Tag: AI

  • How SEO Fundamentals Beat AI in Driving Your Website Traffic

    How SEO Fundamentals Beat AI in Driving Your Website Traffic

    I’ve been observing how AI is transforming search, yet the timeless principles of SEO still seem to bring in the majority of traffic. It’s fascinating to look at data that show which strategies really work.

    Generative AI is a huge trend right now. It’s featured in every conference and is all over my LinkedIn. Businesses, mine included, are rethinking organic search.

    We’re all in a race to optimize for AI Overviews, work on vector embeddings, and reconfigure content models around LLMs. But what’s less talked about is the simple truth: AI isn’t yet the primary driver of web traffic for most of us.

    While AI-driven search is gaining momentum, the LLM platforms collectively account for just a tiny fraction, about 2-3%, of the organic traffic that Google alone provides.

    ```json
{
  "alt": "Bar chart comparing AI referral sessions and Google organic clicks from Jan-25 to Oct-25.",
  "caption": "Diving into the numbers: A bar chart contrasting AI referral sessions with Google organic clicks over a ten-month span in 2025.",
  "description": "This bar chart illustrates the comparison between AI referral sessions and Google organic clicks from January to October 2025. The dark blue bars represent AI referral sessions, ranging from 229,305 in June to 377,416 in April, while orange bars depict Google clicks, which increase from 1,461 in January to 7,056 in October. This visual highlights the varying trends and volumes of online traffic from these two sources over the specified period."
}
```

    However, I’ve noticed that many teams, maybe even yours, are investing more energy in AI strategies instead of reinforcing essential SEO fundamentals that still deliver tangible results. Focusing too much on the future means we’re not making the most of today’s opportunities.

    In my experience, looking closely at proven SEO tactics and real-world data can highlight how they still effectively move the needle today.

    Quick SEO Wins Still Deliver Substantial Gains

    It’s easy to overlook minor updates when we’re caught up with trends like vector embeddings and semantic SEO. Yet, these small changes can have a significant impact.

    ```json
{
  "alt": "SEO ranking table displaying keyword difficulty, position, share of voice, estimated traffic, and volume.",
  "caption": "Explore the latest SEO performance metrics with this detailed ranking table, showcasing keyword difficulty, position shifts, and traffic estimates.",
  "description": "This image shows a table of SEO performance metrics. Columns include Keyword Difficulty (KD%), Position (Pos.), and several 'Diff' metrics showing changes. Share of Voice, Estimated Traffic (Est. Traffic), and Volume (Vol.) columns indicate SEO success and changes over time. Useful for digital marketers analyzing keyword strategy and performance."
}
```

    Take title tags, for instance. They’re among the simplest and most effective SEO tools. I’ve seen many websites fail to use them effectively, often neglecting to target the right keywords, include key variations, or use any keywords at all.

    Just recently, a simple change of adding “& [keyword]” to a client’s homepage title tag resulted in a surge in keyword rankings, clicks, and impressions. No other changes were made, yet the results were significant.

    Combining this with other strategies like on-page copy edits, internal linking, and backlinks can lead to ongoing growth. It might sound basic, but these tactics continue to work wonders. Don’t let advanced GEO strategies blind you to simple, impactful tactics.

    ```json
{
  "alt": "Line graph showing two data trends from late October to mid-November with a noticeable drop on November 6, 2025.",
  "caption": "Visual data comparison: Two trends from Oct 25 to Nov 25, highlighting a drop on Nov 6, 2025. The graph showcases intersecting lines and varying peaks.",
  "description": "This image is a line graph depicting two data trends from October 25 to November 25, 2025, with dates on the x-axis and unmarked values on the y-axis. Both lines display fluctuations with a significant drop in both trends on November 6, indicated by an orange arrow. Peaks and troughs show periodic rises and falls, demonstrating variability in data performance. Ideal for presentations and analytics insights."
}
```

    The Importance of Content Freshness and Authority

    The rise of AI might have pushed some tactics like the skyscraper technique into the shadows.

    This approach involves crafting superior content for keywords and topics that are already ranking, aiming to outperform existing results. While the internet is flooded with similar content, focusing on keyword authority and freshness can be incredibly effective.

    I’ve witnessed this success multiple times. Recently, a client’s article on a well-established topic quickly climbed to the second spot, generating new clicks and impressions almost instantly.

    ```json
{
  "alt": "Line graph showing two data sets over time with peaks and troughs from 9/29/25 to 11/16/25.",
  "caption": "Two fluctuating line graphs reveal trends over time, capturing data dynamics from late September to mid-November 2025.",
  "description": "This image features a line graph displaying two sets of data trends from 9/29/25 to 11/16/25. The lines showcase noticeable peaks and troughs, indicating variations in the data over time. The graph uses purple and blue lines to differentiate the datasets, providing a clear visual comparison of their performance. The x-axis represents dates, while the y-axis represents the measured values, offering an insightful look into the data's progression and behavioral patterns."
}
```

    The success was due to the site’s strong authority and because much of the competing content was outdated. Although this strategy may not suit every situation, ignoring it could mean missing out on clear wins.

    User Experience: A Key Conversion Lever

    Although there’s buzz around AI-driven shopping experiences, the core principles of website optimization remain irreplaceable. Some argue that AI will soon take over interactions and conversions, but this is far from the present reality.

    Many websites still rely on traditional search-driven traffic and website-based conversions. Whether visitors come from organic search, paid ads, AI referrals, or direct, what matters is a fast site, an excellent user experience, and a well-defined conversion funnel.

    ```json
{
  "alt": "Text explaining a 23% improvement in CTR for Apply Now feature due to engaging content and testimonials.",
  "caption": "Boosting engagement by 23%! Enhanced content and testimonials drive Apply Now CTR success.",
  "description": "The image illustrates a 23% increase in CTR for the 'Apply Now' feature. This improvement was achieved by replacing the hero section with engaging content that highlighted value and incorporated social proof and testimonials. The findings suggest implementing the winning variant and moving to the next testing phase. This highlights the importance of content strategy and evidence-based design in digital marketing."
}
```

    Optimizing these aspects can lead to remarkable performance gains, as I’ve seen through a simple CTR test with a client, which yielded impressive results.

    Brands prioritizing user experience and conversion rate optimization will continue to outperform those who don’t. This competitive advantage will only grow if teams delay waiting for AI to perfect conversion mechanisms.

    AI’s Role in Search and the Power of Existing Strategies

    AI is indeed reshaping search by altering user behavior, influencing SERP appearances, and complicating attribution. Yet, the real risk lies in overreacting to AI at the expense of proven strategies.

    For most sites, traditional organic search continues to be the primary traffic source. When well-executed, SEO fundamentals still deliver results. Quick wins and high-quality content are rewarded, and optimizing user experience remains critical.

    These efforts support each other, improving organic visibility and complementing paid search and LLM visibility. Staying updated on AI developments is vital but not at the cost of current growth-driving strategies.


    Inspired by this post on Search Engine Land.


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  • Top Websites AI Models Trust for Shopping Inquiries

    Top Websites AI Models Trust for Shopping Inquiries

    Last Updated: December 22, 2025

    Back in June 2025, I noticed an interesting infographic circulating widely. It highlighted the most-cited websites by AI models, according to a comprehensive SEMrush study of over 150,000 citations. Naturally, seeing Reddit at the top sparked a buzz among marketers, who began to believe that featuring on Reddit was key to their GEO strategy.

    But, here’s the catch: most of these citations were research-oriented, not necessarily geared towards buying intents. For instance, Reddit was frequently mentioned in queries like “Where in Europe should I take a family vacation?” but not so much in “What are the best web design firms?”

    This led to some misguided assumptions. Although Reddit does play a role in AI models like ChatGPT, Perplexity, and Gemini, it represents only about 11% of their commercial recommendation algorithms. So, placing too much emphasis on Reddit won’t really boost your product or service visibility in these AI models.

    The Most Cited Websites by AI Models for Buying-Intent Queries

    Our research team dug deeper into this in October 2025 and later updated it in December 2025. We conducted 36,127 buying-intent queries on ChatGPT and tallied the top-cited websites. Our “buying-intent query” was defined on a scale measuring how close a query was to a purchase decision. A simplified version of this scale is captured in the infographic below:

    Detailed Query Intent Scale

    A query scoring higher than 1.35 was marked as “buying-intent.”

    Simplified Query Intent Scale

    Top Website Types Cited by AI Models for Buying-Intent Queries

    We meticulously categorized the types of websites AI models prefer for such queries. Understanding the types helped us unravel which channels are more effective at GEO—essential in influencing AI chatbots to nominate certain companies.

    ```json
{
  "alt": "Graph depicting buying intent scale for AI chatbot e-bike queries.",
  "caption": "Exploring the buying intent behind various e-bike related queries processed by AI chatbots, from awareness to purchase stages.",
  "description": "The image features a graph titled 'Scale of Buying Intent for AI Chatbot Queries.' It illustrates the progression of buying intent from awareness to purchase for e-bike queries. The x-axis represents buying intent, ranging from 0.07 to 3.00, with labels like 'what is an e-bike' and 'buy e-bike near me.' The spectrum is divided into awareness, evaluation, and purchase stages, providing insights into consumer decision-making processes. Source: Bailyn et al., 2025."
}
```

    Top Website Types Cited by AI Models for Buying-Intent Queries

    #Website TypeDescription# of Citations
    1Product Recommendation
    Media
    “Best of” and “Top 10” review sites largely monetized via affiliate links (e.g., Wirecutter, Tom’s Guide, TechRadar).7,642
    2Consumer Review PlatformsUser-generated review aggregators like Trustpilot, BBB, and Google Reviews.5,983
    3Traditional MediaEstablished publishers including product roundups or consumer coverage (Forbes, NYT, Wired).4,581
    4New MediaDigital-native outlets that frequently review or endorse products (TechCrunch, The Verge).3,826
    5YouTube / Video Review ChannelsVideo-based reviews and product comparisons often transcribed or summarized by AI models.3,211
    6Directory SitesStructured provider listings (Yelp, TripAdvisor, Angi).2,639
    7Commercial / Brand SitesOfficial manufacturer or retailer sites promoting their products.2,208
    8Marketplace Directories (B2B)Listings and SaaS marketplaces such as G2, Clutch, UpCity.1,762
    9eCommerce MarketplacesDirect retail and product pages from major sellers like Amazon, Walmart, and Best Buy.1,413
    10Corporate Blogs / Thought LeadershipBrand-run content hubs (HubSpot Blog, Salesforce Newsroom, Adobe Blog).1,109
    11Influencer / Creator SitesIndependent blogs or Substacks with personal authority and genuine reviews.928
    12Forum CommunitiesPublic discussion boards like Reddit, Quora, and StackExchange.674
    13Deal & Coupon SitesDiscount and promotion aggregators (Honey, RetailMeNot, Slickdeals).505
    14Niche Publications / Enthusiast MediaSpecialized media focused on one domain (Outdoor Gear Lab, PC Gamer).393
    15Local Listings / Maps DataGoogle Maps, Apple Maps, and other local data sources.318
    16Reference SitesGeneral-purpose informational references (Wikipedia, Investopedia).265
    17Social PlatformsCitations to public posts from LinkedIn, X (Twitter), or Facebook Groups.224
    18Academic / Research SourcesScholarly content (Google Scholar, PubMed, arXiv).193
    19Government / Institutional SitesRegulatory or authoritative institutional content (FDA.gov, FTC.gov).159
    20Standards & Certification BodiesOfficial verification or compliance organizations (UL, ISO, Energy Star).119

    The major takeaway from parsing this data? Websites offering list-based product recommendations feature heavily in AI rankings. Being listed on these commercial publications can greatly enhance a product’s visibility in AI recommendations like ChatGPT, Perplexity, and others.

    Industry Breakdown: Websites Most Cited by AI Models for Buying-Intent Queries

    Next, our analysis focused on the top three websites each industry traditionally relies on. This provided a glimpse into which platforms AI models commonly heed within specific verticals, giving GEO marketers a decisive edge in targeting their media placement efforts.

    Top Websites Cited by AI Models in Buying-Intent Queries, by Industry

    IndustryTop-Cited Websites by AI for Buying-Intent Queries
    eCommerceWirecutter, Forbes, Tom’s Guide
    Managed ServicesClutch, G2, UpCity
    HealthcareForbes Health, Verywell Health, Medical News Today
    ManufacturingThomasnet, IndustryWeek, Engineering360 (GlobalSpec)
    Financial ServicesNerdWallet, Investopedia, Forbes Advisor
    CybersecurityCybersecurity Insiders, Gartner Peer Insights, TechRadar
    Real EstateZillow, Realtor.com, Redfin
    PharmaceuticalDrugs.com, FDA.gov, PubMed
    SaaSG2, DesignRush, Clutch
    ConstructionEngineering News-Record, Construction Dive, HomeAdvisor
    Home ServicesAngi (Angie’s List), Thumbtack, HomeAdvisor
    AutomotiveCar and Driver, Kelley Blue Book, Edmunds
    Marketing ServicesClutch, HubSpot Blog, First Page Sage
    Higher EducationUS News Education, Higher Education Marketing Institute, Niche.com
    IndustrialThomasnet, Engineering360 (GlobalSpec), IndustryWeek
    HospitalityTripAdvisor, Booking.com, Yelp
    Software DevelopmentClutch, G2, First Page Sage

    A key insight here is the fragmented nature of website citations. Trade journals contributed more than the top three sites in any category. As with website types, the predominance of review sites and product recommendation platforms was notable.

    Questions, Media Inquiries, or Other Requests

    Curious about our study? Have a media request, or want a PDF copy? Reach out to us here.


    Inspired by this post on First Page Sage Blog.


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  • Google’s Surge in Review Deletions: What It Means for Your Business

    Google’s Surge in Review Deletions: What It Means for Your Business

    In 2025, I’ve noticed Google is removing reviews at an unprecedented rate, and this isn’t by chance.

    According to our industry analysis of 60,000 Google Business Profiles, review deletions are driven by several factors:

    • Automated moderation techniques.
    • Industry-specific risk considerations.
    • Greater enforcement against incentivized reviews.
    • Local regulatory pressures.

    Together, these elements greatly impact business visibility in local search.

    Review Deletions Are Increasing Globally

    Data from tens of thousands of Google Business Profile listings show a notable rise in deleted reviews between January and July 2025.

    The increase gained momentum toward the end of Q1, with more locations seeing at least one review removed weekly.

    This isn’t confined to negative reviews alone.

    Five-star reviews are now frequently removed, indicating Google’s stricter enforcement to maintain authenticity and trust.

    ```json
{
  "alt": "Line graph showing weekly deleted reviews from January to July 2025, with marked data points indicating fluctuations.",
  "caption": "Tracking the rise and fall: Weekly deleted reviews soar in early 2025, peaking in July. A visual insight into the dynamic trends of online reviews.",
  "description": "This image is a line graph illustrating the number of weekly deleted reviews from January to July 2025. The graph highlights significant fluctuations with notable peaks, such as 1100 deletions in early July. Key data points are marked, providing an informative overview of the trends in deleted reviews over the months. This visualization aids in understanding the dynamics of content moderation and review management in the digital space."
}
```

    Google is also asking its Local Guide community about incentivized reviews, likely triggered by AI-flagged suspicious activities.

    Industry Variations in Review Treatment

    Patterns of review deletions differ significantly depending on the business sector.

    Restaurants lead in review deletions, followed by home services, retail, and construction. Both recent and older reviews are subject to removal.

    By contrast, medical, beauty, and professional services experience fewer deletions, but unique patterns are consistent within these categories.

    What Review Ratings Reveal

    Review deletion patterns reveal clear moderation trends. In sectors like restaurants, reviews are deleted across all star ratings.

    Medical and home services show a bias toward removing five-star reviews, indicating more scrutiny in regulated industries.

    This variation is not due to manual policies but reflects Google’s automated adjustments based on perceived industry risks.

    ```json
{
  "alt": "Bar chart showing the share of deleted reviews by rating across top 10 meta categories including restaurants and home services.",
  "caption": "Explore the share of deleted reviews for various industries. This bar chart reveals the blend of 1 to 5 star ratings from deleted reviews across key sectors.",
  "description": "This bar chart illustrates the share of deleted reviews across the top 10 meta categories, such as restaurants, home services, and travel. Each category is represented with stacked bars showing different ratings, from 1 to 5 stars. Red indicates 1-star reviews, with green for 5-star reviews. This visualization helps in understanding the proportion and distribution of deleted reviews in various industries."
}
```

    The Timing of Review Deletions

    The age of a review often determines when it’s removed. In medical and home services, many reviews are deleted within six months.

    In restaurants and retail, older reviews are frequently removed, suggesting ongoing improvement in detection systems.

    For businesses, this means reviews might disappear long after being posted, often without warning.

    Geographical Differences in Review Deletions

    Location adds complexity to review deletion trends. In many English-speaking countries, five-star reviews face increased scrutiny.

    However, in Germany, low-rated reviews are more commonly deleted shortly after being posted, aligning with strict defamation laws.

    In summary:

    • AI-driven enforcement is prevalent in English-speaking markets.
    • Legal challenges are more impactful in Germany.
    ```json
{
  "alt": "Bar chart showing top 10 meta categories by deleted reviews with ratings from 1 to 5 stars.",
  "caption": "Analyzing deleted reviews across categories, restaurants lead with the highest count. The chart stacks ratings from 1 to 5 stars, revealing consumer feedback trends.",
  "description": "This bar chart illustrates the top 10 meta categories by the number of deleted reviews, stacked by rating from 1 to 5 stars. The 'Restaurant' category shows the highest number of deleted reviews, followed by 'Brick & Mortar' and 'Home Services.' Each category's bar is subdivided into colored segments representing different star ratings, illustrating the distribution of consumer feedback across sectors. Key insights can be drawn on which areas face most scrutiny and variability in customer satisfaction."
}
```

    Implications for Local SEO and Business Owners

    Increased review deletions pose two major challenges:

    • Trust erosion: Disappearing legitimate reviews weaken confidence in platforms.
    • Data distortion: Removed reviews alter performance metrics essential for SEO.

    For businesses, monitoring review activity is crucial. Understanding removal patterns is now as vital as acquiring new reviews.

    The Future of Review Visibility

    Three key developments are shaping how reviews are managed:

    • Greater reliance on automated moderation.
    • Legal influences in regions with strict defamation laws.
    • Increased use of third-party monitoring tools for independent tracking.

    As moderation practices evolve, recent and detailed reviews will continue to be critical for SEO authority signals.

    Maintaining a strong reputation now requires constant vigilance.


    Inspired by this post on Search Engine Land.


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  • AI Dominance in Black Friday Shopping Unveiled

    AI Dominance in Black Friday Shopping Unveiled

    This past Black Friday and Cyber Monday, I delved into the fascinating insights from our Black Friday Index, crafted from a vast pool of 400 million genuine conversations. It was enlightening to see which brands stood out as AI’s top recommendations, especially as so many of us relied on Answer Engines to hunt down the best deals.

    As I explored the data, the impact of AI on shopping trends became crystal clear. The technology not only streamlined how we search for deals but also influenced brand visibility and consumer choices. The excitement of seeing how AI is reshaping shopping habits made this year’s Black Friday and Cyber Monday particularly intriguing for me.

    The findings from the Black Friday Index are a testament to the growing importance of AI in retail, showing us how indispensable it has become for both consumers and brands. Being part of this evolution makes me look forward to what future shopping events will bring, especially as technology continues to advance.


    Inspired by this post on Try Profound Blog.


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  • Google vs. ChatGPT: A Deep Dive into 2025 Market Trends

    Google vs. ChatGPT: A Deep Dive into 2025 Market Trends

    Last updated: December 19, 2025

    I’ve delved into a fascinating exploration of the U.S. and global market presence of two internet giants: Google and ChatGPT. By leveraging a combination of client analytics, third-party usage data, and anonymized user logs, our team crafted a model to gauge metrics like monthly active users, engagement time, and the share of total digital queries.

    While Google remains the stalwart champion of online search, ChatGPT’s explosive growth has redefined what’s possible in search tasks, especially in areas requiring long-form conversations and creative input.

    This report offers a comprehensive quantitative comparison of these platforms, beginning with an overview of their market shares. As we progress, we’ll examine how usage breaks down by device type, demographic segments, and user intent.

    Google vs ChatGPT Market Share

    The table below details the digital query market shares of Google and ChatGPT by the end of Q4 2025.

    Google vs ChatGPT Market Share – Q4 2025

    PlatformMonthly Active Users (Global)Share of Total Digital QueriesAvg. Session Duration
    Google Search5 billion77.9%6m 12s
    ChatGPT858 million17.1%13m 09s
    Other (e.g., Bing, Perplexity)580 million5.8%4m 33s

    Key Insights:

    • Google continues to lead with nearly 80% of global digital queries.
    • Commanding 17% of the market, ChatGPT is the most formidable competitor Google has seen in over two decades.
    • Gemini’s latest update has positively impacted market retention, signaling resilience in competition.
    • Despite fewer users, ChatGPT’s notably longer session times indicate robust user engagement.

    Google vs ChatGPT Market Share Over Time

    The graph below illustrates the market share trends for Google and ChatGPT from Q1 2023 to Q4 2025.

    Google vs ChatGPT Market Share, Q1 2023 – Q4 2025

    Google Vs Chatgpt Market Share 2023 2025

    However, when focusing solely on transactional searches, Google’s dominance appears less threatened by ChatGPT.

    Google vs ChatGPT Market Share, Transactional Queries Only
    Q1 2023 – Q4 2025

    Google Vs Chatgpt Transactional Market Share 2023 2025

    Market Share by Device Type

    The following table shows the usage of Google and ChatGPT across mobile and desktop platforms, highlighting differing user behaviors.

    ```json
{
  "alt": "Line chart comparing Google and ChatGPT competitiveness over time, showing Google's gradual decline and ChatGPT's steady rise.",
  "caption": "A dynamic line chart reveals the shifting tides of competitiveness, with Google experiencing a slow decline and ChatGPT rising steadily.",
  "description": "This line chart visually compares the competitiveness of Google and ChatGPT over several months. The green line represents Google, depicting a slight downward trend. The blue line represents ChatGPT, showing a gradual upward trend. The chart uses dots to mark data points and has a clear legend for differentiation. Ideal for discussions on market trends and technological advancements, it highlights how these platforms evolve over time."
}
```

    Google vs ChatGPT Market Share by Device Type – 2025

    PlatformDesktop Usage ShareMobile Usage Share
    Google Search37%63%
    ChatGPT62%38%

    Research Notes:

    • ChatGPT shows more engagement on desktops, indicating a preference among professionals and researchers.
    • Google’s design appeals to those on mobile, capturing the casual and on-the-go demographic.

    Market Share by Age Group

    Below is a breakdown of market share trends segmented by age group.

    Google vs ChatGPT Market Share by Age Group – 2025

    Age GroupGoogle ShareChatGPT Share
    13–2474%17%
    25–4480%13%
    45–6486%8%
    65+89%5%

    Key Takeaways:

    • Younger audiences lean towards ChatGPT, especially for academic and creative pursuits.
    • As age increases, Google’s usage aligns with more traditional search preferences.

    Market Share by User Intent

    Here’s how digital queries are utilized according to intent.

    Google vs ChatGPT Market Share by User Intent – 2025

    Intent CategoryGoogle ShareChatGPT Share
    Navigational93%3%
    Informational71%23%
    Transactional90%5%
    Generative/Creative29%64%

    Analysis:

    • Google dominates in transactional searches due to rich e-commerce and trusted browsing formats in high-stakes scenarios.
    • ChatGPT excels in creative and generative tasks like storytelling and academic work.

    Requesting a Copy of This Report

    If you’re interested in a PDF version of this report or wish to learn more about what we do, feel free to reach out here.

    Source


    Inspired by this post on First Page Sage Blog.


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  • Master Broad Match: Control Smart Bidding Effectively

    Master Broad Match: Control Smart Bidding Effectively

    I’ve learned that broad match now operates alongside Smart Bidding. It’s fascinating how drift happens, why it’s important, and how to align performance with genuine intent.

    Broad match, once synonymous with “more reach, less relevance,” now depends on a machine learning layer to define relevance.

    Over time, Google has nudged us, the advertisers, towards fewer complexities like fewer match types and more automation.

    Since July 2024, broad match has become the default for new Search campaigns, signaling a shift in how we ought to think about it.

    If you’re stuck in the mindset of broad match being the “loosest match type,” you’re stuck in 2016, and that’s where problems like CPC inflation and irrelevant leads arise.

    Today’s broad match works within a system, collaborating with query matching, Smart Bidding, conversion signals, and optional tools like audiences and negatives.

    Google leverages broad match as a growth mechanism for Smart Bidding campaigns rather than a solitary reach tactic.

    In this article, I explore the changes, Google’s motivations behind them, and safe practices to maintain standards while using broad match.

    The real risk with broad match isn’t relevance, it’s direction

    Broad match tends to drift rather than fail completely.

    With shallow optimization goals, broad match coupled with Smart Bidding can find quick ways to meet them, sometimes resulting in:

    • Queries that trigger cheap forms without real sales potential.
    • Users who convert but never purchase.
    • Leads that look good in Google Ads but don’t end up profitable.

    Even when everything seems fine in the interface, the account might drift away from commercial intent.

    This illustrates why understanding broad match’s current behavior is crucial.

    What broad match actually is now

    Broad match no longer stands alone as a keyword setting but works within a larger optimization system.

    It’s built to work with Smart Bidding

    Google specifies that broad match is intended to run with Smart Bidding, as bidding decisions are now made during auctions using signals like:

    • Device
    • Location
    • Time of day
    • Query context
    • User behavior

    Broad match increases eligible queries. Smart Bidding evaluates which ones merit investment.

    Running broad match without Smart Bidding deviates from its intended design.

    Google has materially improved broad match matching

    Google claims that recent AI enhancements have uplifted broad match campaigns using Smart Bidding by 10%.

    This doesn’t imply broad match is inherently safe, but Google feels its matching layer justifies broader use.

    It’s no longer positioned as optional

    Starting July 2024, new Search campaigns activate broad match by default.

    The campaign-level setting enforces broad match when conversion-based Smart Bidding is active, marking a significant paradigm shift.

    Why Google wants advertisers to adopt broad match

    Google’s rationale is straightforward:

    • Search behavior is increasingly unpredictable and long-tail.
    • Manual keyword lists fail to keep up with language and intent shifts.
    • Machine learning can interpret intent at auction time better than rigid logic.

    Google positions broad match as a growth tool for Smart Bidding campaigns, providing algorithms with more opportunities to optimize for conversions.

    You might not agree with this philosophy, but when advertising on Google Search, you’re part of this system.


    A framework for using broad match without losing control

    Broad match expands your reach. Maintaining control requires thoughtful constraints.

    Conversion goals that reflect quality, not convenience

    Smart Bidding optimizes based on defined conversion actions and values.

    If your primary conversions are low-intent, broad match will scale this low intent.

    Successful setups often include:

    • Optimizing for deeper conversion actions.
    • Applying conversion values to identify lead quality tiers.
    • Importing offline conversions, like qualifying leads or revenue.

    This tackles the issue of associating cheap volume with success.

    Intent filters through audience signals

    Broad match identifies queries. Audience signals dictate ad visibility for those queries.

    Audiences should provide context, not just report data:

    • Customer lists favor known buyers.
    • Remarketing lists for measured expansion.
    • Audience insights to recognize quality-segment correlations.

    Even in observation mode, these signals help verify if broad match growth benefits the right areas.

    Negative keyword structures that scale

    With broad match, negative keywords transform from mere cleanup to structural elements.

    Effective accounts often include:

    • Account-level shared negative lists for terms like jobs, free, definition.
    • Campaign-level exclusions aligned with intent boundaries.
    • Regular search term reviews, crucial early on.

    Broad match naturally explores, while negatives determine its limits.

    Brand controls to protect intent

    Google’s brand controls can substantially reduce unwanted behavior in broad match.

    These controls include:

    These controls are handy when broad match starts overlapping with competitor intent or misaligned searches.

    How broad match succeeds and where it breaks

    A sensible rollout usually includes:

    • Choosing a campaign with effective tracking and enough conversion volume.
    • Aligning Smart Bidding with meaningful outcomes.
    • Launching with predetermined negative keywords.
    • Frequent search terms reviews in the initial month.
    • Verifying lead quality outside Google Ads before scaling.

    Broad match has potential and is beneficial if used wisely. However, it isn’t a simple fix.

    Failures often occur due to three common mistakes:

    • Choosing the wrong conversion to optimize: The algorithm follows your instructions meticulously.
    • Lack of a negative keyword system: Unchecked exploration becomes costly.
    • Judging success solely by platform metrics: CPC and CPA can look good, while revenue declines.

    Broad match is a system, not a setting

    Google favors a systemized approach to Search, moving from simple keyword management to a broader strategy.

    Control isn’t lost, but shifted.

    Successful broad match campaigns are defined by:

    • Clear quality definitions.
    • Deliberate intent constraints.
    • Success measured beyond the interface.

    If used judiciously, broad match can reveal new demand opportunities. Casual use, however, might lead you astray.


    Inspired by this post on Search Engine Land.


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  • EU’s Google Probe: Impact on SEO, AI, and Content Rights

    EU’s Google Probe: Impact on SEO, AI, and Content Rights

    I’ve been following the significant regulatory move in which the European Commission launched a formal antitrust investigation into Google.

    At the heart of this issue is Google’s use of publisher content to develop AI Overviews and other generative AI features, potentially diverting traffic from original publishers.

    As someone involved in SEO or content strategy, I’m immediately affected by these developments.

    The question I’m pondering is whether Google is overstepping by using publisher content for AI answers, or if it’s just part of being in an open web environment.

    With regulators stepping in, I’m seeing the industry reevaluate how we use, manage, and value machine-readable content. It raises questions about the cost to brands, publishers, and agencies if regulation doesn’t catch up with innovation.

    Here’s what’s going on, why it’s significant, and how the industry is already responding.

    What’s Actually Happening: Core Allegations in the Complaint

    This move from the EU is unfolding alongside other legal challenges, like those from publishers taking a stand against OpenAI and Penske Media’s recent antitrust suite targeting Google’s AI offerings.

    Many publishers see Google’s actions as a no-choice situation: allow the use of their content for AI, or face losing vital search traffic.

    At the same time, I notice how technical tools like robots.txt, Google-Extended, and new noai/nopreview conventions are reflecting an industry that’s striving to reclaim control.

    The crux of the issue is whether AI training and answer generation stretch the bounds of traditional indexing and require licensing or proper attribution.

    Dig deeper: New web standards could redefine how AI models use your content

    What Does the Complaint Target

    Publishers have seen their traffic drop by 20–50% on informational queries. The complaint highlights three practices:

    • Google’s scraping of publisher content to enhance models like Gemini.
    • A lack of meaningful opt-out options that still preserve search visibility.
    • AI summaries capturing user attention above organic links, thus reducing clicks to the original content.

    Regulators are called to explore three key questions:

    • How Google uses publisher content in model training and grounding.
    • If publishers can meaningfully opt out without losing their search visibility.
    • If AI Overviews enhance Google’s dominance by retaining users within their interface.

    Zero-Click Search Evolution: Is the Market Ready?

    I see this probe as the onset of a post-click era for SEO, shifting the visibility battle from the SERP to the LLM context window.

    The key question on my mind is whether Google is prepared for this transition.

    The zero-click search experience often gets talked about, but for it to be successful for everyone involved, a few things need to happen:

    • Users must find what they need directly on the SERP, within AI Overviews or AI Mode.
    • Google needs to integrate various content types into a coherent experience.
    • Publishers must receive fair compensation for participating in this ecosystem.

    Although Google is moving towards a zero-click model, they’re not yet able to fully support it:

    • Users still face outdated or incorrect answers.
    • Assistive chats remain fragmented and can’t deliver full experiences.
    • Publishers are unsure about compensation for quoted content.

    What is the Opt-Out Version, and How Effective is It?

    Google defends its content repurposing by offering opt-out mechanisms like Google-Extended in robots.txt.

    While Google-Extended can prevent Gemini training, it doesn’t block AI-generated answers from using live data from publisher websites.

    However, opting out of LLM training has its drawbacks:

    • Content may still appear in AI Overviews if it’s already indexed.
    • The process is opt-out rather than opt-in, requiring awareness and action from publishers.
    • No granular control allows for selective blocking between snippets and LLM training.

    Why Opting Out May Be a Bad Idea

    Many publishers are considering opting out of having their content crawled for AI-generated answers.

    Still, as AI answers evolve to become default, relying solely on direct or organic traffic is risky.

    In reality, it creates a lose-lose situation.

    Blocking usage may protect IP but hurts visibility, while staying open compromises control.

    Without regulations, publishers largely have to adapt to the current system.

    Dig deeper: How AI answers are disrupting publisher revenue and advertising


    The Big Debate: ‘Google Doesn’t Owe You’ vs. ‘It’s Not Their Content’

    I often see the assumption that control of web content lies in our hands.

    Yet, without search engines, their reach is quite limited.

    This tension fuels an ongoing debate dividing SEO perspectives.

    On one side is the belief that ‘Google doesn’t owe you anything’.

    • Many argue that the web is open, allowing search engines to crawl freely grants implicit permission for content use.
    • Google facilitates discovery, but clicks or backlinks aren’t guaranteed.

    On the flip side, there’s the perspective that ‘It’s not their content’.

    • Publishers argue against unlicensed use of content for LLM training and AI responses.
    • They see generation without attribution or compensation as disruptive.

    This debate is active across social media and discussion forums.

    Some suggest focusing on generative engine optimization, or GEO, replacing traditional rankings with AI quotes.

    Nonetheless, that approach keeps publishers reliant on Google’s linking decisions.

    In practice, there’s validity to both arguments.

    Yet, the broader trend reveals the trajectory.

    Even if Google faces consequences, search is unlikely to return solely to blue links.

    The zero-click conversion is advancing.

    The Dark Future of a Web Without Unique Content

    Before diving into potential outcomes of the complaint, consider the impact on information itself.

    As creators feel their work is reused without reward, the drive for original content wanes.

    Simultaneously, AI-generated content is growing, often with minimal human input.

    Entire sites now rely heavily on generative systems for content.

    This often involves reworking existing text, with occasional inaccuracies.

    As this cycle continues, the risk is declining informational quality due to a lack of truly fresh inputs.

    The debate over AI training isn’t just about traffic or monetization.

    It questions how the web can sustain unique knowledge creation and why protecting publishers is crucial to prevent information quality degradation.

    What Can Happen if Google Loses

    The traditional Google-publisher agreement was straightforward: “I let you crawl, you give me clicks.”

    Generative AI disrupted this balance.

    If the EU finds Google’s actions anticompetitive, we could witness major shifts:

    • Mandatory opt-out mechanisms: Effective changes could enforce a granular system that protects against AI summaries without sacrificing rankings.
    • The licensing economy: Following the music industry model, licensing could become compulsory, splitting organic search into free and premium sectors.
    • AEO formalization: Attribution could be legally required, turning source citations into a ranking factor.

    Ads and the Shifting Economics of Visibility

    While this primarily concerns AI and content rights, ads still significantly impact SERP dynamics.

    As organic space shrinks due to AI summaries, paid ads remain a strong visibility tool.

    Even if EU pressures curb AI answers, the space for blue links is unlikely to grow.

    The landscape will continue to favor revenue-driven Google products.

    If AI Overviews reduce organic visibility, CPCs could rise, affecting ad positions.

    Whatever the AI outcome, one truth is apparent: the cost of visibility is on the rise.

    How to Adapt Your SEO and Content Strategy

    Before any EU decision, I see top teams already shifting their strategies from merely ranking for keywords to ensuring they are the main entity answer wherever an AI model scans.

    This involves several key actions:

    • Enhancing entity clarity with schema and consistent data for accurate AI association.
    • Auditing brand representation in AI Overviews and tracking emerging visibility KPIs.
    • Reconsidering robots.txt strategies to manage IP protection versus AI visibility.
    • Educating leadership that visibility extends beyond traffic, incorporating citation and AI source value.

    The strategic goal is remaining readable and rights-conscious while ensuring brand presence where AI answers are most trusted.

    Dig deeper: How to build an effective content strategy for 2026


    Inspired by this post on Search Engine Land.


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  • Boost Visibility in AI Search with GEO Strategies

    Boost Visibility in AI Search with GEO Strategies

    Ever felt like your organic traffic is dwindling? I assure you, it’s not just in your head. AI Overviews and answer engines are nudging traditional SEO results off the stage.

    For brands to maintain visibility, swift adaptation is key.

    The upside? You don’t have to overhaul your entire SEO strategy. With some intelligent adjustments, you can transition from SEO to GEO, reclaiming your visibility in the AI era.

    GEO, or generative engine optimization, emphasizes entities—like your brand, products, and experts—over mere pages. By amplifying these signals, you boost your chances of appearing in AI-generated answers and conversational search results.

    This switch to GEO is crucial because AI search tools diverge from traditional search engines. Instead of just presenting a list of links, AI delivers comprehensive answers that predict follow-up inquiries and provide context. Users benefit from swift insights, while brands may observe a drop in clicks.

    The demand for organic search remains, though traffic is shifting. As clicks wane, your presence in AI-generated responses becomes increasingly vital.

    What you need to do now

    Moving from SEO to GEO doesn’t mean starting from scratch. Instead, it builds on existing principles, placing more emphasis on structure, clarity, and consistency.

    1. Prioritize E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness)

    AI engines favor content that’s authoritative and signals genuine expertise. Aligning your content with these quality guidelines ensures its likelihood of appearing in AI-generated answers.

    2. Make your content easy for AI crawlers to read

    While Googlebot processes JavaScript efficiently, other AI crawlers might not. Ensure your content is in fully rendered HTML with a clean structure to facilitate easy referencing by AI systems.

    3. Invest in structured data

    Using schema markup, complete metadata, and detailed alt text helps AI models understand and connect your content to the right entities, improving visibility in AI-generated interactions.

    4. Rethink measurement

    Shifting our focus away from traffic as the primary metric, we should now emphasize conversions, deeper funnel impacts, sentiment, and brand visibility within generative search results.

    Want to go deeper?

    Ready to confidently pivot from SEO to GEO? Check out proven best practices, frameworks, and real-world examples on Contentful’s GEO Hub. It’s your essential resource for understanding the shift and staying ahead.


    Inspired by this post on Search Engine Land.


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  • Discover the Top Brands Shaping AI Search Visibility

    Discover the Top Brands Shaping AI Search Visibility

    I’m excited to share that Semrush has launched the new AI Visibility Awards, highlighting which brands are excelling in AI-generated search results.

    As AI chatbots increasingly become our go-to for travel plans and product recommendations, I often wonder how we can ensure our brands feature prominently in their answers.

    Semrush seems to have found the solution and has introduced this award program to celebrate the trailblazers in this field.

    The AI Visibility Awards honor brands frequently mentioned and recommended in AI-generated responses, assessed using Semrush’s AI Visibility Index—a dataset crafted from over 2,500 real prompts processed through ChatGPT and Google’s AI Mode.

    Andrew Warden, Semrush’s CMO, notes:

    • “This year marks a turning point in how visibility is achieved. It’s driven by actual user behavior rather than submissions or panels. These awards spotlight those marketers who have mastered AI interaction and earned significant trust inside the answers.”

    What the AI Visibility Awards Measure

    The awards recognize three performer types within four major industries:

    • Category Leaders: Brands with the biggest presence in AI searches
    • Growth Engines: Brands rapidly gaining visibility
    • Challengers: Emerging brands gaining AI traction

    To illustrate, Google tops the Business & Professional Services category, while Rippling stands out as a Challenger. In Consumer Electronics, Samsung leads, with Logitech and Nothing Technology recognized as a Growth Engine and Challenger, respectively.

    Other notable winners include:

    • Microsoft, named Category Leader for Digital Tech & Software
    • UNIQLO as a Growth Engine in Fashion & Apparel
    • Anthropic as a Challenger in Digital Tech & Software

    The award insights reveal some emerging truths about AI-powered discovery:

    • Stability among leaders: Top brands display less than 20% monthly volatility in AI share-of-voice, suggesting AI platforms tend to “lock in” trusted names.
    • Niches break through: Brands with niche relevance—like Patagonia in ethical fashion or Logitech in gaming accessories—prove advantageously positioned.
    • Challengers can compete: Newer players, like Nuuly and Anthropic, gain traction with robust positioning and strategic momentum.
    • Verticals behave differently: While some sectors, such as Business & Professional Services, stay fiercely competitive, others benefit from consistency or unique specialization.

    These awards highlight a significant message for marketers: gaining AI visibility is turning into a crucial part of the competitive landscape. For certain brands, it’s already reshaping strategies.


    Inspired by this post on Search Engine Land.


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  • Boost Your Google Citations with AI Fan-Out Strategy

    Boost Your Google Citations with AI Fan-Out Strategy

    Upon evaluating a whopping 10,000 keywords, I’ve discovered an intriguing insight: pages that successfully rank for Google AI Overview ‘fan-out’ queries are significantly more likely to be cited. In fact, they account for more than half of all citations on these platforms.

    From my analysis, it’s clear that pages leveraging these queries dramatically increase their chances of being referenced. As data from Surfer SEO suggests, these pages offer more citation opportunities compared to those focusing solely on the main search query.

    An analysis of these 10,000 keywords revealed a strong correlation—precisely, a Spearman of 0.77—between the volume of fan-out queries a page ranks for and its likelihood of citation in Google’s AI Overviews.

    Diving into the numbers. I found that pages ranking for fan-out queries are 161% more likely to be cited than those ranking exclusively for the main query. Consider this:

    • 76% of the keywords evaluated triggered AI Overviews.
    • Through Gemini, I extracted 33,000 fan-out queries.
    • Pages ranking for both the main query and at least one fan-out constituted 51% of AI Overview citations.
    • In contrast, pages ranking solely for the main query accounted for just under 20%.

    Fan-outs outshine the main query. Recognizing the power of ranking for fan-out queries, I noticed such rankings were 49% more likely to earn citations than merely ranking for the main term. When the AI Overviews chose to reference organic results, here’s what stood out:

    • Approximately 20% of cited pages ranked only for the main query.
    • Conversely, around 30% ranked exclusively for fan-out queries.

    Most AI citations skip top ranks. Fascinatingly, about 68% of cited pages didn’t appear among Google’s top 10 results for either their main or fan-out queries. However, for the top three most prominent citations, this figure dropped to roughly 46%.

    But there’s more. It’s crucial to understand that correlation doesn’t equate to causation. Additionally:

    • Achieving a ranking for fan-out queries alone won’t guarantee an AI Overview citation.
    • User context and personalization affect fan-outs, with only about 27% remaining constant across test runs.
    • Normal SEO practices don’t fully determine citation selection.

    Why this matters to us. If your goal is to be cited in AI Overviews, striving for broader topic authority might be the answer. Surfer SEO advises crafting extensive topical content around core subjects, creating content that naturally responds to a variety of related questions, and allowing AI Overviews to recognize your pertinence across different fan-outs.

    Dive deeper with the report. For more in-depth analysis, check out the full study on Ranking for Multiple Fan-Out Queries Dramatically Increases Your Chances of Getting Cited in AIOs (173,902 URLs Studied).



    Inspired by this post on Search Engine Land.


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