CrushPress.AI

Category: AI SEO

  • Unlocking ChatGPT Mentions: Crafting Content for AI Exposure

    Unlocking ChatGPT Mentions: Crafting Content for AI Exposure

    I’ve been diving into the world of ChatGPT lately, and it turns out there’s a fascinating bias toward commercial intent in its fan-out analysis. Let me break down what this means for our content strategy.

    Over the course of my tests with 90 ChatGPT prompts, I discovered that commercial prompts lead to web searches a whopping 78.3% of the time, while informational prompts only did so 3.1% of the time.

    This discrepancy got me thinking about how to craft our content to increase the chances of being featured in ChatGPT responses.

    ChatGPT doesn’t source every response from the same place. Some responses are derived from its training data, while others are based on live web searches. This process, known as query fan-out, involves expanding a prompt into several background searches, and then curating a synthesized response from multiple topics. If our pages don’t fit into these subtopics, we miss out on getting pulled in.

    So, our challenge now is more than just ranking well; it’s about ensuring our pages open the door to this fan-out process from the get-go.

    In our samples, informational pages fell short. I encourage you to continue reading to uncover the paths the system actually favored.

    I conducted tests across three industries: beauty, legaltech/regtech, and IT. The analysis explored prompt intents, the resulting query expansions, and the intents portrayed by those expansions.

    The main takeaway is that most queries are aligned with commercial, rather than purely informational, intent.

    Why This Question Matters Now and the Role of Query Fan-Outs

    Understanding query fan-outs is crucial because it shifts the content creation approach. The system expands a prompt into several background searches, retrieving and synthesizing information from these subtopics.

    This behavior triggers parallel web searches connected to the initial prompt, providing opportunities for mentions and citations.

    Multi-query expansion is a fundamental design element in today’s search systems. As Google describes AI Mode, it breaks a question into subtopics, searches them simultaneously across sources, and combines the results into one coherent response.

    This raises a key strategic question: should we focus more on top-of-funnel educational content or on lower-funnel comparison, shortlist, and recommendation content?

    I designed this experiment to address that problem.

    We aimed to see where fan-out occurs by intent category across selected industries: informational, commercial, transactional, or branded.

    The hypothesis was clear: while informational prompts wouldn’t trigger fan-out, commercial ones would, and would either remain at the same level or move further down the funnel.

    ChatGPT fan-outs were observed to align predominantly with commercial intent.

    Disclaimer: This analysis reflects observed prompt expansion behavior in ChatGPT. Although Google AI Mode is cited for context to illustrate multi-query expansion as a pattern, it is not evidence of ChatGPT’s architecture.

    The Setup: What We Tested

    The experiment sampled 90 prompts, focusing heavily on informational intent.

    Prompt intentPromptsShare of samplePrompts with fan-outFan-out rate
    Informational6572.2%23.1%
    Commercial2325.6%1878.3%
    Branded11.1%00.0%
    Transactional11.1%00.0%

    Our sample primarily featured informational prompts, with some commercial and very few branded and transactional ones.

    The test was structured around three industries: beauty/personal care, legaltech/regtech, and IT/tech.

    The Result: Commercial Prompts Dominated

    The findings were clear and conclusive.

    Of the 90 prompts, 20 triggered a fan-out. Remarkably, 18 were of commercial intent and only 2 were informational.

    Informational prompts accounted for a mere 10% of fan-out triggers (2 out of 20). When they triggered expansion, they were reframed into more evaluative, solution-seeking subqueries.

    This indicates that, in this sample, commercial intent overwhelmingly influenced fan-out.

    These 20 prompts resulted in 42 fan-out queries, averaging 2.1 per triggered prompt.

    Here’s a breakdown of those fan-out queries:

    • 39 were commercial.
    • 2 were branded.
    • 1 was informational.

    Even if a prompt led to expansion, it typically transformed into a focus on comparison, feature filtering, shortlist creation, or brand-specific exploration, not broad educational discovery.

    Methodology: Our Analytical Approach

    Our experiment involved 90 prompts across three industries, mainly informational with fewer commercial prompts, and minimal branded and transactional queries.

    The analysis involved:

    • Choosing a representative set of prompts.
    • Identifying fan-outs.
    • Classifying each fan-out by intent.
    • Analyzing distribution by prompt metadata.

    Our approach followed three key steps:

    1. Classifying prompts by intent labels.
    2. Counting prompts that triggered any fan-out.
    3. Reviewing expansion queries and their intent labels.

    This process revealed two distinct perspectives:

    • A prompt-level view to determine which prompts instigated fan-out.
    • A fan-out-query view to assess the intent of downstream expansions.

    This distinction is important: the first identifies prompts that initiate the fan-out path, while the second examines where the system proceeds once engaged.

    Interpreting the Results: Fan-Outs Trend Down-Funnel

    The clearest takeaway is that, in this instance, fan-outs behave more like decision support rather than topic exploration.

    Commercial prompts frequently opened new discovery paths.

    Once open, these paths typically remained commercially focused.

    The system often expanded into comparisons, feature-based analyses, product listings, and pricing inquiries.

    Here are some illustrative examples:

    • “Suggest the best accounting software for small business and explain why” expanded to a commercial comparison query on features.
    • “What are the top AI document management systems for lawyers?” led to multiple product-centered legaltech queries.
    • “What are the best products for skin care?” grew into a shortlist-style inquiry around product categories and reviews.

    The rare informational examples expose more about the system’s tendencies than the rules themselves.

    • “I need an open-source document management system. What can you suggest?” initially coded informational, shifted to solution recommendations.
    • “AI tools for legal research and document automation” also redirected into clearly commercial/evaluative queries.

    Ultimately, even broad prompts frequently translate into more focused, commercially driven retrieval paths.

    Implications for Our Content Strategy

    Let’s not abandon informational content; however, we should recognize that informational content alone doesn’t consistently align with fan-out expansions, at least in this dataset.

    If our goal is to shine in AI responses tied to product selection or vendor discovery, we need to strengthen our coverage with content that lines up with these downstream commercial intents.

    Consider the following:

    • Creating “best-of” and shortlist pages
    • Developing thorough comparison pages
    • Writing “which tool should I choose” guides
    • Feature-led category explainers
    • Alternative option pages
    • Evaluation-focused FAQs
    • Incorporating recommendation passages in broader educational pieces

    In practical terms, our content model should integrate both top- and bottom-of-funnel strategies, with strong commercial bridges.

    A comprehensive piece can still be beneficial, provided it contains elements that the system can readily transform into decision-support inquiries.

    An educational piece that lacks direct references to products, tradeoffs, features, use cases, or selection criteria is less likely to match the system’s fan-out paths.

    In short, consider not only answering the obvious inquiries but also forecasting the subsequent evaluative step the system might generate behind the scenes.

    Understanding Our Limitations

    These results offer direction rather than universal truths.

    • 90 prompts highlight a pattern, but don’t establish AI retrieval behavior as a law.
    • The prompt mix skews heavily towards informational content, with few branded or transactional samples. The findings don’t signify absence.
    • While diverse, the dataset isn’t normalized for brand, style, or use case. Some sectors lean easily into product-discovery language.
    • This analysis observed recorded fan-outs rather than controlling for platform-level testing. It reflects what occurred within this set rather than guarantees of ChatGPT’s constant behavior.
    • Google’s fan-out description provides context; however, this isn’t a Google AI Mode test. It’s ChatGPT-centric, with strategic—not architectural—takeaways.

    Next Steps for Testing

    Future versions of this test should further isolate the question while widening the dataset.

    A follow-up should map fan-outs to specific content formats.

    The aim isn’t solely to affirm that commercial intent triumphs, but to pinpoint which page templates and structures proficiently capture AI-preferred fan-out paths.

    Inspired by this post on Search Engine Land.

    crushpress.ai community screenshot
  • Harnessing Brand Signals: The Evolving SEO Authority Model

    Harnessing Brand Signals: The Evolving SEO Authority Model

    For over two decades, I’ve witnessed backlinks as foundational to SEO. Google’s PageRank revolutionized search by using backlinks as proxies for trust.

    Backlinks were more than just pathways; they were votes of confidence. The more votes you gathered from authoritative sources, the better your rankings soared.

    But times have changed. As Google advanced, AI systems evolved, and the necessity for hyperlinks diminished as entity-based understanding gained ground.

    Today, visibility isn’t solely dependent on links. It’s amplified by the broad range of signals signifying your brand’s mentions, citations, and trust across well-regarded platforms.

    This shift sees search engines and AI prioritize these overarching signals.

    AI’s Role in Evolving SEO

    Modern AI models assess trust and expertise in unprecedented ways. They’ve reshaped authority, focusing less on backlinks and more on diverse digital signals.

    AI can now:

    • Identify and relate entities online.
    • Interpret sentiment and context.
    • Spot artificial link patterns.
    • Gauge brand prominence sans hyperlinks.
    • Evaluate reputation from reviews and citations.
    • Integrate information across varying sources.

    Mentions in respected publications, even link-free, enhance entity authority. Consistent expert citations affirm expertise. These are the signals forging a new era where authority becomes a rich network.

    The Shift to Entity-First SEO

    With Google’s move away from pure link signals, the notion of entities—people, brands, concepts—gains importance. Google elevates brands based on identity and conversation rather than just their backlink profile.

    In essence, entity-first SEO involves mapping and understanding brand interactions and references across trusted sources.

    An example: An outdoor brand with a modest backlink profile gained visibility in AI Overviews for “best hiking backpacks” due to mentions in Reddit discussions and YouTube reviews, illustrating real-world relevance sans hyperlinks.

    If your brand consistently figures positively in related talks, it’s seen as relevant and trusted—characteristics essential for success.

    Combining PR-Style Links with Editorial Influence

    PR-style links and editorial coverage indicate real-world authority, shunning algorithmic manipulation.

    Editorial Links Versus Volume-Based Building

    Volume-focused link building loses ground as AI discerns unnatural patterns. Quality-driven, relevant links, coupled with PR signals, grow increasingly essential.

    Editorial PR links from credible sources signal genuine credibility, like a trusted expert affirming a brand’s significance.

    AI not only checks link presence but evaluates surrounding context, striving to reward the most authoritative entities.

    Building Multi-Signal Authority

    The potency of multi-signal authority lies in blending various signals. As the digital landscape evolves, quality shines over quantity.

    AI prompts this evolution by advancing traditional, relevance-based links alongside diversified brand signals.

    Strategic placements can yield:

    • Brand mentions affirming presence.
    • Citations validating expertise.
    • Positive sentiment enhancing trust.
    • Topical relevance and growth-enabling links.
    • Boosted Knowledge Graph associations.
    • Secondary coverage spreading influence.

    Multi-signal authority offers AI the understanding that your brand is recognized, trusted, and worth conversation.

    PR signals, albeit crucial, are but a fragment of the comprehensive authority ecosystem AI evaluates.

    Decoding the New Authority Framework

    Today, authority hinges on varied and consistent validation signals, akin to human assessment—through reputation and recognition.

    It’s no longer just links. Authority encompasses:

    • Brand strength: Upward branded search and direct traffic echo real-world recognition.
    • Entity validation: Consistent NAP, schema, cohesive profiles confirming brand ID.
    • Topical authority: Content depth, expert collaboration underscores knowledge.
    • Reputation signals: Trust reflected in reviews, citations, sentiments.
    • PR signals: News, interviews, industry mentions bolster relevance.

    These interwoven signals forge a comprehensive authority profile, which AI recognizes. The dominating brands have the most impactful multi-signal authority footprint.

    Brand Strength’s Quiet Influence

    Brand strength silently prevails over other signals. Data reveals brands ranking in the top 25% for web mentions average far higher AI Overview citations than their counterparts.

    This aligns with Ahrefs’ analysis of ~75,000 brands, underscoring branded web mentions and search volume as indicators of genuine brand presence.

    Consider two fitness apps: one with extensive generic backlinks, another actively part of social and media conversations. The latter’s real-world engagement ensures consistent AI Overview visibility.

    Leading brands in AI Overviews have robust brand presence supported by consistent links, mentions, and relevance.

    Future Predictions for 2027 and Beyond

    By 2027, link building evolves from a numbers focus to a confidence-driven model with new metrics like Share of Authority.

    Here are my predictions:

    Prediction 1: Visibility via “Share of Model” Metric

    Strategies will shift towards “seeding” information in places AI relies on, moving away from mass low-tier blog outreach to user-chosen platforms like Reddit, which AI values.

    Brands frequently appearing in AI training data will gain visibility, defining the new authority landscape.

    Prediction 2: Brands as Primary News Sources

    In AI-led ecosystems, proprietary data will emerge as critical, offering natural, highly trusted authority signals.

    Data evolves from mere content to a powerful signal engine, enriching PR coverage, citations, and discussions.

    Traditional link building remains vital, but data-driven assets are vital accelerants.

    Prediction 3: Rising Value of Unlinked Mentions

    While foundational, traditional links will gain strength from semantic context and relate directly to brand mentions enhancing entity strength.

    Exploring AI’s Expanding Role in SEO

    The off-page SEO future merges traditional link building with AI-driven signals recognizing links as just one part of a broader array AI processes.

    Both remain essential: links for foundational relevance, AI for context, sentiment, and entity evaluation.

    Links are the foundation. Signals construct the skyscraper.

    Inspired by this post on Search Engine Land.

    crushpress.ai community screenshot
  • Navigating SEO in the Age of AI: A Personal Guide

    Navigating SEO in the Age of AI: A Personal Guide

    SEO is evolving, but it’s certainly not disappearing. In my journey through the changing landscape, I’ve found that blending traditional SEO techniques with emerging AI search practices is crucial for staying ahead.

    SEO is at a fascinating juncture. On one side, there’s a push to optimize for AI and large language models (LLMs), while on the other, some want to stick to the tried-and-true methods. I’ve found a middle path — merging core SEO principles with an awareness of LLMs and their operations.

    Embracing this approach means holding onto effective strategies like on-page SEO and quality backlinks while also exploring new avenues such as optimizing for query fan-out and new prompt intents. Since the rise of tools like ChatGPT, my research has focused on how AI engines present search results and the future direction of SEO.

    ```json { "alt": "The CapmatchOne logo with a gradient circle and bold text.", "caption": "Discover innovation with the CapmatchOne logo, featuring sleek typography and a modern gradient circle.", "description": "The CapmatchOne logo features bold, modern typography coupled with a gradient circle, symbolizing connection and innovation. The sleek design conveys a sense of progress and creativity. This image can be used for branding or promotional purposes, appealing to audiences interested in innovative solutions and forward-thinking designs." } ```

    Here’s what I’ve learned and how you can adjust your strategy to consider human behavior at the forefront of SEO innovations.

    The Red Queen evolutionary model suggests that we must constantly adapt to maintain our position; if we don’t evolve, we risk falling behind. This is exactly the case in the world of AI and SEO — stand still, and you’ll be left behind.

    ```json { "alt": "Recommended anti-aging products list with descriptions and ratings.", "caption": "Explore top-rated anti-aging skincare products curated for their efficacy. See expert picks to keep your skin youthful and glowing.", "description": "This image presents a recommended list of anti-aging skincare products with detailed descriptions, prices, and ratings from various beauty retailers. Featured items include SkinCeuticals C E Ferulic, CeraVe Resurfacing Retinol Serum, Estee Lauder Advanced Night Repair Overnight Treatment, and Clarins Double Serum. Each product is accompanied by user reviews and star ratings, providing insights into their popularity and effectiveness. Keywords: anti-aging, skincare, product recommendations, beauty reviews." } ```

    As you and your competitors adapt, you must maintain your competitive edge. In SEO, failing to adapt means losing visibility and influence.

    How to apply the Red Queen principle to your AI SEO strategy

    The evolution of AI search is a continuation of developments over the past decade. With concepts like RankBrain since 2015, familiar SEO tactics remain relevant. This isn’t about a complete overhaul but rather a series of adaptations and improvements.

    ```json { "alt": "Screenshot discussing February 2026 as a favorable time for home buyers due to low mortgage rates and rising inventory.", "caption": "Considering buying a house? February 2026 is predicted to be ideal for buyers with low mortgage rates, a surplus of sellers, and increased inventory!", "description": "This image highlights a favorable housing market forecast for February 2026, emphasizing low 30-year fixed mortgage rates averaging 5.87% to 5.98%. With 44% more sellers than buyers, the market provides strong negotiating leverage. An increase in listings by over 10% year-over-year reduces bidding wars, and stable home prices (0.9% to 1.2% growth) prevent significant spikes. Relevant sources include Redfin and Freddie Mac." } ```

    Core elements like retrieval-based search engines, content quality, speed, and intent matching are as important as ever. By focusing on these, alongside optimizing for AI retrieval and third-party visibility, you position yourself favorably.

    One effective way I’ve discovered to engage with AI search is by understanding its limitations, particularly their reliance on retrieval-augmented generation (RAG) systems. RAG helps fill the gaps in LLM databases without constant updates, ensuring relevant answers are provided.

    ```json { "alt": "February 2026 snapshot of the U.S. housing market trends and forecasts.", "caption": "Explore the latest trends in the U.S. housing market for February 2026, including mortgage rates and buyer-seller dynamics.", "description": "This image presents a February 2026 overview of the U.S. housing market. It features articles from the Financial Times, Reuters, and New York Post detailing recent mortgage rate changes, construction trends, and market dynamics. Key highlights include mortgage rates hitting the lowest since 2022 and a notable gap with more home sellers than buyers. This image serves as a guide for potential homebuyers evaluating current market conditions." } ```

    In practice, this involves seeing how AI tools like Google AI Mode and ChatGPT respond to prompts and identifying where they draw their information. Using this insight, you can ensure your content is part of the external sources AI assists rely upon.

    Understanding how your content interacts with AI engines’ limitations is critical. AI does its own searching and then provides answers, sometimes without showcasing external sources. Therefore, becoming a trusted source for LLMs is the key to SEO in the AI era.

    ```json { "alt": "Makeup products for Gen Z, including Rare Beauty blush, Morphe face trio, and NYX lip oil.", "caption": "Discover trending makeup gifts perfect for Gen Z! Featuring Rare Beauty's blush, Morphe's face trio, and NYX's vibrant lip oil.", "description": "This image showcases top makeup and beauty gift ideas ideal for Gen Z, featuring three products: Rare Beauty Soft Pinch Liquid Blush ($25.00), Morphe Cheek Thrills Multi-Finish Face Trio ($19.00), and NYX Professional Makeup Fat Oil Lip Drip ($10.00). These products, highlighted for their trendy appeal and versatility, are available at Ulta Beauty and other retailers. The selection emphasizes lightweight, buildable, and vibrant aesthetics that appeal to modern Gen Z preferences." } ```

    It’s essential to analyze AI answers, understand their behavior, and continuously evaluate their preferences. By feeding these systems with quality data, we can ensure we’re among the go-to trusted sources AI assistants reference.

    The long-term future of SEO relies on human behavior

    Long-term SEO strategies should remain focused on understanding human behavior. This involves pinpointing search intent and analyzing how AI-generated queries align with different user needs and intents.

    ```json { "alt": "Search results for best makeup gifts for Gen Z, highlighting viral products from Rare Beauty, Rhode, and Fenty Beauty.", "caption": "Explore the top makeup gifts for Gen Z! Featuring viral products from Rare Beauty, Rhode, and Fenty Beauty, these selections promise high performance and trendy appeal.", "description": "The image displays search results for the best makeup gifts for Gen Z. It highlights popular products like the Rhode Peptide Lip Tint and Rare Beauty Soft Pinch Liquid Blush. Brands such as Rare Beauty, Rhode, and Fenty Beauty are emphasized for their appeal to Gen Z, focusing on high-performance formulas and 'glass skin' effects. The section also mentions TikTok's influence on beauty trends. Keywords: makeup gifts, Gen Z, Rare Beauty, Rhode, Fenty Beauty, TikTok trends." } ```

    Being successful means considering both traditional search intents and new AI-induced intents to provide valuable content that resonates with user needs. It’s about dynamically adapting approaches based on observed behavior and striving to stay ahead in this ever-evolving field.

    Inspired by this post on Search Engine Land.

    crushpress.ai community screenshot
  • Mastering AI Search Visibility: Key Signals You Need to Know

    Mastering AI Search Visibility: Key Signals You Need to Know

    I’ve discovered that rankings alone no longer guarantee visibility in AI search. In today’s digital landscape, four key signals dictate whether a brand appears in AI-generated responses and how they’re portrayed.

    Ranking and visibility have diverged. For years, SEO was all about securing that sweet spot on the SERPs, boosting visibility, clicks, and traffic. This connection is unraveling.

    Earlier this year, Ahrefs reported that only 38% of pages featured in Google AI Overviews also ranked in the traditional top 10. Compare this to eight months prior when it was 76%, and you’ll see the shift.

    The message is clear: a high rank doesn’t necessarily mean visibility.

    Visibility in AI-generated responses hinges on inclusion and the portrayal of your brand upon inclusion, determined by a unique set of signals.

    So, how exactly does visibility work within the realm of AI search? There are four critical signals I need to focus on:

    ```json { "alt": "Search result page highlighting best CRMs for startups including HubSpot, Pipedrive, and Attio.", "caption": "Explore the top CRM platforms for startups, featuring HubSpot, Pipedrive, and Attio, known for their scalability, ease of use, and affordability. Is your brand or resource listed?", "description": "This image showcases a Google search results page for 'what’s the best CRM for a new startup.' Featured CRMs include HubSpot, Pipedrive, and Attio, recommended for their functionality and cost-effectiveness. The page emphasizes considerations like affordability and ease of use, while highlighting resources from Reddit. Keywords: CRM, startup, HubSpot, Pipedrive, Attio, Google search." } ```
    • Mention order.
    • Depth of explanation.
    • Authority signals.
    • Comparative positioning.

    Let me dive deeper into them, starting with mention order.

    The order in which AI models list options is crucial. According to a study by Growth Memo and Citation Labs, a whopping 74% of users tend to go with the AI’s top suggestion.

    Yet, 26% of users overturn the AI’s order if they recognize a brand they trust. This is quite a change from traditional search behavior. In AI Mode, most users accept the AI’s shortlist without further checks.

    However, the mention order is unstable. SE Ranking’s research shows AI Mode only overlaps with itself 9.2% of the time when running the same query thrice, indicating variable sources and order.

    Lesson learned: While mention order gives an edge, it’s not a sure thing. Brand recognition can surpass position.

    ```json { "alt": "Four quadrants describing content relevance factors: Mention Order, Depth of Explanation, Authority Signals, Comparative Positioning.", "caption": "Boost your content's relevance! Explore how Mention Order, Depth of Explanation, Authority Signals, and Comparative Positioning enhance credibility and value.", "description": "This image is divided into four quadrants, each illustrating a factor that enhances the relevance of content. Mention Order notes that earlier mentions carry more weight. Depth of Explanation emphasizes comprehensive coverage for greater relevance. Authority Signals focus on citations and trust markers for credibility. Comparative Positioning underlines the importance of context and value clarification. These insights collectively aim at improving content strategy." } ```

    Next, let’s explore the depth of explanation.

    Not every mention is equal. Some brands earn only a sentence, while others get full paragraphs detailing their strengths and uniqueness.

    This comes down to how much citation-worthy information AI systems have gathered about you.

    When Semrush launched its AI Visibility Awards in December 2025, it reviewed over 2,500 prompts using ChatGPT and Google AI Mode. Category leaders like Samsung in consumer electronics didn’t just show up more—they received more in-depth mentions.

    Challenger brands, like Logitech in gaming accessories, appeared too, but typically with shorter, focused mentions highlighting a single differentiator.

    ```json { "alt": "Bar chart showing 74% of participants chose rank 1 items, compared to 10% for rank 3+ in AI mode.", "caption": "In a compelling AI study, the first choice dominated with 74% preference, leaving rank 3+ far behind at just 10%.", "description": "This image depicts a bar chart comparing choice rates in AI mode, where 74% of participants favored the first-ranked item, while only 10% selected items ranked third or lower. This visualization highlights the significant preference for top-ranked options in AI-derived responses. Source: Growth Memo / Citation Labs AI Mode Study." } ```

    Pages that are comprehensive, answering “what is it,” “who uses it,” and “how to choose” in one place, rose to the top in AI citations.

    Lesson learned: If AI systems only find sparse data on your brand, expect sparse mentions.

    Third on the list: authority signals.

    AI systems not only cite but also characterize sources by tone, indicating how much confidence they place in a brand’s authority.

    HubSpot’s AEO Grader classifies brands as leaders, challengers, or niche players, labels influencing how AI conveys their authority.

    ```json { "alt": "The CapmatchOne logo with a gradient circle and bold text.", "caption": "Discover innovation with the CapmatchOne logo, featuring sleek typography and a modern gradient circle.", "description": "The CapmatchOne logo features bold, modern typography coupled with a gradient circle, symbolizing connection and innovation. The sleek design conveys a sense of progress and creativity. This image can be used for branding or promotional purposes, appealing to audiences interested in innovative solutions and forward-thinking designs." } ```

    Semrush’s data shows that brands identified as leaders exhibit less than 20% monthly volatility in AI share of voice, maintaining consistent authority.

    Leaders are described using strong terms like “the industry standard,” while challengers are termed “gaining traction.”

    Lesson learned: AI doesn’t just name-drop; it frames your reputation.

    Finally, comparative positioning is akin to traditional rankings in AI answers—how you’re positioned among multiple brands.

    Amsive’s research demonstrates clear positioning hierarchies within sectors.

    ```json { "alt": "Line graph comparing visibility scores of banks and credit unions, including Bank of America, SoFi, and JPMorgan Chase, dated June 2025.", "caption": "Explore the visibility scores of top banking institutions like Bank of America and JPMorgan Chase over a week in June 2025. See which financial giants are leading the digital arena!", "description": "This image displays a line graph titled 'Visibility Score Comparisons' by Profound, illustrating the visibility scores of banks and credit unions as of June 2025. The data compares entities like Bank of America, SoFi, LightStream, Capital One, and others, showing subtle fluctuations over several days. Bank of America leads with a score of 32.2%, while Upstart is at the lower end with 11.1%. The graph provides insights into the digital presence and performance of these financial institutions." } ```
    • In banking, Bank of America leads, followed by SoFi and LightStream.
    • In healthcare, Mayo Clinic stands out significantly.

    Kevin Indig’s research highlights how users self-select based on AI’s framing, regardless of actual capabilities.

    Lesson learned: It’s not about being number one; it’s about owning a niche in AI’s mental map.

    Traditional rankings’ correlation with AI visibility is minimal. The concept of query fan-out explains why visibility dropped so swiftly.

    During an AI Overview, Google processes not just the top pages for a query but various sub-queries to synthesize a complete response.

    This means your page might rank first for one query but may be overlooked if AI finds more relevant passages elsewhere.

    ```json { "alt": "Line graph showing Google's share of ChatGPT referral traffic from October 2024 to February 2026, displaying upward trend.", "caption": "Google's influence grows as its share of ChatGPT referral traffic rises steadily over time, peaking in early 2026.", "description": "This graph illustrates Google's share of total ChatGPT referral traffic, derived from Semrush US clickstream data between October 2024 and February 2026. The line graph, highlighted in purple, shows a general upward trend starting around mid-2025, reaching its highest point in early 2026. The chart provides insights into Google's impact on ChatGPT referral traffic over this period. Keywords: Google, ChatGPT, referral traffic, Semrush, clickstream data." } ```

    Research shows Google’s Gemini 3 update altered approximately 42% of cited domains, making traditional rank positions less predictive.

    Where does AI traffic land? Interestingly, a substantial portion of ChatGPT traffic eventually ends up on Google. Users seek answers from ChatGPT, then confirm their findings on Google.

    Most prompts to ChatGPT are too specific for traditional keywords, intensifying the shift.

    So, how can I measure visibility in AI answers?

    • Track citation frequency to gauge how often your brand appears in AI answers.
    • Measure brand mention rate for category penetration.
    • Focus on recommendation rates, especially in B2B and high-consideration sectors.
    • Analyze sentiment and context of mentions to evaluate impact.
    • Citation position provides an edge, even if it’s not organic rank.

    The 2026 measurement model demands dual tracking—traditional and AI-focused metrics for accurate visibility insights.

    New tools have emerged for this purpose, complementing but not replacing traditional SEO tools.

    For citation tracking, platforms like Profound and Peec AI keep tabs on cited URLs across AI responses.

    For brand analysis, tools like Semrush’s AI Visibility Toolkit check mention frequency, portrayal, and recommendations.

    For competitive positioning, Bluefish and HubSpot’s AEO Grader assess your brand’s AI categorization against competitors.

    Traditional rank obsession persists, but visibility in AI requires a broader view with a distinct measurement model.

    Inspired by this post on Search Engine Land.

    crushpress.ai community screenshot
  • Transform Your SEO: Why More Is Not Always Better

    Transform Your SEO: Why More Is Not Always Better

    I’ve realized that just adding more content won’t automatically boost my SEO. In fact, it can dilute my website’s authority, split rankings, and waste crawl budget. So what’s really driving visibility now? Let’s explore!

    Many believe the best way to grow organic visibility was to publish more and more content, thinking that covering every angle of a topic would ensure traffic growth. I used to think that too.

    Like many SEO teams, I used to follow content calendars based on search volume targets, believing content quantity equaled growth. But lately, I’ve noticed the effort doesn’t always match the outcomes.

    I’ve learned that simply adding more pages doesn’t guarantee increased visibility. Instead, it can dilute the overall performance. I find maintaining a large content library challenging, as it can lead to internal competition and fewer pages appearing in search results.

    The real challenge now is understanding why a lot of my content fails to enhance visibility, not just producing more of it.

    For a long time, simply increasing content volume worked well. Search engines relied on keyword matching and topical coverage, which meant expanding into different keyword variations often captured more demand.

    I found that competition was significantly lower, and the limited high-quality search results made it easier to gain visibility quickly. Publishing frequently seemed to enhance domain authority, signaling freshness and relevance.

    But now, the conditions have changed. The search ecosystem evolved, making the relationship between content volume and visibility less predictable.

    Dig deeper: Content marketing in an AI era: From SEO volume to brand fame

    Entering this new landscape, I’ve encountered content saturation. Most relevant topics have established pages with links and data years in the making. A new page tends to be at a disadvantage.

    When creating content around adjacent keyword variations, I noticed a trend of similar queries being directed to the same URL, making it hard for multiple pages to perform well.

    The development of AI overviews impacted a significant share of informational queries, reshaping the landscape of informational content and consequently the efforts I’ve put into volume strategies.

    I’ve come to understand Google’s indexing limits and that low-value URLs drain valuable crawl activity. Thin or redundant content becomes deprioritized, never contributing meaningfully to search competition despite constant additions.

    Dig deeper: The authority era: How AI is reshaping what ranks in search

    The reality I’ve faced is that the content library behaves as a system at scale, which can lead to problems compounding over time.

    ```json { "alt": "The CapmatchOne logo with a gradient circle and bold text.", "caption": "Discover innovation with the CapmatchOne logo, featuring sleek typography and a modern gradient circle.", "description": "The CapmatchOne logo features bold, modern typography coupled with a gradient circle, symbolizing connection and innovation. The sleek design conveys a sense of progress and creativity. This image can be used for branding or promotional purposes, appealing to audiences interested in innovative solutions and forward-thinking designs." } ```

    Publishing each page creates an obligation—a debt, so to speak—to keep it updated and relevant. At scale, this quickly becomes overwhelming; a library isn’t merely a collection of assets, but a series of commitments.

    I’ve realized that focusing editorial resources on keeping a library from becoming a liability prevents us from strengthening existing high-performing pages.

    Google allocates a finite crawl budget. If my site’s content volume expands without quality or authority gains, it can reduce the crawl frequency and reliability for high-value pages.

    Search engines prefer signals being consolidated rather than rewarding each competing page individually. Without clear authority, overlapping queries often perform worse.

    Broadly expanding my content range without depth erodes topical authority rather than building it. Maintaining consistent subject matter expertise is crucial for SEO success.

    Sites publishing high volumes without strong engagement harm domain-level quality assessments, thereby affecting better-performing pages. I learned the hard way that more mediocre content introduces risks to overall engagement.

    Dig deeper: Content alone isn’t enough: Why SEO now requires distribution

    Turning to a new model means shifting focus from sheer volume to impactful content. Publishing is about creating pieces that truly add value and earn visibility.

    Auditing reveals that a few pages generate most traffic while many offer little to none, diverting precious resources and attention.

    My strategy now involves merging overlapping intent pages and removing thin content. Producing new pages with authority and signal potential is key.

    To impact SEO, content must address truly unaddressed issues, providing unique perspectives and targeting specific intents.

    As I move forward, my focus will be on creating fewer, but quality-driven sources of information relevant to users and credible to search engines.

    Depth ensures authority and relevance, while targeted distribution and being citation-worthy enhance the chance to stand out and drive SEO success.

    Inspired by this post on Search Engine Land.

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  • AI Shopping: 77% Use It, But Trust It to Spend?

    AI Shopping: 77% Use It, But Trust It to Spend?

    In my latest dive into the world of AI commerce, I discovered that over 77% of people, like myself, are tapping into AI to make shopping decisions. However, when it comes to allowing it to spend our money, trust dramatically drops.

    When we consider the current landscape of AI shopping, tools such as ChatGPT and Google Gemini are becoming staples for weekly shopping routines. They help us compare prices and perform product research, but hand over our credit cards? Not so fast.

    ```json { "alt": "Pie chart showing frequency of AI usage in shopping decisions over the past 6 months.", "caption": "Exploring AI's impact on consumer behavior: 43.21% use AI weekly for shopping decisions, highlighting its growing role in everyday life.", "description": "This image features a pie chart from a survey about using AI in consumer shopping decisions over the past 6 months. The chart is divided into four segments: 43.21% weekly usage, 13.48% monthly, 20.91% a few times, and 22.40% not at all. The total number of respondents is 1,009. The chart illustrates the growing reliance on AI for product research and price comparison." } ```

    From the research conducted by Exploding Topics, discomfort still looms around AI’s potential to handle our payments. Even though I’m using AI more, especially for researching the best deals, there’s still significant skepticism about allowing AI to make autonomous purchases.

    ```json { "alt": "Bar chart showing AI usage in shopping tasks, with product research as the highest.", "caption": "Discover how AI is revolutionizing shopping, with product research topping the chart.", "description": "This survey results image displays a bar chart illustrating the use of AI in shopping tasks. The chart ranks tasks like product research, finding deals, and brand decision-making, with percentages and response counts. Product research leads with 68.50%, followed by finding deals at 55.19%. The data represents responses from 781 individuals, providing insights into AI’s role in modern shopping behaviors." } ```

    Fast forward to the future, our shopping habits might evolve, but certain barriers, such as consumer trust, will need to be addressed for AI to play an even larger role.

    ```json { "alt": "Bar chart showing usage of AI tools for shopping, led by ChatGPT and Gemini.", "caption": "Discover the preferred AI tools for shopping, with ChatGPT and Gemini taking the lead according to a recent survey.", "description": "This image features a bar chart from a survey question asking which AI tools are used for shopping purposes. ChatGPT leads with 77.56% usage, followed by Gemini at 58.21%. Other tools like Perplexity, Grok, Claude, and DeepSeek show varied usage, with the least being 'Other' at 4.10%. The chart visualizes preferences among 780 respondents." } ```

    Download the summary of our findings.

    ```json { "alt": "Bar chart showing use of AI tools for shopping by gender, comparing usage rates of ChatGPT, Perplexity, Gemini, Claude, Grok, DeepSeek, and others.", "caption": "An insightful bar chart reveals gender differences in using AI tools for shopping, highlighting preferences for ChatGPT, Perplexity, and others.", "description": "The image depicts a bar chart and table illustrating survey results on the use of AI tools for shopping by gender. Respondents indicated preferences among tools like ChatGPT, Perplexity, Gemini, and others. The chart breaks down usage, showing significant use of ChatGPT by both genders, while other preferences vary. Data details, including response rates and percentages, are presented in a table below the chart, providing an in-depth view of AI tool utilization for shopping." } ```

    Here are some quick insights: 77.6% of us have used AI for shopping in the last six months, with 43.21% using it weekly. AI influences purchase decisions for clothing and technology, but when it comes to storing payment details or allowing autonomous purchases, the hesitation persists.

    ```json { "alt": "Pie chart showing use of AI tools for shopping over the last six months, with options and response counts.", "caption": "Exploring AI's Retail Impact: Majority of respondents are using AI tools for shopping more frequently in the last six months.", "description": "This image features a pie chart and data table analyzing changes in AI tool usage for shopping over the past six months. The chart shows categories such as 'I use AI much more' with 39.10% and 'I use AI a bit more' with 28.97%, reflecting increased usage. Meanwhile, 25.90% report usage staying the same. The dataset includes responses from 780 participants, highlighting shifting trends in retail technology adoption." } ```

    People like me are cautious, with the mode average for trusting AI to spend being a whopping $0. The uncertainty is real, but one thing’s for sure, AI in commerce isn’t going anywhere.

    ```json { "alt": "Bar chart showing survey responses on AI's influence on buying decisions.", "caption": "Survey insights reveal AI's sway on purchases, with over a third influenced many times. Discover how technology shifts consumer behavior.", "description": "This image displays a bar chart from a survey where respondents answered if AI influenced their purchasing decisions. Out of 778 respondents, 36.89% said 'Yes, many times,' 31.75% said 'Yes, once or twice,' 23.91% 'Not that I can recall,' and 7.46% 'No, definitely not.' The data reflects AI's significant impact on consumer choices. Keywords: AI influence, consumer behavior, survey results." } ```

    For businesses, leveraging tools like Semrush’s Exploding Topics Pro could provide insights into these AI shopping trends, ensuring they stay ahead in this evolving market.

    ```json { "alt": "Bar chart showing survey results on AI influence on purchasing decisions by income brackets.", "caption": "Explore how AI impacts buying habits across different income levels, from less than $10K to over $200K annually. Insights reveal varied influence.", "description": "This image displays a horizontal stacked bar chart representing a survey question about AI's influence on purchasing decisions. Different income brackets, ranging from under $10,000 to over $200,000, are analyzed. The color-coded responses include options like 'Yes, many times,' 'Yes, once or twice,' 'Not that I can recall,' and 'No, definitely not.' It shows how people perceive AI's impact on their purchasing behavior, based on their annual income." } ```

    Download the complete findings for a deep dive into the data and discover potential strategies for tapping into this growing AI-driven shopping landscape.

    ```json { "alt": "Pie chart displaying trust levels in AI for shopping among 778 respondents.", "caption": "Exploring Trust: Most respondents show partial trust in AI for shopping, preferring some level of supervision.", "description": "This image shows a pie chart from a survey about trust in AI as a shopping tool. Out of 778 respondents, 21.08% completely trust AI, 39.33% mostly trust with some manual checking, 22.49% are neutral, 14.65% have limited trust, and 2.44% do not trust AI at all. The chart is designed with varied colors for each category and is accompanied by a table detailing the percentages and number of respondents for each response option. Keywords: AI, trust, shopping, survey, pie chart." } ```

    Inspired by this post on Search Engine Land.

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  • Streamline Your SEO Workflow: 8 Tasks You Should Automate Now

    Streamline Your SEO Workflow: 8 Tasks You Should Automate Now

    I often find myself overwhelmed by repetitive SEO tasks that eat up valuable time. That’s why I’ve started identifying tasks that can be automated, allowing me to concentrate on strategy, quality assurance, and crucial decision-making.

    While tasks like note-taking and setting team reminders are obviously automatable, I’ve discovered that content audits, page outlines, and keyword research can also benefit from automation.

    I recommend beginning with basic strategies that help save time on daily repetitive work before diving into more advanced AI tools for automation. It’s essential to conduct a final check personally, as relying solely on AI can sometimes lead to less-than-perfect outcomes.

    One way I assess which tasks to automate is by asking myself: Would I assign this task to an intern? Tasks suitable for new employees are often ideal for automation. Whether it’s research or drafting, I let AI handle 70% of it, then I fine-tune the remaining 30% myself.

    Some tasks that I’ve found can be automated include data analysis, ensuring best practices are used in updates, creating detailed SEO reports, identifying content gaps, scaling SEO-optimized templates, building editorial calendars, and documenting prompts and standards.

    To discover more automation opportunities, I audit existing workflows, review onboarding processes, gather team input on disliked tasks, and explore AI capabilities.

    However, automation won’t fix every issue. Core challenges like broken systems, incomplete assets, and a lack of resources still need human intervention.

    For instance, I recently automated my team’s content calendar. Using Excel formulas, I quickly identify which content needs updating. By integrating a performance audit with custom AI tools, I can streamline these updates even further.

    Similarly, for keyword research, I employ AI to sift through data and generate relevant keywords, saving me valuable time.

    ```json { "alt": "The CapmatchOne logo with a gradient circle and bold text.", "caption": "Discover innovation with the CapmatchOne logo, featuring sleek typography and a modern gradient circle.", "description": "The CapmatchOne logo features bold, modern typography coupled with a gradient circle, symbolizing connection and innovation. The sleek design conveys a sense of progress and creativity. This image can be used for branding or promotional purposes, appealing to audiences interested in innovative solutions and forward-thinking designs." } ```

    For internal linking, tools like Ahrefs can automate the identification of pages that require more links, enhancing site crawling efficiency without manual labor.

    By automating outlines and briefs, I ensure consistency and quality across my team’s work, streamlining communication and reducing redundant effort.

    On the brand compliance front, custom AI tools help me catch simple errors in high-risk drafts, ensuring they adhere to brand standards before final review.

    Manual data validation can be a painstaking process, but with automation, I’m able to swiftly identify and address anomalies in reports, enhancing accuracy.

    When it comes to metadata and schema, automating these tasks minimizes errors and ensures that content is optimized for search engines.

    Finally, for formatting and shortcoding, I use Excel functions to concatenate code, vastly speeding up what used to be a time-intensive process.

    To make automation truly beneficial, it’s critical it complements, rather than complicates, the workflow. Using custom AI solutions allows my team to focus on more impactful, strategic tasks.

    Inspired by this post on Search Engine Land.

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  • How Agile Competitors Outshine with AI Search Visibility

    How Agile Competitors Outshine with AI Search Visibility

    I’ve often faced the challenge of watching enormous digital budgets return less and less, while more nimble competitors seem to pull ahead effortlessly. It’s frustrating knowing the potential is there, yet being unable to act swiftly enough.

    Examining how AI Overviews and responses from tools like ChatGPT and Claude cite sources, I’ve noticed an unsettling trend: smaller, more agile companies are capturing the most valuable, bottom-of-funnel commercial queries.

    This reality is a call to action, challenging the notion that simply having a well-known brand name can protect my market share. Agility is increasingly becoming more important than relying solely on brand heritage.

    To stay relevant, AI models require quick, machine-readable data to form a credible consensus. The bureaucracy I’ve encountered, which I call the “bureaucracy tax,” often hinders established companies like ours from deploying such knowledge quickly.

    Unintentionally, as my business expanded, the structures built for stability began to stifle our agility.

    In my experience, when deployment lags, it’s often marketing teams pointing fingers at legal, risk, or compliance departments. Yet, in sectors where regulation is strict, compliance is a necessity.

    The operational shortcoming isn’t with the legal department but with what we’re providing them. Winning in the AI search space requires that we separate factual data from marketing narratives.

    The truth is, legal teams debate adjectives—not APIs. They take months to scrutinize creative marketing copy. Conversely, they can review static data tables or product specifications in days.

    I recall how a global payments company struggled with this. A proposed 2,000-word marketing article was a compliance nightmare. However, when the same data was presented as a structured table, approval came within 24 hours.

    When a CFO asks Perplexity to “compare enterprise payment gateway fees,” it skips over blocked competitor blogs and cites your factual table as the authoritative source.

    Dig deeper: Why most SEO failures are organizational, not technical

    How Much Does the Bureaucracy Tax Actually Cost?

    From my perspective, the bureaucracy tax is a tangible and damaging effect on profit and loss statements. For a new initiative, the deployment cycle can take up to 180 days from idea to execution, hampering responsiveness to market shifts.

    Imagine being a global shipping company. While awaiting IT staging, your competitors publish a straightforward “Current freight delay and tariff matrix,” seizing AI consensus and lucrative leads before you can react.

    ```json { "alt": "The CapmatchOne logo with a gradient circle and bold text.", "caption": "Discover innovation with the CapmatchOne logo, featuring sleek typography and a modern gradient circle.", "description": "The CapmatchOne logo features bold, modern typography coupled with a gradient circle, symbolizing connection and innovation. The sleek design conveys a sense of progress and creativity. This image can be used for branding or promotional purposes, appealing to audiences interested in innovative solutions and forward-thinking designs." } ```

    An analysis of AI citations across platforms revealed that disruptors deploying data within 14 days achieve a significantly higher share of AI voice compared to legacy companies that take much longer. The cost of delay is persistent, demanding both time and financial resources to recapture lost ground.

    Dig deeper: How to build an enterprise SEO strategy that gets buy-in

    The Technical Bypass: The Schema-Locked GEO Template

    I’ve come to understand that the loss in this race is partly due to outdated technology. Many of us are stuck on heavyweight, legacy CMS platforms.

    Generative Engine Optimization (GEO) demands a quick rollout of JSON-LD schema and data tables. If an IT ticket is required merely to update author info, the advantage is lost to faster disruptors.

    The remedy isn’t to circumvent systems insecurely. We must advocate for schema-locked GEO templates. This requires IT to create a non-modifiable template designed specifically for data, ensuring rapid deployment without risking architecture.

    From Compliance to Consideration in Record Time

    Workflows must balance keeping risk officers satisfied while drastically speeding up market delivery. These strategic frameworks are critical to protecting your AI consensus.

    If legal bottlenecks your progress, shift your strategy to use pre-approved, factual tables. If developing resources are scarce, implement a “schema-locked GEO template.” If your analytics indicate stability but pipeline velocity drops, audit your LLM visibility immediately.

    Agility is the New Authority

    It’s clear to me that digital acquisition rules have shifted. Winning isn’t just about budget size anymore; it’s about being the fastest to establish a machine-readable agreement.

    Legacy systems and poorly aligned compliance procedures can’t continue to define our market share. The bureaucracy tax siphons resources needlessly, hurting our bottom line.

    I urge you to audit your deployment processes promptly. Treat GEO as a high-speed data operation, not just a marketing campaign. Remove the barriers, and empower your teams to be the definitive resource consumers and machines turn to.

    Inspired by this post on Search Engine Land.

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  • Evading AI’s ‘Bland Tax’: How to Maintain Brand Visibility

    Evading AI’s ‘Bland Tax’: How to Maintain Brand Visibility

    When I think about brand visibility today, it’s clear that being chosen by AI systems is crucial. Authority, unique insights, and consistent signals now determine if my brand makes the cut.

    I’ve realized that AI isn’t just reshaping search; it’s deciding which brands are seen and which are ignored.

    I learned from Andrew Warden, CMO of Semrush, at the Adobe Summit that visibility is evolving fundamentally, and our brands risk being systematically filtered out by AI systems.

    “The idea of standing out is no longer optional. There’s a real risk of sameness,” he pointed out.

    With AI systems deciding what to highlight and what to ignore, I know I must compete more fiercely for visibility in AI-generated answers.

    AI is Changing How Discovery Works

    The change is evident in the data: 60% of Google searches now end without a click to a website. People are still seeking information but aren’t always visiting websites. They’re getting their answers directly from AI systems like Google AI Overviews and ChatGPT.

    These AI systems have become, as Warden described, the “new gatekeepers.”

    This shift ushers us into the agentic era, where AI systems act as intermediaries, guiding users from inquiry to decision in one seamless interface.

    Meanwhile, user behavior is evolving. People engage more in conversational environments, posing follow-up questions, refining queries, and surveying options within the interface, all resulting in fewer clicks but often attracting higher-intent users.

    Warden noted that consumers using LLMs convert at least four times higher than those relying solely on search.

    SEO is the Foundation

    Despite some claims that AI could replace search, Warden reassured us that SEO is not dead.

    SEO has become more foundational than ever. It’s essential to ensure my brand exists in the data layer AI systems rely on.

    Warden emphasized, “SEO isn’t just for humans anymore. This is a training manual for AI right now.”

    This involves ensuring:

    • Crawlability
    • Indexability
    • Structured data
    • Authority signals

    Without these, my brand won’t appear at all.

    Research backs this up: 94% of Google AI Overviews cite at least one top organic result, reaffirming that traditional search signals still support AI outcomes.

    The Rise of the ‘Bland Tax’

    One striking concept from the session was what Warden dubbed the “bland tax.”

    AI conditions itself to overlook blandness, causing generic or repetitive content to vanish.

    If I’m generic, Warden warned I’m perceived as average, and if I’m bland, I’m effectively invisible.

    AI systems don’t reward sameness. Rather than highlighting my brand, they often condense similar content into a single, attribution-lacking response.

    “This is an invisible penalty,” Warden noted.

    The consequences manifest in several ways:

    • My brand identity gets erased in AI-generated summaries
    • My content is filtered out as low-value
    • My work becomes training data for AI without offering visibility to my brand

    “You also become a free training ground for LLMs,” he said.

    What Visibility Depends On

    Warden redefined brand visibility as a blend of:

    • Discoverability: Can LLMs easily find me?
    • Authority: Do they trust my brand enough to include it?

    “You absolutely need both,” Warden asserted.

    SEO ensures I’m discoverable. Authority determines whether my brand shows up in AI-generated responses.

    Without authority, I risk turning into a “commodity that isn’t worth being mentioned.”

    How to Win: Three Key Signals

    Warden outlined three crucial areas determining whether my brand appears or gets filtered out:

    1. Entity Authority

    AI systems map entities and relationships, and they must recognize my brand as an authority on a topic.

    One key signal is brand demand. If people aren’t seeking out my brand, neither will AI.

    Strong brands emphasize their authority across various platforms—owned content, media exposure, and community discussions—demonstrating their niche.

    2. Information Density and Originality

    AI systems prioritize content that offers new insights. It’s vital to not just publish content but contribute something meaningful.

    They emphasize new facts with proprietary data, original research, unique perspectives, and expert insights.

    According to Warden, original insights can enhance visibility by 30 to 40%.

    3. Signal Alignment

    AI evaluates not just what I convey but also what others say about my brand.

    This includes reviews, discussions on platforms like Reddit and YouTube, media mentions, and customer conversations.

    Warden warned that conflicting signals could prompt AI to flag my brand as unreliable.

    Consistency across these channels creates what he called a “consensus signal” that AI systems can trust.

    Why Most Organizations Aren’t Ready

    One of our biggest challenges is organizational, as visibility isn’t just a channel issue; it’s an organizational one.

    Currently, responsibilities are fragmented. SEO teams focus solely on rankings, PR and brand teams manage messaging, and growth teams conduct experiments. This leaves no one clearly owning AI visibility.

    This fragmentation leads to inconsistent signals and missed opportunities for us.

    To truly compete, we need alignment across teams, working on a shared strategy about how my brand appears wherever LLMs gather data.

    The Measurement Problem

    Meanwhile, traditional performance metrics are unraveling.

    Many marketers, including myself, notice a gap where rankings hold steady, but traffic declines. Meanwhile, leads might increase, yet attribution remains murky.

    Warden explained that demand remains, but traffic no longer serves as its proxy. Our content is utilized, but not in ways directing users back to us.

    This creates a growing disparity between impact and the ability to measure that impact accurately.

    From Rankings to Relevance

    The nature of competition has evolved. I’m no longer vying for a mere position; instead, I’m competing to be featured in a synthesized AI answer.

    Authority, once easier to influence, now hinges on external validation—emphasizing what others say over what I publish.

    Algorithms have shifted from being my allies to arbiters of meaning, marking a significant change in search dynamics since Google itself emerged.

    The New Rules of Brand Visibility

    AI has not altered what makes a brand strong but has transformed how that strength is measured and rewarded. The brands that win today will build real authority in a focused niche, publish original and high-value content, and ensure consistent messaging across every platform.

    The need for consistent third-party validation across an ecosystem is paramount.

    As Warden urged, I must make it impossible for LLMs to ignore my brand.

    Inspired by this post on Search Engine Land.

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  • Why AI Is Revolutionizing Acquisition with a Bottom-Up Approach

    Why AI Is Revolutionizing Acquisition with a Bottom-Up Approach

    AI has reshaped how we think about acquisition strategy. It’s no longer about starting at the top of the funnel with broad awareness campaigns. Instead, we begin at the bottom, focusing on building understanding, credibility, and reach in the right sequence.

    For the past 30 years, the industry followed a top-down model: raising awareness, gaining visibility, and then guiding potential customers through the purchase funnel. This approach made sense during the broadcast era and was somewhat effective in the search era, but today, in AI-driven environments, it’s outdated.

    ```json { "alt": "The CapmatchOne logo with a gradient circle and bold text.", "caption": "Discover innovation with the CapmatchOne logo, featuring sleek typography and a modern gradient circle.", "description": "The CapmatchOne logo features bold, modern typography coupled with a gradient circle, symbolizing connection and innovation. The sleek design conveys a sense of progress and creativity. This image can be used for branding or promotional purposes, appealing to audiences interested in innovative solutions and forward-thinking designs." } ```

    Today’s search engines and AI-powered assistants build brand recommendations from the ground up. They need to grasp who we are before they can evaluate our credibility. Only after establishing credibility can they recommend us. If we prioritize top-down strategies, we’re essentially wasting budget on awareness without a strong foundational understanding for AI to work with.

    ```json { "alt": "Diagram comparing user display funnel with brand build funnel, showing stages like awareness, consideration, decision versus understandability, credibility, deliverability.", "caption": "Exploring the user journey with the display funnel and contrasting it with the brand-focused build funnel.", "description": "This image presents a comparative diagram of 'The Display Funnel' for users, highlighting stages such as Awareness, Consideration, and Decision, and 'The Build Funnel' for brands, featuring Understandability, Credibility, and Deliverability. The layout emphasizes the user journey and machine build paths, showing how these funnels align and differ. Keywords: user journey, display funnel, build funnel, awareness, credibility." } ```

    AI systems hold the key to successful brand recommendations — if they don’t understand our brand, or find us less credible compared to our competitors, they’ll likely recommend someone else. This AI-led shift is what I call the ultimate zero-sum game: the unseen recommendation to prospects we might not even know about.

    ```json { "alt": "Flowchart titled 'The Funnel Pathway' illustrating customer journey from research to purchase.", "caption": "Discover the Funnel Pathway: guiding your ideal customer profile (ICP) through strategic stages, leading to a winning outcome.", "description": "This flowchart, titled 'The Funnel Pathway: many paths lead to one Zero-Sum Moment,' visually represents a customer's journey from ToFu (Top of Funnel) with topical research, through MoFu (Middle of Funnel) for consideration, to BoFu (Bottom of Funnel) for a Zero-Sum Moment. Nodes A to I represent initial touchpoints, L to N for interim stages, culminating in a 'WON' outcome." } ```

    The acquisition funnel hasn’t altered for users. They still journey from awareness to consideration to decision. Essentially, Elias St. Elmo Lewis’s model from 1898 still applies. All marketing models have been based on this, although channels have evolved. The mantra remains: reach first, relationship second, commitment third.

    ```json { "alt": "Infographic showing acquisition funnel stages in search engine pipeline with a funnel diagram.", "caption": "Explore how the acquisition funnel integrates into the search engine pipeline through a detailed infographic, showcasing each critical stage.", "description": "This infographic details the stages of the acquisition funnel as it fits into the search engine pipeline. The funnel is divided into stages for awareness, consideration, and decision-making, corresponding to different phases like discovery, crawling, and indexing. The Kalicube Process logo appears at the top. Each step of the pipeline is marked with initial letters and descriptions, providing a clear pathway from discovery to winning potential customers. Keywords: acquisition funnel, search engine pipeline, Kalicube Process." } ```

    In my experience, the digital landscape changed with Google’s Knowledge Graph in 2012. It allowed machines to form independent opinions about brands, highlighting the need for brand understanding and reputation over mere awareness. Since then, my focus has centered on these aspects because AI-driven engines and agents rely on it to direct users towards credible destinations.

    ```json { "alt": "Build vs. Display Framework diagram explaining layers of marketing and failure tax.", "caption": "Explore the Build vs. Display Framework, which outlines the layered marketing approach and associated taxes of failure at each stage.", "description": "This image presents the Build vs. Display Framework, focusing on layered marketing and the 'tax' of failure. It illustrates three stages: Deliverability (D), Credibility (C), and Understandability (U), each paired with potential failures: Invisibility, Ghost, and Doubt taxes. The process builds U to C to D and displays D to C to U, highlighting consequences of faltering at any level. Ideal resource for understanding strategic marketing layers." } ```

    This marks a structural shift in marketing since 1898. While the user still travels from awareness to decision, in AI engines and agents, it’s our understanding and credibility that position us at the top of their funnel, achieved by training AI to guide users to us.

    ```json { "alt": "The Kalicube Framework diagram illustrating SEO processes in three phases: record, activate, and serve.", "caption": "Explore the Kalicube Framework, a strategic guide for digital branding that outlines the process from data recording to audience engagement.", "description": "The Kalicube Framework visualizes the journey of digital content through three phases: Record, Activate, and Serve. Starting with discovery and indexing by bots, it progresses to algorithm activation with annotation and display. The process concludes with serving content through onboarding and performance. Key components include traditional bots, IndexNow, and the Kalicube Flywheel. Keywords: Kalicube Framework, SEO, digital branding, content indexing, algorithmic activation." } ```

    The coexistence of top-down and bottom-up strategies is real. We can still build awareness through controlled channels—paid media, broadcasts, and direct outreach. However, in the realm of organic engines, we must start from the bottom of the funnel, building a foundation for AI to guide users efficiently.

    Every algorithm, AI engine, and agent operates based on entity and brand signals. Social media reach, too, hinges on brand recognition and engagement. Therefore, investing in a solid brand understanding orients us favorably within the AI framework, where roadmaps to our brand are increasingly machine-built.

    This content reflects my approach to developing robust brand presences that resonate with both AI systems and human audiences.

    Inspired by this post on Search Engine Land.

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