I was thrilled to learn that Google has rolled out its Google Search Live globally, expanding its reach to over 200 countries and territories where AI Mode is available. You can check which languages and regions are supported.
Google attributes this remarkable expansion to its cutting-edge audio and voice model, Gemini 3.1 Flash Live. This model offers more natural and intuitive conversations, and because it is bilingual, it allows individuals worldwide to engage with Search in their language of choice.
How it works. To get started with Search Live, I simply open the Google app on my Android or iOS device and tap the Live icon beneath the Search bar. From there, I can speak my question out loud and receive a helpful audio response. It’s seamless to continue the conversation with follow-up questions or delve deeper using the provided web links. When I need visual context, like figuring out how to install a new shelving unit, I just enable my camera, and it complements Search Live’s suggestions with relevant information from the web.
Moreover, if I’m already using Google Lens to capture an image, tapping on the Live option lets me have a real-time conversation about what I see, bringing what’s in front of me to life.
More. Back in September, Google made Search Live with video available in the U.S., appealing to those who enjoyed its earlier iterations. Initially, it was an opt-in beta, and before that, it featured a talk and listen mode, minus the video component.
Why we care. This development offers a fresh approach for users to interact with Google’s AI through conversation rather than text queries. While this might reduce traditional web traffic, since users get direct answers, the inclusion of citations and links might still benefit content creators and brands, even if users are less compelled to click through for more depth.
I’ve often wondered how much schema markup actually aids AI search optimization. There are claims it can increase citations or significantly enhance AI visibility, yet the truth is more complex and nuanced.
Let’s dive into separating facts from assumptions and explore how schema truly integrates into an AI search strategy.
How Schema Fits into AI Search Now
Search is evolving from simple SERP links to dynamic AI Overviews, with generative answers and chat-style summaries compiling content beyond just links. My goal is to ensure my content is recognized within this model, and that’s achieved by focusing on ‘entities’—distinct concepts such as a person, place, or event—not just strings of text.
Schema markup is a powerful tool I use to clarify these entities and their relationships, making them comprehensible to AI. For instance, identifying a person, their organization, the price of a product, or the author of an article.
AI systems focus on three key elements:
Entity definition: Identifying brands, authors, services, or SKUs on the page.
Attribute clarity: Distinguishing which properties relate to which entity (like prices or ratings).
Entity relationships: Understanding connections between entities (using tags like offeredBy or authoredBy).
By employing schema with stable values and structured methods, it begins to function like a mini knowledge graph. AI systems no longer guess who I am or how my content ties together; they follow explicit links between my brand, authors, and subjects.
Microsoft Bing Copilot: Microsoft’s product manager confirmed in March 2025 that schema aids Microsoft’s LLMs in understanding content for Copilot.
Exploring ChatGPT, Perplexity, and Other AI Search Platforms
The usage of schema by these platforms remains uncertain. They haven’t publicly clarified if they maintain schema during crawling or use it for data extraction. Though LLMs can technically process structured data, it doesn’t guarantee their search systems do.
It doesn’t negate schema’s value, but highlights that schema alone doesn’t drive citations. LLM systems prioritize relevance, authority, and clarity over structured markup presence.
LLMs excel when given a structured format to fill out instead of a blank canvas, minimizing errors when extracting defined data fields.
Schema markup resembles this structured format, providing clear entity, brand, and topic fields.
Interpreting the Research
The findings suggest that LLMs can better process structured data than unstructured text. However, we still lack confirmation on whether AI search systems preserve schema data during crawling or use it during extraction.
For Microsoft Bing and Google AI Overviews, schema likely improves data extraction accuracy, given their confirmed usage. Other platforms remain unverified regarding implementation.
Given the novelty of AI search—exemplified by ChatGPT’s launch in October 2024—companies haven’t revealed their indexing methods. Measuring impact remains challenging due to non-deterministic AI responses.
No peer-reviewed studies yet explore schema’s AI search visibility impact, nor are there controlled studies on LLM citation behavior with schema.
This gap persists as AI search is relatively new, with companies withholding indexing details and difficulties in assessing AI interactions.
Building an Entity Graph with Schema
In traditional SEO, schema is often limited to adding individual markup like Article or Organization. For AI search, connecting nodes into a cohesive graph through @id is more beneficial.
Create an Organization node with a permanent @id for your brand.
Develop a Person node for each author linked to your organization.
Form an Article node linking the author to the publication with detailed topics.
This interconnected pattern transforms schema into a useful entity graph. For AI systems preserving the JSON-LD, it clearly identifies brand ownership, human responsibility, and topic focus, unaffected by page changes over time.
Aspect
Traditional SEO schema
Entity graph schema
Structure
Single @type object per page
@graph array of interconnected nodes
Entity ID
None (anonymous)
Stable @id URLs for reuse across site
Relationships
Nested, one‑way (author: “name”)
Bidirectional via @id refs (worksFor, authoredBy)
Primary benefit
Rich snippets, SERP CTR
Entity disambiguation, extraction accuracy for AI
AI impact
Minimal (tokenization often strips)
Makes site a unified knowledge graph source if preserved
Schema markup acts as infrastructure rather than a miracle solution. Although it may not automatically raise citation rates, it’s an aspect I control that’s explicitly used by platforms such as Bing and Google AI Overviews.
The key isn’t just implementing schema in isolation, but integrating structured data with proper entity connections, high-quality authoritative content, and clear entity identity and brand signals. Strategic use of @graph and @id to build these connections is crucial.
I recently delved into a fascinating study exploring how AI citations are significantly influenced by certain content formats. It turns out listicles, articles, and product pages are at the forefront, driving over 52% of mentions across various AI language models.
The research, conducted by Wix Studio AI Search Lab, analyzed a whopping 75,000 AI answers and more than a million citations across platforms like ChatGPT, Google AI Mode, and Perplexity. It’s an exciting revelation that showcases the power of content structure in digital landscapes.
The findings? Listicles claimed the top spot with 21.9% of citations, followed by articles at 16.7% and product pages at 13.7%. When combined, these formats make up a majority of the citations AI references.
What’s interesting is that articles tend to dominate when it comes to informational queries, being cited 2.7 times more than other formats. Meanwhile, listicles capture nearly 40% of commercial-intent citations, almost double compared to any other type.
The Why Behind Intent. It’s fascinating to see how query intent, rather than industry or AI model, is the strongest predictor of which content gets cited. This trend doesn’t shift much across different sectors, from SaaS to health industries.
Informational queries skew towards articles (45.5%) and listicles (21.7%), while commercial queries are dominated by listicles (40.9%). Interestingly, transactional and navigational queries favor product and category pages, with those two formats comprising about 40% of the citations combined.
The Impact for Us. This study is incredibly insightful, illustrating why aligning content types with user intent is more strategic than simply generating content. Articles serve to inform, listicles foster comparisons, and product pages drive conversions. Tailoring content to align with user goals might just be the key to snagging more AI citations and enhancing visibility.
Not all listicles perform equally. In professional services, third-party listicles account for 80.9% of citations, showing a preference for neutral editorial comparisons over branded lists by large language models.
Looking at Model Preferences. While all models have a penchant for listicles, their other preferences vary. ChatGPT leans heavily towards articles and informational content, Google AI Mode shows a balanced approach, and Perplexity stands out with 17% of its citations coming from discussions on platforms like Reddit and forums.
Industry-Specific Trends. Though preferences shifted slightly among industries, there are notable trends. SaaS and professional services veer towards listicles, health sectors favor authoritative articles, and ecommerce spreads its citations across listicles, articles, and category pages. Interestingly, home repair maintains an even distribution across different formats.
I’m intrigued to know more! The comprehensive research can be found here.
I recently had the opportunity to attend the Industrial Marketing Summit, where Rand Fishkin delivered a keynote highlighting our current “zero-click world”. His perspective resonated with me, emphasizing that while fewer users are visiting websites, their impact remains crucial.
Diving deeper, it’s evident that the structural dynamics of how information is assessed and trusted online have shifted profoundly. This change has led many to misunderstand the true value of websites today.
Despite the drop in clicks, websites still play a vital role. They are the bedrock of visibility and trustworthiness on the internet.
Why ‘zero-click’ discussions often lead to the wrong conclusion
There’s an undeniable trend: clicks are on the decline, and here’s why.
Search engines readily display answers directly on results pages.
Social media platforms have become discovery hubs, allowing users to explore without ever needing to leave.
AI assistants synthesize comprehensive responses from the web even before presenting a user with links.
The focus on zero-click results disrupts traditional metrics for measuring online visibility. For decades, traffic and click-through rates have been the cornerstones for evaluating search performance.
Yet, when answers are given directly by search results or AI systems, often outside our typical analytics frameworks, many assume websites are losing significance. This is far from the truth.
Websites still underpin the information ecosystem. Their role in shaping visibility is arguably becoming more significant, especially with AI and modern information systems relying heavily on widespread, consistent signals from multiple sources on the web.
Fishkin is right about the trend
Information today is discovered in various environments, including search results, social media, and AI interfaces, leading to a real fragmentation of how we consume content.
While these interactions might appear as lost website traffic, the true question is: where does the original information come from?
Although people consume information through expanding platforms, these systems fundamentally depend on credible, original knowledge sources.
Zero-click doesn’t mean zero influence
The critical takeaway is differentiating between traffic and information influence.
Traffic measures visits to your site.
Influence assesses if your information shaped the answers people received.
AI creates responses based on patterns from the web, and content creators who provide valuable information remain crucial in this ecosystem.
Even without direct clicks, reliable sources continue to influence the information pipeline, helping shape the responses generated by AI systems.
The role of ‘rented land’
In adapting to a zero-click landscape, the focus might shift towards platforms where brands lack control, such as social networks or other “rented lands”.
Visibility stems from both types of territory — owned and rented.
Owned land encompasses your controlled content like websites.
Rented land includes platforms that distribute your message but aren’t owned by you.
In an AI-driven discovery setting, both are valuable. Owned content serves as essential knowledge sources, while rented platforms amplify these insights.
Yet, authority primarily emerges from robust original content, typically housed on first-party sites, which remains pivotal in influencing AI systems.
Why AI often favors primary sources
Contrary to some beliefs, AI systems value primary sources more than aggregated content.
When AI generates answers, it frequently relies on sources with clear, expert explanations and well-reasoned content, mostly found in single-source publishing like legal blogs or technical documentation.
This move places emphasis on creating authoritative content, which can enhance your influence in an AI-led world even as click metrics may reduce.
The real shift you should understand
Websites are evolving beyond their historical role as mere traffic generators. They are now key players in the AI-mediated informational landscape as sources of knowledge and bastions of expertise.
The goal now is to ensure expertise is accessible and can be assimilated across various digital environments, be it search engines, AI responses, or social discussions.
In our zero-click world, influence takes root earlier, reinforcing the importance of creating valuable, knowledgeable content.
When I hear about someone ranking first but still being invisible, it seems strange, right? But here’s the real story:
A potential customer might ask ChatGPT or Perplexity for the best tool or agency in your category—and your competitor is the one that gets mentioned, not you. Your top ranking isn’t helping in this scenario.
This is the new reality in SEO that surprises many experienced marketers. Large language models (LLMs) gather consensus from multiple sources instead of relying on just one. This shift means it’s no longer just about ranking—it’s about being consistently mentioned across various sources. Missing this understanding means you’re losing ground.
Let’s unravel what’s happening and, more importantly, how we can navigate this new landscape.
From Rankings to Consensus: Understanding the Shift
Traditional SEO was straightforward: rank high to get clicks and drive traffic. Google searches found pages, and users decided which ones to visit.
However, AI-driven search introduces a new method. Platforms like Google’s AI Overviews and ChatGPT now create their responses by compiling information from numerous sources. They check for consistency to form a single, synthesized answer.
Data reveals the magnitude of this shift: since mid-2024, organic click-through rates have dropped significantly for queries showing AI Overviews. Even queries without AI results saw a decrease.
The technology behind this is retrieval-augmented generation (RAG), where AI pulls from across the web to discern repeating claims from credible publishers. The objective isn’t just publishing a great page—it’s about becoming one of those consistently cited sources.
What the Consensus Layer Actually Is
I think of the consensus layer as AI systems producing consistent outputs about your brand. It’s a large-scale pattern recognition.
When AI systems find your brand mentioned in the same way across several credible sources, they build confidence in those claims. When they don’t, your brand becomes an outlier, which risks exclusion.
This system prevents AI hallucinations, using corroboration as their defense. If multiple sources independently agree on a claim, AI considers it reliable. Sole sources tend to be ignored.
I’ve observed brands being invisible despite their high rankings because they rely solely on traditional authority without corroborated recognition.
Will Scott’s insight is valuable: Visibility issues arise because brands aren’t mentioned in AI answers, despite being high-ranked in traditional search.
Explore more: When search demand surpasses keyword limits.
The Signals That Actually Build Consensus
What signals do AI systems rely on to build consensus? Here’s where we need to focus:
Traditional Authority Is Just a Starting Point
Foundational elements like backlinks and domain authority get you in the game. But achieving consensus is what truly sets you apart.
Unlinked Mentions Matter More Than We Think
AI scans for brand mentions, even when unlinked. Unlinked mentions signal both traditional and AI visibility, like when an unlinked mention in an industry publication serves as a consensus signal.
Approximately 9 out of 10 webpages cited by ChatGPT fall outside the top 20 organic results, highlighting the game’s transformative nature.
Publisher Diversity Strengthens Credibility
Repeating mentions on the same site doesn’t build consensus. Diverse mentions across credible publishers are key.
Community Platforms Are Consensus Gold
Platforms like Reddit and Quora are becoming pivotal for consensus, as AI recognizes genuine user discussions as reliable data sources.
With Reddit leading in SERPs, positive mentions in subreddits significantly contribute to AI perceptions. Genuine community trust can’t be fabricated—it must be earned.
Entity Clarity Simplifies Retrieval
Search engines use knowledge graphs to connect entities. If your brand is inconsistently presented or your category is vague, AI systems struggle to recognize you in answers.
Structured data, schema markup, and JSON-LD are crucial. The clearer your entity’s profile, the easier it is for AI to reference and cite you.
How to Actually Build Consensus
Alright, let’s dive into some tactical steps. Understanding your current standing is vital before taking action.
Begin with an LLM Audit
Use ChatGPT, Perplexity, Gemini, and Google AI Overviews to ask questions just as your customers would.
“What’s the best tool/service for the problem you solve?”
“Who are the leading providers in your category?”
“What do people say about your brand?”
Focus on three outcomes:
Is your brand even mentioned?
If so, is the information accurate and current?
How are you compared to competitors?
This assessment reveals gaps, misinformation, and your weakest points in the consensus landscape.
Build Your Owned Media Foundation
Ensure your website is technically sound with clear semantic structures. Utilize structured data, clearly define your entity, roles, and solutions, and affirm these consistently across your site.
Develop topic clusters and pillar pages with related content to demonstrate expertise and depth. Without a robust foundation, efforts may falter.
Leverage Earned Media for Consensus
Press, guest posts, podcasts, and expert quotes help distribute your authority across the web. It’s about more than links; it’s about managing your narrative.
Sustained visibility across reputable platforms amplifies your consensus reach. Balance unlinked mentions with traditional link building.
Conduct and Share Original Research
Original data and proprietary surveys serve as high-impact consensus assets. Other publishers referencing your research naturally boosts your credibility, offering long-term citation opportunities.
Invest in Expert-Led Content
Position team members as experts. When recognized continuously, they gain trust from AI systems. Optimize author profiles with structured data to enhance this.
Engage Authentically in Communities
It’s not merely about sharing links on Reddit. It’s about real participation—answering questions and building your brand reputation organically.
When users naturally recommend your brand, it’s the strongest signal of consensus.
Tracking What’s Vital Now
Traditional rankings indicate where you stand in search results but don’t show AI citations. New metrics focus on visibility and share of voice rather than mere clicks.
Experiment with high-value queries to check AI Overviews and ChatGPT responses. Note your brand’s mentions, descriptors, and accompanying sources.
Measure share of voice across AI responses and monitor cross-domain mention density and entity co-occurrence to assess your consensus reach accurately.
The New SEO Playbook
Success now lies with brands building distributed credibility through a mix of owned media, earned media, and community platforms.
While traditional SEO basics are necessary, they’re just the start. Integrate SEO, digital PR, and community efforts into a unified strategy to build a durable visibility moat.
Building this network of mentions and citations is the defense against competitors, and the timing for action is critical.
Dive deeper: Why distribution is essential in conjunction with content for SEO success.
I’ve realized that when my law firm’s referrals don’t convert, the issue often lies in the validation process. This crucial phase can break conversions if my firm’s credibility, specificity, and authority don’t align with the lead’s expectations.
Referred prospects aren’t direct conversions. They engage in research and verification on various platforms, like my website or search engines, to ensure what they’ve heard matches reality.
Despite being premium leads — pre-sold through trusted recommendations — if their validation needs aren’t met, they lose momentum.
This issue, known as the referral validation gap, is where trust falters rather than strengthens during the research phase. Addressing this is key for all referral-based businesses, even beyond law firms.
The four types of referral validation failure
Spotting and fixing predictable patterns of referral loss is essential. The main types are:
Credibility gaps: When my digital presence fails to meet the reputable image conveyed by the referral.
Specificity gaps: When my content doesn’t address the specific issue for which the prospect was referred.
Authority gaps: When independent validations or AI tools don’t confirm my expertise.
Friction gaps: When ready-to-act leads face unnecessary hurdles.
Credibility gaps occur when visitors form impressions in seconds. If my website doesn’t immediately back up what the referrer promised, their trust wavers.
To combat this, I need targeted landing pages, specific H1s, and visible credentials that match the referral’s expectations.
Specificity gaps arise when my homepage doesn’t align with the specific issue that brought the referral. Simple headlines like ‘family law’ or ‘commercial real estate services’ don’t suffice.
It’s crucial to have content reflecting the search intent, proving the specific expertise that prompted the referral.
Authority gaps hinder validation if AI tools can’t find structured data supporting my firm’s claims.
Regularly running queries through AI tools can show whether competitors are outranking my firm, and adjusting content strategies based on these findings is imperative.
Friction gaps lead to loss when prospects are ready but face difficulties in contacting us. Immediate and clear action steps are necessary to maintain momentum.
Ensuring prospects can engage without delay, with clear contact information and easy processes, prevents loss at this critical stage.
Your roadmap to close the referral validation gap
To bridge this gap, I need strategic, step-by-step changes, starting with removing immediate friction and then building validation infrastructure.
These actions range from simple technical fixes to comprehensive content strategies, ultimately ensuring that my firm stands out in both traditional and AI-driven environments.
2026 is your firm’s inflection point
Prospects now find answers without even visiting a firm’s website. Bridging the gap between digital presence and authority is critical, or the gap will widen, with leads slipping away.
Mastering this process will not only enhance conversion rates but also capitalize on high-value leads, reduce costs, and build a competitive edge in an AI-driven environment.
Ultimately, gaining an initial consideration through referrals is just the beginning. How we manage our digital presence to close the referral validation gap truly matters.
When I think about how much AI search has evolved, I’m amazed by how it’s deeply rooted in years-old patents. These historical blueprints are the architects of AEO, GEO, and our modern SEO strategies.
It’s fascinating to me that whenever a new large language model (LLM) is released or Google makes an AI update, the SEO community seems to panic. We tend to overlook that the features we’re scrambling to optimize for were often designed in the patent offices a decade ago. Our focus on the present and future blinds us to the wisdom of the past.
If we want to stay ahead in 2026, we need to shift from being futurists to becoming archaeologists.
To truly serve our clients, a balanced research framework is essential. By revisiting foundational patents, we can grasp the core rules, while also keeping an eye on how current AI developments breathe life into those regulations.
There’s a myth that understanding AI search requires being a prompt engineer or diving into every research paper from OpenAI. In reality, many of the algorithms powering today’s innovations were penned in mathematical language over a decade ago.
I deeply respect Bill Slawski, the late, great SEO archaeologist, who spent over 20 years unearthing insights from dry, technical patent filings to forecast the present we are experiencing now.
Looking back, his method of analyzing history certainly proved its relevance.
The SEO algorithms aren’t mysterious; they’re mathematical. Many features introduced today are based on blueprints filed between 2007 and 2016. To succeed, it’s vital to dive into historical documentation.
Understanding strategy versus mechanics is crucial. We need to categorize our learning as either strategic or mechanical. The transition from ‘strings to things’, or entities, required verification to distinguish real from fabricated.
It’s crucial to separate AEO from GEO, as they demand distinctive content architectures and fulfill different objectives. AEO targets direct answers, while GEO requires synthesis and demonstrates the interplay between concepts.
It’s easy to neglect basic SEO fundamentals amidst the influx of AI developments. The essentials, like technical SEO, remain pivotal.
The persistence of technical debt exposes how the tolerance for neglecting foundational SEO tasks has vanished.
The technical backend of our websites, whether using traditional CMS or modern headless architectures, requires careful attention to succeed in AEO and GEO.
To become a proactive SEO architect rather than a reactive time traveler, we must integrate verified facts and trusted source connections into our strategic framework.
As I look back on 2025, it’s astonishing to see the AI search traffic growth leap by an impressive 180% year-over-year. I’m diving into the data to better understand how this impacts our visibility strategies. We’ll explore insights on ChatGPT, Gemini, Perplexity, and Claude usage trends in this review.
With AI technologies rapidly advancing, I’ve noticed how they continue to reshape how we think about search and brand visibility. The increased use of AI-powered tools signifies a pivotal shift in the way we approach digital marketing strategies.
In 2025, ChatGPT saw a remarkable surge in use, closely followed by interest in platforms like Gemini and Claude. This data is crucial as we plan for future visibility tactics, ensuring that our brand remains competitive in an ever-evolving digital landscape.
How does this data affect your brand’s approach? I believe understanding and leveraging these trends will be key to optimizing AI-driven search capabilities and visibility while crafting more personalized and effective content strategies.
Join me on April 1 for the inaugural SMX Now event, where iPullRank will unveil their presentation, ‘AI Search Picks Winners: Here’s the GEO Strategy Behind It’.
Visibility today means more than just ranking well; it’s about ensuring your content is found, evaluated, and chosen by AI-driven search platforms. On April 1 at 1 p.m. ET, I’m excited to launch our new monthly SMX Now webinar series featuring insights from iPullRank’s experts Zach Chahalis, Patrick Schofield, and Garrett Sussman.
During the session, you’ll be introduced to iPullRank’s innovative Relevance Engineering (r19g) framework, which applies Generative Engine Optimization (GEO) using a comprehensive omnichannel content strategy. Engaging with this will deepen your understanding of how AI search leverages query fan-outs to discover and elect content sources, and how best to structure your content for optimal retrieval, visibility, and citation.
It’s crucial to note that success with GEO is not a one-size-fits-all solution. It demands continuous testing, tailored strategies, and a robust three-tier measurement model that covers discovery, selection, and citation impact.
Reserve your spot now and explore how you can elevate your brand’s visibility in an AI-powered world.
I’m proud to partner with Search Engine Land as a media sponsor for the upcoming SEO Week by iPullRank.
Most content out there tends to be too generic, making it less effective in AI search. I’ve discovered that using customer personas allows me to pinpoint real problems and step into the search space much earlier.
Whenever buyers pose a question, my goal is to deliver a clear answer. That’s essentially the “They Ask, You Answer” (TAYA) framework, which thrives even in AI-driven discovery.
Though it sounds straightforward, I’ve seen many teams struggle to anchor their approach. This typically results in generic questions that lead to generic content.
This is problematic since AI is transforming search behavior, shifting from simple queries to in-depth, context-rich questions. The difference lies in the questions we choose to answer, and that’s where customer personas shine.
The Problem with Generic Questions
Chances are, both I and my competitors have tackled these generic questions already or could do so quite easily.
The trap of generic questions occurs when marketing teams, including mine at times, begin brainstorming content ideas with broad topics like:
What is CRM software?
What is marketing automation?
What is warehouse management?
While reasonable, these questions are not what real buyers ask. Real buyers ask questions based on their specific situations, such as:
“What CRM should a 10-person sales team use?”
“Why are leads slipping through the cracks in our marketing?”
“Why is our warehouse picking speed so slow?”
This distinction is subtle but crucial. The second set of questions integrates a person and a problem, transforming the quality of the content I produce.
Why This Matters More in AI-Driven Discovery
With AI, buyers are asking detailed, context-rich questions, such as:
“I run a 15-person marketing team, and we’re struggling to track leads properly. What should we do?”
The AI provides explanations, outlines solutions, and suggests vendors, essentially giving the buyer a consultation. My content’s job is to explain why a specific persona faces a specific issue, framing how it should be perceived.
This positions me into the conversation earlier, increasing the likelihood of staying top of mind as the user’s understanding evolves.
Imagine this scenario, using myself as the subject:
Marcus.
50 years old.
Meeting old friends in Birmingham, UK.
Looking for things to do for the day.
I might start with a broad question:
“I’m looking for some things to do with friends in Birmingham on the weekend. I’m 50, and I have some old friends visiting for a day. We’ll enjoy some beers, but need activities too.”
The answers might include bars, food, and activity bars. An F1 gaming arcade could be suggested, sparking my interest since I enjoy games but not cars, which prompts my follow-up question:
“Ah, we all like games. What gaming arcades could you recommend?”
The responses might highlight a pinball arcade in Digbeth.
“Pinball Factory in Digbeth sounds fun. What else is there to do around there, food- and drinks-wise?”
This kind of dialogue allows me to refine my day’s plan perfectly for my friends.
Being part of the conversation from the start helps shape the dialogue and boosts the chance of being included in the final decision.
Personas Make TAYA Far More Precise
With personas, I think like my customers, identifying the questions they might ask long before they reach my offerings.
When I define a customer segment, I delve into that persona, understanding their problems and goals to think like them, which helps in crafting content that answers their early-stage questions.
Instead of creating content for a vague audience, I focus on real people, addressing specific needs like, “The best day out in Birmingham for a group of 50-year-old gamers.”
This small shift often leads to valuable content, positioning me within meaningful conversations rather than competing on crowded commercial queries.
A Simple Way to Uncover Better Questions
No need for a complex persona framework. Often, a simple three-question exercise reveals the problems buyers seek to solve.
For each persona, I ask:
What are they responsible for? Examples include sales targets, marketing leads, or warehouse operations.
What problems complicate that responsibility? Issues like missed targets or inefficient operations might arise.
What might they search for when facing these problems?
Now, the questions I generate differ greatly from generic ones:
Instead of saying: “What is CRM software?”
I see questions like:
“Why are leads slipping through the cracks in our CRM?”
“What CRM should a small sales team use?”
“Why is our warehouse picking speed so slow?”
These questions reflect real situations, providing the most substantial content opportunities.
‘They Ask, You Answer’ Works Better with Personas
TAYA covers five key areas: cost, problems, comparisons, reviews, and best-of. These topics offer structure, but approached generically, they mirror what everyone else is doing.
Generic questions like:
“How much does CRM software cost?”
“What problems do warehouse systems have?”
“HubSpot vs. Salesforce”
“Best CRM systems”
“Salesforce review”
Can be transformed into more targeted questions:
“What does CRM cost for a 10-person sales team?”
“Why do my warehouse managers struggle with picking accuracy?”
“HubSpot vs. Salesforce for a small B2B marketing team”
“Best CRM for growing sales teams”
“Is Salesforce suitable for a mid-size sales organization?”
Although the topic remains the same, the approach is tailored to the buyer’s reality. This makes the content more useful and aligns with AI interactions.
Targeted questions might include:
“We’re a small marketing team struggling to track leads properly. What CRM should we use?”
If my content already answers these persona-centered questions, it increases the chance of my explanations becoming part of their conversation.
In short, personas enhance TAYA by transitioning from broad topics to specific questions associated with real problems, improving the content and aligning better with buyers’ needs.
Start with the Problem, Not the Product
A common misstep in content marketing is leading with the product. Buyers, however, start with a problem.
By using personas, I anchor content in the buyer’s perspective rather than my own, ensuring the focus is on the customer.
This change can mean the difference between influence and mere existence of my content.
Where You Enter the Conversation Matters
“They Ask, You Answer” is an effective framework when the questions I address are of high quality.
Personas help in turning vague topics into precise problems, resulting in content that resonates with buyers and AI systems while earning their trust.
