<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/How-to-use-Performance-Planner-and-Reach-Planner-in-Google-Ads.png" class="attachment-large size-large wp-post-image" alt="How to use Performance Planner and Reach Planner in Google Ads" style="margin-bottom: 15px;" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/02/How-to-use-Performance-Planner-and-Reach-Planner-in-Google-Ads.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/How-to-use-Performance-Planner-and-Reach-Planner-in-Google-Ads-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/How-to-use-Performance-Planner-and-Reach-Planner-in-Google-Ads-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/02/How-to-use-Performance-Planner-and-Reach-Planner-in-Google-Ads-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Google is shifting away from impression-based planning, pushing advertisers toward more conversion-focused strategies.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/02/How-to-use-Performance-Planner-and-Reach-Planner-in-Google-Ads.png" class="attachment-large size-large wp-post-image" alt="How to use Performance Planner and Reach Planner in Google Ads" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/02/How-to-use-Performance-Planner-and-Reach-Planner-in-Google-Ads.png 1920w, https://searchengineland.com/wp-content/seloads/2026/02/How-to-use-Performance-Planner-and-Reach-Planner-in-Google-Ads-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/02/How-to-use-Performance-Planner-and-Reach-Planner-in-Google-Ads-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/02/How-to-use-Performance-Planner-and-Reach-Planner-in-Google-Ads-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google is narrowing the scope of its Performance Planner tool, signaling a shift toward conversion-focused campaign types and away from impression-based planning.</p>



<p><strong>What’s happening</strong>. As of last month Performance Planner no longer supports planning for Display and Video campaigns, and removes access to plans using impression share, top impression share or absolute top impression share metrics.</p>



<p><strong>Why we care. </strong>Google is deprioritizing impression-based planning, making it harder to forecast and optimize upper-funnel campaigns like Display and Video within native tools. This could mean a shift toward conversion-focused strategies and automation, meaning advertisers may need to rethink how they plan awareness campaigns and measure success outside of traditional impression share metrics.</p>



<p><strong>The big picture</strong>. Google Ads is continuing to prioritize automation and performance-driven outcomes, aligning its planning tools more closely with campaign types like Search, Shopping, App, Demand Gen, Local and Performance Max.</p>



<p><strong>How it works now</strong>. Advertisers can still use Performance Planner for supported campaign types, but any existing plans that include Display or Video campaigns — or rely on impression share metrics — can no longer be viewed or edited.</p>



<p><strong>What to watch</strong>. How advertisers adapt their forecasting and planning for upper-funnel channels like Display and Video, which now lack native support in the tool.</p>



<p><strong>Bottom line</strong>. <a href="https://support.google.com/google-ads/answer/9230124?hl=en#:~:text=goal%20drop%20down.-,How%20it%20works,and%20Performance%20Max%20impact%20estimates.">Google is doubling down on performance-driven planning</a> — and leaving impression-based strategies increasingly on the sidelines.</p>


Google is narrowing the scope of its Performance Planner tool, signaling a shift toward conversion-focused campaign types and away from impression-based planning.
What’s happening. As of last month Performance Planner no longer supports planning for Display and Video campaigns, and removes access to plans using impression share, top impression share or absolute top impression share metrics.
Why we care. Google is deprioritizing impression-based planning, making it harder to forecast and optimize upper-funnel campaigns like Display and Video within native tools. This could mean a shift toward conversion-focused strategies and automation, meaning advertisers may need to rethink how they plan awareness campaigns and measure success outside of traditional impression share metrics.
The big picture. Google Ads is continuing to prioritize automation and performance-driven outcomes, aligning its planning tools more closely with campaign types like Search, Shopping, App, Demand Gen, Local and Performance Max.
How it works now. Advertisers can still use Performance Planner for supported campaign types, but any existing plans that include Display or Video campaigns — or rely on impression share metrics — can no longer be viewed or edited.
What to watch. How advertisers adapt their forecasting and planning for upper-funnel channels like Display and Video, which now lack native support in the tool.
Bottom line. Google is doubling down on performance-driven planning — and leaving impression-based strategies increasingly on the sidelines.

Google is shifting away from impression-based planning, pushing advertisers toward more conversion-focused strategies.

Google Ads drops Display and Video planning from Performance Planner

<div><img width="1920" height="1079" src="https://searchengineland.com/wp-content/seloads/2026/04/google-discover-pipelines.png" class="attachment-large size-large wp-post-image" alt="Google Discover pipelines" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/google-discover-pipelines.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/google-discover-pipelines-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/google-discover-pipelines-1536x863.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/google-discover-pipelines-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Three months of feeds reveal how trends, news, video, and ads flow through pipelines — and why some content gets broadcast-level visibility.

<div><img width="1920" height="1079" src="https://searchengineland.com/wp-content/seloads/2026/04/google-discover-pipelines.png" class="attachment-large size-large wp-post-image" alt="Google Discover pipelines" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/google-discover-pipelines.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/google-discover-pipelines-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/google-discover-pipelines-1536x863.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/google-discover-pipelines-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Metehan Yesilyurt’s SDK analysis revealed the pipeline names. We captured months of real Discover feeds to show what each pipeline actually does — volume, reach, timing, and which publishers dominate. Here’s what 42 million cards reveal about Discover’s internal architecture.</p>



<h2 class="wp-block-heading" id="h-what-we-did">What we did</h2>



<p>Over three months (December 2025 – February 2026), we observed real Discover feeds from <strong>hundreds of devices</strong>. The result: <strong>42 million feed cards</strong> analyzed. We linked each card to the precise pipeline that selected it.</p>



<p>Some of the names were already known from the SDK, You likely <a href="https://metehan.ai/blog/google-discover-architecture/" target="_blank" rel="noopener">saw the SDK Analysis by Metehan Yesilyurt</a> already. What was missing: what each pipeline does in practice. How much content it selects, how many devices see it, how fast it operates, and which publishers it favors. That’s what our data reveals.</p>



<p>For each pipeline, we compute four metrics:</p>



<ul class="wp-block-list">
<li><strong>Reach</strong> — percentage of devices that see each URL per day</li>



<li><strong>Speed</strong> — median age of articles at time of appearance</li>



<li><strong>Exclusivity</strong> — percentage of URLs unique to that pipeline</li>



<li><strong>Volume</strong> — share of total feed</li>
</ul>



<p><strong><em>Explore all 20 pipelines visually: <a href="https://1492.vision/interactive/xpl/" target="_blank" rel="noopener">Open the interactive explorer →</a></em></strong></p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1475" height="1617" src="https://searchengineland.com/wp-content/seloads/2026/04/google-discover-20-pipelines-decoded.png" alt="" class="wp-image-473987" srcset="https://searchengineland.com/wp-content/seloads/2026/04/google-discover-20-pipelines-decoded.png 1475w, https://searchengineland.com/wp-content/seloads/2026/04/google-discover-20-pipelines-decoded-768x842.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/google-discover-20-pipelines-decoded-1401x1536.png 1401w" sizes="(max-width: 1475px) 100vw, 1475px" /><figcaption class="wp-element-caption"><em>Screenshot of the interactive explorer — EN toggle.</em></figcaption></figure>
</div>


<h2 class="wp-block-heading" id="h-not-one-algorithm-a-layered-system">Not one algorithm — a layered system</h2>



<p>The common assumption: Discover uses a single recommendation algorithm. Our data tells a different story: it’s a <strong>structured system with six functional layers</strong>, each with distinct logic, speed, and audience.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1837" height="1321" src="https://searchengineland.com/wp-content/seloads/2026/04/pipeline-map-freshness-reach.png" alt="" class="wp-image-473989" srcset="https://searchengineland.com/wp-content/seloads/2026/04/pipeline-map-freshness-reach.png 1837w, https://searchengineland.com/wp-content/seloads/2026/04/pipeline-map-freshness-reach-768x552.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/pipeline-map-freshness-reach-1536x1105.png 1536w" sizes="(max-width: 1837px) 100vw, 1837px" /><figcaption class="wp-element-caption"><em>Each pipeline positioned by speed (X axis, log) and reach (Y). neoncluster stands out at 13% reach — the highest editorial pipeline. feedads is the extreme outlier at 58.4%. Breaking pipelines (nsh, mustntmiss) cluster top-left; personalization pipelines bottom-right.</em></figcaption></figure>
</div>

<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1999" height="1276" src="https://searchengineland.com/wp-content/seloads/2026/04/top-20-category-label-by-hits.png" alt="" class="wp-image-473990" srcset="https://searchengineland.com/wp-content/seloads/2026/04/top-20-category-label-by-hits.png 1999w, https://searchengineland.com/wp-content/seloads/2026/04/top-20-category-label-by-hits-768x490.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/top-20-category-label-by-hits-1536x980.png 1536w" sizes="(max-width: 1999px) 100vw, 1999px" /><figcaption class="wp-element-caption"><em>The 20 EN pipelines ranked by total volume. content dominates at 34.2%, followed by feedads (11.1%) and aura (8.7%).</em></figcaption></figure>
</div>

<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1475" height="1617" src="https://searchengineland.com/wp-content/seloads/2026/04/google-discover-20-pipelines-decoded.png" alt="" class="wp-image-473987" srcset="https://searchengineland.com/wp-content/seloads/2026/04/google-discover-20-pipelines-decoded.png 1475w, https://searchengineland.com/wp-content/seloads/2026/04/google-discover-20-pipelines-decoded-768x842.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/google-discover-20-pipelines-decoded-1401x1536.png 1401w" sizes="(max-width: 1475px) 100vw, 1475px" /><figcaption class="wp-element-caption"><em><em>The 20 EN pipelines organized into 6 functional layers. Same structure as French, radically different proportions.</em></em></figcaption></figure>
</div>


<p>The six layers:</p>



<ol class="wp-block-list">
<li><strong>Core editorial</strong> — content (34.2% of volume), moonstone (7.8%, reach 9.4%), aura (8.7%, science/tech over-represented), paginationpanoptic (5.5%, scroll infrastructure), relatedcontentruby (6.7%, click-triggered related content).</li>



<li><strong>News urgency</strong> — mustntmiss (0.5% volume but 7.3% reach, ~2x priority boost, <strong>29% AI Overviews content</strong>) and newsstoriesheadlines (10.6% reach, Google News story clusters).</li>



<li><strong>Trends</strong> — deeptrendsfable detects, deeptrends persists. Sequential pipeline: 27% pass rate, 21-hour delay. x.com is a trend signal source even in EN.</li>



<li><strong>Local/geo</strong> — geotargetingstories (x.com dominates at 43.2% in EN), webkicklocalstories (hyperlocal UK/US press, 67% exclusive URLs), astria (BBC 29.3%, horse racing, astrology, Showcase).</li>



<li><strong>Social/video</strong> — the YouTube cascade: creatorcontent (YouTube 72.4%) → freshvideos (+15h, 94% YouTube) → neoncluster (+23h, 100% YouTube, <strong>13% reach</strong>). The cascade that doesn’t exist in French.</li>



<li><strong>Commercial</strong> — shoppinginspiration (13.1% reach, 2.5-day lifespan) and feedads (<strong>58.4% reach</strong> — the highest of any pipeline in any language).</li>



<li><strong>AI Overview</strong> — discover_ai_summary (1.1% of volume, 99.997% AI Overviews content, EN-only). Quality press: Reuters, New York Times, CNBC, Financial Times, Guardian.</li>
</ol>



<h2 class="wp-block-heading">The four EN-specific findings</h2>



<h3 class="wp-block-heading">The YouTube cascade: three pipelines, one content journey</h3>



<p>This is the most distinctive feature of the English feed. Three pipelines form a sequential amplifier:</p>



<figure class="wp-block-table"><table class="has-fixed-layout"><thead><tr><th>Stage</th><th>Pipeline</th><th>Content mix</th><th>Reach</th><th>Timing</th></tr></thead><tbody><tr><td>1. Intake</td><td>creatorcontent</td><td>72% YouTube, 23% x.com</td><td>6.7%</td><td>T₀</td></tr><tr><td>2. Filter</td><td>freshvideos</td><td>94% YouTube</td><td>7.1%</td><td>T₀ + 15h</td></tr><tr><td>3. Broadcast</td><td>neoncluster</td><td>100% YouTube</td><td>13.0%</td><td>T₀ + 23h</td></tr></tbody></table></figure>



<p>At each stage, the content narrows (from mixed to pure video) and reach increases (from 6.7% to 13%). The best YouTube content is filtered, then projected to 13% of devices — broadcast-level distribution.</p>



<p>Growth is explosive across all three stages: creatorcontent 7.8x, freshvideos 7.2x, neoncluster <strong>18x</strong> over three months. The video cascade is the fastest-growing part of Discover EN.</p>



<p>In French Discover, this cascade doesn’t exist. neoncluster has 36 hits in 3 months. The conditions — YouTube-dominant social, pure video content, broadcast audience — are only met in English.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="667" height="946" src="https://searchengineland.com/wp-content/seloads/2026/04/each-stage-narrows-content-type-while-amplifying-reach.png" alt="" class="wp-image-473991"/><figcaption class="wp-element-caption"><em>The three-stage video cascade: creatorcontent (intake, 1.9h) → freshvideos (amplifier, 8.6h) → neoncluster (broadcast, 17.3h, 13% reach). At each stage, content narrows toward pure video and reach increases.</em></figcaption></figure>
</div>


<h3 class="wp-block-heading" id="h-ai-overviews-have-landed-in-discover-but-only-in-en">AI Overviews have landed in Discover — but only in EN</h3>



<p>AI Overviews — the AI-generated summary card — has been added to Discover. But only in English.</p>



<ul class="wp-block-list">
<li><strong>discover_ai_summary</strong>: 1.1% of EN volume, 99.997% AI Overview content. Reuters (12.3%), New York Times (7.5%), CNBC (7.3%). Finance and space over-represented.</li>



<li><strong>mustntmiss</strong>: <strong>29% AI Overviews content</strong> — the highest penetration of any non-dedicated pipeline.</li>



<li><strong>paginationpanoptic</strong>: 7.8% AI Overviews — even the scroll infrastructure carries AI summaries</li>



<li><strong>In French</strong>: Almost no AI Overview hits in 3 months.</li>
</ul>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="774" height="1437" src="https://searchengineland.com/wp-content/seloads/2026/04/ai-overviews-in-google-discover.png" alt="" class="wp-image-473992" srcset="https://searchengineland.com/wp-content/seloads/2026/04/ai-overviews-in-google-discover.png 774w, https://searchengineland.com/wp-content/seloads/2026/04/ai-overviews-in-google-discover-768x1426.png 768w" sizes="(max-width: 774px) 100vw, 774px" /><figcaption class="wp-element-caption"><em>AI Overviews content rate per pipeline. discover_ai_summary at 99.997%, mustntmiss at 29%, paginationpanoptic at 7.8%. AI Overviews exist only in English Discover.</em></figcaption></figure>
</div>


<p>The AI Overviews source club is small and elite: Reuters, New York Times, CNBC, Financial Times, Guardian. Factual, structured, financial press. AI Overviews in Discover don’t democratize visibility — they concentrate it.</p>



<h3 class="wp-block-heading">feedads reaches 58% of English devices</h3>



<p>feedads is the single most powerful pipeline by reach — <strong>58.4%</strong> of EN devices see each ad. YouTube accounts for 53.7% of ads (video advertising dominates). Campaigns run for a median of 57 days. The ecosystem is hermetically sealed: 99.8% exclusive URLs.</p>



<p>For context: the highest-reach editorial pipeline (neoncluster) reaches 13%. feedads reaches 4.5x more. The EN Discover feed is heavily monetized — significantly more than French (24% reach).</p>



<h3 class="wp-block-heading">The EPL exclusion</h3>



<p>The most surprising finding. Premier League content is <strong>systematically under-represented</strong> across 7+ EN pipelines.</p>



<p>The affected terms: <em>Premier League</em>, <em>football</em>, <em>Arsenal</em>, <em>Liverpool</em>, <em>Chelsea</em>, <em>Manchester United</em>, <em>Tottenham</em>. Each shows strong negative signals in aura, deeptrendsfable, deeptrends, geotargetingstories, astria, freshvideos, and others.</p>



<p>The terms <strong>not</strong> affected: NFL, NBA, Olympics, rugby, cricket, Formula 1. The exclusion is specific to EPL.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="768" height="701" src="https://searchengineland.com/wp-content/seloads/2026/04/premier-league-systematic-exclusion-across-en-discover.png" alt="" class="wp-image-473993"/><figcaption class="wp-element-caption"><em>Premier League terms are systematically under-represented across 7+ EN pipelines. Other sports (NFL, NBA, F1) are unaffected.</em></figcaption></figure>
</div>


<p>The most likely hypothesis: EPL broadcasting rights and licensing constraints create editorial restrictions that propagate through the selection system. But we can’t confirm this — it’s an observation, not an explanation.</p>



<h2 class="wp-block-heading" id="h-three-publisher-profiles">Three publisher profiles</h2>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1844" height="1521" src="https://searchengineland.com/wp-content/seloads/2026/04/pipeline-dna-top-30-domains.png" alt="" class="wp-image-473995" srcset="https://searchengineland.com/wp-content/seloads/2026/04/pipeline-dna-top-30-domains.png 1844w, https://searchengineland.com/wp-content/seloads/2026/04/pipeline-dna-top-30-domains-768x633.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/pipeline-dna-top-30-domains-1536x1267.png 1536w" sizes="(max-width: 1844px) 100vw, 1844px" /><figcaption class="wp-element-caption"><em>Each row = a domain, each column = a pipeline family, color = percentage of hits. Find YouTube (49.9% social), Guardian, BBC, New York Times, and see their pipeline fingerprint at a glance.</em></figcaption></figure>
</div>

<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="910" height="1864" src="https://searchengineland.com/wp-content/seloads/2026/04/domain-dominance-by-pipeline.png" alt="" class="wp-image-473998" srcset="https://searchengineland.com/wp-content/seloads/2026/04/domain-dominance-by-pipeline.png 910w, https://searchengineland.com/wp-content/seloads/2026/04/domain-dominance-by-pipeline-768x1573.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/domain-dominance-by-pipeline-750x1536.png 750w" sizes="(max-width: 910px) 100vw, 910px" /><figcaption class="wp-element-caption"><em>For each pipeline, the top 5 domains and their share. neoncluster: 100% YouTube. feedads: 53.7% YouTube ads. content: YouTube, Guardian, BBC. shopping: TechRadar, Tom’s Hardware, Digital Camera World.</em></figcaption></figure>
</div>


<h3 class="wp-block-heading" id="h-quality-press-guardian-bbc-new-york-times">Quality press (Guardian, BBC, New York Times)</h3>



<p>Present in 8-10 pipelines. Guardian shows the broadest spread — top-5 in 12 different pipelines, from content and aura to newsstoriesheadlines and deeptrends. BBC dominates astria (29.3%) and deeptrends (24.7%). mustntmiss gives a ~2x priority boost, and with 29% AI Overviews content, <strong>AI Overviews-readiness is now a competitive advantage</strong> for quality press in EN.</p>



<h3 class="wp-block-heading">Tech/review site (TechRadar, Tom’s Hardware)</h3>



<p>shoppinginspiration: 13.1% reach, 2.5-day lifespan — a strong visibility window. But shopping is a silo: low co-occurrence with other pipelines. A Samsung Galaxy S25 review stays in shopping.</p>



<p>The opportunity: diversify beyond pure product testing. aura over-represents science/tech content by 2-2.4x. Adding editorial context — a trend analysis, a market comparison — can open doors to aura and content, breaking out of the shopping silo.</p>



<h3 class="wp-block-heading">Video-first publisher (YouTube channels)</h3>



<p>The cascade is a 3-stage amplifier. neoncluster at 13% reach is broadcast-level distribution. The content that makes it through: news/politics (WION, NBC — 46% international news), science, and current affairs. Entertainment and gaming are present but don’t dominate.</p>



<p>For a YouTube creator focused on news/politics/science, Discover’s cascade is one of the most powerful organic distribution channels available — and it’s growing at 18x in three months.</p>



<p>Full per-profile recommendations (quality press, tech, video, local, lifestyle, finance, pure player) will be published in our <a href="https://1492dotvision.substack.com/">Substack series</a>.</p>



<h2 class="wp-block-heading">Explore further</h2>



<p>This article is an overview. The complete analysis — 20 pipelines, per-pipeline data, domain leaders, typical titles — is available:</p>



<ul class="wp-block-list">
<li><strong>Interactive explorer</strong>: <a href="https://1492.vision/interactive/xpl/">navigate all 20 pipelines</a>, compare metrics, see top domains and typical titles</li>



<li><strong>EN Substack</strong>: <a href="https://1492dotvision.substack.com/">weekly deep-dives</a> with data, charts, and recommendations</li>



<li><strong>Full reference</strong>: <a href="https://1492.vision/research/">1492.vision</a> — the complete pipeline-by-pipeline analysis, with 3 detailled articles.</li>
</ul>



<p>The Discover system evolves. These findings are a snapshot from December 2025 to February 2026. The video cascade that didn’t exist in December already represents 13% of EN reach in February. Monitoring the evolution — not just photographing a moment — is where the real advantage lies.</p>



<p><em>Data: 42 million Discover cards, December 2025 – February 2026. Analysis: 1492.vision. Credit to Metehan Yesilyurt for the Google SDK analysis — our data shows what each pipeline does in practice.</em><br></p>


Metehan Yesilyurt’s SDK analysis revealed the pipeline names. We captured months of real Discover feeds to show what each pipeline actually does — volume, reach, timing, and which publishers dominate. Here’s what 42 million cards reveal about Discover’s internal architecture.
What we did
Over three months (December 2025 – February 2026), we observed real Discover feeds from hundreds of devices. The result: 42 million feed cards analyzed. We linked each card to the precise pipeline that selected it.
Some of the names were already known from the SDK, You likely saw the SDK Analysis by Metehan Yesilyurt already. What was missing: what each pipeline does in practice. How much content it selects, how many devices see it, how fast it operates, and which publishers it favors. That’s what our data reveals.
For each pipeline, we compute four metrics:
Reach — percentage of devices that see each URL per day
Speed — median age of articles at time of appearance
Exclusivity — percentage of URLs unique to that pipeline
Volume — share of total feed
Explore all 20 pipelines visually: Open the interactive explorer →
Screenshot of the interactive explorer — EN toggle.



Not one algorithm — a layered system
The common assumption: Discover uses a single recommendation algorithm. Our data tells a different story: it’s a structured system with six functional layers, each with distinct logic, speed, and audience.
Each pipeline positioned by speed (X axis, log) and reach (Y). neoncluster stands out at 13% reach — the highest editorial pipeline. feedads is the extreme outlier at 58.4%. Breaking pipelines (nsh, mustntmiss) cluster top-left; personalization pipelines bottom-right.



The 20 EN pipelines ranked by total volume. content dominates at 34.2%, followed by feedads (11.1%) and aura (8.7%).



The 20 EN pipelines organized into 6 functional layers. Same structure as French, radically different proportions.



The six layers:
Core editorial — content (34.2% of volume), moonstone (7.8%, reach 9.4%), aura (8.7%, science/tech over-represented), paginationpanoptic (5.5%, scroll infrastructure), relatedcontentruby (6.7%, click-triggered related content).
News urgency — mustntmiss (0.5% volume but 7.3% reach, ~2x priority boost, 29% AI Overviews content) and newsstoriesheadlines (10.6% reach, Google News story clusters).
Trends — deeptrendsfable detects, deeptrends persists. Sequential pipeline: 27% pass rate, 21-hour delay. x.com is a trend signal source even in EN.
Local/geo — geotargetingstories (x.com dominates at 43.2% in EN), webkicklocalstories (hyperlocal UK/US press, 67% exclusive URLs), astria (BBC 29.3%, horse racing, astrology, Showcase).
Social/video — the YouTube cascade: creatorcontent (YouTube 72.4%) → freshvideos (+15h, 94% YouTube) → neoncluster (+23h, 100% YouTube, 13% reach). The cascade that doesn’t exist in French.
Commercial — shoppinginspiration (13.1% reach, 2.5-day lifespan) and feedads (58.4% reach — the highest of any pipeline in any language).
AI Overview — discover_ai_summary (1.1% of volume, 99.997% AI Overviews content, EN-only). Quality press: Reuters, New York Times, CNBC, Financial Times, Guardian.
The four EN-specific findings
The YouTube cascade: three pipelines, one content journey
This is the most distinctive feature of the English feed. Three pipelines form a sequential amplifier:

StagePipelineContent mixReachTiming

1. Intakecreatorcontent72% YouTube, 23% x.com6.7%T₀
2. Filterfreshvideos94% YouTube7.1%T₀ + 15h
3. Broadcastneoncluster100% YouTube13.0%T₀ + 23h





At each stage, the content narrows (from mixed to pure video) and reach increases (from 6.7% to 13%). The best YouTube content is filtered, then projected to 13% of devices — broadcast-level distribution.
Growth is explosive across all three stages: creatorcontent 7.8x, freshvideos 7.2x, neoncluster 18x over three months. The video cascade is the fastest-growing part of Discover EN.
In French Discover, this cascade doesn’t exist. neoncluster has 36 hits in 3 months. The conditions — YouTube-dominant social, pure video content, broadcast audience — are only met in English.
The three-stage video cascade: creatorcontent (intake, 1.9h) → freshvideos (amplifier, 8.6h) → neoncluster (broadcast, 17.3h, 13% reach). At each stage, content narrows toward pure video and reach increases.



AI Overviews have landed in Discover — but only in EN
AI Overviews — the AI-generated summary card — has been added to Discover. But only in English.
discover_ai_summary: 1.1% of EN volume, 99.997% AI Overview content. Reuters (12.3%), New York Times (7.5%), CNBC (7.3%). Finance and space over-represented.
mustntmiss: 29% AI Overviews content — the highest penetration of any non-dedicated pipeline.
paginationpanoptic: 7.8% AI Overviews — even the scroll infrastructure carries AI summaries
In French: Almost no AI Overview hits in 3 months.
AI Overviews content rate per pipeline. discover_ai_summary at 99.997%, mustntmiss at 29%, paginationpanoptic at 7.8%. AI Overviews exist only in English Discover.



The AI Overviews source club is small and elite: Reuters, New York Times, CNBC, Financial Times, Guardian. Factual, structured, financial press. AI Overviews in Discover don’t democratize visibility — they concentrate it.
feedads reaches 58% of English devices
feedads is the single most powerful pipeline by reach — 58.4% of EN devices see each ad. YouTube accounts for 53.7% of ads (video advertising dominates). Campaigns run for a median of 57 days. The ecosystem is hermetically sealed: 99.8% exclusive URLs.
For context: the highest-reach editorial pipeline (neoncluster) reaches 13%. feedads reaches 4.5x more. The EN Discover feed is heavily monetized — significantly more than French (24% reach).
The EPL exclusion
The most surprising finding. Premier League content is systematically under-represented across 7+ EN pipelines.
The affected terms: Premier League, football, Arsenal, Liverpool, Chelsea, Manchester United, Tottenham. Each shows strong negative signals in aura, deeptrendsfable, deeptrends, geotargetingstories, astria, freshvideos, and others.
The terms not affected: NFL, NBA, Olympics, rugby, cricket, Formula 1. The exclusion is specific to EPL.
Premier League terms are systematically under-represented across 7+ EN pipelines. Other sports (NFL, NBA, F1) are unaffected.



The most likely hypothesis: EPL broadcasting rights and licensing constraints create editorial restrictions that propagate through the selection system. But we can’t confirm this — it’s an observation, not an explanation.
Three publisher profiles
Each row = a domain, each column = a pipeline family, color = percentage of hits. Find YouTube (49.9% social), Guardian, BBC, New York Times, and see their pipeline fingerprint at a glance.



For each pipeline, the top 5 domains and their share. neoncluster: 100% YouTube. feedads: 53.7% YouTube ads. content: YouTube, Guardian, BBC. shopping: TechRadar, Tom’s Hardware, Digital Camera World.



Quality press (Guardian, BBC, New York Times)
Present in 8-10 pipelines. Guardian shows the broadest spread — top-5 in 12 different pipelines, from content and aura to newsstoriesheadlines and deeptrends. BBC dominates astria (29.3%) and deeptrends (24.7%). mustntmiss gives a ~2x priority boost, and with 29% AI Overviews content, AI Overviews-readiness is now a competitive advantage for quality press in EN.
Tech/review site (TechRadar, Tom’s Hardware)
shoppinginspiration: 13.1% reach, 2.5-day lifespan — a strong visibility window. But shopping is a silo: low co-occurrence with other pipelines. A Samsung Galaxy S25 review stays in shopping.
The opportunity: diversify beyond pure product testing. aura over-represents science/tech content by 2-2.4x. Adding editorial context — a trend analysis, a market comparison — can open doors to aura and content, breaking out of the shopping silo.
Video-first publisher (YouTube channels)
The cascade is a 3-stage amplifier. neoncluster at 13% reach is broadcast-level distribution. The content that makes it through: news/politics (WION, NBC — 46% international news), science, and current affairs. Entertainment and gaming are present but don’t dominate.
For a YouTube creator focused on news/politics/science, Discover’s cascade is one of the most powerful organic distribution channels available — and it’s growing at 18x in three months.
Full per-profile recommendations (quality press, tech, video, local, lifestyle, finance, pure player) will be published in our Substack series.
Explore further
This article is an overview. The complete analysis — 20 pipelines, per-pipeline data, domain leaders, typical titles — is available:
Interactive explorer: navigate all 20 pipelines, compare metrics, see top domains and typical titles
EN Substack: weekly deep-dives with data, charts, and recommendations
Full reference: 1492.vision — the complete pipeline-by-pipeline analysis, with 3 detailled articles.
The Discover system evolves. These findings are a snapshot from December 2025 to February 2026. The video cascade that didn’t exist in December already represents 13% of EN reach in February. Monitoring the evolution — not just photographing a moment — is where the real advantage lies.
Data: 42 million Discover cards, December 2025 – February 2026. Analysis: 1492.vision. Credit to Metehan Yesilyurt for the Google SDK analysis — our data shows what each pipeline does in practice.

Three months of feeds reveal how trends, news, video, and ads flow through pipelines — and why some content gets broadcast-level visibility.

Inside Google Discover: 20 pipelines, 42 million cards, and what they mean for publishers

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/dell-agentic-ai-search.png" class="attachment-large size-large wp-post-image" alt="Dell agentic AI search" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/dell-agentic-ai-search.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/dell-agentic-ai-search-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/dell-agentic-ai-search-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/dell-agentic-ai-search-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Early data shows AI drives discovery, not sales. Dell says a great search experience still matters most for ecommerce success.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/dell-agentic-ai-search.png" class="attachment-large size-large wp-post-image" alt="Dell agentic AI search" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/dell-agentic-ai-search.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/dell-agentic-ai-search-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/dell-agentic-ai-search-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/dell-agentic-ai-search-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Traffic from agentic AI sources is rising at Dell, but the impact remains minimal and inconsistent, according to the company’s ecommerce lead.</p>



<p><strong>The details</strong>. Dell is seeing increased visits from platforms like ChatGPT, Perplexity, and Claude, according to Breanna Fowler, head of global consumer revenue programs. But the growth isn&#8217;t &#8220;earth-shaking,” and agentic shopping has yet to deliver meaningful results, Fowler told Digital Commerce 360.</p>



<ul class="wp-block-list">
<li>Dell is still testing how to integrate with LLM-driven shopping, with efforts in early proof-of-concept stages and internal debate over long-term strategy, Fowler said.</li>



<li>Fowler expects agentic AI to function more like an aggregation layer — similar to travel sites or delivery platforms — rather than a primary purchasing channel.</li>



<li>Fowler doesn’t expect consumers to adopt agentic shopping en masse for transactions, at least in the near term.</li>
</ul>



<p><strong>Agentic AI vs. search.</strong> Fowler said that, with or without LLMs and agentic commerce, ecommerce sites “can do the most good for their customers” through a “really great search experience.”</p>



<ul class="wp-block-list">
<li>“If I can’t find your products easily and effortlessly, no amount of content and configurator capabilities — nobody really gives a crap about that stuff,” she said.</li>
</ul>



<p><strong>Why we care</strong>. Agentic AI is emerging as a discovery layer, but it hasn’t shown signs of replacing core search behavior. You still win or lose on how easily products can be found, whether by humans or AI agents.</p>



<p><strong>The context.</strong> Dell ranks highly in emerging AI-driven discovery metrics, despite not being among the largest ecommerce players.</p>



<ul class="wp-block-list">
<li>That mismatch suggests AI surfaces may reward different product types or content structures than traditional search.</li>
</ul>



<p><strong>Bottom line. </strong>Agentic AI is sending more traffic, but it behaves like a top-of-funnel channel, not a conversion engine. Search — especially on-site — remains the primary driver of ecommerce performance.</p>



<p><strong>The report.</strong> <a href="https://www.digitalcommerce360.com/2026/04/08/dell-agentic-ai-use-case/" target="_blank" rel="noopener">Dell use case for agentic AI could revolve around search rather than commerce</a></p>


Traffic from agentic AI sources is rising at Dell, but the impact remains minimal and inconsistent, according to the company’s ecommerce lead.
The details. Dell is seeing increased visits from platforms like ChatGPT, Perplexity, and Claude, according to Breanna Fowler, head of global consumer revenue programs. But the growth isn’t “earth-shaking,” and agentic shopping has yet to deliver meaningful results, Fowler told Digital Commerce 360.
Dell is still testing how to integrate with LLM-driven shopping, with efforts in early proof-of-concept stages and internal debate over long-term strategy, Fowler said.
Fowler expects agentic AI to function more like an aggregation layer — similar to travel sites or delivery platforms — rather than a primary purchasing channel.
Fowler doesn’t expect consumers to adopt agentic shopping en masse for transactions, at least in the near term.
Agentic AI vs. search. Fowler said that, with or without LLMs and agentic commerce, ecommerce sites “can do the most good for their customers” through a “really great search experience.”
“If I can’t find your products easily and effortlessly, no amount of content and configurator capabilities — nobody really gives a crap about that stuff,” she said.
Why we care. Agentic AI is emerging as a discovery layer, but it hasn’t shown signs of replacing core search behavior. You still win or lose on how easily products can be found, whether by humans or AI agents.
The context. Dell ranks highly in emerging AI-driven discovery metrics, despite not being among the largest ecommerce players.
That mismatch suggests AI surfaces may reward different product types or content structures than traditional search.
Bottom line. Agentic AI is sending more traffic, but it behaves like a top-of-funnel channel, not a conversion engine. Search — especially on-site — remains the primary driver of ecommerce performance.
The report. Dell use case for agentic AI could revolve around search rather than commerce

Early data shows AI drives discovery, not sales. Dell says a great search experience still matters most for ecommerce success.

Dell: Agentic AI is growing, but search still wins

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/09/youtube-ai.png" class="attachment-large size-large wp-post-image" alt="YouTube AI citations" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/09/youtube-ai.png 1920w, https://searchengineland.com/wp-content/seloads/2025/09/youtube-ai-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/09/youtube-ai-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>YouTube’s move toward longer TV-style ads signals a shift to more premium inventory for brand-focused campaigns.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/09/youtube-ai.png" class="attachment-large size-large wp-post-image" alt="YouTube AI citations" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/09/youtube-ai.png 1920w, https://searchengineland.com/wp-content/seloads/2025/09/youtube-ai-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/09/youtube-ai-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>YouTube is pushing further into traditional TV-style advertising, signaling a shift that could reshape the viewing experience — and attract bigger brand budgets.</p>



<p><strong>What’s happening</strong>. Some TV viewers are being shown ads up to 90 seconds long before they can skip, a significant jump from the 30-second unskippable formats introduced recently.</p>



<p><strong>How it works</strong>. The longer ad blocks appear primarily on TV devices and may exceed 90 seconds in total length, with the skip option only becoming available after that initial window.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1080" height="1440" src="https://searchengineland.com/wp-content/seloads/2026/04/so-it-has-begun-v0-8zv2769yzttg1.webp" alt="" class="wp-image-474079" srcset="https://searchengineland.com/wp-content/seloads/2026/04/so-it-has-begun-v0-8zv2769yzttg1.webp 1080w, https://searchengineland.com/wp-content/seloads/2026/04/so-it-has-begun-v0-8zv2769yzttg1-768x1024.webp 768w" sizes="(max-width: 1080px) 100vw, 1080px" /></figure>



<p><strong>Why we care.</strong> YouTube is creating more premium, TV-like ad inventory that allows for longer, more impactful storytelling on the big screen. This opens the door for brand advertisers to run campaigns similar to traditional TV but with digital targeting and measurement. As Google pushes further into connected TV, budgets may increasingly shift toward YouTube as a core channel for reach and brand awareness.</p>



<p><strong>Zoom in</strong>. Early reports suggest the format is not tied to video length, appearing on both short and long content, and is currently limited to TV audiences rather than mobile or desktop.</p>



<p><strong>User reaction</strong>. Feedback so far has been largely negative, with viewers criticizing the longer interruptions and exploring alternatives like ad blockers or third-party clients.</p>



<p><strong>Context</strong>. The test follows recent efforts to monetize more aggressively, including new ad formats and the rollout of a lighter subscription tier offering reduced ads.</p>



<p><strong>What to watch</strong>. Whether YouTube expands the format beyond TV and how it balances ad load with user retention.</p>



<p><strong>Bottom line</strong>. <a href="https://9to5google.com/2026/04/08/youtube-starts-showing-90-second-unskippable-ads-to-tv-viewers/">YouTube is leaning into its role as a TV platform</a> — and longer, less skippable ads may be part of the tradeoff.</p>



<p><strong>What Google are saying.</strong> Google released a statement on <a href="https://x.com/TeamYouTube/status/2042252676709134424?s=20">X</a> saying that they do not have a 90 second ad format. </p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><a href="https://x.com/TeamYouTube/status/2042252676709134424?s=20"><img decoding="async" width="616" height="159" src="https://searchengineland.com/wp-content/seloads/2026/04/YouTube-statement.png" alt="" class="wp-image-474081"/></a></figure>
</div>

YouTube is pushing further into traditional TV-style advertising, signaling a shift that could reshape the viewing experience — and attract bigger brand budgets.
What’s happening. Some TV viewers are being shown ads up to 90 seconds long before they can skip, a significant jump from the 30-second unskippable formats introduced recently.
How it works. The longer ad blocks appear primarily on TV devices and may exceed 90 seconds in total length, with the skip option only becoming available after that initial window.




Why we care. YouTube is creating more premium, TV-like ad inventory that allows for longer, more impactful storytelling on the big screen. This opens the door for brand advertisers to run campaigns similar to traditional TV but with digital targeting and measurement. As Google pushes further into connected TV, budgets may increasingly shift toward YouTube as a core channel for reach and brand awareness.
Zoom in. Early reports suggest the format is not tied to video length, appearing on both short and long content, and is currently limited to TV audiences rather than mobile or desktop.
User reaction. Feedback so far has been largely negative, with viewers criticizing the longer interruptions and exploring alternatives like ad blockers or third-party clients.
Context. The test follows recent efforts to monetize more aggressively, including new ad formats and the rollout of a lighter subscription tier offering reduced ads.
What to watch. Whether YouTube expands the format beyond TV and how it balances ad load with user retention.
Bottom line. YouTube is leaning into its role as a TV platform — and longer, less skippable ads may be part of the tradeoff.
What Google are saying. Google released a statement on X saying that they do not have a 90 second ad format.

YouTube’s move toward longer TV-style ads signals a shift to more premium inventory for brand-focused campaigns.

YouTube 90-second unskippable ads on TVs spotted

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/How-to-measure-intent-gaps-using-your-Google-Search-Console-data.png" class="attachment-large size-large wp-post-image" alt="How to measure intent gaps using your Google Search Console data" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/How-to-measure-intent-gaps-using-your-Google-Search-Console-data.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/How-to-measure-intent-gaps-using-your-Google-Search-Console-data-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/How-to-measure-intent-gaps-using-your-Google-Search-Console-data-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/How-to-measure-intent-gaps-using-your-Google-Search-Console-data-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Use this free tool to compare your page’s positioning with real search demand using GSC data, and see where it aligns or falls short.

There’s often a disconnect between what a webpage says it’s about and what its audience is actually searching for. 
This mismatch has always existed. But the stakes are higher now. 
If your page fails to match user intent, it won’t show up in AI-powered search surfaces. Search engines will find a page that delivers.
You can see the mismatch, but it’s hard to quantify. The data to measure it is already in your Google Search Console account. Below, you can analyze your own pages to see how closely your content aligns with what your audience is searching for.
Measuring the gap between positioning and demand
Most web content today is designed to accommodate multiple target audiences, tens or hundreds of keywords, and brand positioning. As a result, it drifts away from the problems people are trying to solve.
I’ve had this argument many times and learned that observations create interesting conversations, but numbers create urgency and action. In this case, the numbers you need are already in your data, and the intent gap analysis tool uses that data to measure them.
Google Search Console captures what your audience searches for when they find each page. The meta description captures what the page says it’s about. One is demand. The other is positioning.
Intent gap analysis scores the distance between your meta description and your audience’s queries. Vector embeddings make that score possible by measuring meaning rather than just matching words. The result is a single intent gap score (0-100) that shows how well your page aligns with what your audience is searching for. 

  
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Connecting positioning to demand
Google’s Search Central documentation describes the meta description as “a pitch that convinces the user that the page is exactly what they’re looking for.” 
The meta description also functions as a machine-readable signal. LLMs and generative engines consume it as a compact summary of what the page claims to deliver.
Achieving “durable visibility in AI ecosystems” requires “consistent metadata, provenance, and trust signals that can be interpreted by search crawlers and generative engines,” IDC’s December 2025 Market Note on brand visibility found.
Scoring a page’s meta description requires an anchor in audience behavior. Google Search Console provides that anchor — the queries where Google chose to surface your page, regardless of whether the page was built for that intent.
The intent gap analysis tool expresses the gap as a score. In the sample analysis below of LumonHR, a fictional SaaS platform inspired by Severance, the homepage scores a 32. 
The meta description uses vague aspirational language that doesn’t match the functional, software-focused queries driving traffic. The page isn’t attracting the audience it targeted.
LumonHR’s homepage scores a 32 out of 100. The colored bar shows how impressions distribute across topic clusters.



Dig deeper: How to use AI to diagnose and improve search intent alignment
Why intent is measurable now
Search engines now use vector embeddings as a core part of how they match content to queries. Intent matching runs on meaning, not just keywords. When a user searches, the engine embeds the query and compares it against content candidates in a shared vector space. 
Semantic similarity is one of the signals that determines whether your page gets surfaced, cited, or used to generate an answer, alongside authority, trust, freshness, and other ranking factors.
Vector embeddings let you see your page the way a search engine does.
Where existing tools stop
N-gram analysis and TF-IDF have been the standard tools for analyzing search queries. N-grams surface recurring phrases, revealing the vocabulary your audience uses. TF-IDF highlights which terms matter most in your query set. 
These approaches match words, not meaning. “Setting boundaries between office and personal time” and “maintaining employee work-life balance” share zero words. To a word-matching tool, they’re separate topics. To a search engine running on embeddings, they express the same intent. 
When brands match words and search engines match intent, you’re working at a disadvantage.
Measuring meaning, not words
Vector embeddings encode meaning. An embedding converts text into numbers, allowing you to create a map of relationships rather than a list of terms. When two pieces of text mean similar things, their vectors land close together in a shared mathematical space.
Once your meta description and your audience’s queries are plotted in the same space, the distance between them is measurable. 
Queries close to the meta description align with the page’s positioning. Queries far from it represent demand the page wasn’t built for. That distance is the intent gap score.
The map below breaks the intent gap into clusters, showing where your page aligns with audience demand and where it doesn’t.
LumonHR’s query clusters mapped by the relationship between positioning and demand.



Dig deeper: SEO gap analysis: How to find content and keyword gaps
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See terms.
What the intent gap reveals
Clustering your queries into topics reveals which audiences the page is reaching and which it’s missing. Each cluster has two properties: 
How closely it aligns with the meta description.
How much search demand it carries. 
Those two dimensions place every cluster into one of four quadrants: defend, create, optimize, or monitor.
Defend
High alignment, high demand. The audience is finding your page for the reasons you built it, and in volume. This is where your topical authority lives.
Protect and reinforce. Keep the content current, and update the meta description if the language has drifted from how the audience phrases their searches.
Create
Low alignment, high demand. The audience is arriving with intent the page was never built to serve. This is demand you’re visible for but not capturing.
Create new content for the clusters that fit your strategy, using the language your audience is already using. Ignore the ones that don’t. Each cluster that passes the filter is a signal for new content.
Optimize
High alignment, low demand. The page matches what these searchers need, but few are finding it. The content is right. The visibility isn’t.
Investigate the constraint. The alignment is there, but the audience is small. Rankings may be too low, the positioning too narrow, or the topic may need supporting content to grow.
Monitor
Low alignment, low demand. Some clusters may grow into Create or Optimize territory over time.
Watch for growth. This is often where emerging topics are first detected. If demand increases, re-evaluate.
Query clusters analyzed, scored, and assigned a recommended action.



Dig deeper: How and why to ‘be the primary source’ for organic search
Your data, your score: Running the intent gap analysis
Here’s the tool and how to run the analysis on your own pages.

Step 1: Export your page data
In Google Search Console, navigate to Performance > Search results, filter by a single page, and export as a .zip file. 
Step 2: Upload and score
Upload the .zip file to the tool (your data is not stored) to get your intent gap score. The tool scrapes the meta description, scores every query against it, and clusters the results. 
Step 3: Explore the map
Each cluster is plotted by alignment and demand. Click any bubble to see the individual queries with clicks, impressions, CTR, and position.
Step 4: Review the breakdown
Every cluster in one view with its quadrant, alignment score, and performance metrics.
Step 5: Get rewrite recommendations
The tool generates recommended changes to your page’s title and meta description, grounded in the search language from your highest-demand clusters.
Step 6: Share your results
Download the table as CSV or use the “Copy as Image” buttons to share individual views with your team.
Sample suggested title and meta description revisions based on intent gap findings.



Dig deeper: How to master user intent with SEO personas

  
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Turning the score into a decision
The intent gap score assigns a number to the disconnect, and that number gives it traction. It turns observations into actions you can take in stakeholder conversations, whether that means changing a page or defending it.
Your audience is already telling you what they need. That signal is always shifting. Now you can monitor it, measure it, and close the gap.
The tool featured in this article was created by Robin Tully, co-founder at Forecast.ing.

Use this free tool to compare your page’s positioning with real search demand using GSC data, and see where it aligns or falls short.

How to measure intent gaps using Google Search Console data

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/ai-prompt-engine.png" class="attachment-large size-large wp-post-image" alt="AI prompt engine" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/ai-prompt-engine.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/ai-prompt-engine-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/ai-prompt-engine-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/ai-prompt-engine-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Turn AI into a strategic ad partner with prompts that help reveal buyer emotions, high-intent audiences, and objections.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/ai-prompt-engine.png" class="attachment-large size-large wp-post-image" alt="AI prompt engine" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/ai-prompt-engine.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/ai-prompt-engine-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/ai-prompt-engine-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/ai-prompt-engine-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Many of us use various generative AI tools to generate marketing ideas and improve ad campaign outcomes.</p>



<p>Prompting can be a powerful alternative to working solo or brainstorming with colleagues. It improves productivity and expands your options.</p>



<p>In this article, I’ll cover some of my favorite marketing prompts for ad campaigns. Use these suggestions to spark ideas for your own prompts.</p>



<h2 class="wp-block-heading" id="h-why-use-prompts-for-online-ads">Why use prompts for online ads?</h2>



<p>Prompts quickly give you a range of ad elements — triggers, emotions, actions, and audiences.</p>



<p>You can often repurpose prompt outputs across channels and initiatives — ads, email, landing pages, social media, and offers.</p>



<p>When you get closer to optimal campaigns from the start, you save cycles. That’s especially useful for lower-budget efforts that take longer to generate feedback.</p>



<p>The prompts themselves matter. You need to ask strong questions to get useful output from large language models (LLMs).</p>



<p>Feeling stumped? Ask AI tools which prompts they recommend for your situation.</p>



<p>Or use mine. Here are several prompts I use for online ads.</p>



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<h2 class="wp-block-heading" id="h-emotional-trigger-prompt">Emotional trigger prompt</h2>



<p>Purchases are often emotional, so it helps to understand your buyers’ emotions.</p>



<p>Use this prompt: “What are the top emotional triggers that would make X audience buy Y product?”</p>



<p>For example, I asked which three emotional triggers would make parents buy math learning software for their kids. The LLM identified key triggers and provided hooks and language with scarcity and urgency:</p>



<ul class="wp-block-list">
<li><strong>Fear of falling behind:</strong> Anxiety and a protective instinct. Example: “Make sure your child never falls behind in math.”</li>



<li><strong>Desire to give kids a competitive advantage:</strong> Ambition and pride. Example: “Give your kid the math skills top students develop years ahead.”</li>



<li><strong>Relief from homework stress at home:</strong> Relief and peace of mind. Example: “No more math homework battles at home.”</li>
</ul>



<h2 class="wp-block-heading" id="h-purchase-intent-prompt">Purchase intent prompt</h2>



<p>Ask these questions to understand who is ready to buy your product or service:</p>



<ul class="wp-block-list">
<li>Who is most likely to buy immediately?</li>



<li>Who needs convincing?</li>



<li>Who will never buy?</li>
</ul>



<p>To avoid wasted ad spend, focus on audiences likely to purchase and avoid those who won’t.</p>



<p>Keep asking which audiences are most likely to convert. Use the LLM’s rationale for more specific inputs for your ads.</p>



<p>In the math software example, the LLM suggested parents of kids struggling in math would convert best, citing high urgency and low friction.</p>



<p>The second-best group was homeschooling parents, who are motivated because they manage the full curriculum.</p>



<p id="h-">We were already targeting parents of kids struggling in math, but hadn’t considered homeschooling parents. From there, it was easy to create ads and test whether that audience drove conversions.</p>



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<h2 class="wp-block-heading" id="h-overcoming-objections-prompt">Overcoming objections prompt</h2>



<p>Overcoming objections is key to making sales. Ask for three to five objections someone might have to buying your product.</p>



<p>In the math software example, the LLM identified these objections:</p>



<ul class="wp-block-list">
<li>My child already has too much screen time</li>



<li>Will this actually improve my child’s math skills?</li>



<li>It’s too expensive</li>
</ul>



<p>Then write a persuasive rebuttal for each using logic, emotion, and proof. For “it’s too expensive,” you could use:</p>



<ul class="wp-block-list">
<li><strong>Logic:</strong> “Less than the cost of a tutor.” This sets a higher anchor, making the price feel more reasonable without calling it inexpensive.</li>



<li><strong>Emotion:</strong> “Don’t let your kids fall behind in math.”</li>



<li><strong>Proof:</strong> “80% of students improve by one letter grade in two months.”</li>
</ul>



<h2 class="wp-block-heading" id="h-psychological-profile-prompt">Psychological profile prompt</h2>



<p>Ask an LLM for a detailed psychological profile of your ideal customer. Use questions like:</p>



<ul class="wp-block-list">
<li>What are your ideal customer’s fears?</li>



<li>What are your ideal customer’s frustrations?</li>



<li>What do your ideal customers envy?</li>



<li>What do they pretend doesn’t bother them?</li>



<li>What keeps them up at night?</li>
</ul>



<p>In the math software example, I asked: “What or who do my ideal customers envy?”</p>



<p>One answer suggested parents envy kids in enrichment programs or advanced classes, reflecting a desire for future opportunities.</p>



<p>A message for this target audience: “Help your child stay ahead instead of playing catchup.”</p>



<h2 class="wp-block-heading" id="h-the-lifetime-value-prompt">The lifetime value prompt</h2>



<p>To thrive long term, focus on customer lifetime value (LTV), not one-and-done sales.</p>



<p>Ask questions like:</p>



<ul class="wp-block-list">
<li>Why might your customers stay longer?</li>



<li>Why might your customers buy more?</li>



<li>What retention strategies work best?</li>
</ul>



<p>For a higher-end furniture brand, we expanded these into a short playbook to increase LTV. The LLM grouped ideas under: “Shift from a transactional relationship to a long-term design partner.”</p>



<p>For example, it suggested segmenting your customer base and using direct mail for your highest-potential group (sending a lookbook). It’s unexpected and sounds old-school, but the potential for higher LTV than broad, generic mailings makes it worth testing.</p>



<p>Your clients value clear priorities and strategic thinking, not just execution. We value that AI tools can support strategy, not just tactics.</p>



<h2 class="wp-block-heading" id="h-fix-lagging-average-order-value-prompt">Fix lagging average order value prompt</h2>



<p>When performance lags, it’s easy to ask broad questions about metrics like return on ad spend (ROAS).</p>



<p>But that’s what everyone else does, and it often leads to generic checklists.</p>



<p>We deal with overlap between B2C and B2B search queries. Focusing on B2B users isn’t always easy, but it’s critical for acquiring high-value, long-term customers.</p>



<p>We noticed a likely driver of a B2B materials client’s lagging ROAS: average order value (AOV), shown in Google Ads as Value/Conv., had declined. Smart Bidding had shifted toward high-converting but lower-quality sessions, hurting performance.</p>



<p>We asked an LLM to help diagnose and correct the issue.</p>



<p>Using Ads Advisor (Gemini) in Google Ads, the first response focused on trivial consumer use cases, like holiday themes.</p>



<p>After refining the prompt, it returned more targeted, actionable suggestions, saving significant time.</p>



<p>We leaned further into audience targeting, using value rules to emphasize specific Google audience segments and first-party audiences.</p>



<p>AOV increased. This didn’t guarantee higher order values, but it refocused spend on B2B intent and reduced low-priority consumer purchases.</p>



<p>Other business metrics also improved, moving toward growth and profitability.</p>



<h2 class="wp-block-heading" id="h-better-prompts-lead-to-better-campaigns">Better prompts lead to better campaigns</h2>



<p>Start simple — test one or two of these prompts in your next campaign, refine the outputs, and build from there. Over time, you’ll develop a repeatable system that turns AI from a novelty into a core part of your marketing workflow.</p>

Many of us use various generative AI tools to generate marketing ideas and improve ad campaign outcomes.
Prompting can be a powerful alternative to working solo or brainstorming with colleagues. It improves productivity and expands your options.
In this article, I’ll cover some of my favorite marketing prompts for ad campaigns. Use these suggestions to spark ideas for your own prompts.
Why use prompts for online ads?
Prompts quickly give you a range of ad elements — triggers, emotions, actions, and audiences.
You can often repurpose prompt outputs across channels and initiatives — ads, email, landing pages, social media, and offers.
When you get closer to optimal campaigns from the start, you save cycles. That’s especially useful for lower-budget efforts that take longer to generate feedback.
The prompts themselves matter. You need to ask strong questions to get useful output from large language models (LLMs).
Feeling stumped? Ask AI tools which prompts they recommend for your situation.
Or use mine. Here are several prompts I use for online ads.
shows up.
      
Start Free Trial
    
Get started with
Emotional trigger prompt
Purchases are often emotional, so it helps to understand your buyers’ emotions.
Use this prompt: “What are the top emotional triggers that would make X audience buy Y product?”
For example, I asked which three emotional triggers would make parents buy math learning software for their kids. The LLM identified key triggers and provided hooks and language with scarcity and urgency:
Fear of falling behind: Anxiety and a protective instinct. Example: “Make sure your child never falls behind in math.”
Desire to give kids a competitive advantage: Ambition and pride. Example: “Give your kid the math skills top students develop years ahead.”
Relief from homework stress at home: Relief and peace of mind. Example: “No more math homework battles at home.”
Purchase intent prompt
Ask these questions to understand who is ready to buy your product or service:
Who is most likely to buy immediately?
Who needs convincing?
Who will never buy?
To avoid wasted ad spend, focus on audiences likely to purchase and avoid those who won’t.
Keep asking which audiences are most likely to convert. Use the LLM’s rationale for more specific inputs for your ads.
In the math software example, the LLM suggested parents of kids struggling in math would convert best, citing high urgency and low friction.
The second-best group was homeschooling parents, who are motivated because they manage the full curriculum.
We were already targeting parents of kids struggling in math, but hadn’t considered homeschooling parents. From there, it was easy to create ads and test whether that audience drove conversions.
Get the newsletter search marketers rely on.
 
				
								
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See terms.
Overcoming objections prompt
Overcoming objections is key to making sales. Ask for three to five objections someone might have to buying your product.
In the math software example, the LLM identified these objections:
My child already has too much screen time
Will this actually improve my child’s math skills?
It’s too expensive
Then write a persuasive rebuttal for each using logic, emotion, and proof. For “it’s too expensive,” you could use:
Logic: “Less than the cost of a tutor.” This sets a higher anchor, making the price feel more reasonable without calling it inexpensive.
Emotion: “Don’t let your kids fall behind in math.”
Proof: “80% of students improve by one letter grade in two months.”
Psychological profile prompt
Ask an LLM for a detailed psychological profile of your ideal customer. Use questions like:
What are your ideal customer’s fears?
What are your ideal customer’s frustrations?
What do your ideal customers envy?
What do they pretend doesn’t bother them?
What keeps them up at night?
In the math software example, I asked: “What or who do my ideal customers envy?”
One answer suggested parents envy kids in enrichment programs or advanced classes, reflecting a desire for future opportunities.
A message for this target audience: “Help your child stay ahead instead of playing catchup.”
The lifetime value prompt
To thrive long term, focus on customer lifetime value (LTV), not one-and-done sales.
Ask questions like:
Why might your customers stay longer?
Why might your customers buy more?
What retention strategies work best?
For a higher-end furniture brand, we expanded these into a short playbook to increase LTV. The LLM grouped ideas under: “Shift from a transactional relationship to a long-term design partner.”
For example, it suggested segmenting your customer base and using direct mail for your highest-potential group (sending a lookbook). It’s unexpected and sounds old-school, but the potential for higher LTV than broad, generic mailings makes it worth testing.
Your clients value clear priorities and strategic thinking, not just execution. We value that AI tools can support strategy, not just tactics.
Fix lagging average order value prompt
When performance lags, it’s easy to ask broad questions about metrics like return on ad spend (ROAS).
But that’s what everyone else does, and it often leads to generic checklists.
We deal with overlap between B2C and B2B search queries. Focusing on B2B users isn’t always easy, but it’s critical for acquiring high-value, long-term customers.
We noticed a likely driver of a B2B materials client’s lagging ROAS: average order value (AOV), shown in Google Ads as Value/Conv., had declined. Smart Bidding had shifted toward high-converting but lower-quality sessions, hurting performance.
We asked an LLM to help diagnose and correct the issue.
Using Ads Advisor (Gemini) in Google Ads, the first response focused on trivial consumer use cases, like holiday themes.
After refining the prompt, it returned more targeted, actionable suggestions, saving significant time.
We leaned further into audience targeting, using value rules to emphasize specific Google audience segments and first-party audiences.
AOV increased. This didn’t guarantee higher order values, but it refocused spend on B2B intent and reduced low-priority consumer purchases.
Other business metrics also improved, moving toward growth and profitability.
Better prompts lead to better campaigns
Start simple — test one or two of these prompts in your next campaign, refine the outputs, and build from there. Over time, you’ll develop a repeatable system that turns AI from a novelty into a core part of your marketing workflow.

Turn AI into a strategic ad partner with prompts that help reveal buyer emotions, high-intent audiences, and objections.

How to use AI prompts to generate better ad campaigns

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/ppc-best-practices-playbook.png" class="attachment-large size-large wp-post-image" alt="PPC best practices playbook" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/ppc-best-practices-playbook.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/ppc-best-practices-playbook-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/ppc-best-practices-playbook-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/ppc-best-practices-playbook-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Stop chasing 'best practice' gains. These tested tactics show where real performance lifts actually come from in modern PPC.

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<p>PPC performance conversations often focus on best practices.</p>



<ul class="wp-block-list">
<li>Account structures should be clean. </li>



<li>Match types controlled. </li>



<li>Budgets scaled gradually. </li>



<li>Campaigns should avoid overlap. </li>



<li>Everything should be logical, efficient, and easy to explain.</li>
</ul>



<p>That foundation matters. It creates consistency and avoids obvious inefficiencies.</p>



<p>But it’s not where the biggest gains come from.</p>



<p>Looking back over the past 10 years, many of the most meaningful performance improvements didn’t come from refining those frameworks. They came from testing ideas that didn’t quite fit them — things that felt slightly uncomfortable but aligned with how platforms actually behave.</p>



<p>In practice, Google Ads and Meta don’t optimize toward best practice. They optimize toward signals. Once you think in those terms, your approach to performance changes.</p>



<h2 class="wp-block-heading" id="h-control-still-matters-more-than-you-think-skags-aren-t-actually-dead">Control still matters more than you think (SKAGs aren’t actually &#8216;dead&#8217;)</h2>



<p>Single Keyword Ad Groups were widely written off as automation improved.</p>



<p>The narrative was simple: machine learning removed the need for granular control. Structure mattered less.</p>



<p>In practice, that wasn’t entirely true.</p>



<p>In several accounts, reintroducing SKAGs on a small subset of high-intent, high-revenue keywords led to immediate performance gains. Query matching tightened, ad relevance improved, and conversion rates increased.</p>



<p>This wasn’t about reverting to old structures across the board. It was about recognizing where precision still adds value.</p>



<p>The takeaway is more nuanced than “SKAGs work” or “SKAGs are dead.”</p>



<p>Control still matters, but only where intent justifies it.</p>



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<h2 class="wp-block-heading" id="h-broad-match-works-better-when-you-aggressively-constrain-it">Broad match works better when you aggressively constrain it</h2>



<p>Broad match has always carried distrust.</p>



<p>Too much expansion. Too little control. Too much reliance on Google’s interpretation of intent.</p>



<p>In practice, one of the most effective setups combines broad match with aggressive negative keyword management.</p>



<p>Instead of restricting input, you let Google explore — then shape the output.</p>



<p>Search term mining becomes the control layer. You continuously remove irrelevant queries and reinforce valuable ones.</p>



<p>This creates a system where reach expands without fully sacrificing relevance.</p>



<p>The shift is how you apply control.</p>



<p>You don’t control broad match upfront. You control what it learns from.</p>



<h2 class="wp-block-heading" id="h-target-impression-share-changes-behavior-when-visibility-matters-more-than-efficiency">Target Impression Share changes behavior when visibility matters more than efficiency</h2>



<p>Target Impression Share is usually positioned as a defensive metric.</p>



<p>Used for brand campaigns. Used to protect coverage. Rarely used for growth.</p>



<p>Applying it to non-branded, high-value terms feels counterintuitive. You prioritize presence over efficiency.</p>



<p>In certain cases, that trade-off is worth it.</p>



<p>By pushing aggressive impression share targets on commercially important queries, you increase SERP dominance and reduce competitor visibility. Conversion volume increases, even if cost efficiency softens.</p>



<p>Here, bidding strategy becomes less about optimization and more about intent.</p>



<p>If your goal is to own a space — not just compete — efficiency can’t be the only metric.</p>



<h2 class="wp-block-heading" id="h-conversion-tracking-isn-t-the-issue-conversion-weighting-is">Conversion tracking isn’t the issue. Conversion weighting is.</h2>



<p>Most lead gen accounts correctly track multiple conversion actions.</p>



<p>Form fills, phone calls, email inquiries — all captured and visible.</p>



<p>The issue isn’t tracking. It’s interpretation.</p>



<p>When you treat every action equally, the platform has no reason to prioritize one over another.</p>



<p>In one account, assigning values based on the likelihood of becoming an MQL changed optimization almost immediately. Phone calls were weighted highest, email clicks lower, and generic form fills lower.</p>



<p>Switching to Maximize Conversion Value didn’t increase total conversions. It improved their quality.</p>



<p>The distinction matters.</p>



<p>The platform isn’t misoptimizing. It’s optimizing exactly what you tell it to.</p>



<h2 class="wp-block-heading" id="h-competitor-bidding-works-because-intent-already-exists">Competitor bidding works because intent already exists</h2>



<p>Competitor campaigns are often dismissed as inefficient.</p>



<p>Higher CPCs. Lower CTRs. Messier reporting.</p>



<p>All true.</p>



<p>They also deliver something most prospecting campaigns struggle to create: existing intent.</p>



<p>Users searching competitor brands are further along in the decision process. They’re not exploring the category — they’re choosing within it.</p>



<p>In multiple accounts, competitor campaigns consistently converted—not at the lowest cost, but with high commercial intent.</p>



<p>With clear positioning, strong differentiation, and relevant landing pages, they became a reliable strategic layer.</p>



<p>You’re not creating demand. You’re intercepting it.</p>



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<h2 class="wp-block-heading" id="h-top-of-funnel-keywords-don-t-convert-they-still-drive-performance">Top-of-funnel keywords don’t convert. They still drive performance.</h2>



<p>There’s a tendency to remove anything that doesn’t convert.</p>



<p>Informational queries. Early-stage searches. Broad, non-commercial terms.</p>



<p>Individually, they rarely justify spend.</p>



<p>Collectively, they influence the entire account.</p>



<p>Introducing top-of-funnel keywords not expected to convert improved lower-funnel performance. Remarketing pools strengthened, audience signals improved, and high-intent campaigns became more efficient.</p>



<p>The value is indirect but measurable.</p>



<p>This challenges a common assumption.</p>



<p>Not every keyword needs to convert. Some build the signal that drives conversion elsewhere.</p>



<h2 class="wp-block-heading" id="h-audience-assumptions-are-often-wrong-the-data-usually-isn-t">Audience assumptions are often wrong. The data usually isn’t.</h2>



<p>Audience targeting often starts with a clear hypothesis.</p>



<p>Who the customer is. What they care about. How they behave.</p>



<p>In one account, the data told a different story.</p>



<p>The “ideal” demographic underperformed, while adjacent audiences converted more efficiently.</p>



<p>Instead of forcing the account toward expectations, you follow the data. You shift budget, expand targeting, and improve performance.</p>



<p>This tension is common in PPC.</p>



<p>What you expect to work and what actually works aren’t always aligned.</p>



<h2 class="wp-block-heading" id="h-clean-structure-is-often-a-management-preference-not-a-performance-driver">Clean structure is often a management preference, not a performance driver</h2>



<p>There’s a strong bias toward clean account structure.</p>



<p>No overlap. Clear segmentation. Tight control.</p>



<p>It makes accounts easier to manage, explain, and audit.</p>



<p>It doesn’t always make them more effective.</p>



<p>In several cases, allowing controlled overlap between campaigns, match types, and keyword themes improved coverage and performance. Instead of cannibalization, the system used overlapping signals to make better auction decisions.</p>



<p>This challenges a long-standing assumption.</p>



<p>Structure should support performance—not limit it.</p>



<h2 class="wp-block-heading" id="h-product-feeds-are-strategic">Product feeds are strategic</h2>



<p>In Shopping campaigns, the product feed is often treated as backend work.</p>



<p>Ensure accuracy. Ensure completeness. Then leave it.</p>



<p>But the feed directly shapes how products are interpreted and matched to queries.</p>



<p>Rewriting titles to prioritize high-intent keywords, reordering attributes based on performance, and testing naming variations all improved visibility and CTR.</p>



<p>These weren’t cosmetic changes.</p>



<p>They changed how the algorithm understood the product.</p>



<p>In Shopping, your feed is your targeting. Treating it as static limits performance.</p>



<h2 class="wp-block-heading" id="h-retargeting-is-one-of-the-fastest-ways-to-test-what-actually-works">Retargeting is one of the fastest ways to test what actually works</h2>



<p>Retargeting is usually treated as the safest part of the funnel.</p>



<p>High intent. High conversion rates. Predictable performance.</p>



<p>That makes it an ideal testing environment.</p>



<p>Using retargeting audiences to test messaging, offers, and creative variations creates faster feedback loops and clearer results than prospecting.</p>



<p>You can then scale winning ideas with confidence.</p>



<p>This reframes retargeting.</p>



<p>It’s not just a conversion layer. It’s a testing environment.</p>



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<h2 class="wp-block-heading" id="h-how-top-accounts-really-win">How top accounts really win</h2>



<p>After 10 years in PPC, the biggest shift isn’t the platforms — it’s how they actually work. </p>



<p>Best practices still matter. They’re the foundation.</p>



<p>But they’re not where advantage comes from.</p>



<p>The accounts that outperform understand how signals are interpreted, where systems can be influenced, and when to step outside what’s considered “correct.”</p>



<p>Because the goal was never to follow the playbook.</p>



<p>It was to outperform it.</p>

PPC performance conversations often focus on best practices.
Account structures should be clean. 
Match types controlled. 
Budgets scaled gradually. 
Campaigns should avoid overlap. 
Everything should be logical, efficient, and easy to explain.
That foundation matters. It creates consistency and avoids obvious inefficiencies.
But it’s not where the biggest gains come from.
Looking back over the past 10 years, many of the most meaningful performance improvements didn’t come from refining those frameworks. They came from testing ideas that didn’t quite fit them — things that felt slightly uncomfortable but aligned with how platforms actually behave.
In practice, Google Ads and Meta don’t optimize toward best practice. They optimize toward signals. Once you think in those terms, your approach to performance changes.
Control still matters more than you think (SKAGs aren’t actually ‘dead’)
Single Keyword Ad Groups were widely written off as automation improved.
The narrative was simple: machine learning removed the need for granular control. Structure mattered less.
In practice, that wasn’t entirely true.
In several accounts, reintroducing SKAGs on a small subset of high-intent, high-revenue keywords led to immediate performance gains. Query matching tightened, ad relevance improved, and conversion rates increased.
This wasn’t about reverting to old structures across the board. It was about recognizing where precision still adds value.
The takeaway is more nuanced than “SKAGs work” or “SKAGs are dead.”
Control still matters, but only where intent justifies it.
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Broad match works better when you aggressively constrain it
Broad match has always carried distrust.
Too much expansion. Too little control. Too much reliance on Google’s interpretation of intent.
In practice, one of the most effective setups combines broad match with aggressive negative keyword management.
Instead of restricting input, you let Google explore — then shape the output.
Search term mining becomes the control layer. You continuously remove irrelevant queries and reinforce valuable ones.
This creates a system where reach expands without fully sacrificing relevance.
The shift is how you apply control.
You don’t control broad match upfront. You control what it learns from.
Target Impression Share changes behavior when visibility matters more than efficiency
Target Impression Share is usually positioned as a defensive metric.
Used for brand campaigns. Used to protect coverage. Rarely used for growth.
Applying it to non-branded, high-value terms feels counterintuitive. You prioritize presence over efficiency.
In certain cases, that trade-off is worth it.
By pushing aggressive impression share targets on commercially important queries, you increase SERP dominance and reduce competitor visibility. Conversion volume increases, even if cost efficiency softens.
Here, bidding strategy becomes less about optimization and more about intent.
If your goal is to own a space — not just compete — efficiency can’t be the only metric.
Conversion tracking isn’t the issue. Conversion weighting is.
Most lead gen accounts correctly track multiple conversion actions.
Form fills, phone calls, email inquiries — all captured and visible.
The issue isn’t tracking. It’s interpretation.
When you treat every action equally, the platform has no reason to prioritize one over another.
In one account, assigning values based on the likelihood of becoming an MQL changed optimization almost immediately. Phone calls were weighted highest, email clicks lower, and generic form fills lower.
Switching to Maximize Conversion Value didn’t increase total conversions. It improved their quality.
The distinction matters.
The platform isn’t misoptimizing. It’s optimizing exactly what you tell it to.
Competitor bidding works because intent already exists
Competitor campaigns are often dismissed as inefficient.
Higher CPCs. Lower CTRs. Messier reporting.
All true.
They also deliver something most prospecting campaigns struggle to create: existing intent.
Users searching competitor brands are further along in the decision process. They’re not exploring the category — they’re choosing within it.
In multiple accounts, competitor campaigns consistently converted—not at the lowest cost, but with high commercial intent.
With clear positioning, strong differentiation, and relevant landing pages, they became a reliable strategic layer.
You’re not creating demand. You’re intercepting it.
Get the newsletter search marketers rely on.
 
				
								
					MktoForms2.loadForm("https://app-sj02.marketo.com", "727-ZQE-044", 16298, function(form) {
						// form.onSubmit(function(){
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See terms.
Top-of-funnel keywords don’t convert. They still drive performance.
There’s a tendency to remove anything that doesn’t convert.
Informational queries. Early-stage searches. Broad, non-commercial terms.
Individually, they rarely justify spend.
Collectively, they influence the entire account.
Introducing top-of-funnel keywords not expected to convert improved lower-funnel performance. Remarketing pools strengthened, audience signals improved, and high-intent campaigns became more efficient.
The value is indirect but measurable.
This challenges a common assumption.
Not every keyword needs to convert. Some build the signal that drives conversion elsewhere.
Audience assumptions are often wrong. The data usually isn’t.
Audience targeting often starts with a clear hypothesis.
Who the customer is. What they care about. How they behave.
In one account, the data told a different story.
The “ideal” demographic underperformed, while adjacent audiences converted more efficiently.
Instead of forcing the account toward expectations, you follow the data. You shift budget, expand targeting, and improve performance.
This tension is common in PPC.
What you expect to work and what actually works aren’t always aligned.
Clean structure is often a management preference, not a performance driver
There’s a strong bias toward clean account structure.
No overlap. Clear segmentation. Tight control.
It makes accounts easier to manage, explain, and audit.
It doesn’t always make them more effective.
In several cases, allowing controlled overlap between campaigns, match types, and keyword themes improved coverage and performance. Instead of cannibalization, the system used overlapping signals to make better auction decisions.
This challenges a long-standing assumption.
Structure should support performance—not limit it.
Product feeds are strategic
In Shopping campaigns, the product feed is often treated as backend work.
Ensure accuracy. Ensure completeness. Then leave it.
But the feed directly shapes how products are interpreted and matched to queries.
Rewriting titles to prioritize high-intent keywords, reordering attributes based on performance, and testing naming variations all improved visibility and CTR.
These weren’t cosmetic changes.
They changed how the algorithm understood the product.
In Shopping, your feed is your targeting. Treating it as static limits performance.
Retargeting is one of the fastest ways to test what actually works
Retargeting is usually treated as the safest part of the funnel.
High intent. High conversion rates. Predictable performance.
That makes it an ideal testing environment.
Using retargeting audiences to test messaging, offers, and creative variations creates faster feedback loops and clearer results than prospecting.
You can then scale winning ideas with confidence.
This reframes retargeting.
It’s not just a conversion layer. It’s a testing environment.
complete picture of your search visibility.
      
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How top accounts really win
After 10 years in PPC, the biggest shift isn’t the platforms — it’s how they actually work. 
Best practices still matter. They’re the foundation.
But they’re not where advantage comes from.
The accounts that outperform understand how signals are interpreted, where systems can be influenced, and when to step outside what’s considered “correct.”
Because the goal was never to follow the playbook.
It was to outperform it.

Stop chasing 'best practice' gains. These tested tactics show where real performance lifts actually come from in modern PPC.

What 10 years of PPC testing reveals about breaking best practices

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/server-side-tagging.png" class="attachment-large size-large wp-post-image" alt="Server-side tagging" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/server-side-tagging.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/server-side-tagging-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/server-side-tagging-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/server-side-tagging-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>As data loss grows, brands are rethinking tracking. Explore when a server-based setup delivers cleaner data and stronger ad optimization.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/server-side-tagging.png" class="attachment-large size-large wp-post-image" alt="Server-side tagging" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/server-side-tagging.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/server-side-tagging-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/server-side-tagging-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/server-side-tagging-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Privacy laws are tightening, browser extensions are blocking data, and ad platforms are demanding cleaner data. As a result, how you track user behavior online is changing fast.</p>



<p>Server-side tagging can help you reduce data loss while collecting cleaner, privacy-compliant data.</p>



<p>Here’s what server-side tagging is, when it makes sense to implement it, and our experience with providers like Elevar and Littledata.</p>



<h2 class="wp-block-heading" id="h-what-is-server-side-tagging">What is server-side tagging?</h2>



<p>Traditionally, tracking scripts like Meta Pixel or Google Analytics run in the browser. This is client-side (browser-side) tagging. With server-side tagging, those scripts run on a server you control instead of the visitor’s browser.</p>



<p>Instead of the browser sending data directly to multiple third parties, it sends events to a first-party server endpoint (often a Google Tag Manager server-side container). The server processes, enriches, and forwards the data to tools like Meta and Google Analytics — on infrastructure you host (such as Google Cloud Run) or through managed providers like Elevar or Stape.</p>



<p>This allows for:</p>



<ul class="wp-block-list">
<li>More control over your data.</li>



<li>Cleaner page performance.</li>



<li>Better alignment with privacy laws.</li>
</ul>



<p>Beyond these benefits, server-side tagging gives you more flexibility in how you enrich and transform data before it reaches ad platforms. </p>



<p>You can standardize event names, filter out low-quality events, and add custom parameters to improve audience segmentation. This creates a more reliable, unified data foundation across tools like Meta, Google Ads, and Klaviyo, leading to stronger optimization signals and more confident decisions.</p>



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<h2 class="wp-block-heading" id="h-is-server-side-tagging-right-for-you">Is server-side tagging right for you?</h2>



<p>Server-side tagging isn’t a one-size-fits-all solution, but for many brands it’s becoming essential. It may be the right choice for you if:</p>



<h3 class="wp-block-heading" id="h-you-need-to-meet-strict-privacy-or-compliance-requirements">You need to meet strict privacy or compliance requirements</h3>



<p>Server-side setups don’t automatically anonymize data, but they give you more control over how it’s processed and shared. </p>



<p>This makes it easier to support compliance with regulations like GDPR, CCPA, and platform consent requirements. </p>



<p>You can apply consent logic, filter or hash sensitive fields, and control which events and parameters you send to each platform based on user permissions.</p>



<h3 class="wp-block-heading" id="h-you-want-faster-website-performance">You want faster website performance</h3>



<p>Client-side tracking can slow page load times. </p>



<p>Server-side tagging helps your site run faster by shifting data processing off the browser and onto the server, boosting user experience and SEO. </p>



<p>Faster pages can improve conversion rates and reduce bounce rates, especially on mobile, where every millisecond counts.</p>



<h3 class="wp-block-heading" id="h-you-want-more-accurate-tracking-despite-ad-blockers">You want more accurate tracking (despite ad blockers)</h3>



<p>Ad blockers can disrupt client-side scripts, but server-side tagging bypasses many of these limits. </p>



<p>It helps platforms like Meta and Google Ads collect more reliable data for attribution and optimization. </p>



<p>It doesn’t eliminate blocking or consent requirements or recover all lost data, but it can improve signal reliability compared to browser-only setups.</p>



<h3 class="wp-block-heading" id="h-you-re-investing-heavily-in-paid-media">You’re investing heavily in paid media</h3>



<p>If you’re spending heavily on Meta, Google, or other paid channels, better data accuracy can directly impact your return on ad spend. </p>



<p>Server-side tagging helps ensure conversion events are delivered consistently, allowing algorithms to learn faster and optimize toward higher-value users.</p>



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						// });
						
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						// });
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<h2 class="wp-block-heading" id="h-how-to-implement-server-side-tagging">How to implement server-side tagging</h2>



<p>You have two main options: build it yourself or use a service provider.</p>



<h3 class="wp-block-heading" id="h-option-1-internal-setup">Option 1: Internal setup</h3>



<p>Building your own setup gives you full control but requires technical expertise and ongoing maintenance. You’ll also need clear documentation and QA processes to keep events accurate as your site evolves and new features launch.</p>



<p>Your setup might look like:</p>



<ul class="wp-block-list">
<li>Set up a GTM server-side container</li>



<li>Host on a platform like Google Cloud Run or Stape</li>



<li>Migrate key tags (GA4, Meta CAPI, etc.)</li>



<li>Add logic for PII hashing, cookie consent, and filtering</li>



<li>Monitor server costs, performance, and data integrity</li>
</ul>



<h3 class="wp-block-heading" id="h-option-2-use-a-server-side-tagging-service">Option 2: Use a server-side tagging service</h3>



<p>Platforms like Elevar, Littledata, and Triple Whale simplify this by offering turnkey solutions that integrate with tools like Shopify, Klaviyo, Meta, and Google Ads. They handle infrastructure, updates, and many edge cases, so you can focus on strategy instead of engineering.</p>



<h2 class="wp-block-heading" id="h-our-direct-experience-littledata-vs-elevar">Our direct experience: Littledata vs. Elevar</h2>



<p>We’ve implemented Littledata and Elevar across multiple Shopify environments. Each serves a different type of brand, and understanding the difference is key.</p>



<h3 class="wp-block-heading" id="h-littledata-for-simpler-technology-stacks-and-budget-conscious-brands">Littledata: For simpler technology stacks and budget-conscious brands</h3>



<p>We first implemented Littledata for a Shopify brand focused on improving visibility into Klaviyo and strengthening owned marketing channels. In that context, it performed well, passing additional Klaviyo events and improving email-driven reporting.</p>



<p>Littledata is a strong fit for:</p>



<ul class="wp-block-list">
<li>Early-stage or emerging Shopify brands</li>



<li>Teams with moderate paid media budgets</li>



<li>Teams prioritizing email/SMS over aggressive paid scaling</li>



<li>Brands with relatively simple tech stacks</li>



<li>Businesses that want better tracking without enterprise-level cost</li>
</ul>



<p>Littledata&#8217;s affordability makes it accessible if you want better attribution without highly customized, platform-specific data engineering.</p>



<p>In more performance-intensive environments — especially where paid media relies on precise in-platform reporting — we’ve seen limitations with fully aligned Meta conversion data and consistent GA4 session and revenue reporting. At scale, even small discrepancies can affect bidding strategies.</p>



<p>Littledata&#8217;s support is relatively lean, so resolving nuanced discrepancies may require additional internal validation. If you have in-house technical resources or less aggressive optimization needs, this may not be a major concern.</p>



<h3 class="wp-block-heading" id="h-elevar-when-brands-outgrow-entry-level-solutions">Elevar: When brands outgrow entry-level solutions</h3>



<p>As you scale paid media spend and demand more precise attribution in Meta and Google Ads, the margin for tracking error shrinks.</p>



<p>You’ll likely reach a point where you:</p>



<ul class="wp-block-list">
<li>Spend heavily on paid acquisition.</li>



<li>Optimize toward blended ROAS across channels.</li>



<li>Make bid decisions based on in-platform conversion data.</li>
</ul>



<p>At that point, you need more advanced infrastructure, deeper data enrichment, and specialized troubleshooting support. When your needs shift, so should your solution.</p>



<p>This is where a solution like Elevar comes in.</p>



<h2 class="wp-block-heading" id="h-set-yourself-up-for-success-with-server-side-tagging-nbsp">Set yourself up for success with server-side tagging&nbsp;</h2>



<p>Server-side tagging is a technical and strategic upgrade. It:</p>



<ul class="wp-block-list">
<li>Supports privacy compliance.</li>



<li>Speeds up your site.</li>



<li>Improves performance by making your data more accurate and reliable. </li>



<li>Future-proofs your measurement stack as browsers limit third-party cookies and platforms prioritize first-party data.</li>
</ul>



<p>As privacy restrictions grow and ad platforms evolve, this setup only becomes more valuable. If you invest early, you’re better positioned to maintain strong attribution, build richer audiences, and make smarter media buying decisions.</p>



<p>The goal isn’t just to recover lost data. It’s to build a more resilient measurement framework that adapts as platforms and regulations change.</p>



<p>Server-side tagging gives you ownership over your data pipeline and reduces reliance on fragile browser-based signals. When implemented well, it becomes a long-term advantage, enabling clearer insights, stronger attribution, and more confident optimization across every channel you invest in.</p>

Privacy laws are tightening, browser extensions are blocking data, and ad platforms are demanding cleaner data. As a result, how you track user behavior online is changing fast.
Server-side tagging can help you reduce data loss while collecting cleaner, privacy-compliant data.
Here’s what server-side tagging is, when it makes sense to implement it, and our experience with providers like Elevar and Littledata.
What is server-side tagging?
Traditionally, tracking scripts like Meta Pixel or Google Analytics run in the browser. This is client-side (browser-side) tagging. With server-side tagging, those scripts run on a server you control instead of the visitor’s browser.
Instead of the browser sending data directly to multiple third parties, it sends events to a first-party server endpoint (often a Google Tag Manager server-side container). The server processes, enriches, and forwards the data to tools like Meta and Google Analytics — on infrastructure you host (such as Google Cloud Run) or through managed providers like Elevar or Stape.
This allows for:
More control over your data.
Cleaner page performance.
Better alignment with privacy laws.
Beyond these benefits, server-side tagging gives you more flexibility in how you enrich and transform data before it reaches ad platforms. 
You can standardize event names, filter out low-quality events, and add custom parameters to improve audience segmentation. This creates a more reliable, unified data foundation across tools like Meta, Google Ads, and Klaviyo, leading to stronger optimization signals and more confident decisions.
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Is server-side tagging right for you?
Server-side tagging isn’t a one-size-fits-all solution, but for many brands it’s becoming essential. It may be the right choice for you if:
You need to meet strict privacy or compliance requirements
Server-side setups don’t automatically anonymize data, but they give you more control over how it’s processed and shared. 
This makes it easier to support compliance with regulations like GDPR, CCPA, and platform consent requirements. 
You can apply consent logic, filter or hash sensitive fields, and control which events and parameters you send to each platform based on user permissions.
You want faster website performance
Client-side tracking can slow page load times. 
Server-side tagging helps your site run faster by shifting data processing off the browser and onto the server, boosting user experience and SEO. 
Faster pages can improve conversion rates and reduce bounce rates, especially on mobile, where every millisecond counts.
You want more accurate tracking (despite ad blockers)
Ad blockers can disrupt client-side scripts, but server-side tagging bypasses many of these limits. 
It helps platforms like Meta and Google Ads collect more reliable data for attribution and optimization. 
It doesn’t eliminate blocking or consent requirements or recover all lost data, but it can improve signal reliability compared to browser-only setups.
You’re investing heavily in paid media
If you’re spending heavily on Meta, Google, or other paid channels, better data accuracy can directly impact your return on ad spend. 
Server-side tagging helps ensure conversion events are delivered consistently, allowing algorithms to learn faster and optimize toward higher-value users.
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See terms.
How to implement server-side tagging
You have two main options: build it yourself or use a service provider.
Option 1: Internal setup
Building your own setup gives you full control but requires technical expertise and ongoing maintenance. You’ll also need clear documentation and QA processes to keep events accurate as your site evolves and new features launch.
Your setup might look like:
Set up a GTM server-side container
Host on a platform like Google Cloud Run or Stape
Migrate key tags (GA4, Meta CAPI, etc.)
Add logic for PII hashing, cookie consent, and filtering
Monitor server costs, performance, and data integrity
Option 2: Use a server-side tagging service
Platforms like Elevar, Littledata, and Triple Whale simplify this by offering turnkey solutions that integrate with tools like Shopify, Klaviyo, Meta, and Google Ads. They handle infrastructure, updates, and many edge cases, so you can focus on strategy instead of engineering.
Our direct experience: Littledata vs. Elevar
We’ve implemented Littledata and Elevar across multiple Shopify environments. Each serves a different type of brand, and understanding the difference is key.
Littledata: For simpler technology stacks and budget-conscious brands
We first implemented Littledata for a Shopify brand focused on improving visibility into Klaviyo and strengthening owned marketing channels. In that context, it performed well, passing additional Klaviyo events and improving email-driven reporting.
Littledata is a strong fit for:
Early-stage or emerging Shopify brands
Teams with moderate paid media budgets
Teams prioritizing email/SMS over aggressive paid scaling
Brands with relatively simple tech stacks
Businesses that want better tracking without enterprise-level cost
Littledata’s affordability makes it accessible if you want better attribution without highly customized, platform-specific data engineering.
In more performance-intensive environments — especially where paid media relies on precise in-platform reporting — we’ve seen limitations with fully aligned Meta conversion data and consistent GA4 session and revenue reporting. At scale, even small discrepancies can affect bidding strategies.
Littledata’s support is relatively lean, so resolving nuanced discrepancies may require additional internal validation. If you have in-house technical resources or less aggressive optimization needs, this may not be a major concern.
Elevar: When brands outgrow entry-level solutions
As you scale paid media spend and demand more precise attribution in Meta and Google Ads, the margin for tracking error shrinks.
You’ll likely reach a point where you:
Spend heavily on paid acquisition.
Optimize toward blended ROAS across channels.
Make bid decisions based on in-platform conversion data.
At that point, you need more advanced infrastructure, deeper data enrichment, and specialized troubleshooting support. When your needs shift, so should your solution.
This is where a solution like Elevar comes in.
Set yourself up for success with server-side tagging 
Server-side tagging is a technical and strategic upgrade. It:
Supports privacy compliance.
Speeds up your site.
Improves performance by making your data more accurate and reliable. 
Future-proofs your measurement stack as browsers limit third-party cookies and platforms prioritize first-party data.
As privacy restrictions grow and ad platforms evolve, this setup only becomes more valuable. If you invest early, you’re better positioned to maintain strong attribution, build richer audiences, and make smarter media buying decisions.
The goal isn’t just to recover lost data. It’s to build a more resilient measurement framework that adapts as platforms and regulations change.
Server-side tagging gives you ownership over your data pipeline and reduces reliance on fragile browser-based signals. When implemented well, it becomes a long-term advantage, enabling clearer insights, stronger attribution, and more confident optimization across every channel you invest in.

As data loss grows, brands are rethinking tracking. Explore when a server-based setup delivers cleaner data and stronger ad optimization.

When server-side tagging makes sense for your brand

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/inbound-unbound.png" class="attachment-large size-large wp-post-image" alt="Inbound Unbound" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/inbound-unbound.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/inbound-unbound-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/inbound-unbound-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/inbound-unbound-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>After declaring inbound dead last year, HubSpot is renaming its annual conference to something less limiting.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/inbound-unbound.png" class="attachment-large size-large wp-post-image" alt="Inbound Unbound" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/inbound-unbound.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/inbound-unbound-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/inbound-unbound-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/inbound-unbound-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>If you shelved your inbound strategy this past year, you can shelve your Inbound conference mugs and swag with it.</p>



<p>HubSpot renamed its annual Inbound conference in Boston this September to Unbound. A <a href="https://unbound.hubspot.com/">note on the event site</a> explains the thinking:</p>



<ul class="wp-block-list">
<li>“This evolution is our response to that reality. INBOUND is becoming UNBOUND because growth no longer fits within a single framework or function. Today, it covers marketing, sales, service, and operations across the full customer journey in an AI-driven environment. UNBOUND reflects that expanded reality and the mindset required to lead through it.” </li>
</ul>



<p><strong>Inbound is outbound. </strong>HubSpot pioneered inbound marketing, which uses content and search rankings to attract visitors, then convert them on-site.</p>



<p>Recent Google core updates appeared to <a href="https://searchengineland.com/hubspot-seo-organic-traffic-drop-451096">hurt the HubSpot blog</a>, possibly because its content drifted from core topics like CRM, sales, and marketing into broader business areas like interview tips.</p>



<p>Inbound strategy has declined as search shifts from platforms like Google to LLMs like ChatGPT, which drive fewer clicks to websites.</p>



<p><strong>From inbound to loop marketing. </strong>In 2025, HubSpot introduced its <a href="https://martech.org/hubspot-brings-a-new-playbook-for-a-new-era-of-marketing-to-inbound/" target="_blank" rel="noopener">Loop marketing strategy</a> to replace inbound. Loop focuses educating consumers in an AI-driven world.</p>



<p>The conference rebrand acknowledges that no single framework works for you in today’s marketing landscape.</p>


If you shelved your inbound strategy this past year, you can shelve your Inbound conference mugs and swag with it.
HubSpot renamed its annual Inbound conference in Boston this September to Unbound. A note on the event site explains the thinking:
“This evolution is our response to that reality. INBOUND is becoming UNBOUND because growth no longer fits within a single framework or function. Today, it covers marketing, sales, service, and operations across the full customer journey in an AI-driven environment. UNBOUND reflects that expanded reality and the mindset required to lead through it.” 
Inbound is outbound. HubSpot pioneered inbound marketing, which uses content and search rankings to attract visitors, then convert them on-site.
Recent Google core updates appeared to hurt the HubSpot blog, possibly because its content drifted from core topics like CRM, sales, and marketing into broader business areas like interview tips.
Inbound strategy has declined as search shifts from platforms like Google to LLMs like ChatGPT, which drive fewer clicks to websites.
From inbound to loop marketing. In 2025, HubSpot introduced its Loop marketing strategy to replace inbound. Loop focuses educating consumers in an AI-driven world.
The conference rebrand acknowledges that no single framework works for you in today’s marketing landscape.

After declaring inbound dead last year, HubSpot is renaming its annual conference to something less limiting.

HubSpot rebrands its flagship conference from Inbound to Unbound

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/ai-bot-traffic-surge.png" class="attachment-large size-large wp-post-image" alt="AI bot traffic surge" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/ai-bot-traffic-surge.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/ai-bot-traffic-surge-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/ai-bot-traffic-surge-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/ai-bot-traffic-surge-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI fetcher bots siphon traffic and revenue from publishers, delivering real-time answers that bypass your site and cut referral visits.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/04/ai-bot-traffic-surge.png" class="attachment-large size-large wp-post-image" alt="AI bot traffic surge" style="margin-bottom: 15px;" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/04/ai-bot-traffic-surge.png 1920w, https://searchengineland.com/wp-content/seloads/2026/04/ai-bot-traffic-surge-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/04/ai-bot-traffic-surge-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/04/ai-bot-traffic-surge-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>AI bot activity surged 300% in 2025, with media and publishing among the most targeted sectors, according to a new Akamai report. </p>



<p><strong>Why we care. </strong>AI bots are reshaping how content is discovered and consumed, shifting users from search clicks to instant answers in chat interfaces. Publishers are seeing fewer visits from organic search and often don&#8217;t get attribution in AI-generated answers. It&#8217;s also eroding ad and subscription models.</p>



<p><strong>The threat is real. </strong>Publishers now face two threats:</p>



<ul class="wp-block-list">
<li>Training bots that ingest content for models.</li>



<li>Fetcher bots that extract real-time content for immediate answers. These pose the bigger risk because they capture value as it’s created.</li>
</ul>



<p><strong>The impact</strong>. Pageviews are declining, costs are rising (because scraping bots increase infrastructure costs by consuming server and CDN resources without generating revenue), and brand visibility is weakening.</p>



<ul class="wp-block-list">
<li>AI chatbot referrals drive ~96% less traffic than traditional search</li>



<li>Users click cited sources in AI answers only ~1% of the time</li>
</ul>



<p><strong>What publishers are doing</strong>. Publishers are adopting nuanced controls (rather than blanket blocking AI bots), such as:</p>



<ul class="wp-block-list">
<li>Monitoring and classifying bot traffic.</li>



<li>Selectively blocking or slowing malicious scrapers (e.g., tarpitting).</li>



<li>Allowing approved bots tied to licensing or partnerships.</li>
</ul>



<p><strong>What they&#8217;re saying.</strong> According to Akamai&#8217;s report: </p>



<ul class="wp-block-list">
<li>&#8220;These bots are not just a security nuisance, they represent a profound business challenge that threatens the sustainability of quality journalism in an age dominated by zero-click searches and AI-generated content.&#8221;</li>



<li>&#8220;The publishing industry today faces an existential crisis &#8230; Many readers and visitors still value trustworthy reporting and original content. Yet, instead of clicking through search results, users now turn to AI-driven platforms like ChatGPT and Gemini for instant answers and summaries.&#8221;</li>
</ul>



<p><strong>What’s next</strong>? A <a href="https://searchengineland.com/cloudflare-pay-per-crawl-seo-geo-458310">“pay-per-crawl” model</a> is emerging. Tools like identity verification (Know Your Agent) and platforms like TollBit aim to authenticate bots and charge for access in real time.</p>



<ul class="wp-block-list">
<li>The goal is to turn scraping into a measurable, monetizable transaction instead of uncontrolled extraction.</li>
</ul>



<p><strong>About the data</strong>. The report analyzed Akamai bot management data from July to December 2025, covering application-layer traffic across websites, apps, and APIs.</p>



<p><strong>The report.</strong> <a href="https://www.akamai.com/resources/state-of-the-internet/publishing-ai-botnet-report">SOTI Security Insight Series: Navigating the AI Bot Era</a> (registration required)</p>


AI bot activity surged 300% in 2025, with media and publishing among the most targeted sectors, according to a new Akamai report. 
Why we care. AI bots are reshaping how content is discovered and consumed, shifting users from search clicks to instant answers in chat interfaces. Publishers are seeing fewer visits from organic search and often don’t get attribution in AI-generated answers. It’s also eroding ad and subscription models.
The threat is real. Publishers now face two threats:
Training bots that ingest content for models.
Fetcher bots that extract real-time content for immediate answers. These pose the bigger risk because they capture value as it’s created.
The impact. Pageviews are declining, costs are rising (because scraping bots increase infrastructure costs by consuming server and CDN resources without generating revenue), and brand visibility is weakening.
AI chatbot referrals drive ~96% less traffic than traditional search
Users click cited sources in AI answers only ~1% of the time
What publishers are doing. Publishers are adopting nuanced controls (rather than blanket blocking AI bots), such as:
Monitoring and classifying bot traffic.
Selectively blocking or slowing malicious scrapers (e.g., tarpitting).
Allowing approved bots tied to licensing or partnerships.
What they’re saying. According to Akamai’s report: 
“These bots are not just a security nuisance, they represent a profound business challenge that threatens the sustainability of quality journalism in an age dominated by zero-click searches and AI-generated content.”
“The publishing industry today faces an existential crisis … Many readers and visitors still value trustworthy reporting and original content. Yet, instead of clicking through search results, users now turn to AI-driven platforms like ChatGPT and Gemini for instant answers and summaries.”
What’s next? A “pay-per-crawl” model is emerging. Tools like identity verification (Know Your Agent) and platforms like TollBit aim to authenticate bots and charge for access in real time.
The goal is to turn scraping into a measurable, monetizable transaction instead of uncontrolled extraction.
About the data. The report analyzed Akamai bot management data from July to December 2025, covering application-layer traffic across websites, apps, and APIs.
The report. SOTI Security Insight Series: Navigating the AI Bot Era (registration required)

AI fetcher bots siphon traffic and revenue from publishers, delivering real-time answers that bypass your site and cut referral visits.

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Read the Latest Trends and Headlines in Digital Marketing.

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

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