentions form a competitive advantage, especially as they accumulate over time, creating a protective ‘ranking moat’ when competitors don’t invest similarly.
To properly prioritize, ensure your brand’s technical and content fundamentals are solid. This includes crawlability, structured data, and clear on-page content. Afterward, focus on brand mentions before engaging in large-scale content production without an existing citation footprint.
When seeking impactful brand mentions, it’s crucial to examine their sources. My agency goes beyond standard tools, looking for opportunities through systems like Profound that highlight relevant brand mentions aligned with key topics.
We also review AI Overview links for SEO queries and dive into top-ranking Reddit threads to identify frequently mentioned entities related to important keywords.
You can uncover links to source articles in AI Overviews by selecting the chain-link icon, enhancing your brand’s topical visibility.
Driving Passive Brand Mentions
Passive brand mentions come when your content naturally fills an informational gap. The aim is to become the go-to reference for certain topics, achieving this by creating assets that are easily referenced.
These can include original data, insightful reports, or highly scannable explanatory pages. By establishing your brand as the primary source, you’re better positioned for more mentions.
Actively Soliciting Brand Mentions
For proactive outreach to earn brand mentions, focus on building genuine relationships and providing valuable information. Start by sharing assets that offer clear benefits, without immediately asking for something in return.
When contacting journalists or content creators, make your pitches relevant and timely, with a clear angle that increases your inclusion chances. Combining outreach with thought leadership, through podcasts or panels, enhances discovery possibilities.
Our goal is to establish a robust outreach engine, nurturing relationships so that those individuals may naturally reference your brand in the future, potentially leading to collaborative content opportunities.
Deciding When to Engage a PR Resource
PR support is particularly beneficial when you have compelling stories or data but face distribution challenges. It’s also crucial for quick scaling of brand mentions, especially during fundraising, launches, or when competing in aggressive markets, like health or AI.
However, if foundational SEO or assets are lacking, focus on establishing those first. Once ready, PR will accelerate visibility across search engines and LLMs.
The core tenets of link building still apply: aim for quality over quantity and avoid low-impact sources. By keeping a clear focus on key sources and strategy, your brand can achieve significant improvements in search visibility.
Hey there! I’ve been diving into ways to develop an effective AI-ready content strategy that’s perfect for large language models (LLMs) to parse, trust, and cite. It’s fascinating how the focus has shifted from just getting clicks to ensuring understanding through visibility. Let me walk you through my journey of crafting this strategy.
Imagine building a content framework where AI tools not only recognize but also rely on the information you provide. This is where content tailored for LLMs comes into play. It’s all about providing data that these models find credible and resourceful. Essentially, visibility is now measured by how well the content communicates rather than just its ability to attract clicks.
As I started building my strategy, I focused on ensuring that the content is structured and detailed enough for LLMs to easily process and extract valuable insights. This involves more than just surface-level content optimization but delves into creating comprehensive narratives that AI can effectively utilize.
Every year, Black Friday offers a unique glimpse into how consumers search, compare, and decide. This year, it added another layer: it became a real-world arena to see how AI models comprehend commerce amidst genuine demand.
I embarked on a journey to test major large language models (LLMs), analyzing 10,000 responses to understand how these systems perceive the retail landscape and the signals that shape their responses.
As I dissected the dataset, a pattern was unmistakable: Black Friday acts as a genuine stress test for AI-driven discovery.
The sheer number of queries and the diversity of categories reveal the sources, structures, and behaviors LLMs rely on for reasoning about products, retailers, and consumer intent.
The outcomes offer a sneak peek into how AI search is transforming—and how this will impact the broader commerce ecosystem.
TLDR; LLMs lean heavily on a limited range of external domains with YouTube, large retailers, and U.S. review media leading the charge.
Generalist retailers dominantly capture nearly half of all retail citations, serving as the recurring funnel LLMs use to address shopping queries.
Social and user-generated content see an 8.1% surge during Black Friday, as conventional retail and media sites experience a decline.
Off-page signals like Reddit, YouTube, Amazon, and Consumer Reports are vital, equally important as on-page content for shaping LLM comparisons and recommendations.
Structured comparison content wields significant influence, far surpassing branded assets.
The behavior of LLMs differs not only from Google but also from each other, with each platform like Gemini, OpenAI, and Perplexity offering unique formats, lengths, and reasoning patterns.
Unlike traditional search, where the process begins with a query leading to a list of ranked results, AI search reverses this. It starts with a model’s internal web of relationships, sources, and signals to construct a response.
In our review of the top 50 most-cited domains across 10,000 LLM responses—all centered around deals, reviews, and product recommendations—the distribution was notably skewed:
YouTube led with 1,509 citations, followed by Best Buy with 950, Walmart with 885, Target with 477, TechRadar with 355, RTings with 342, and Consumer Reports with 325.
This cluster shapes much of the commercial “knowledge” from which LLMs draw. It gravitates towards large retailers, global media outlets, and platforms specializing in comparisons and reviews.
In analyzing 10,000 responses, I compared the week leading up to Black Friday with the event itself. Pre-Black Friday, responses reins focused on planning behavior.
Retail and brand domains: 59.6%
Media: 23.4%
Social and user-generated content: 17%
When Black Friday commenced, the mix rapidly evolved. Social and UGC content jumped to 25.1%, gaining significant share, while retail and media slightly retreated.
This shift within the models mirrors consumer behavior but also highlights the models’ reliance on conversation-driven content for in-the-moment decision cues.
One of the most transparent insights is the weight third-party domains carry on AI reasoning. Today’s LLMs thrive by absorbing as much human interest in products as possible. Huge volumes of consumer insights, reviews, product demos, sentiment, and structured data guide how models reason and decide.
An analysis revealed key off-page signals LLMs depend on:
Reddit: 34%
YouTube: 19.5%
Amazon: 15.5%
Business Insider: 9.2%
Walmart: 8.9%
Each domain influences different aspects of the model’s decision-making. Across the board, LLMs lean on content that captures human interest, organizes consumer options, and mitigates uncertainty through verifiable data.
While third-party domains reign supreme, brand websites still hold measurable sway. They are vital for any consumer brand aiming to excel in AI discovery.
A site’s architecture plays a crucial role in how a model interprets a brand. Homepages account for 40% and serve as the primary identity layer—establishing tone, positioning, and offering quick semantic signals to models.
Blogs and product pages clarify brand definitions and long-tail context, providing the factual details models need.
Brands that rely too heavily on promotional copy, weak hierarchies, or thin product content risk sacrificing major visibility.
Across the entire dataset, certain retailer categories led the charge in model responses.
Generalist retailers hold 48% of the conversation. Walmart, Target, and Best Buy capture almost half of all retail citations. Their range, familiarity, and content depth make them central figures in LLM commerce reasoning.
Electronics specialists grasp 23% of the share. Best Buy leads, trailed by Newegg and Micro Center, with tech-focused queries often directing models toward these sources.
Other verticals lag behind. Despite strong category leaders, sectors like fashion, beauty, and home capture smaller portions due to the content volume disparity compared with generalist retailers.
Reviewing the platforms uncovered another pattern: major LLMs not only offer different answers but exhibit distinct thinking styles. Each platform has its own rhythm, structures, and styles for presenting commercial information.
Gemini provides the most detailed responses, with essays averaging 606 words, using lists and headings extensively.
OpenAI stands in the middle, averaging 401 words per response, with high list usage and balanced headings.
Perplexity shifts towards brevity with an average of 288 words, favoring short summaries akin to executive briefs.
These differences define unique retrieval and reasoning methods, shaping how each platform interprets brands, categories, and commercial intent.
The data presents a clear direction: AI search is forging its ecosystem, driven by familiar SEO inputs, source quality, content structure, and off-page signals, all interpreted to deliver precise answers.
If your content isn’t well-structured and present across the web, it risks becoming invisible to AI platforms delivering answers or product suggestions.
As this new environment evolves, it’s crucial for retailers and brands to rethink their communication strategies across the entire digital landscape.
On-page actions that matter:
Develop semantically coherent homepages that convey the brand, product categories, and relevance to core queries. LLMs prioritize clarity over cleverness.
Strengthen product pages with factual content, clear specifications, and Q&A sections aligned with user research intents.
Establish educational content clusters tied to core product themes, serving as reusable frameworks for AI models.
Off-page actions that matter:
Foster comprehensive review ecosystems and discussion forums to validate trust signals LLMs recognize with product quality.
Ensure visibility in media driven by comparisons and recommendations. Regularly appear in “best of” lists, product roundups, and influencer content.
Invest in rich media showcasing product value, particularly on YouTube and TikTok. Video content helps train LLMs on product use cases, reflecting sentiment, and experiential value.
Maintain accurate, indexable product data in marketplaces like Amazon, Walmart, and Etsy to enhance AI discovery pathways.
OpenAI’s Shopping Research announcement escalates the stakes. With ChatGPT, OpenAI tracks real-time consumer research behavior, turning preferences into a user-trained targeting engine for commerce.
This isn’t just AI learning about your product. It’s AI absorbing consumer shopping behavior, revolutionizing discovery through an active AI participation model.
Brands not infused into these AI systems risk invisibility during AI-driven consumer journeys.
What Black Friday revealed was more than top-selling products; it showed how LLMs operate under real demand, revealing their reasoning, referencing, and prioritizing patterns.
The advent of AI-native visibility requires structured, semantically rich content, adequately represented across the right off-page ecosystems to align with major AI models’ reasoning.
Black Friday might be the stress test, but the real transformation is only just beginning.
See how collaborating with LLMs can transform your content by converting customer, expert, and competitor data into actionable insights.
When I think about large language models (LLMs), one major discussion point is their ability to scale content creation. It’s a tool we’re all tempted to lean on heavily. However, balancing efficiency with creativity is key.
With our busy schedules, boosting productivity is essential. Imagine using tools like Claude and ChatGPT not just for speeding up processes, but also for adding a personal touch to your website and making your day-to-day tasks easier, all without sacrificing creativity.
This journey explores how to:
Analyze customer feedback and questions comprehensively.
Streamline the gathering of detailed insights from subject matter experts.
Conduct competitive analysis effectively.
These tasks, often done manually, can be remarkably enhanced with automation, giving you an edge by rooting your approach in customer and market realities instead of working in a vacuum.
By tapping into this information, I can better connect with my audience, avoiding the pitfalls of an echo chamber.
Analyzing Customer Feedback at Scale
One outstanding feature of LLMs is their scalability in processing data, identifying patterns, and uncovering trends—tasks that might otherwise take me or a colleague days or even weeks to complete.
If you’re not part of a global enterprise with a dedicated data team, LLMs are your next best ally to substitute those capabilities. Focusing on customer feedback, for instance, could mean the difference between success and redundancy. The thought of sifting through thousands of NPS surveys doesn’t sound appealing to me, and I doubt it does to you either.
Utilizing raw data uploads into a project knowledge space and having my LLM of choice run its analysis is one way to go. However, I prefer uploading this data into something like BigQuery, using LLMs to write relevant SQL queries for in-depth analysis, ensuring integrity and accuracy.
This approach not only lets me peek behind the analytical curtain, learning SQL by osmosis but also serves as a safeguard against potential inaccuracies or hallucinations often seen with direct LLM data uploads.
The separate handling of data fosters a more reliable, accurate, and actionable insight, preventing the wild goose chases that could arise from misleading automated responses.
Practically speaking, unless overwhelmed by enormous datasets, BigQuery is a free resource (setup might require a credit card, though). And fear not if SQL is new to you; with an LLM, you’re set for success with full query support in place.
Here’s a glimpse into my workflow:
Generate SQL functions using the LLM.
Debug and validate data entries.
Feed LLM with results from SQL queries.
Create visualizations either with the LLM or via further SQL queries.
Frustrations abound when attempting to secure time with subject matter experts, whose schedules often leave them stretched thin.
Why would they want to regurgitate information they’ve already discussed ad nauseam with the manufacturing team? Yet, for marketing purposes, I still need this information to clearly present new features on our platform, offering customers precise details beyond mere specifications.
How to get this coveted expertise? By crafting a customized GPT that can assume the role of interviewer, asking the right questions.
Be advised: customization may vary depending on the launch, product, or service in question. A ChatGPT Plus subscription should suffice for this task.
The guidelines should entail the following:
Role and tone: Define the interviewer’s persona.
Context: Clarify learning objectives and rationale.
Interview structure: Outline initial topics and follow-ups.
Pacing: Implement a structure of query-response dynamics.
Closing: Craft a concluding summary or call to action.
Testing it myself, I pretended to be a subject matter expert to refine this tool, always seeking to fit within their limited downtime.
The responses provided can then be further analyzed or converted into draft articles thanks to an LLM.
While potentially tricky, the strategic examination of competitors can yield profound insights regarding the competitive landscape and personal business gaps.
Here’s a few things I’ve found valuable when dissecting competitor data:
Aggregating competitors’ reviews helps identify common themes, benefits, and problem areas.
An analysis of their web copy gives clues into the type of audience they’re targeting and their unique positioning. Combine this with the Wayback Machine to track how messages have evolved over time.
Job postings can highlight strategic priorities or areas of potential experimentation.
Social media engagement data can provide insight into customer satisfaction and desire, revealing potential gaps in their customer service.
Using LLMs alongside extensive datasets allows me to remain grounded in customer realities while being swift in delivering specific, actionable insights through pair programming.
The methods explored within are just starting points. Consider other useful data sources you might already have access to:
Call transcripts from sales teams.
Query data from Google Search Console.
Insights from on-site searches.
Heatmaps tracking user interactions.
A note of caution—while analytics data is tempting, sticking to qualitative, customer-focused data rather than quantitative metrics leads to richer insights.
I’m always fascinated by how technology evolves, especially when it comes to AI models. Recently, I stumbled upon some compelling data showing how these AI systems are reshaping brand hierarchies and influencing buyer decisions at an unprecedented speed.
AI models like ChatGPT, Gemini, and Claude have become a part of our daily interactions, from search to content creation and product recommendations.
According to a survey conducted by Responsive, a significant 80% of tech buyers now use generative AI to research vendors just as often as they use traditional search methods. This shift in how buyers build trust with AI-driven discovery tools quietly determines which brands stay top-of-mind and which fade into oblivion.
At Previsible, we’ve been analyzing this intriguing phenomenon through what we call LLM perception drift. It’s a new metric revealing how AI models are dynamically organizing brands within specific categories over time. (Disclosure: I am the CEO and co-founder of Previsible.)
Our case study on project management software, comparing data from September to October 2025, highlights just how quickly AI brand perception can change. This volatility is set to become the next major metric for SEO strategies.
Key insights
The concept of LLM perception drift is emerging as a crucial visibility metric in SEO and B2B marketing.
Brands like Atlassian gained prominence, while others like Trello and Slack saw declines, indicating the dynamic nature of AI perception.
Understanding AI brand perception is pivotal for marketers aiming to grasp authority and relevance in language models.
By 2026, maintaining digital visibility will hinge on AI brand signal stability as LLMs rapidly evolve.
A subtle shake-up inside the AI mind
Evertune’s AI brand score provides insights into how likely a model is to recommend a brand without specific prompting. It measures both visibility and ranking within AI responses.
September to October shifts highlight considerable changes in the internal brand landscape of AI models. Notably, Slack saw a significant decline, while Atlassian experienced a boost.
This seemingly simple reshuffle reveals a deeper transformation in AI’s nonspecific brand awareness, altering how the model discerns and prioritizes brands despite market stability.
The meaning behind the drift
We’re seeing two main forces driving these shifts:
Category entanglement
Rather than declining, categories are blurring — project management tools are being integrated into broader conceptual frameworks.
Operations
Digital transformation
Workflow orchestration
Enterprise productivity
IT consulting
Names like Deloitte and KPMG rise alongside Smartsheet and Atlassian.
Ecosystem advantage
Brands with multi-product ecosystems are getting noticed more. Atlassian’s lift, for example, stems from its robust documentation and integration abilities. Brands like Microsoft, Google, and Amazon are also seeing positive movement.
Models increasingly prefer brands that span multiple ecosystems, echoing entity-based SEO patterns but at a faster, more volatile pace.
We observe emerging trends in newer brands like Celoxis and Workfront, showcasing how fine-tuned LLMs draw from diverse datasets.
SaaS directories
GitHub repositories
Technical documentation
Reviews
Community content
For smaller B2B brands, this represents a gateway to visibility without needing to dominate traditional SEO metrics.
Why this shift matters for B2B discovery – and why it’s speeding up
Traditional SEO focuses on visible search results, whereas LLMs synthesize knowledge based on associations and contextual richness.
This means that brand recall in AI systems relies on deeper semantic connections, and these can fluctuate significantly over short periods.
Understanding and leveraging this LLM perception drift is crucial — being consistently recognized in AI outputs is now as vital as traditional search rank.
A new AI optimization KPI: AI brand signal stability
In working with B2B clients, we’re focusing on AI brand signal stability as an emerging metric — tracking how consistently a brand’s presence is maintained in AI outputs.
Fluctuations suggest fragile brand perception, influenced by data changes and model retraining, while stable scores indicate strong semantic grounding.
In coming years, AI brand signal stability will be essential alongside share of voice and traditional SEO metrics.
From project management to every B2B vertical
This transformation isn’t limited to project management — it’s happening across all B2B sectors.
The recalibration of category contexts by AI models alters the buying journey, influencing brand appearance in AI-generated content.
The rise or fall of brand attention affects which brands occupy summative or comparative outputs, making AI memory a new realm of marketing focus.
This shift marks SEO’s evolution — from focusing on search indices to emphasizing model memory optimization. Our goals now include measuring how AI interprets and recalls brand identity.
It’s about ensuring that AI systems correctly interpret and represent brands across their expansive digital landscapes.
This demands new strategies and tools tailored to how dynamic perception systems function, rather than treating them as static outcomes.
Evertune’s dataset highlights more than monthly position changes — it showcases a quick shift in AI’s category perception, which marketing teams must monitor to stay competitive.
By 2026, brand appearance in AI-generated summaries will play a bigger role in decision-making than traditional metrics like pageviews or clicks. Brands that effectively manage their model-driven visibility will set themselves apart as AI becomes a mainstay in digital research.
