<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/Why-Google-Ads-auctions-now-run-on-intent-not-keywords.jpg" class="attachment-large size-large wp-post-image" alt="Why Google Ads auctions now run on intent, not keywords" style="margin-bottom: 15px;" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/02/Why-Google-Ads-auctions-now-run-on-intent-not-keywords.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/02/Why-Google-Ads-auctions-now-run-on-intent-not-keywords-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/02/Why-Google-Ads-auctions-now-run-on-intent-not-keywords-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google Ads now shows where Performance Max campaigns run, giving advertisers clear insights to optimize performance.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/Why-Google-Ads-auctions-now-run-on-intent-not-keywords.jpg" class="attachment-large size-large wp-post-image" alt="Why Google Ads auctions now run on intent, not keywords" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/Why-Google-Ads-auctions-now-run-on-intent-not-keywords.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/02/Why-Google-Ads-auctions-now-run-on-intent-not-keywords-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/02/Why-Google-Ads-auctions-now-run-on-intent-not-keywords-1536x864.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google Ads now surfaces Performance Max (PMax) campaign data in the “Where ads showed” report, giving advertisers clearer insight into placements, networks, and impressions — data that was previously unavailable.</p>



<p><strong>What’s new.</strong> The update makes it possible to see exactly where PMax ads are appearing across Google’s network, including search partners, display, and other placements. Advertisers can now track impressions by placement type and network, helping them understand how campaigns are performing in detail.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="848" height="644" src="https://searchengineland.com/wp-content/seloads/2026/02/Screenshot-2026-02-18-at-21.24.20.png" alt="" class="wp-image-469534" srcset="https://searchengineland.com/wp-content/seloads/2026/02/Screenshot-2026-02-18-at-21.24.20.png 848w, https://searchengineland.com/wp-content/seloads/2026/02/Screenshot-2026-02-18-at-21.24.20-768x583.png 768w" sizes="(max-width: 848px) 100vw, 848px" /></figure>



<p><strong>Why we care.</strong> This update finally gives visibility into where PMax campaigns are running, including Google Search Partners, display, and other networks. With placement, type, and impression data now available, marketers can better understand campaign performance, optimize budgets, and make informed decisions instead of relying on guesswork. It turns previously opaque PMax reporting into actionable insights.</p>



<p><strong>User reaction</strong>. Digital marketer Thomas Eccel shared on <a href="https://www.linkedin.com/posts/thomaseccel_ppc-googleads-activity-7429061262946369536-5va6/">LinkedIn</a> that the report was historically empty, but now finally shows real data.</p>



<ul class="wp-block-list">
<li>“I finally see where and how PMax is being displayed,” he wrote.</li>



<li>He also noted the clarity on Google Search Partners, previously a “blurry grey zone.”</li>
</ul>



<p><strong>The bottom line</strong>. This update gives marketers actionable visbility into PMax campaigns, helping them understand placement performance, optimize spend, and identify which networks are driving results — all in one report.</p>


Google Ads now surfaces Performance Max (PMax) campaign data in the “Where ads showed” report, giving advertisers clearer insight into placements, networks, and impressions — data that was previously unavailable.
What’s new. The update makes it possible to see exactly where PMax ads are appearing across Google’s network, including search partners, display, and other placements. Advertisers can now track impressions by placement type and network, helping them understand how campaigns are performing in detail.




Why we care. This update finally gives visibility into where PMax campaigns are running, including Google Search Partners, display, and other networks. With placement, type, and impression data now available, marketers can better understand campaign performance, optimize budgets, and make informed decisions instead of relying on guesswork. It turns previously opaque PMax reporting into actionable insights.
User reaction. Digital marketer Thomas Eccel shared on LinkedIn that the report was historically empty, but now finally shows real data.
“I finally see where and how PMax is being displayed,” he wrote.
He also noted the clarity on Google Search Partners, previously a “blurry grey zone.”
The bottom line. This update gives marketers actionable visbility into PMax campaigns, helping them understand placement performance, optimize spend, and identify which networks are driving results — all in one report.

Google Ads now shows where Performance Max campaigns run, giving advertisers clear insights to optimize performance.

Google Ads shows PMax placements in “Where ads showed” report

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/self-reinforcing-cycle.png" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/self-reinforcing-cycle.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/self-reinforcing-cycle-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/self-reinforcing-cycle-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Text ad clicks surged year over year as classic organic fell across key verticals, with paid listings now taking up to one-third of clicks.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/self-reinforcing-cycle.png" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/self-reinforcing-cycle.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/self-reinforcing-cycle-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/self-reinforcing-cycle-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Organic search clicks are shrinking across major verticals — and it’s not just because of Google&#8217;s AI Overviews. </p>



<ul class="wp-block-list">
<li>Classic organic click share fell sharply across headphones, jeans, greeting cards, and online games queries in the U.S., new Similarweb data comparing January 2025 to January 2026 shows.</li>



<li>The biggest winner: text ads.</li>
</ul>



<p><strong>Why we care.</strong> You aren&#8217;t just competing with AI Overviews. You’re competing with Google&#8217;s aggressive expansion of paid search real estate. Across every vertical analyzed, text ads gained more click share than any other measurable surface. In product categories, paid listings now capture roughly one-third of all clicks. As a result, several brands that are losing organic visibility are increasing their paid investment.</p>



<p><strong>By the numbers. </strong>Across four verticals, text ads showed the most consistent, measurable click-share gains.</p>



<ul class="wp-block-list">
<li>Classic organic lost 11 to 23 percentage points of click share year over year.</li>



<li>Text ads gained 7 to 13 percentage points in every case.</li>



<li>Paid click share doubled in major product categories.</li>



<li>AI Overviews SERP presence rose ~10 to ~30 percentage points, depending on the vertical.</li>
</ul>



<p><strong>Classic organic is down everywhere.</strong> Year-over-year classic organic click share declined across all four verticals. Headphones saw the steepest drop. Even online games — historically organic-heavy — lost double digits. In two verticals (headphones, jeans), total clicks also fell.</p>



<ul class="wp-block-list">
<li>Headphones: Down from 73% to 50%</li>



<li>Jeans: Down from 73% to 56%</li>



<li>Greeting cards: Down from 88% to 75%</li>



<li>Online games: Down from 95% to 84%</li>
</ul>



<p><strong>Text ads are the biggest winner. </strong>Text ads gained share in every vertical; no other surface showed this level of consistent growth:</p>



<ul class="wp-block-list">
<li>Headphones: Up from 3% to 16%</li>



<li>Online games: Up from 3% to 13%</li>



<li>Jeans: Up from 7% to 16%</li>



<li>Greeting cards: Up from 9% to 16%</li>
</ul>



<p>In product categories, PLAs compounded the shift:</p>



<ul class="wp-block-list">
<li>Headphones: Up from 16% to 36%</li>



<li>Jeans: Up from 18% to 34%</li>



<li>Greeting cards: Up from 10% to 19%</li>
</ul>



<p><strong>AI Overviews surged unevenly.</strong> The presence of Google AI Overviews expanded sharply, but varied by vertical:</p>



<ul class="wp-block-list">
<li>Headphones: 2.28% → 32.76%</li>



<li>Online games: 0.38% → 29.80%</li>



<li>Greeting cards: 0.94% → 21.97%</li>



<li>Jeans: 2.28% → 12.06%</li>
</ul>



<p><strong>Zero-click searches are high — and mostly stable.</strong> Except for online games, zero-click rates didn’t change dramatically:</p>



<ul class="wp-block-list">
<li>Headphones: 63% (flat)</li>



<li>Jeans: Down from 65% to 61%</li>



<li>Online games: Up from 43% to 50%</li>



<li>Greeting cards: Up from 51% to 53%</li>
</ul>



<p><strong>Brands losing organic traffic are buying it back.</strong> In headphones:</p>



<ul class="wp-block-list">
<li>Amazon increased paid clicks 35% while losing organic volume.</li>



<li>Walmart nearly 6x’d paid clicks.</li>



<li>Bose boosted paid 49%.</li>
</ul>



<p>In jeans:</p>



<ul class="wp-block-list">
<li>Gap grew paid clicks 137% to become the top paid player.</li>



<li>True Religion entered the paid top tier without top-10 organic presence.</li>
</ul>



<p>In online games:</p>



<ul class="wp-block-list">
<li>CrazyGames quadrupled paid clicks while organic declined.</li>



<li>Arkadium entered paid after losing 68% of organic clicks.</li>
</ul>



<p><strong>The result</strong>? We&#8217;re seeing a self-reinforcing cycle, according to the study&#8217;s author, <a href="https://www.linkedin.com/in/aleyda/" target="_blank" rel="noopener">Aleyda Solis</a>:</p>



<ul class="wp-block-list">
<li>Organic share declines.</li>



<li>Competition intensifies.</li>



<li>More brands increase paid budgets.</li>



<li>Paid surfaces capture more clicks.</li>
</ul>



<p><strong>About the data. </strong>This analysis used Similarweb data to examine SERP composition and click distribution for the top 5,000 U.S. queries in headphones, jeans, and online games, and the top 956 queries in greeting cards and ecards. It compares January 2025 to January 2026, tracking how clicks shifted across classic organic results, organic SERP features, text ads, PLAs, zero-click searches, and AI Overviews.</p>



<p><strong>The study.</strong> <a href="https://www.aleydasolis.com/en/search-engine-optimization/serp-shifts-ads-remonetized/" target="_blank" rel="noopener">Search Isn’t Just Turning to AI, it’s being Re-Monetized: Text Ads Are Taking a Bigger Share of Google SERP Clicks (Data)</a></p>


Organic search clicks are shrinking across major verticals — and it’s not just because of Google’s AI Overviews. 
Classic organic click share fell sharply across headphones, jeans, greeting cards, and online games queries in the U.S., new Similarweb data comparing January 2025 to January 2026 shows.
The biggest winner: text ads.
Why we care. You aren’t just competing with AI Overviews. You’re competing with Google’s aggressive expansion of paid search real estate. Across every vertical analyzed, text ads gained more click share than any other measurable surface. In product categories, paid listings now capture roughly one-third of all clicks. As a result, several brands that are losing organic visibility are increasing their paid investment.
By the numbers. Across four verticals, text ads showed the most consistent, measurable click-share gains.
Classic organic lost 11 to 23 percentage points of click share year over year.
Text ads gained 7 to 13 percentage points in every case.
Paid click share doubled in major product categories.
AI Overviews SERP presence rose ~10 to ~30 percentage points, depending on the vertical.
Classic organic is down everywhere. Year-over-year classic organic click share declined across all four verticals. Headphones saw the steepest drop. Even online games — historically organic-heavy — lost double digits. In two verticals (headphones, jeans), total clicks also fell.
Headphones: Down from 73% to 50%
Jeans: Down from 73% to 56%
Greeting cards: Down from 88% to 75%
Online games: Down from 95% to 84%
Text ads are the biggest winner. Text ads gained share in every vertical; no other surface showed this level of consistent growth:
Headphones: Up from 3% to 16%
Online games: Up from 3% to 13%
Jeans: Up from 7% to 16%
Greeting cards: Up from 9% to 16%
In product categories, PLAs compounded the shift:
Headphones: Up from 16% to 36%
Jeans: Up from 18% to 34%
Greeting cards: Up from 10% to 19%
AI Overviews surged unevenly. The presence of Google AI Overviews expanded sharply, but varied by vertical:
Headphones: 2.28% → 32.76%
Online games: 0.38% → 29.80%
Greeting cards: 0.94% → 21.97%
Jeans: 2.28% → 12.06%
Zero-click searches are high — and mostly stable. Except for online games, zero-click rates didn’t change dramatically:
Headphones: 63% (flat)
Jeans: Down from 65% to 61%
Online games: Up from 43% to 50%
Greeting cards: Up from 51% to 53%
Brands losing organic traffic are buying it back. In headphones:
Amazon increased paid clicks 35% while losing organic volume.
Walmart nearly 6x’d paid clicks.
Bose boosted paid 49%.
In jeans:
Gap grew paid clicks 137% to become the top paid player.
True Religion entered the paid top tier without top-10 organic presence.
In online games:
CrazyGames quadrupled paid clicks while organic declined.
Arkadium entered paid after losing 68% of organic clicks.
The result? We’re seeing a self-reinforcing cycle, according to the study’s author, Aleyda Solis:
Organic share declines.
Competition intensifies.
More brands increase paid budgets.
Paid surfaces capture more clicks.
About the data. This analysis used Similarweb data to examine SERP composition and click distribution for the top 5,000 U.S. queries in headphones, jeans, and online games, and the top 956 queries in greeting cards and ecards. It compares January 2025 to January 2026, tracking how clicks shifted across classic organic results, organic SERP features, text ads, PLAs, zero-click searches, and AI Overviews.
The study. Search Isn’t Just Turning to AI, it’s being Re-Monetized: Text Ads Are Taking a Bigger Share of Google SERP Clicks (Data)

Text ad clicks surged year over year as classic organic fell across key verticals, with paid listings now taking up to one-third of clicks.

Paid search click share doubles as organic clicks fall: Study

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2024/11/Microsoft-Ads-How-it-compares-to-Google-Ads-and-tips-for-getting-started-800x450.png" class="attachment-large size-large wp-post-image" alt="Microsoft Ads: How it compares to Google Ads and tips for getting started" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2024/11/Microsoft-Ads-How-it-compares-to-Google-Ads-and-tips-for-getting-started-800x450.png 800w, https://searchengineland.com/wp-content/seloads/2024/11/Microsoft-Ads-How-it-compares-to-Google-Ads-and-tips-for-getting-started-600x338.png 600w, https://searchengineland.com/wp-content/seloads/2024/11/Microsoft-Ads-How-it-compares-to-Google-Ads-and-tips-for-getting-started-200x113.png 200w, https://searchengineland.com/wp-content/seloads/2024/11/Microsoft-Ads-How-it-compares-to-Google-Ads-and-tips-for-getting-started-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2024/11/Microsoft-Ads-How-it-compares-to-Google-Ads-and-tips-for-getting-started-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2024/11/Microsoft-Ads-How-it-compares-to-Google-Ads-and-tips-for-getting-started.png 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>Microsoft Advertising’s multi-image Shopping ads let brands show more product visuals in search to boost engagement.

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2024/11/Microsoft-Ads-How-it-compares-to-Google-Ads-and-tips-for-getting-started-800x450.png" class="attachment-large size-large wp-post-image" alt="Microsoft Ads: How it compares to Google Ads and tips for getting started" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2024/11/Microsoft-Ads-How-it-compares-to-Google-Ads-and-tips-for-getting-started-800x450.png 800w, https://searchengineland.com/wp-content/seloads/2024/11/Microsoft-Ads-How-it-compares-to-Google-Ads-and-tips-for-getting-started-600x338.png 600w, https://searchengineland.com/wp-content/seloads/2024/11/Microsoft-Ads-How-it-compares-to-Google-Ads-and-tips-for-getting-started-200x113.png 200w, https://searchengineland.com/wp-content/seloads/2024/11/Microsoft-Ads-How-it-compares-to-Google-Ads-and-tips-for-getting-started-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2024/11/Microsoft-Ads-How-it-compares-to-Google-Ads-and-tips-for-getting-started-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2024/11/Microsoft-Ads-How-it-compares-to-Google-Ads-and-tips-for-getting-started.png 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>
<p>Microsoft Advertising is rolling out multi-image ads for Shopping campaigns in Bing search results, giving ecommerce brands a richer way to showcase products and capture shopper attention before the click.</p>



<p><strong>What’s new.</strong> Advertisers can now display multiple product images within a single Shopping ad, letting shoppers preview different angles, styles or variations directly in search.</p>



<p>The format is designed to make ads more visually engaging and informative, helping consumers compare options quickly without leaving the results page.</p>



<p><strong>How it works</strong>:</p>



<ul class="wp-block-list">
<li>Additional images are uploaded through the optional additional_image_link attribute in the product feed.</li>



<li>Advertisers can include up to 10 images, separated by commas.</li>



<li>The images appear alongside pricing and retailer information in Shopping results.</li>
</ul>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="552" height="302" src="https://searchengineland.com/wp-content/seloads/2026/02/Microsoft-image-ads.png" alt="" class="wp-image-469497"/></figure></div>


<p><strong>Why we care</strong>. Multi-image ads could increase engagement and purchase intent by presenting a fuller picture of a product. More visuals can highlight features, colors and design details that a single image might miss. </p>



<p><strong>Discovery.</strong> The feature was first spotted by digital marketer Arpan Banerjee who shared spotting it on <a href="https://www.linkedin.com/posts/arpan301_microsoftadvertising-shoppingads-ecommerceads-activity-7429483312928239616-4-OV/?utm_source=share&amp;utm_medium=member_desktop&amp;rcm=ACoAABU5RtAB1yM61HMd6Ska99csDVjiJlgRo6A">LinkedIn.</a></p>



<p><strong>The bottom line.</strong> Multi-image Shopping ads give retailers more creative flexibility and shoppers more context at a glance — a shift that could improve ad performance and reshape how products compete in search results.</p>


Microsoft Advertising is rolling out multi-image ads for Shopping campaigns in Bing search results, giving ecommerce brands a richer way to showcase products and capture shopper attention before the click.
What’s new. Advertisers can now display multiple product images within a single Shopping ad, letting shoppers preview different angles, styles or variations directly in search.
The format is designed to make ads more visually engaging and informative, helping consumers compare options quickly without leaving the results page.
How it works:
Additional images are uploaded through the optional additional_image_link attribute in the product feed.
Advertisers can include up to 10 images, separated by commas.
The images appear alongside pricing and retailer information in Shopping results.

Why we care. Multi-image ads could increase engagement and purchase intent by presenting a fuller picture of a product. More visuals can highlight features, colors and design details that a single image might miss. 
Discovery. The feature was first spotted by digital marketer Arpan Banerjee who shared spotting it on LinkedIn.
The bottom line. Multi-image Shopping ads give retailers more creative flexibility and shoppers more context at a glance — a shift that could improve ad performance and reshape how products compete in search results.

Microsoft Advertising’s multi-image Shopping ads let brands show more product visuals in search to boost engagement.

Microsoft Advertising adds a multi image creative to Shopping ads

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2023/02/Microsoft-Ads-800x450.jpeg" class="attachment-large size-large wp-post-image" alt="Microsoft Ads" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2023/02/Microsoft-Ads-800x450.jpeg 800w, https://searchengineland.com/wp-content/seloads/2023/02/Microsoft-Ads-600x338.jpeg 600w, https://searchengineland.com/wp-content/seloads/2023/02/Microsoft-Ads-200x113.jpeg 200w, https://searchengineland.com/wp-content/seloads/2023/02/Microsoft-Ads-768x432.jpeg 768w, https://searchengineland.com/wp-content/seloads/2023/02/Microsoft-Ads-1536x864.jpeg 1536w, https://searchengineland.com/wp-content/seloads/2023/02/Microsoft-Ads-150x84.jpeg 150w, https://searchengineland.com/wp-content/seloads/2023/02/Microsoft-Ads.jpeg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>Microsoft Advertising’s new Performance Max learning path helps advertisers run better campaigns and showcase verified expertise.

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2023/02/Microsoft-Ads-800x450.jpeg" class="attachment-large size-large wp-post-image" alt="Microsoft Ads" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2023/02/Microsoft-Ads-800x450.jpeg 800w, https://searchengineland.com/wp-content/seloads/2023/02/Microsoft-Ads-600x338.jpeg 600w, https://searchengineland.com/wp-content/seloads/2023/02/Microsoft-Ads-200x113.jpeg 200w, https://searchengineland.com/wp-content/seloads/2023/02/Microsoft-Ads-768x432.jpeg 768w, https://searchengineland.com/wp-content/seloads/2023/02/Microsoft-Ads-1536x864.jpeg 1536w, https://searchengineland.com/wp-content/seloads/2023/02/Microsoft-Ads-150x84.jpeg 150w, https://searchengineland.com/wp-content/seloads/2023/02/Microsoft-Ads.jpeg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>
<p>A new applied learning path from Microsoft Advertising is designed to help marketers get more value from Performance Max campaigns through hands-on, scenario-based training — not just theory.</p>



<p><strong>What&#8217;s happening</strong>. The new Performance Max learning path bundles three progressive courses that focus on real-world setup, optimization and troubleshooting. The structure is meant to let advertisers learn at their own pace while building practical skills they can immediately apply to live campaigns.</p>



<p>Each course targets a different stage of expertise, from beginner fundamentals to advanced strategy and credentialing.</p>



<p><strong>What’s included</strong>:</p>



<p><strong>Course 1: Foundations</strong></p>



<ul class="wp-block-list">
<li>Introducing Microsoft Advertising Performance Max campaigns covers the essentials.</li>



<li>Ideal for beginners who want to understand how PMax campaigns work.</li>



<li>Focuses on core concepts and terminology.</li>
</ul>



<p><strong>Course 2: Hands-on setup</strong></p>



<ul class="wp-block-list">
<li>Setting up Microsoft Advertising Performance Max campaigns provides a guided walkthrough.</li>



<li>Designed for advertisers launching their first PMax campaign or refreshing their skills.</li>



<li>Walks step-by-step through campaign creation and answers common setup questions.</li>
</ul>



<p><strong>Course 3: Advanced implementation</strong></p>



<ul class="wp-block-list">
<li>Implementing &amp; optimizing Microsoft Advertising Performance Max centers on scenario-based applied learning.</li>



<li>Targets advanced users developing strategic and optimization skills.</li>



<li>Includes practical tools like checklists, videos and reusable reference materials.</li>
</ul>



<p><strong>How it works</strong>. The third course introduces embedded support features that let learners access targeted educational resources mid-assessment via a “Help me understand” option. Users can review specific concepts in context and return directly to their questions.</p>



<p><strong>The benefit</strong>. Learners can spend more time on weak areas while quickly progressing through familiar material.</p>



<p><strong>Credential payoff</strong>. Completing the advanced course unlocks the chance to earn a Performance Max badge. The credential signals proficiency in implementing and optimizing PMax campaigns and applying best practices in real-world scenarios.</p>



<p>The badge is digitally shareable and verifiable through Credly, making it easy to display on professional platforms like LinkedIn.</p>



<p><strong>Why we care. </strong>This update from Microsoft Advertising makes it faster and easier to build real, job-ready skills for running Performance Max campaigns — not just theoretical knowledge. The applied, scenario-based training helps marketers avoid common setup mistakes, optimize campaigns more confidently, and improve performance in live accounts. </p>



<p>Plus, the shareable credential adds professional credibility, signaling proven expertise to clients and employers.</p>



<p><strong>The bottom line</strong>. <a href="https://about.ads.microsoft.com/en/blog/post/february-2026/build-real-world-performance-max-expertise-with-the-new-microsoft-advertising-learning-path">The new learning path</a> aims to close the gap between training and execution. By combining applied scenarios, embedded support and credentialing, it offers a structured route for advertisers to build confidence — and prove it — in Performance Max campaign management.</p>


A new applied learning path from Microsoft Advertising is designed to help marketers get more value from Performance Max campaigns through hands-on, scenario-based training — not just theory.
What’s happening. The new Performance Max learning path bundles three progressive courses that focus on real-world setup, optimization and troubleshooting. The structure is meant to let advertisers learn at their own pace while building practical skills they can immediately apply to live campaigns.
Each course targets a different stage of expertise, from beginner fundamentals to advanced strategy and credentialing.
What’s included:
Course 1: Foundations
Introducing Microsoft Advertising Performance Max campaigns covers the essentials.
Ideal for beginners who want to understand how PMax campaigns work.
Focuses on core concepts and terminology.
Course 2: Hands-on setup
Setting up Microsoft Advertising Performance Max campaigns provides a guided walkthrough.
Designed for advertisers launching their first PMax campaign or refreshing their skills.
Walks step-by-step through campaign creation and answers common setup questions.
Course 3: Advanced implementation
Implementing & optimizing Microsoft Advertising Performance Max centers on scenario-based applied learning.
Targets advanced users developing strategic and optimization skills.
Includes practical tools like checklists, videos and reusable reference materials.
How it works. The third course introduces embedded support features that let learners access targeted educational resources mid-assessment via a “Help me understand” option. Users can review specific concepts in context and return directly to their questions.
The benefit. Learners can spend more time on weak areas while quickly progressing through familiar material.
Credential payoff. Completing the advanced course unlocks the chance to earn a Performance Max badge. The credential signals proficiency in implementing and optimizing PMax campaigns and applying best practices in real-world scenarios.
The badge is digitally shareable and verifiable through Credly, making it easy to display on professional platforms like LinkedIn.
Why we care. This update from Microsoft Advertising makes it faster and easier to build real, job-ready skills for running Performance Max campaigns — not just theoretical knowledge. The applied, scenario-based training helps marketers avoid common setup mistakes, optimize campaigns more confidently, and improve performance in live accounts. 
Plus, the shareable credential adds professional credibility, signaling proven expertise to clients and employers.
The bottom line. The new learning path aims to close the gap between training and execution. By combining applied scenarios, embedded support and credentialing, it offers a structured route for advertisers to build confidence — and prove it — in Performance Max campaign management.

Microsoft Advertising’s new Performance Max learning path helps advertisers run better campaigns and showcase verified expertise.

Microsoft rolls out applied Performance Max learning path

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/chatgpt-citations-attention.png" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/chatgpt-citations-attention.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/chatgpt-citations-attention-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/chatgpt-citations-attention-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>ChatGPT pulls most from early sections, favoring direct definitions, balanced tone, and dense entities, new research finds.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/chatgpt-citations-attention.png" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/chatgpt-citations-attention.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/chatgpt-citations-attention-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/chatgpt-citations-attention-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>ChatGPT heavily favors the top of content when selecting citations, according to an analysis of 1.2 million AI answers and 18,012 verified citations by Kevin Indig, Growth Advisor.</p>



<p><strong>Why we care</strong>. Traditional search rewarded depth and delayed payoff. AI favors immediate classification — clear entities and direct answers up front. If your substance isn’t surfaced early, it’s less likely to appear in AI answers.</p>



<p><strong>By the numbers</strong>. Indig’s team found a consistent “ski ramp” citation pattern that held across randomized validation batches. He called the results statistically indisputable:</p>



<ul class="wp-block-list">
<li>44.2% of citations come from the first 30% of content.</li>



<li>31.1% come from the middle (30–70%).</li>



<li>24.7% come from the final third, with a sharp drop near the footer.</li>
</ul>



<p>At the paragraph level, AI reads more deeply:</p>



<ul class="wp-block-list">
<li>53% of citations come from the middle of paragraphs.</li>



<li>24.5% come from first sentences.</li>



<li>22.5% come from last sentences.</li>
</ul>



<p><strong>The big takeaway</strong>. Front-load key insights at the article level. Within paragraphs, prioritize clarity and information density over forced first sentences.</p>



<p><strong>Why this happens</strong>. Large language models are trained on journalism and academic writing that follow a “bottom line up front” structure. The model appears to weight early framing more heavily, then interpret the rest through that lens.</p>



<ul class="wp-block-list">
<li>Modern models can process massive token windows, but they prioritize efficiency and establish context quickly.</li>
</ul>



<p><strong>What gets cited</strong>. Indig identified five traits of highly cited content:</p>



<ul class="wp-block-list">
<li><strong>Definitive language</strong>: Cited passages were nearly twice as likely to use clear definitions (“X is,” “X refers to”). Direct subject-verb-object statements outperform vague framing.</li>



<li><strong>Conversational Q&amp;A structure</strong>: Cited content was 2x more likely to include a question mark. 78.4% of citations tied to questions came from headings. AI often treats H2s as prompts and the following paragraph as the answer.</li>



<li><strong>Entity richness</strong>: Typical English text contains 5% to 8% proper nouns. Heavily cited text averaged 20.6%. Specific brands, tools, and people anchor answers and reduce ambiguity.</li>



<li><strong>Balanced sentiment</strong>: Cited text clustered around a subjectivity score of 0.47 — neither dry fact nor emotional opinion. The preferred tone resembles analyst commentary: fact plus interpretation.</li>



<li><strong>Business-grade clarity</strong>: Winning content averaged a Flesch-Kincaid grade level of 16 versus 19.1 for lower-performing content. Shorter sentences and plain structure beat dense academic prose.</li>
</ul>



<p><strong>About the data</strong>. Indig analyzed 3 million ChatGPT responses and 30 million citations, isolating 18,012 verified citations to examine where and why AI pulls content. His team used sentence-transformer embeddings to match responses to specific source sentences, then measured their page position and linguistic traits such as definitions, entity density, and sentiment.</p>



<p><strong>Bottom line. </strong>Narrative “ultimate guide” writing may underperform in AI retrieval. Structured, briefing-style content performs better.</p>



<ul class="wp-block-list">
<li>Indig argues this creates a “clarity tax.” Writers must surface definitions, entities, and conclusions early—not save them for the end.</li>
</ul>



<p><strong>The report.</strong> <a href="https://www.growth-memo.com/p/the-science-of-how-ai-pays-attention">The science of how AI pays attention</a></p>


ChatGPT heavily favors the top of content when selecting citations, according to an analysis of 1.2 million AI answers and 18,012 verified citations by Kevin Indig, Growth Advisor.
Why we care. Traditional search rewarded depth and delayed payoff. AI favors immediate classification — clear entities and direct answers up front. If your substance isn’t surfaced early, it’s less likely to appear in AI answers.
By the numbers. Indig’s team found a consistent “ski ramp” citation pattern that held across randomized validation batches. He called the results statistically indisputable:
44.2% of citations come from the first 30% of content.
31.1% come from the middle (30–70%).
24.7% come from the final third, with a sharp drop near the footer.
At the paragraph level, AI reads more deeply:
53% of citations come from the middle of paragraphs.
24.5% come from first sentences.
22.5% come from last sentences.
The big takeaway. Front-load key insights at the article level. Within paragraphs, prioritize clarity and information density over forced first sentences.
Why this happens. Large language models are trained on journalism and academic writing that follow a “bottom line up front” structure. The model appears to weight early framing more heavily, then interpret the rest through that lens.
Modern models can process massive token windows, but they prioritize efficiency and establish context quickly.
What gets cited. Indig identified five traits of highly cited content:
Definitive language: Cited passages were nearly twice as likely to use clear definitions (“X is,” “X refers to”). Direct subject-verb-object statements outperform vague framing.
Conversational Q&A structure: Cited content was 2x more likely to include a question mark. 78.4% of citations tied to questions came from headings. AI often treats H2s as prompts and the following paragraph as the answer.
Entity richness: Typical English text contains 5% to 8% proper nouns. Heavily cited text averaged 20.6%. Specific brands, tools, and people anchor answers and reduce ambiguity.
Balanced sentiment: Cited text clustered around a subjectivity score of 0.47 — neither dry fact nor emotional opinion. The preferred tone resembles analyst commentary: fact plus interpretation.
Business-grade clarity: Winning content averaged a Flesch-Kincaid grade level of 16 versus 19.1 for lower-performing content. Shorter sentences and plain structure beat dense academic prose.
About the data. Indig analyzed 3 million ChatGPT responses and 30 million citations, isolating 18,012 verified citations to examine where and why AI pulls content. His team used sentence-transformer embeddings to match responses to specific source sentences, then measured their page position and linguistic traits such as definitions, entity density, and sentiment.
Bottom line. Narrative “ultimate guide” writing may underperform in AI retrieval. Structured, briefing-style content performs better.
Indig argues this creates a “clarity tax.” Writers must surface definitions, entities, and conclusions early—not save them for the end.
The report. The science of how AI pays attention

ChatGPT pulls most from early sections, favoring direct definitions, balanced tone, and dense entities, new research finds.

44% of ChatGPT citations come from the first third of content: Study

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2025/01/Google-Ads-logo-on-smartphone-800x450.png" class="attachment-large size-large wp-post-image" alt="Google Ads logo on smartphone" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/01/Google-Ads-logo-on-smartphone-800x450.png 800w, https://searchengineland.com/wp-content/seloads/2025/01/Google-Ads-logo-on-smartphone-600x338.png 600w, https://searchengineland.com/wp-content/seloads/2025/01/Google-Ads-logo-on-smartphone-200x113.png 200w, https://searchengineland.com/wp-content/seloads/2025/01/Google-Ads-logo-on-smartphone-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/01/Google-Ads-logo-on-smartphone-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2025/01/Google-Ads-logo-on-smartphone.png 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>Google Ads now shows the real performance impact of applied recommendations in a new Results tab, comparing outcomes against estimated baseline results.

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2025/01/Google-Ads-logo-on-smartphone-800x450.png" class="attachment-large size-large wp-post-image" alt="Google Ads logo on smartphone" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/01/Google-Ads-logo-on-smartphone-800x450.png 800w, https://searchengineland.com/wp-content/seloads/2025/01/Google-Ads-logo-on-smartphone-600x338.png 600w, https://searchengineland.com/wp-content/seloads/2025/01/Google-Ads-logo-on-smartphone-200x113.png 200w, https://searchengineland.com/wp-content/seloads/2025/01/Google-Ads-logo-on-smartphone-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/01/Google-Ads-logo-on-smartphone-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2025/01/Google-Ads-logo-on-smartphone.png 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>
<p>Google Ads has launched a new Results tab inside its Recommendations section that shows advertisers the measured performance impact after they apply bid and budget suggestions.</p>



<p><strong>How it works.</strong> After an advertiser applies a bid or budget recommendation, Google analyzes campaign performance one week later and compares it to an estimated baseline of what would have happened without the change. The system then highlights the incremental lift, such as additional conversions generated by raising a budget or adjusting targets.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="800" height="1000" src="https://searchengineland.com/wp-content/seloads/2026/02/Results-tab-in-recommendation.jpeg" alt="" class="wp-image-469481" srcset="https://searchengineland.com/wp-content/seloads/2026/02/Results-tab-in-recommendation.jpeg 800w, https://searchengineland.com/wp-content/seloads/2026/02/Results-tab-in-recommendation-768x960.jpeg 768w" sizes="(max-width: 800px) 100vw, 800px" /></figure>



<p><strong>Where to find it.</strong> Impact reporting appears in the Recommendations area of an account. A summary callout shows recent results on the main page, while a dedicated Results tab provides a deeper breakdown grouped by Budget and Target recommendations, with filtering options for each.</p>



<p><strong>Why we care.</strong> Advertisers can now see whether Google’s automated recommendations actually drive incremental results — not just projected gains — helping teams evaluate the business value of platform guidance.</p>



<p><strong>What to expect.</strong> Results are reported as a seven-day rolling average measured across a 28-day window after a recommendation is applied. Metrics focus on the campaign’s primary bidding objective — such as conversions, conversion value, or clicks.</p>



<p><strong>Between the lines.</strong> The feature adds a layer of accountability to automated recommendations at a time when advertisers are relying more heavily on platform-driven optimization.</p>



<p><strong>Spotted by.</strong> Hana Kobzová founder of PPCNewsFeed who shared a screenshot of the help doc on <a href="https://www.linkedin.com/posts/hanakobzova_breaking-results-tab-for-recommendations-activity-7429057005983547393-IceG/?utm_source=share&amp;utm_medium=member_desktop&amp;rcm=ACoAAAJTV04Bw5M-1qqJtnImkpsEOFw9bHvfSSs">LinkedIn</a>. </p>



<p><strong>Help doc.</strong> Even though there isn&#8217;t a live Google help doc, a Google spokesperson has confirmed that there&#8217;s an early pilot running.</p>


Google Ads has launched a new Results tab inside its Recommendations section that shows advertisers the measured performance impact after they apply bid and budget suggestions.
How it works. After an advertiser applies a bid or budget recommendation, Google analyzes campaign performance one week later and compares it to an estimated baseline of what would have happened without the change. The system then highlights the incremental lift, such as additional conversions generated by raising a budget or adjusting targets.




Where to find it. Impact reporting appears in the Recommendations area of an account. A summary callout shows recent results on the main page, while a dedicated Results tab provides a deeper breakdown grouped by Budget and Target recommendations, with filtering options for each.
Why we care. Advertisers can now see whether Google’s automated recommendations actually drive incremental results — not just projected gains — helping teams evaluate the business value of platform guidance.
What to expect. Results are reported as a seven-day rolling average measured across a 28-day window after a recommendation is applied. Metrics focus on the campaign’s primary bidding objective — such as conversions, conversion value, or clicks.
Between the lines. The feature adds a layer of accountability to automated recommendations at a time when advertisers are relying more heavily on platform-driven optimization.
Spotted by. Hana Kobzová founder of PPCNewsFeed who shared a screenshot of the help doc on LinkedIn. 
Help doc. Even though there isn’t a live Google help doc, a Google spokesperson has confirmed that there’s an early pilot running.

Google Ads now shows the real performance impact of applied recommendations in a new Results tab, comparing outcomes against estimated baseline results.

Google Ads adds Results tab to show impact of applied recommendations

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/AI-search-is-about-the-consideration-set-not-ranking-first.png" class="attachment-large size-large wp-post-image" alt="AI search is about the consideration set, not ranking first" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/AI-search-is-about-the-consideration-set-not-ranking-first.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/AI-search-is-about-the-consideration-set-not-ranking-first-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/AI-search-is-about-the-consideration-set-not-ranking-first-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI users consider 3.7 businesses per response, and 60% decide without clicking. Here’s what that means for your AI visibility strategy.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/AI-search-is-about-the-consideration-set-not-ranking-first.png" class="attachment-large size-large wp-post-image" alt="AI search is about the consideration set, not ranking first" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/AI-search-is-about-the-consideration-set-not-ranking-first.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/AI-search-is-about-the-consideration-set-not-ranking-first-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/AI-search-is-about-the-consideration-set-not-ranking-first-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>We need to have a talk about KPIs and AI search.</p>



<p>I’ve observed numerous <a href="https://searchengineland.com/guide/what-is-seo">SEO</a> professionals on LinkedIn and at conferences talking about “ranking No. 1 on ChatGPT” as if it’s the equivalent of a No. 1 ranking on Google:</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1782" height="968" src="https://searchengineland.com/wp-content/seloads/2026/02/LinkedIn-post-ranking-1-on-ChatGPT.png" alt="LinkedIn post - ranking # 1 on ChatGPT" class="wp-image-469345" srcset="https://searchengineland.com/wp-content/seloads/2026/02/LinkedIn-post-ranking-1-on-ChatGPT.png 1782w, https://searchengineland.com/wp-content/seloads/2026/02/LinkedIn-post-ranking-1-on-ChatGPT-768x417.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/LinkedIn-post-ranking-1-on-ChatGPT-1536x834.png 1536w" sizes="(max-width: 1782px) 100vw, 1782px" /></figure></div>


<p>On Google, being the first result is often a golden ticket.</p>



<p>Going from No. 2 to No. 1 in Google search will <a href="https://searchengineland.com/organic-click-thru-rates-tumbling-study-97338">often result in 100%-300% increases in traffic</a> and conversions.</p>



<p>This is almost certainly not the case with AI responses – even if they weren’t constantly changing.</p>



<p>Our team’s research shows AI users consider an average of 3.7 businesses before deciding who to contact.</p>



<p>Being the first result in that list on ChatGPT isn&#8217;t the golden ticket it is in Google search.</p>



<p>This being the case, the focus of AI search really should be on “inclusion in the consideration set” – not necessarily being “the first mentioned in that set” – as well as crafting what AI is saying about us.</p>



<h2 class="wp-block-heading" id="h-user-behavior-on-ai-platforms-differs-from-google-search">User behavior on AI platforms differs from Google search</h2>



<p>Over the past several months, my team has spent more than 100 hours observing people use ChatGPT and Google’s AI Mode to find services.</p>



<p>One thing came into focus within the first dozen or so sessions: User behavior on AI platforms differs from Google search in ways that extend far beyond using “natural language” and having conversations versus performing keyword searches.</p>



<p>Which is overstated, by the way. About <a href="https://searchengineland.com/chatgpt-users-keywords-for-local-services-data-467715">75% of the sessions</a> we observed included “keyword searches.”</p>



<p>One key difference: Users consider more businesses in AI responses than in organic search.</p>



<p>It makes sense — it’s much easier to compare multiple options in a chat window than to click through three to five search results and visit each site.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/from-searching-to-delegating-adapting-to-ai-first-search-behavior-467888">From searching to delegating: Adapting to AI-first search behavior</a></em></strong></p>



<h2 class="wp-block-heading" id="h-ai-users-don-t-stop-at-the-first-result">AI users don’t stop at the first result</h2>



<p>In both Google AI Mode and ChatGPT, users considered an average of 3.7 businesses from the results.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1145" height="1219" src="https://searchengineland.com/wp-content/seloads/2026/02/The-average-AI-chat-user-considers-3.7-businesses.jpeg" alt="The average AI chat user considers 3.7 businesses" class="wp-image-469346" srcset="https://searchengineland.com/wp-content/seloads/2026/02/The-average-AI-chat-user-considers-3.7-businesses.jpeg 1145w, https://searchengineland.com/wp-content/seloads/2026/02/The-average-AI-chat-user-considers-3.7-businesses-768x818.jpeg 768w" sizes="(max-width: 1145px) 100vw, 1145px" /></figure></div>


<p>This has strong implications for the No. 1 result – as well as No. 4.</p>



<p>The value of appearing first drops sharply — and the value of appearing lower rises — when, in 75% of sessions, users also consider businesses in Positions 2 to 8.</p>



<p>What’s driving conversions isn&#8217;t your position in that list.</p>



<div class="nl-inline-form border py-2 px-1 my-2">
	
	<div class="row align-items-center nl-inline-container">
		<div class="col-12 col-lg-3 col-xl-4 pe-md-0 pb-2 pb-lg-0">
			<p class="inline-form-text text-center mb-0">Get the newsletter search marketers rely on.</p>
		</div>
		<div class="col-12 col-lg-7 col-xl-6  pe-lg-0">
			<div class="form-nl-inline">
				<script src="https://app-sj02.marketo.com/js/forms2/js/forms2.min.js"></script> <form id="mktoForm_16298"></form> 
				
				<script>				
					MktoForms2.loadForm("https://app-sj02.marketo.com", "727-ZQE-044", 16298, function(form) {
						// form.onSubmit(function(){
						// });
						
						// form.onSuccess(function (values, followUpUrl) {
						// });
					});
				</script>
			</div>
		</div>
		<div class="col-12 col-lg-2 col-xl-2 ps-1">
			<p class="text-center mb-0" style="font-size: .75rem;"><a class="nl-terms" href="https://searchengineland.com/termsofuse" target="_blank" aria-label="opens in a new tab">See terms.</a></p>
		</div>
	</div>
</div>




<hr class="wp-block-separator has-text-color has-cyan-bluish-gray-color has-css-opacity has-cyan-bluish-gray-background-color has-background"/>



<h2 class="wp-block-heading" id="h-why-do-businesses-with-lower-rankings-end-up-in-the-consideration-set-in-llms">Why do businesses with lower rankings end up in the consideration set in LLMs?</h2>



<p>First of all, these aren’t rankings.</p>



<p>They are a list of recommendations that will likely get shuffled, reformatted from a list to a table, and completely changed, given the probabilistic nature of AI.</p>



<p>That aside, AI chat makes it much easier to scan and consider more options than Google search does.</p>



<p>Let’s look at the Google search results for “fractional CMO.”</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1552" height="1600" src="https://searchengineland.com/wp-content/seloads/2026/02/Google-SERPS-Fractional-CMO.png" alt="" class="wp-image-469347" srcset="https://searchengineland.com/wp-content/seloads/2026/02/Google-SERPS-Fractional-CMO.png 1552w, https://searchengineland.com/wp-content/seloads/2026/02/Google-SERPS-Fractional-CMO-768x792.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/Google-SERPS-Fractional-CMO-1490x1536.png 1490w" sizes="(max-width: 1552px) 100vw, 1552px" /></figure></div>


<p>If a user wants to evaluate multiple fractional CMO options for their startup, it’s more work to do so in Google Search than in ChatGPT.</p>



<p>Only two options appear above the fold, and each requires a click-through to read their website content.</p>



<p>Contrast this with the experience on ChatGPT.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1095" height="1600" src="https://searchengineland.com/wp-content/seloads/2026/02/ChatGPT-Fractional-CMO.png" alt="ChatGPT - Fractional CMO" class="wp-image-469348" srcset="https://searchengineland.com/wp-content/seloads/2026/02/ChatGPT-Fractional-CMO.png 1095w, https://searchengineland.com/wp-content/seloads/2026/02/ChatGPT-Fractional-CMO-768x1122.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/ChatGPT-Fractional-CMO-1051x1536.png 1051w" sizes="(max-width: 1095px) 100vw, 1095px" /></figure></div>


<p>The model gave them eight options, along with information about each one.</p>



<p>It’s easy to read all eight blurbs and decide whom to explore further.</p>



<p>Which leads to the other thing we really need to focus on: what the model is saying about you.</p>



<p><a href="https://www.semrush.com/lp/semrush-one/en/?utm_campaign=ic_semrush_one&amp;utm_source=searchengineland.com&amp;utm_medium=overlay&amp;onboarding=off" target="_blank" style="display: block; text-decoration: none; cursor: pointer;">
  <div style="background: radial-gradient(circle at 30% 40%, rgba(184, 111, 255, 0.15), rgba(0, 169, 255, 0.15) 40%, #CDE8FD 70%); padding: 30px; width: 100%; max-width: 802px; color: #000000 !important; font-family: Arial, sans-serif; margin: 25px 0 30px 0; border-radius: 8px; box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1); position: relative; box-sizing: border-box;">

    <div style="width: 100%; max-width: 100%; margin-bottom: 20px; text-align: left; padding-right: 20px; box-sizing: border-box;">

      <div id="semrush-one-headline" class="headline-responsive" style="font-family: Oswald, sans-serif; font-size: 30px; font-weight: normal; margin: 0; color: #000000 !important; line-height: 1.2;">
        Your customers search everywhere. Make sure your brand <span style="background: linear-gradient(90deg, #D56EFE 0%, #068EF8 51%); -webkit-background-clip: text; -webkit-text-fill-color: transparent; background-clip: text;">shows up</span>.
      </div>
      <p id="semrush-one-subhead" style="font-family: Roboto, sans-serif; font-size: 18px; font-weight: 300; line-height: 25px; margin: 12px 0 0 0; color: #000000 !important;">
        The SEO toolkit you know, plus the AI visibility data you need.
      </p>
    </div>
    <div style="margin-bottom: 15px;">
      <span id="semrush-one-cta" style="display: inline-block; background-color: #FF642D; color: white; height: 44px; border: none; border-radius: 5px; cursor: pointer; font-size: 16px; padding: 0 24px; font-weight: bold; white-space: nowrap; box-sizing: border-box; text-decoration: none; line-height: 44px;">Start Free Trial</span>
    </div>

    <div style="font-size: 12px;">
      <div style="font-family: Roboto, sans-serif; font-weight: 300; color: #000000; margin-bottom: 4px;">Get started with</div>
      <img decoding="async" src="https://searchengineland.com/wp-content/seloads/2025/11/semrush-one.webp" alt="Semrush One Logo" style="height: 16px; width: auto; display: block;">
    </div>
  </div>
</a>
<style>
  @media (max-width: 768px) {
    .headline-responsive {
      font-size: 30px !important;
      line-height: 1.3 !important;
    }
  }
</style></p>



<h2 class="wp-block-heading" id="h-a-bigger-driver-than-being-first-on-chatgpt-being-a-good-fit">A bigger driver than being first on ChatGPT: Being a good fit</h2>



<p>Many search marketers focus on rankings and traffic, but rarely on messaging and positioning.</p>



<p>This needs to change.</p>



<p>In the case of the response for an ophthalmologist in southern New Jersey, you get an easily scannable list:</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1019" height="1600" src="https://searchengineland.com/wp-content/seloads/2026/02/A-bigger-driver-than-being-first-on-ChatGPT-Being-a-good-fit.png" alt="" class="wp-image-469350" srcset="https://searchengineland.com/wp-content/seloads/2026/02/A-bigger-driver-than-being-first-on-ChatGPT-Being-a-good-fit.png 1019w, https://searchengineland.com/wp-content/seloads/2026/02/A-bigger-driver-than-being-first-on-ChatGPT-Being-a-good-fit-768x1206.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/A-bigger-driver-than-being-first-on-ChatGPT-Being-a-good-fit-978x1536.png 978w" sizes="(max-width: 1019px) 100vw, 1019px" /></figure></div>


<p>Roughly 60% make their entire decision based on the response, without visiting the website or switching to Google, according to our study.</p>



<p>So how do you drive conversion?</p>



<p>Deliver the right message — and make sure the model shares it.</p>



<p>Dr. Lanciano may be the best glaucoma specialist in the area. But if the model highlights Ravi D. Goel and Bannett Eye Centers for glaucoma care, and that’s what the user needs, they’ll go there.</p>



<p>Bannett Eye Centers appears last in the AI response but may still win the conversion because of what the model says about it — something that rarely happens in Google Search.</p>



<p>Visibility doesn’t pay the bills. Conversions do. And conversions don’t happen when customers think someone else is a better fit.</p>



<p><em><strong>Dig deeper: <a href="https://searchengineland.com/measure-brand-visibility-ai-search-464524">How to measure your AI search brand visibility and prove business impact</a></strong></em></p>



<h2 class="wp-block-heading" id="h-as-seos-shift-toward-dig-deeper-a-mindset-shift-needs-to-occur">As SEOs shift toward <strong><em>Dig deeper: </em></strong>, a mindset shift needs to occur</h2>



<p>We’re still thinking about <a href="https://searchengineland.com/optimize-ai-search-llm-visibility-tactics-468106">AI search</a> the way we’ve thought about SEO.</p>



<p>In SEO, the top result captures most of the traffic. In AI search, it doesn’t.</p>



<p>AI users consider more available options.</p>



<p>Responses — and their format — change dramatically with each request.</p>



<p>“Winning” in AI search means getting into the consideration set and being presented compellingly.</p>



<p>It’s not about being first on a list, especially if what’s said about you misses the mark.</p>



<p>In other words, SEOs who think like copywriters and salespeople will drive outcomes for their organizations.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/seo-brand-performance-channel-468095">Is SEO a brand channel or a performance channel? Now it’s both</a></em></strong></p>

We need to have a talk about KPIs and AI search.
I’ve observed numerous SEO professionals on LinkedIn and at conferences talking about “ranking No. 1 on ChatGPT” as if it’s the equivalent of a No. 1 ranking on Google:

On Google, being the first result is often a golden ticket.
Going from No. 2 to No. 1 in Google search will often result in 100%-300% increases in traffic and conversions.
This is almost certainly not the case with AI responses – even if they weren’t constantly changing.
Our team’s research shows AI users consider an average of 3.7 businesses before deciding who to contact.
Being the first result in that list on ChatGPT isn’t the golden ticket it is in Google search.
This being the case, the focus of AI search really should be on “inclusion in the consideration set” – not necessarily being “the first mentioned in that set” – as well as crafting what AI is saying about us.
User behavior on AI platforms differs from Google search
Over the past several months, my team has spent more than 100 hours observing people use ChatGPT and Google’s AI Mode to find services.
One thing came into focus within the first dozen or so sessions: User behavior on AI platforms differs from Google search in ways that extend far beyond using “natural language” and having conversations versus performing keyword searches.
Which is overstated, by the way. About 75% of the sessions we observed included “keyword searches.”
One key difference: Users consider more businesses in AI responses than in organic search.
It makes sense — it’s much easier to compare multiple options in a chat window than to click through three to five search results and visit each site.
Dig deeper: From searching to delegating: Adapting to AI-first search behavior
AI users don’t stop at the first result
In both Google AI Mode and ChatGPT, users considered an average of 3.7 businesses from the results.

This has strong implications for the No. 1 result – as well as No. 4.
The value of appearing first drops sharply — and the value of appearing lower rises — when, in 75% of sessions, users also consider businesses in Positions 2 to 8.
What’s driving conversions isn’t your position in that list.
Get the newsletter search marketers rely on.
  
				
								
					MktoForms2.loadForm("https://app-sj02.marketo.com", "727-ZQE-044", 16298, function(form) {
						// form.onSubmit(function(){
						// });
						
						// form.onSuccess(function (values, followUpUrl) {
						// });
					});
				
			
See terms.
Why do businesses with lower rankings end up in the consideration set in LLMs?
First of all, these aren’t rankings.
They are a list of recommendations that will likely get shuffled, reformatted from a list to a table, and completely changed, given the probabilistic nature of AI.
That aside, AI chat makes it much easier to scan and consider more options than Google search does.
Let’s look at the Google search results for “fractional CMO.”

If a user wants to evaluate multiple fractional CMO options for their startup, it’s more work to do so in Google Search than in ChatGPT.
Only two options appear above the fold, and each requires a click-through to read their website content.
Contrast this with the experience on ChatGPT.

The model gave them eight options, along with information about each one.
It’s easy to read all eight blurbs and decide whom to explore further.
Which leads to the other thing we really need to focus on: what the model is saying about you.

  
shows up.
      
Start Free Trial
    
Get started with




A bigger driver than being first on ChatGPT: Being a good fit
Many search marketers focus on rankings and traffic, but rarely on messaging and positioning.
This needs to change.
In the case of the response for an ophthalmologist in southern New Jersey, you get an easily scannable list:

Roughly 60% make their entire decision based on the response, without visiting the website or switching to Google, according to our study.
So how do you drive conversion?
Deliver the right message — and make sure the model shares it.
Dr. Lanciano may be the best glaucoma specialist in the area. But if the model highlights Ravi D. Goel and Bannett Eye Centers for glaucoma care, and that’s what the user needs, they’ll go there.
Bannett Eye Centers appears last in the AI response but may still win the conversion because of what the model says about it — something that rarely happens in Google Search.
Visibility doesn’t pay the bills. Conversions do. And conversions don’t happen when customers think someone else is a better fit.
Dig deeper: How to measure your AI search brand visibility and prove business impact
As SEOs shift toward Dig deeper: , a mindset shift needs to occur
We’re still thinking about AI search the way we’ve thought about SEO.
In SEO, the top result captures most of the traffic. In AI search, it doesn’t.
AI users consider more available options.
Responses — and their format — change dramatically with each request.
“Winning” in AI search means getting into the consideration set and being presented compellingly.
It’s not about being first on a list, especially if what’s said about you misses the mark.
In other words, SEOs who think like copywriters and salespeople will drive outcomes for their organizations.
Dig deeper: Is SEO a brand channel or a performance channel? Now it’s both

AI users consider 3.7 businesses per response, and 60% decide without clicking. Here’s what that means for your AI visibility strategy.

AI search KPIs: Focus on inclusion, not position

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/perplexity-no-ads.png" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/perplexity-no-ads.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/perplexity-no-ads-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/perplexity-no-ads-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Perplexity began testing sponsored placements in 2024. It’s abandoning them, saying labeled ads can erode credibility.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/perplexity-no-ads.png" class="attachment-large size-large wp-post-image" alt="" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/perplexity-no-ads.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/perplexity-no-ads-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/perplexity-no-ads-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Perplexity is abandoning advertising, for now at least. The company believes sponsored placements — even labeled ones — risk undermining the trust on which its AI answer engine depends.</p>



<ul class="wp-block-list">
<li>Perplexity phased out the <a href="https://searchengineland.com/perplexity-begins-testing-ads-448277">ads it began testing in 2024</a> and has no plans to bring them back, the Financial Times reported.</li>



<li>The AI search company could revisit advertising or “never ever need to do ads,” the report said.</li>
</ul>



<p><strong>Why we care.</strong> If Perplexity remains ad-free, brands lose paid access to a fast-growing audience. The company previously reported that it gets <a href="https://searchengineland.com/perplexity-780-million-monthly-queries-month-456725">780 million monthly queries</a>. With sponsored placements gone, brands have no way to get visibility inside Perplexity&#8217;s answers other than via organic citations.</p>



<p><strong>What changed.</strong> Perplexity was one of the first AI search companies to test ads, placing sponsored answers beneath chatbot responses. It said at the time that ads were clearly labeled and didn&#8217;t influence outputs. Executives now say perception matters as much as policy.</p>



<ul class="wp-block-list">
<li>“A user needs to believe this is the best possible answer,” one executive said, adding that once ads appear, users may second-guess response integrity.</li>
</ul>



<p><strong>Meanwhile.</strong> Perplexity’s exit comes as other AI platforms experiment with ads.</p>



<ul class="wp-block-list">
<li><a href="https://searchengineland.com/openai-starts-testing-chatgpt-ads-468593">OpenAI is now testing ads in ChatGPT</a> for free users, placing labeled sponsored results below answers. </li>



<li>Google runs <a href="https://searchengineland.com/google-ads-inside-ai-mode-tests-expand-464979">ads in AI Mode </a>and <a href="https://searchengineland.com/google-ads-ai-overviews-spotted-455889">AI Overviews</a> within Search, though <a href="https://searchengineland.com/google-rules-out-ads-in-gemini-for-now-467780">not in Gemini</a>. </li>



<li>Anthropic has publicly committed to <a href="https://searchengineland.com/anthropic-claude-no-ads-468232">keeping Claude ad-free</a>.</li>
</ul>



<p><strong>Perplexity says subscriptions are its core business. </strong>It offers a free tier and paid plans from $20 to $200 per month. It has more than 100 million users and about $200 million in annualized revenue, according to executives.</p>



<ul class="wp-block-list">
<li>Perplexity also introduced <a href="https://searchengineland.com/perplexity-launches-ai-powered-shopping-search-448424">shopping features</a>, but doesn&#8217;t take a cut of transactions, another indication it’s cautious about revenue models that could create conflicts of interest.</li>



<li>“We are in the accuracy business, and the business is giving the truth, the right answers,” one executive said.</li>
</ul>



<p><strong>The report. </strong><a href="https://www.ft.com/content/6eec07a5-34a8-4f78-a9ed-93ab4263d43c" target="_blank" rel="noopener">Perplexity drops advertising as it warns it will hurt trust in AI</a> (subscription required)</p>


Perplexity is abandoning advertising, for now at least. The company believes sponsored placements — even labeled ones — risk undermining the trust on which its AI answer engine depends.
Perplexity phased out the ads it began testing in 2024 and has no plans to bring them back, the Financial Times reported.
The AI search company could revisit advertising or “never ever need to do ads,” the report said.
Why we care. If Perplexity remains ad-free, brands lose paid access to a fast-growing audience. The company previously reported that it gets 780 million monthly queries. With sponsored placements gone, brands have no way to get visibility inside Perplexity’s answers other than via organic citations.
What changed. Perplexity was one of the first AI search companies to test ads, placing sponsored answers beneath chatbot responses. It said at the time that ads were clearly labeled and didn’t influence outputs. Executives now say perception matters as much as policy.
“A user needs to believe this is the best possible answer,” one executive said, adding that once ads appear, users may second-guess response integrity.
Meanwhile. Perplexity’s exit comes as other AI platforms experiment with ads.
OpenAI is now testing ads in ChatGPT for free users, placing labeled sponsored results below answers. 
Google runs ads in AI Mode and AI Overviews within Search, though not in Gemini. 
Anthropic has publicly committed to keeping Claude ad-free.
Perplexity says subscriptions are its core business. It offers a free tier and paid plans from $20 to $200 per month. It has more than 100 million users and about $200 million in annualized revenue, according to executives.
Perplexity also introduced shopping features, but doesn’t take a cut of transactions, another indication it’s cautious about revenue models that could create conflicts of interest.
“We are in the accuracy business, and the business is giving the truth, the right answers,” one executive said.
The report. Perplexity drops advertising as it warns it will hurt trust in AI (subscription required)

Perplexity began testing sponsored placements in 2024. It’s abandoning them, saying labeled ads can erode credibility.

Perplexity stops testing advertising

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/Why-answering-pricing-problems-and-comparisons-drives-AI-visibility.png" class="attachment-large size-large wp-post-image" alt="Why answering pricing, problems, and comparisons drives AI visibility" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/Why-answering-pricing-problems-and-comparisons-drives-AI-visibility.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/Why-answering-pricing-problems-and-comparisons-drives-AI-visibility-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/Why-answering-pricing-problems-and-comparisons-drives-AI-visibility-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI rewards clarity over spin. Map real buyer questions on pricing, problems, and reviews to win trust, citations, and sales.

Search behavior is no longer just people typing keywords into Google. It’s people asking questions and, in some cases, outsourcing their thinking to LLMs.
As Google evolves from a traditional search engine into a more question-and-answer machine, businesses need a robust, time-tested way to respond to customer questions.
AI changes how people research and compare options. Tasks that once felt painful and time-consuming are now easy. But there’s a catch. The machine only knows what it can find about you.
If you want visibility across the widest possible range of questions, you need to understand your customers’ wants, needs, and concerns in depth.
That’s where the “they ask, you answer” framework comes in. It helps businesses identify and create the many questions and answers prospective customers already have in mind. Always useful, it’s a practical, actionable way forward in the age of AI.
An answer-first content strategy and why it matters now
“They Ask, You Answer” (TAYA) is a book by Marcus Sheridan. (I strongly recommend you read it.)
The concept is simple: buyers have questions, and businesses should answer them honestly, clearly, and publicly — especially the ones sales teams avoid.
No dodging. No “contact us for a quote.” No “it depends” – sorry, SEO folks.
TAYA isn’t just an inbound marketing strategy. It’s a practical way to map a customer-facing content strategy with an E-E-A-T mindset.
The framework centers on five core content categories:
Pricing and cost.
Problems.
Versus and comparisons.
Reviews.
Best in class.
These categories align with the moments when a buyer is seeking the best solution, reducing risk, and making a decision.
More of those moments now happen inside AI environments — on your smartphone, your PC, in apps like ChatGPT or Gemini, or anywhere else AI shows up, which at this point is nearly everywhere.
At their core, these are question-and-answer machines. You ask. The machine answers. That’s why the TAYA process fits so well.
The modern web is chaotic. Finding what you need can be exhausting — dodging ads, navigating layers of SERP features, and avoiding pop-ups on the site you finally click.
AI is gaining ground because it feels better. Easier. Faster. Cleaner. Less chaos. More order.

  
shows up.
      
Start Free Trial
    
Get started with




Turning E-E-A-T into a practical content strategy
You could argue we already have a north star for content creation in E-E-A-T. But have you ever tried to build a content strategy around it? Great in principle, harder in practice.
They ask, you answer puts an E-E-A-T-focused content strategy on rails:
Pricing supports trust, experience, and expertise.
Problems show experience and trust.
Versus content builds authority and expertise.
Reviews build experience and trust.
Best-in-class content builds authority and trust.
E-E-A-T can be difficult to target because there are many ways to build trust, show experience, and demonstrate authority. TAYA maps those signals across multiple areas within each category, helping you build a comprehensive database of people-first content that AI readily surfaces. 
Dig deeper: How to build an effective content strategy for 2026
How to integrate TAYA with traditional SEO research
The skills and tools we use as SEOs already put us in a strong position for the AI era. They can help us build an integrated SEO, PPC, and AI strategy. 
The action plan:
Google Search Console: Go to Google Search Console > Performance. Filter queries by question modifiers such as who, what, why, how, and cost. These are your raw TAYA topics.
Google Business Profile: Review keywords and queries in your Google Business Profile for additional ideas.
The semantic map: Use AnswerThePublic or Also Asked. Look for secondary questions. If you’re writing about cost, you’ll often see related concerns such as financing or hidden fees.
The competitor gap: Use Semrush or Ahrefs Keyword Gap tools. Don’t focus on what competitors rank for. Look for “how-to” and “versus” keywords where they have no meaningful content. That’s your land grab.
Method marketing: Immerse yourself in the mindset of your ideal customer and start searching. What comes up? What does AI say? What’s missing? Tools like the Value Proposition Canvas and SCAMPER can help you evaluate these angles in structured ways.
Often, you won’t go wrong by simply searching for your own products and services. AI tools and search results will surface a wide range of questions, answers, and perspectives that can feed directly into your AI and SEO content strategy. 
Also consider the internal sources available to you:
Sales calls and sales teams.
Live chat transcripts.
Emails.
Customer service tickets.
Proposal feedback.
Complaints.
All of this helps you understand the question landscape. From there, you can begin organizing those insights within the five TAYA categories.
TAYA and your AI-era content marketing strategy
The framework centers on five core categories, reinterpreted for an answer-driven environment where Google, Gemini, and ChatGPT-like systems anticipate user needs.
For each, here’s what it is, why it matters now, and examples to get you started.
1. Pricing and cost: Why we must talk about money
Buyers want cost clarity early. Businesses avoid it because “it depends.” Both are true, but only one is useful.
AI systems will readily summarize typical costs, using someone else’s numbers if you don’t publish your own. If you fail to provide a credible range with context, you’re effectively handing the narrative to competitors, directories, or a generic blog with a stock photo handshake.
How to do it
Publish ranges, not unrealistic single prices.
Explain what drives costs up or down.
Include example packages, such as good, better, and best.
Be explicit about what’s included and excluded.
Add country-specific variables where relevant, such as tax or VAT in the UK.
Content examples
How much does [service] cost in the UK? Include price ranges and what influences them.
X vs. Y pricing: what you get at each level.
The hidden costs of [solution] and how to avoid them.
Budget checklist: what to prepare before you buy [product or service].
One of the most cited examples in the TAYA world is Yale Appliance. The company embraced transparent, buyer-focused content and saw inbound become its largest sales channel, alongside significant reported growth.
The takeaway isn’t “go sell fridges.” It’s to answer money questions more clearly and honestly than anyone else. Do that, and you build trust at scale.
2. Problems: Turning problems into strengths
This category focuses on being honest about drawbacks, limitations, risks, and who a product or service isn’t for. You have to think beyond pure SEO or GEO. 
A core communication strategy is taking a perceived weakness, such as being a small business, and reframing it as a strength, like a more personalized approach.
Own the areas that could be seen as problems. Present them clearly and constructively so customers understand the trade-offs and context.
The answer layer aims to provide balanced guidance. Pages that focus only on benefits read like marketing. Pages that acknowledge trade-offs read like advice.
People can spot spin quickly. Be direct. Own your limitations. When you do, credibility increases.
How to do it
Create problem-and-solution guides.
Include “avoid if …” sections.
Address common failure modes and misuses.
Be explicit about prerequisites, such as budget, timeline, skill, or access.
Content examples
The biggest problems with [solution] and how to mitigate them.
Is [product or service] worth it? When it’s a great choice and when it isn’t.
Common mistakes when buying or implementing [solution].
What can go wrong with [approach] and how to reduce risk.
This is where your “experience” in E-E-A-T becomes tangible. “We’ve seen this go wrong when …” carries far more weight than “we’re passionate about excellence.”
Get the newsletter search marketers rely on.
  
				
								
					MktoForms2.loadForm("https://app-sj02.marketo.com", "727-ZQE-044", 16298, function(form) {
						// form.onSubmit(function(){
						// });
						
						// form.onSuccess(function (values, followUpUrl) {
						// });
					});
				
			
See terms.
3. Versus and comparisons
People rely on comparisons to reduce cognitive load. They want clarity. What’s the difference?
Comparison queries are ideal for answer engines because they lend themselves to structured summaries, tables, and recommendations. If you don’t publish the clearest comparison, you won’t be the source used to generate the clearest answer.
How to do it
Compare by use case, not just features.
Use a consistent framework, such as price, setup, outcomes, risks, and who it suits.
Include clear guidance, such as “If you’re X, choose Y.”
Content examples
X vs. Y: which is better for [specific scenario]?
In-house vs. outsourced for [service]: cost, risk, and results.
Tool A vs. Tool B vs. Tool C: an honest comparison for UK teams.
Alternatives to [popular option]: when to choose each.
SEO bonus: These pieces tend to earn links because they’re genuinely useful and because many competitors hesitate to name alternatives directly.
Dig deeper: Chunk, cite, clarify, build: A content framework for AI search
4. Reviews, case studies, and credibility
This isn’t about asking for a five-star review. It’s about creating review-style content that helps buyers evaluate their options.
AI summaries often rely on review-style pages because they’re structured around evaluation. But generic affiliate reviews can be, at best, inconsistent in sincerity. Your advantage is first-hand experience and contextual truth.
How to do it
Review your own services honestly, including what clients value and where they struggle.
Review the tools you use with clear pros and cons.
Publish “what we’d choose and why” for different buyer types.
Content examples
Is [solution] worth it? Our honest take after implementing it for X clients.
Best [category] tools for [persona], including limitations.
The top questions to ask before choosing a [provider].
What good looks like: a checklist to evaluate [service].
If you want to be cited in AI answers, you have to sound like a source, not an ad.
5. Best in class – and the courage to recommend others
Sheridan’s view, and it’s a bold one, is that you should sometimes publish “best in class” recommendations even when the best option isn’t you. That’s how trust is built.
The answer layer rewards utility. If your page genuinely helps users choose well, it becomes the kind of resource systems are more likely to reference.
How to do it
Build “best for” lists based on clear criteria, not hype.
Explain how you evaluated the options.
Include scenarios where each option wins or loses.
Content examples
Best [solutions] for [use case] in 2026, including criteria and picks.
Best [service] providers for [industry] and what to look for.
Best budget, best premium, best for speed, best for compliance.
If I were buying this today: the decision framework I’d use.
The goal is for your brand to become a trusted educator, not just a vendor.

  
complete picture of your search visibility.
      
Start Free Trial
    
Get started with




TAYA as the playbook for answer-first visibility
Strategic content marketing in the age of AI centers on middle-of-the-funnel content, where AI helps prospects make informed decisions. The content you publish and organize on your website remains the foundation, and SEO remains the backbone of AI visibility. 
When leveraged effectively, TAYA is a powerful way to map what you should be addressing and to build a content strategy that ensures you’re represented across the AI landscape.
In practice, that means building an editorial program where:
Every piece begins with a real buyer question.
The five core categories prioritize decision-stage content, not just awareness content.
Traditional SEO research validates language and demand.
Content is written to satisfy both the human, through clarity and confidence, and the machine, through structure, specificity, evidence, and balanced trade-offs.
This shift also changes how success is measured.
In classic SEO, the win was rank, click, convert.
In the AI era, the win is often be the source, earn trust, be chosen, with or without the click.
If your content is the clearest, most in-depth, most honest, and most experience-backed explanation available for the questions buyers are already asking, then whether someone discovers it through Google, Gemini, ChatGPT, or elsewhere, you’ve built something durable.
Which is what strong SEO has always been about. The window has changed. The principles haven’t.
Dig deeper: Mentions, citations, and clicks: Your 2026 content strategy

AI rewards clarity over spin. Map real buyer questions on pricing, problems, and reviews to win trust, citations, and sales.

How to apply ‘They Ask, You Answer’ to SEO and AI visibility

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/How-to-build-AI-confidence-inside-your-SEO-team.png" class="attachment-large size-large wp-post-image" alt="How to build AI confidence inside your SEO team" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/How-to-build-AI-confidence-inside-your-SEO-team.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/How-to-build-AI-confidence-inside-your-SEO-team-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/How-to-build-AI-confidence-inside-your-SEO-team-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>From shared tool selection to celebrating wins, here are four practical ways to reduce fear and turn AI into a strategic partner.


<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/How-to-build-AI-confidence-inside-your-SEO-team.png" class="attachment-large size-large wp-post-image" alt="How to build AI confidence inside your SEO team" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/How-to-build-AI-confidence-inside-your-SEO-team.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/How-to-build-AI-confidence-inside-your-SEO-team-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/How-to-build-AI-confidence-inside-your-SEO-team-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>With more than two decades in <a href="https://searchengineland.com/guide/what-is-seo">SEO</a>, I’ve lived through every major disruption the industry has faced — from stuffing meta keywords to rank on AltaVista to Google reshaping search, to mobile-first indexing, and now AI.</p>



<p>What feels different today is the speed of change and the emotional weight it carries. I see growing pressure across teams, even among seasoned professionals who have weathered every major shift before this one.</p>



<p>Many have a legitimate concern: If AI can do this faster, where do I fit in? That’s not a technical question. It’s a human one.</p>



<p>That uncertainty affects morale and adoption. Productivity slows. Experimentation stalls. Teams either overuse AI without judgment or avoid it altogether. </p>



<p>The real leadership challenge is about building confidence, capability, and trust in AI-assisted teams.</p>



<h2 class="wp-block-heading" id="h-4-tips-for-building-ai-confidence-in-seo-teams">4 tips for building AI confidence in SEO teams</h2>



<p>Building real confidence in AI within an SEO team isn’t about deploying new tools. It’s about shifting the culture.</p>



<p>The most effective SEO teams aren’t the ones adopting the most tools. They use AI intentionally and with discipline. They automate data pulls, summarize research, and cluster keywords. This allows teams to focus on strategy, storytelling, and stakeholder alignment.</p>



<p>Technology adoption is largely cultural, as <a href="https://online.hbs.edu/blog/post/build-team-confidence-with-ai" target="_blank" rel="noopener">Harvard Business School</a> has noted. Tools alone don’t drive change. Trust does. That insight applies directly to SEO teams navigating AI today.</p>



<p>Below are four strategies for building AI confidence in your teams through clarity, participation, and shared ownership, not pressure or hype.</p>



<h3 class="wp-block-heading" id="h-1-earn-trust-by-involving-the-team-in-ai-tool-selection-and-workflow-design">1. Earn trust by involving the team in AI tool selection and workflow design</h3>



<p>A practical way to strengthen trust is to move from top-down implementation to shared ownership. People trust what they help create.</p>



<p>When AI is imposed on a team, resistance increases. Inviting people into evaluation and workflow design makes AI feel less intimidating and more empowering. Bringing teams in early also surfaces real-world insight into where AI reduces friction or introduces new risks.</p>



<p>Effective leaders:</p>



<ul class="wp-block-list">
<li>Invite teams to test tools and share feedback.</li>



<li>Run small experiments before scaling adoption.</li>



<li>Communicate clearly about what you’re adopting, what you’re rejecting, and why.</li>
</ul>



<p>When teams feel included, they’re more willing to experiment. They learn and stretch into new capabilities. That openness fuels growth and innovation.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/seo-teams-use-ai-468242">Why SEO teams need to ask ‘should we use AI?’ not just ‘can we?’</a></em></strong></p>



<h3 class="wp-block-heading" id="h-2-meet-people-where-they-are-not-where-you-want-them-to-be">2. Meet people where they are – not where you want them to be</h3>



<p>AI capability varies widely across SEO teams. Some practitioners experiment daily. Others feel overwhelmed or skeptical, often because they’ve seen past automation trends come and go.</p>



<p>Leaders who strengthen confidence understand that capability develops at different speeds. They create environments that encourage curiosity, where uncertainty is normal, and learning happens continuously, not just when it’s mandated.</p>



<p>That means:</p>



<ul class="wp-block-list">
<li>Normalizing different comfort levels.</li>



<li>Creating psychological safety around “I don’t know yet.”</li>



<li>Avoiding shame or over-celebration of early adopters.</li>



<li>Offering multiple learning paths.</li>
</ul>



<p>Recognizing different starting points makes progress feel achievable rather than threatening.</p>



<p><a href="https://www.semrush.com/lp/semrush-one/en/?utm_campaign=ic_semrush_one&amp;utm_source=searchengineland.com&amp;utm_medium=overlay&amp;onboarding=off" target="_blank" style="display: block; text-decoration: none; cursor: pointer;">
  <div style="background: radial-gradient(circle at 30% 40%, rgba(184, 111, 255, 0.15), rgba(0, 169, 255, 0.15) 40%, #CDE8FD 70%); padding: 30px; width: 100%; max-width: 802px; color: #000000 !important; font-family: Arial, sans-serif; margin: 25px 0 30px 0; border-radius: 8px; box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1); position: relative; box-sizing: border-box;">

    <div style="width: 100%; max-width: 100%; margin-bottom: 20px; text-align: left; padding-right: 20px; box-sizing: border-box;">

      <div id="semrush-one-headline" class="headline-responsive" style="font-family: Oswald, sans-serif; font-size: 30px; font-weight: normal; margin: 0; color: #000000 !important; line-height: 1.2;">
        Your customers search everywhere. Make sure your brand <span style="background: linear-gradient(90deg, #D56EFE 0%, #068EF8 51%); -webkit-background-clip: text; -webkit-text-fill-color: transparent; background-clip: text;">shows up</span>.
      </div>
      <p id="semrush-one-subhead" style="font-family: Roboto, sans-serif; font-size: 18px; font-weight: 300; line-height: 25px; margin: 12px 0 0 0; color: #000000 !important;">
        The SEO toolkit you know, plus the AI visibility data you need.
      </p>
    </div>
    <div style="margin-bottom: 15px;">
      <span id="semrush-one-cta" style="display: inline-block; background-color: #FF642D; color: white; height: 44px; border: none; border-radius: 5px; cursor: pointer; font-size: 16px; padding: 0 24px; font-weight: bold; white-space: nowrap; box-sizing: border-box; text-decoration: none; line-height: 44px;">Start Free Trial</span>
    </div>

    <div style="font-size: 12px;">
      <div style="font-family: Roboto, sans-serif; font-weight: 300; color: #000000; margin-bottom: 4px;">Get started with</div>
      <img decoding="async" src="https://searchengineland.com/wp-content/seloads/2025/11/semrush-one.webp" alt="Semrush One Logo" style="height: 16px; width: auto; display: block;">
    </div>
  </div>
</a>
<style>
  @media (max-width: 768px) {
    .headline-responsive {
      font-size: 30px !important;
      line-height: 1.3 !important;
    }
  }
</style></p>



<h3 class="wp-block-heading" id="h-3-celebrate-wins-and-highlight-champions">3. Celebrate wins and highlight champions</h3>



<p>Confidence grows through visible success.</p>



<p>When someone uses AI to cut a task from hours to minutes, it’s more than a productivity gain. It proves AI can support real work without replacing human judgment.</p>



<p>Effective teams:</p>



<ul class="wp-block-list">
<li>Share clear examples of AI improving quality and efficiency.</li>



<li>Highlight internal champions who can mentor others.</li>



<li>Create space for demos and knowledge sharing.</li>



<li>Reinforce a culture of experimentation, not judgment.</li>
</ul>



<p>My agency formed AI focus groups with members from across the organization. One group focused on integrating AI into project management, with representatives from SEO, operations, and leadership.</p>



<p>That shared ownership made adoption more successful. Teams weren’t just implementing AI; they were shaping how it fit into real workflows. The result was stronger buy-in, better collaboration, and greater confidence across the team.</p>



<p>Each group shared its successes and lessons. This built awareness of what worked and why. Momentum builds when teams see their peers using AI responsibly and effectively.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/future-seo-teams-human-led-agent-powered-463872">The future of SEO teams is human-led and agent-powered</a></em></strong></p>



<h3 class="wp-block-heading" id="h-4-frame-ai-as-a-collaborative-partner-not-a-replacement">4. Frame AI as a collaborative partner, not a replacement</h3>



<p>Fear of replacement is real. Ignoring it doesn’t make it disappear. Teams need explicit clarity about where human expertise still matters.</p>



<p>Reframing AI as a partner means emphasizing:</p>



<ul class="wp-block-list">
<li><strong><a href="https://searchengineland.com/guide/agentic-ai-in-seo">AI handles volume</a>.</strong> Humans handle nuance.</li>



<li><strong>AI accelerates analysis.</strong> Humans interpret meaning.</li>



<li><strong>AI drafts.</strong> Humans validate, refine and contextualize.</li>



<li><strong>AI scales output.</strong> Humans build trust and influence.</li>
</ul>



<p>AI can help with execution, but it <a href="https://mitsloan.mit.edu/press/new-mit-sloan-research-suggests-ai-more-likely-to-complement-not-replace-human-workers" target="_blank" rel="noopener">can’t replace strategic instincts</a>, contextual judgment, or cross-functional leadership. Those are the skills that ultimately move performance forward.</p>



<h2 class="wp-block-heading" id="h-why-experience-still-matters-in-ai-driven-seo">Why experience still matters in AI-driven SEO</h2>



<p>AI has lowered the barrier to entry for many SEO tasks. With effective prompts, almost anyone can generate keyword lists, outlines, or summaries. With that accessibility, we see many short-lived tactics and recycled “quick wins.”&nbsp;</p>



<p>Anyone who’s been in SEO long enough has seen this cycle before. The tactics change. The fundamentals don’t. This is where experience becomes the differentiator.</p>



<h3 class="wp-block-heading" id="h-ai-can-generate-outputs-not-accountability">AI can generate outputs, not accountability</h3>



<p>AI can produce content and analyze data, but it doesn’t own outcomes. It doesn’t carry responsibility for brand reputation, compliance, or long-term performance.</p>



<p>SEO professionals remain accountable for:</p>



<ul class="wp-block-list">
<li>Deciding what to exclude from publication.</li>



<li>Assessing technical, reputational, and compliance risks.</li>



<li>Weighing long-term consequences against short-term gains.</li>
</ul>



<p>AI executes. Humans decide. That distinction matters more than ever.</p>



<h3 class="wp-block-heading" id="h-pattern-recognition-is-learned-not-automated">Pattern recognition is learned, not automated</h3>



<p>AI excels at surfacing patterns. It struggles to explain why they matter or whether they apply in a specific context.</p>



<p>Experienced SEOs bring a depth of understanding that AI can’t replicate. Their historical background helps them distinguish true shifts from industry noise.&nbsp;</p>



<p>Few industries have seen as many tactics rise and fall as SEO. Experience enables strategic thinking beyond what worked before and helps avoid repeating tactics that once succeeded but later failed.</p>



<p>AI suggests possibilities. Experience evaluates relevance.</p>



<h3 class="wp-block-heading" id="h-professional-integrity-remains-a-differentiator">Professional integrity remains a differentiator</h3>



<p>In high-visibility search environments, mistakes scale quickly. AI can produce inaccuracies and hallucinations. These errors can put brands at risk of losing trust and facing compliance issues.</p>



<p>Teams with strong professional SEO foundations:</p>



<ul class="wp-block-list">
<li>Validate AI output instead of assuming correctness.</li>



<li>Prioritize accuracy over speed.</li>



<li>Maintain ethical SEO standards.</li>



<li>Protect brand voice and credibility.</li>
</ul>



<p>Integrity isn’t automated. It’s practiced. In a high-speed AI environment, that discipline matters even more.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/remote-seo-team-build-lead-464333">How to build and lead a successful remote SEO team</a></em></strong></p>



<h2 class="wp-block-heading" id="h-growing-the-seo-profession-in-an-ai-era">Growing the SEO profession in an AI era</h2>



<p>AI is accelerating SEO execution.</p>



<p>As routine tasks become automated, the SEO professional’s role shifts to strategic oversight. Time once spent on manual analysis can be redirected to interpreting user intent, shaping search strategy, guiding stakeholders, and assessing risk.</p>



<p>This makes fundamentals more important. Teams still need sound judgment, technical expertise, and accountability. AI can support execution, but professionals remain responsible for decisions, quality, and long-term performance.</p>



<p>Developing the next generation of SEOs requires more than proficiency with tools. It requires teaching:</p>



<ul class="wp-block-list">
<li>When to rely on AI.</li>



<li>When to challenge it.</li>



<li>How to apply experience and context to its output.</li>
</ul>


With more than two decades in SEO, I’ve lived through every major disruption the industry has faced — from stuffing meta keywords to rank on AltaVista to Google reshaping search, to mobile-first indexing, and now AI.
What feels different today is the speed of change and the emotional weight it carries. I see growing pressure across teams, even among seasoned professionals who have weathered every major shift before this one.
Many have a legitimate concern: If AI can do this faster, where do I fit in? That’s not a technical question. It’s a human one.
That uncertainty affects morale and adoption. Productivity slows. Experimentation stalls. Teams either overuse AI without judgment or avoid it altogether. 
The real leadership challenge is about building confidence, capability, and trust in AI-assisted teams.
4 tips for building AI confidence in SEO teams
Building real confidence in AI within an SEO team isn’t about deploying new tools. It’s about shifting the culture.
The most effective SEO teams aren’t the ones adopting the most tools. They use AI intentionally and with discipline. They automate data pulls, summarize research, and cluster keywords. This allows teams to focus on strategy, storytelling, and stakeholder alignment.
Technology adoption is largely cultural, as Harvard Business School has noted. Tools alone don’t drive change. Trust does. That insight applies directly to SEO teams navigating AI today.
Below are four strategies for building AI confidence in your teams through clarity, participation, and shared ownership, not pressure or hype.
1. Earn trust by involving the team in AI tool selection and workflow design
A practical way to strengthen trust is to move from top-down implementation to shared ownership. People trust what they help create.
When AI is imposed on a team, resistance increases. Inviting people into evaluation and workflow design makes AI feel less intimidating and more empowering. Bringing teams in early also surfaces real-world insight into where AI reduces friction or introduces new risks.
Effective leaders:
Invite teams to test tools and share feedback.
Run small experiments before scaling adoption.
Communicate clearly about what you’re adopting, what you’re rejecting, and why.
When teams feel included, they’re more willing to experiment. They learn and stretch into new capabilities. That openness fuels growth and innovation.
Dig deeper: Why SEO teams need to ask ‘should we use AI?’ not just ‘can we?’
2. Meet people where they are – not where you want them to be
AI capability varies widely across SEO teams. Some practitioners experiment daily. Others feel overwhelmed or skeptical, often because they’ve seen past automation trends come and go.
Leaders who strengthen confidence understand that capability develops at different speeds. They create environments that encourage curiosity, where uncertainty is normal, and learning happens continuously, not just when it’s mandated.
That means:
Normalizing different comfort levels.
Creating psychological safety around “I don’t know yet.”
Avoiding shame or over-celebration of early adopters.
Offering multiple learning paths.
Recognizing different starting points makes progress feel achievable rather than threatening.

  
shows up.
      
Start Free Trial
    
Get started with




3. Celebrate wins and highlight champions
Confidence grows through visible success.
When someone uses AI to cut a task from hours to minutes, it’s more than a productivity gain. It proves AI can support real work without replacing human judgment.
Effective teams:
Share clear examples of AI improving quality and efficiency.
Highlight internal champions who can mentor others.
Create space for demos and knowledge sharing.
Reinforce a culture of experimentation, not judgment.
My agency formed AI focus groups with members from across the organization. One group focused on integrating AI into project management, with representatives from SEO, operations, and leadership.
That shared ownership made adoption more successful. Teams weren’t just implementing AI; they were shaping how it fit into real workflows. The result was stronger buy-in, better collaboration, and greater confidence across the team.
Each group shared its successes and lessons. This built awareness of what worked and why. Momentum builds when teams see their peers using AI responsibly and effectively.
Dig deeper: The future of SEO teams is human-led and agent-powered
4. Frame AI as a collaborative partner, not a replacement
Fear of replacement is real. Ignoring it doesn’t make it disappear. Teams need explicit clarity about where human expertise still matters.
Reframing AI as a partner means emphasizing:
AI handles volume. Humans handle nuance.
AI accelerates analysis. Humans interpret meaning.
AI drafts. Humans validate, refine and contextualize.
AI scales output. Humans build trust and influence.
AI can help with execution, but it can’t replace strategic instincts, contextual judgment, or cross-functional leadership. Those are the skills that ultimately move performance forward.
Why experience still matters in AI-driven SEO
AI has lowered the barrier to entry for many SEO tasks. With effective prompts, almost anyone can generate keyword lists, outlines, or summaries. With that accessibility, we see many short-lived tactics and recycled “quick wins.” 
Anyone who’s been in SEO long enough has seen this cycle before. The tactics change. The fundamentals don’t. This is where experience becomes the differentiator.
AI can generate outputs, not accountability
AI can produce content and analyze data, but it doesn’t own outcomes. It doesn’t carry responsibility for brand reputation, compliance, or long-term performance.
SEO professionals remain accountable for:
Deciding what to exclude from publication.
Assessing technical, reputational, and compliance risks.
Weighing long-term consequences against short-term gains.
AI executes. Humans decide. That distinction matters more than ever.
Pattern recognition is learned, not automated
AI excels at surfacing patterns. It struggles to explain why they matter or whether they apply in a specific context.
Experienced SEOs bring a depth of understanding that AI can’t replicate. Their historical background helps them distinguish true shifts from industry noise. 
Few industries have seen as many tactics rise and fall as SEO. Experience enables strategic thinking beyond what worked before and helps avoid repeating tactics that once succeeded but later failed.
AI suggests possibilities. Experience evaluates relevance.
Professional integrity remains a differentiator
In high-visibility search environments, mistakes scale quickly. AI can produce inaccuracies and hallucinations. These errors can put brands at risk of losing trust and facing compliance issues.
Teams with strong professional SEO foundations:
Validate AI output instead of assuming correctness.
Prioritize accuracy over speed.
Maintain ethical SEO standards.
Protect brand voice and credibility.
Integrity isn’t automated. It’s practiced. In a high-speed AI environment, that discipline matters even more.
Dig deeper: How to build and lead a successful remote SEO team
Growing the SEO profession in an AI era
AI is accelerating SEO execution.
As routine tasks become automated, the SEO professional’s role shifts to strategic oversight. Time once spent on manual analysis can be redirected to interpreting user intent, shaping search strategy, guiding stakeholders, and assessing risk.
This makes fundamentals more important. Teams still need sound judgment, technical expertise, and accountability. AI can support execution, but professionals remain responsible for decisions, quality, and long-term performance.
Developing the next generation of SEOs requires more than proficiency with tools. It requires teaching:
When to rely on AI.
When to challenge it.
How to apply experience and context to its output.

From shared tool selection to celebrating wins, here are four practical ways to reduce fear and turn AI into a strategic partner.

All About
Digital Marketing

Read the Latest Trends and Headlines in Digital Marketing.

Here are some of the latest headlines on digital marketing insights, search, and ecommerce:

Free Consultation

Reach out with any questions you might have regarding how RSO can help you with digital marketing.

All About Digital Marketing

Read the Latest Trends and Headlines in Digital Marketing.

Here are some of the latest headlines on digital marketing insights, search, and ecommerce:

Free Consultation

Reach out with any questions you might have regarding how RSO can help you with digital marketing.

All About
Digital Marketing