<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/06/Google-Analytics-source-grouping.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Google-Analytics-source-grouping.png 1672w, https://searchengineland.com/wp-content/seloads/2026/06/Google-Analytics-source-grouping-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Google-Analytics-source-grouping-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Google-Analytics-source-grouping-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Google Analytics is introducing cleaner source attribution and new filtering controls, for easier cross-channel performance analysis

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/06/Google-Analytics-source-grouping.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Google-Analytics-source-grouping.png 1672w, https://searchengineland.com/wp-content/seloads/2026/06/Google-Analytics-source-grouping-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Google-Analytics-source-grouping-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Google-Analytics-source-grouping-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p>Google Analytics is introducing a new Source Group reporting dimension and hostname filtering controls aimed at improving attribution analysis and data quality.</p>



<p>The updates are designed to help advertisers clean up fragmented traffic source reporting, better analyze cross-channel performance and reduce noise in their analytics data.</p>



<p><strong>What&#8217;s new</strong>. Source Group is a new reporting dimension that consolidates multiple variations of the same traffic source into a standardized category.</p>



<p>For example, traffic from Facebook can now be grouped under a single reporting value instead of appearing across multiple naming conventions such as &#8220;facebook,&#8221; &#8220;fb&#8221; or other variations.</p>



<p>At the same time, Google is updating its existing Source Platform field to align with the new grouping structure and provide more consistent classifications across advertising channels.</p>



<p><strong>Why we care. </strong>Cleaner source classification means more accurate attribution and cross-channel reporting. Instead of traffic being fragmented across inconsistent labels, marketers can more easily understand which platforms are actually driving conversions and where budgets are performing best.</p>



<p>The inclusion of AI traffic sources like ChatGPT and Perplexity is particularly noteworthy, as it gives advertisers a standardized way to measure and compare emerging AI-driven referral traffic alongside traditional channels. The new hostname filters also help improve data quality by ensuring only traffic from approved domains is included in reporting.</p>



<p><strong>The big picture</strong>. As advertisers manage campaigns across a growing number of platforms, inconsistent source naming can make attribution and budget analysis more difficult. The new reporting structure is intended to simplify performance comparisons across channels.</p>



<p><strong>Between the lines</strong>. The update expands source standardization beyond Google&#8217;s own properties, creating consistent classifications for platforms including TikTok, Pinterest and Amazon while also introducing support for emerging AI-driven traffic sources such as ChatGPT and Perplexity.</p>



<p><strong>Also new</strong>. Google is launching hostname filters within the Admin section, allowing advertisers to exclude events from unapproved domains before they enter reporting.</p>



<p>The feature is designed to help improve data accuracy by preventing unwanted traffic from influencing analysis.</p>



<p><strong>What advertisers get</strong>. Standardized source reporting, retroactive access to historical source group data, cleaner attribution analysis and greater control over which domains contribute data to reporting.</p>



<p><strong>The bottom line</strong>. <a href="https://support.google.com/analytics/answer/9164320?hl=en#06112026">Google is adding new tools to help advertisers improve reporting consistency</a>, strengthen attribution analysis and maintain cleaner datasets as traffic sources become more fragmented.</p>


Google Analytics is introducing a new Source Group reporting dimension and hostname filtering controls aimed at improving attribution analysis and data quality.
The updates are designed to help advertisers clean up fragmented traffic source reporting, better analyze cross-channel performance and reduce noise in their analytics data.
What’s new. Source Group is a new reporting dimension that consolidates multiple variations of the same traffic source into a standardized category.
For example, traffic from Facebook can now be grouped under a single reporting value instead of appearing across multiple naming conventions such as “facebook,” “fb” or other variations.
At the same time, Google is updating its existing Source Platform field to align with the new grouping structure and provide more consistent classifications across advertising channels.
Why we care. Cleaner source classification means more accurate attribution and cross-channel reporting. Instead of traffic being fragmented across inconsistent labels, marketers can more easily understand which platforms are actually driving conversions and where budgets are performing best.
The inclusion of AI traffic sources like ChatGPT and Perplexity is particularly noteworthy, as it gives advertisers a standardized way to measure and compare emerging AI-driven referral traffic alongside traditional channels. The new hostname filters also help improve data quality by ensuring only traffic from approved domains is included in reporting.
The big picture. As advertisers manage campaigns across a growing number of platforms, inconsistent source naming can make attribution and budget analysis more difficult. The new reporting structure is intended to simplify performance comparisons across channels.
Between the lines. The update expands source standardization beyond Google’s own properties, creating consistent classifications for platforms including TikTok, Pinterest and Amazon while also introducing support for emerging AI-driven traffic sources such as ChatGPT and Perplexity.
Also new. Google is launching hostname filters within the Admin section, allowing advertisers to exclude events from unapproved domains before they enter reporting.
The feature is designed to help improve data accuracy by preventing unwanted traffic from influencing analysis.
What advertisers get. Standardized source reporting, retroactive access to historical source group data, cleaner attribution analysis and greater control over which domains contribute data to reporting.
The bottom line. Google is adding new tools to help advertisers improve reporting consistency, strengthen attribution analysis and maintain cleaner datasets as traffic sources become more fragmented.

Google Analytics is introducing cleaner source attribution and new filtering controls, for easier cross-channel performance analysis

Google Analytics adds source grouping and hostname filtering

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/top-10-search-ranking-ai-answers.png" class="attachment-large size-large wp-post-image" alt="Top 10 search ranking AI answers" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/top-10-search-ranking-ai-answers.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/top-10-search-ranking-ai-answers-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/top-10-search-ranking-ai-answers-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/top-10-search-ranking-ai-answers-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Claude frequently pulls from Brave Search results, with ranking, recency, and comparison prompts triggering searches most often.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/top-10-search-ranking-ai-answers.png" class="attachment-large size-large wp-post-image" alt="Top 10 search ranking AI answers" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/top-10-search-ranking-ai-answers.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/top-10-search-ranking-ai-answers-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/top-10-search-ranking-ai-answers-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/top-10-search-ranking-ai-answers-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Claude may be more directly tied to Brave Search rankings than other AI answer engines, according to information <a href="https://searchengineland.com/author/jon-clark">Jonathan Clark</a> shared <a href="https://www.linkedin.com/posts/ppcmarketing_claude-doesnt-re-rank-search-results-it-activity-7470936887109664769-TOBd/">on LinkedIn</a> from a Zero Click by Profound session.</p>



<p>Clark, managing partner at Moving Traffic Media, said the session’s key takeaway was that Claude “doesn’t re-rank search results” and instead appears to use Brave’s top 10 results directly in its answers.</p>



<p><strong>Claude searched less often. </strong>Claude used web search in 36.6% of prompts, compared with about 90% for ChatGPT, according to Clark.</p>



<ul class="wp-block-list">
<li>Claude was most likely to search when prompts signaled freshness, rankings, location, or comparison intent. Recency-focused prompts such as “best XYZ” triggered search 81% of the time, while ranking-focused prompts triggered search 67% of the time.</li>



<li>Location-focused prompts triggered search 55% of the time, while comparison prompts such as “X vs. Y” triggered search 51% of the time.</li>
</ul>



<p><strong>Brave rankings carried weight. </strong>Claude’s citations overlapped with ChatGPT’s in only 8% of cases when responding to the same prompts, according to Clark.</p>



<ul class="wp-block-list">
<li>Claude’s results had much higher overlap with Google rankings, at 64%. This suggests that Google SEO efforts may carry over more readily to Claude than strategies focused specifically on improving visibility in ChatGPT, according to Clark.</li>



<li>The finding also increased the importance of Brave rank tracking. Clark said Claude uses Brave, and that ranking well in Brave gives us “something we can monitor and correlate to data.”</li>
</ul>



<p><strong>Some prompts stayed in memory. </strong>Prompts such as “how does,” “what is,” and “steps to” were less likely to trigger Claude to search the web. When Claude doesn&#8217;t search, it can&#8217;t cite web pages. Claude searched most often for prompts containing terms such as “best,” “top,” “near me,” and comparison-style queries, according to Clark.</p>



<p><strong>Years showed up often. </strong>Clark also noted two patterns that could make Claude easier to test:</p>



<ul class="wp-block-list">
<li>Claude’s query fan-outs were nearly deterministic, producing the same fan-out 65% of the time across users. </li>



<li>The fan-outs often included years.
<ul class="wp-block-list">
<li>That means page titles with current-year signals may have an advantage in Claude-triggered searches, especially for ranking and freshness-driven prompts.</li>
</ul>
</li>
</ul>



<p><strong>Why we care.</strong> Claude visibility appears to depend more heavily on ranking in the search results Claude uses. Clark’s takeaway was that Claude may be one of the most optimizable AI answer engines today because its search behavior appears more consistent and more closely tied to observable search rankings.</p>


Claude may be more directly tied to Brave Search rankings than other AI answer engines, according to information Jonathan Clark shared on LinkedIn from a Zero Click by Profound session.
Clark, managing partner at Moving Traffic Media, said the session’s key takeaway was that Claude “doesn’t re-rank search results” and instead appears to use Brave’s top 10 results directly in its answers.
Claude searched less often. Claude used web search in 36.6% of prompts, compared with about 90% for ChatGPT, according to Clark.
Claude was most likely to search when prompts signaled freshness, rankings, location, or comparison intent. Recency-focused prompts such as “best XYZ” triggered search 81% of the time, while ranking-focused prompts triggered search 67% of the time.
Location-focused prompts triggered search 55% of the time, while comparison prompts such as “X vs. Y” triggered search 51% of the time.
Brave rankings carried weight. Claude’s citations overlapped with ChatGPT’s in only 8% of cases when responding to the same prompts, according to Clark.
Claude’s results had much higher overlap with Google rankings, at 64%. This suggests that Google SEO efforts may carry over more readily to Claude than strategies focused specifically on improving visibility in ChatGPT, according to Clark.
The finding also increased the importance of Brave rank tracking. Clark said Claude uses Brave, and that ranking well in Brave gives us “something we can monitor and correlate to data.”
Some prompts stayed in memory. Prompts such as “how does,” “what is,” and “steps to” were less likely to trigger Claude to search the web. When Claude doesn’t search, it can’t cite web pages. Claude searched most often for prompts containing terms such as “best,” “top,” “near me,” and comparison-style queries, according to Clark.
Years showed up often. Clark also noted two patterns that could make Claude easier to test:
Claude’s query fan-outs were nearly deterministic, producing the same fan-out 65% of the time across users. 
The fan-outs often included years.

That means page titles with current-year signals may have an advantage in Claude-triggered searches, especially for ranking and freshness-driven prompts.
Why we care. Claude visibility appears to depend more heavily on ranking in the search results Claude uses. Clark’s takeaway was that Claude may be one of the most optimizable AI answer engines today because its search behavior appears more consistent and more closely tied to observable search rankings.

Claude frequently pulls from Brave Search results, with ranking, recency, and comparison prompts triggering searches most often.

Claude visibility may depend heavily on Brave Search rankings, new data suggests

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/How-to-build-a-Claude-Code-powered-second-brain-for-agency-work.png" class="attachment-large size-large wp-post-image" alt="How to build a Claude Code-powered second brain for agency work" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/How-to-build-a-Claude-Code-powered-second-brain-for-agency-work.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-build-a-Claude-Code-powered-second-brain-for-agency-work-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-build-a-Claude-Code-powered-second-brain-for-agency-work-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-build-a-Claude-Code-powered-second-brain-for-agency-work-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>See how memory, search, MCP integrations, and AI skills work together to reduce context-switching and keep client work moving.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/How-to-build-a-Claude-Code-powered-second-brain-for-agency-work.png" class="attachment-large size-large wp-post-image" alt="How to build a Claude Code-powered second brain for agency work" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/How-to-build-a-Claude-Code-powered-second-brain-for-agency-work.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-build-a-Claude-Code-powered-second-brain-for-agency-work-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-build-a-Claude-Code-powered-second-brain-for-agency-work-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/How-to-build-a-Claude-Code-powered-second-brain-for-agency-work-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>If you run or work in an agency, manage clients, or own anything more than a single workflow, you know how the first 45 minutes of the morning vanish into Gmail, Slack, Fireflies, the CRM, and whatever doc was last on screen, just trying to remember what mattered the day before.&nbsp;</p>



<p>It used to be I&#8217;d be thinking about a pricing decision I owed the agency team, a roadmap call for my habit-tracking app, three Slack threads I&#8217;d half-read on my phone the night before, and a sales follow-up that needed to go out today.</p>



<p>Those days are over. About six months ago, I rebuilt how I work using Claude Code as the engine of what I&#8217;ll call my second brain. The Monday morning synthesis now takes about a minute.</p>



<p>Here&#8217;s what I built, why, and how you can do the same.</p>



<h2 class="wp-block-heading" id="h-why-most-second-brain-setups-break-down">Why most second-brain setups break down</h2>



<p>The &#8220;second brain&#8221; idea isn&#8217;t new. Tiago Forte&#8217;s “Building a Second Brain,” the PARA method, Notion, and Obsidian were all built on the same instinct: externalize what you&#8217;d otherwise try to remember.</p>



<p>Capturing information works. Recall mostly works. But the bit that adds value is what happens after recall: turning what&#8217;s stored into something you can act on.</p>



<p>Three failure modes show up in almost every implementation I&#8217;ve seen:</p>



<ul class="wp-block-list">
<li><strong>Passive storage.</strong> Information goes in. It sits there. The only way out is search plus your own memory of what you tagged it. Meeting notes are a perfect example.</li>



<li><strong>Context-switching tax.</strong> Even when you find the right note, you&#8217;re still copy-pasting and re-prompting your way to a usable output.</li>



<li><strong>No action layer.</strong> A second brain that can&#8217;t draft, retrieve, or execute tasks can end up giving you more work than you started with. Over time, it builds up into an excess of notes that just leads to cognitive overload.</li>
</ul>



<p>Documenting tasks isn&#8217;t the problem. The problem is that the documents and conversations are scattered across a hundred different apps, and nothing reads across them.</p>



<p>What&#8217;s missing is the layer on top: something that can pull from all of it and actually do the work.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/claude-code-seo-work-470668">How to turn Claude Code into your SEO command center</a></em></strong>



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<h2 class="wp-block-heading" id="h-how-claude-code-changes-the-equation">How Claude Code changes the equation</h2>



<p>Most general-purpose AI assistants are stuck behind a chat window. They can answer questions, but they can&#8217;t reach into your file system, remember what you said last week, or do anything outside their own UI.</p>



<p>Claude Code (Anthropic&#8217;s developer-focused agent) ticks four boxes that, together, change how you can operate day to day:</p>



<ul class="wp-block-list">
<li><strong>Native file system access:</strong> It reads and writes inside an actual project folder, accessing your local files like any directory.</li>



<li><strong>Persistent, structured memory:</strong> It remembers across sessions through Markdown files I curate.</li>



<li><strong>MCP integrations into the tools you already use:</strong> The Model Context Protocol lets it plug directly into Gmail, Slack, Google Drive, HubSpot, and Scoro. No data migration. No rebuilding workflows around it.</li>



<li><strong>An action layer:</strong> It drafts internal docs, prepares analysis, and handles the repeatable parts of my workflow.</li>
</ul>



<p>The last one is the most useful: moving from something that just stores information to something that acts on it. This is where I can save most of my time (and you can, too).</p>



<h2 class="wp-block-heading" id="h-the-four-layers-of-an-ai-second-brain">The four layers of an AI second brain</h2>



<p>The architecture of my second brain is organized into four layers, stacked on top of each other.</p>



<h3 class="wp-block-heading" id="h-1-memory-nbsp">1. Memory&nbsp;</h3>



<p>This is contained in a small set of plain Markdown files. One describes me and what I work on. One holds running facts worth keeping, such as pricing decisions, client preferences, and retainer structures. One defines the personality I want it to take when working with me.</p>



<p>These load automatically every session, so I never re-explain context.</p>



<p>The interesting bit is that the memory grows by itself. Every conversation gets dumped into a daily log. Once a day, a small job runs over that log and decides what&#8217;s worth promoting into long-term memory.</p>



<p>Throwaway chat doesn&#8217;t make the cut, and over six months, this has built a surprisingly accurate model of how I work and what each client needs.</p>



<h3 class="wp-block-heading" id="h-2-search">2. Search</h3>



<p>The long-term memory file stays small on purpose. So every daily log gets indexed into a local database.</p>



<p>When I ask something specific like, &#8216;What did we agree with this client on internal linking back in February?&#8217; it pulls the actual conversation and context to review.</p>



<h3 class="wp-block-heading" id="h-3-skills">3. Skills</h3>



<p>A skill is a focused capability I&#8217;ve defined once and can invoke by name: draft a client brief, build a sales proposal, reply to an email in my voice, summarize a discovery call into a scope of work.</p>



<p>Each skill is small, single-purpose, and inherits the memory layer underneath.</p>



<p>For example, the &#8216;draft email&#8217; skill isn&#8217;t generic because it knows my tone, the recipient, and what we last discussed, by training on all the data it reviews day to day.</p>



<p>I deliberately avoided one giant do-everything agent because what I really wanted was an advanced assistant that can work on small, composable skills that improve each day to help support me while I work on the bigger decisions.</p>



<h3 class="wp-block-heading" id="h-4-a-heartbeat">4. A heartbeat</h3>



<p>Every hour during the working day, a small process pulls a snapshot across my tools, such as new emails, calendar shifts, Slack threads I&#8217;m in, and HubSpot pipeline movement, and decides whether any of it needs me.</p>



<p>If it does, I get a Slack ping with a one-line summary and, where appropriate, a starting draft for me to work from.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/client-brain-ai-context-seo-479095">How a ‘client brain’ gives AI the context SEO work needs</a></em></strong>



<h2 class="wp-block-heading" id="h-where-it-pays-back-hours-every-week">Where it pays back hours every week</h2>



<p>Here&#8217;s where the time actually comes back:</p>



<h3 class="wp-block-heading" id="h-faster-context-gathering-for-client-work">Faster context-gathering for client work</h3>



<p>When a client emails for a status update, the second brain has already pulled together every Fireflies transcript, Slack thread, and HubSpot deal note for that account.</p>



<p>What used to be 30 minutes of searching before I write back is now 30 seconds of context, so I can spend the time on the reply itself, not the digging.</p>



<h3 class="wp-block-heading" id="h-faster-data-analysis">Faster data analysis</h3>



<p>Whether it&#8217;s analytics, rank-tracker data, or Search Console information, the second brain can pull what I need with context for review.</p>



<h3 class="wp-block-heading" id="h-discovery-to-scope">Discovery to scope</h3>



<p>New retainers used to take days of back-and-forth. Now the second brain reads the discovery transcript, the email thread, and historic data on similar engagements, and it produces a scope I can pressure-test rather than build from scratch.</p>



<p>All of it adds up to working more efficiently and giving clients better service because the important stuff isn&#8217;t slipping through.</p>



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<h2 class="wp-block-heading" id="h-the-guardrails-that-make-this-work">The guardrails that make this work</h2>



<p>Setting up a tool this powerful needs guardrails. Otherwise, you end up with an agent doing things you didn&#8217;t ask for.</p>



<h3 class="wp-block-heading" id="h-read-only-by-default">Read-only by default</h3>



<p>Every integration starts read-only. So for Slack, calendar, and Gmail, it can see and draft, but nothing goes out without me.</p>



<p>Write access gets added one tool at a time (and only once I trust the outputs from that tool). An agent with broad write access is one prompt injection away from doing something you&#8217;ll regret, and that risk compounds the more tools you wire in.</p>



<h3 class="wp-block-heading" id="h-memory-hygiene-matters">Memory hygiene matters</h3>



<p>The temptation is to dump everything into the tool. Don&#8217;t.</p>



<p>Long-term memory should hold things that change how the agent acts: client pricing, ongoing decisions, and ways of working.</p>



<h3 class="wp-block-heading" id="h-trust-the-draft-verify-the-action">Trust the draft, verify the action</h3>



<p>Read every email it drafts before it sends. Read every brief before it lands with a client.</p>



<p>The point isn&#8217;t to remove yourself from the work. It&#8217;s to give you a head start based on what it knows, while you still use your own expertise to finesse the final decisions.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/train-claude-sound-like-your-brand-479023">How to train Claude to sound like your brand</a></em></strong>



<h2 class="wp-block-heading" id="h-how-to-build-your-own-second-brain">How to build your own second brain</h2>



<p>You don&#8217;t need my exact stack. Use the tools you&#8217;re most comfortable with, along with this process.</p>



<ul class="wp-block-list">
<li>Pick the four or five places real decisions live for you. Email, calendar, one messaging tool, your CRM, and your task tool. That&#8217;s enough.</li>



<li>Add a transcript layer. Calls are where the most context gets lost in agency work.</li>



<li>Build memory first. A &#8216;this is me&#8217; file plus a daily log that gets distilled. Don&#8217;t move on until talking to it feels like talking to someone who knows your business.</li>



<li>Add skills one at a time. Pick the most repetitive thing you do and build a skill for it.</li>



<li>Add the heartbeat last. Once retrieval and skills both work, give it a schedule. Start with notifications only. Add write access slowly.</li>
</ul>



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<h2 class="wp-block-heading" id="h-this-is-a-second-brain-but-don-t-let-it-replace-your-actual-one">This is a second brain, but don&#8217;t let it replace your actual one</h2>



<p>The goal of a second brain isn&#8217;t to replace your existing one. It&#8217;s to make sure you can be as efficient as possible with your day-to-day work, providing more value for your team and clients.</p>



<p>The tools to build this didn&#8217;t exist 18 months ago. They do now, and the time it takes to set up is paid back within a fortnight.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/build-custom-seo-reports-claude-code-google-search-console-477894">How to build custom SEO reports with Claude Code and Google Search Console</a></em></strong>

If you run or work in an agency, manage clients, or own anything more than a single workflow, you know how the first 45 minutes of the morning vanish into Gmail, Slack, Fireflies, the CRM, and whatever doc was last on screen, just trying to remember what mattered the day before. 
It used to be I’d be thinking about a pricing decision I owed the agency team, a roadmap call for my habit-tracking app, three Slack threads I’d half-read on my phone the night before, and a sales follow-up that needed to go out today.
Those days are over. About six months ago, I rebuilt how I work using Claude Code as the engine of what I’ll call my second brain. The Monday morning synthesis now takes about a minute.
Here’s what I built, why, and how you can do the same.
Why most second-brain setups break down
The “second brain” idea isn’t new. Tiago Forte’s “Building a Second Brain,” the PARA method, Notion, and Obsidian were all built on the same instinct: externalize what you’d otherwise try to remember.
Capturing information works. Recall mostly works. But the bit that adds value is what happens after recall: turning what’s stored into something you can act on.
Three failure modes show up in almost every implementation I’ve seen:
Passive storage. Information goes in. It sits there. The only way out is search plus your own memory of what you tagged it. Meeting notes are a perfect example.
Context-switching tax. Even when you find the right note, you’re still copy-pasting and re-prompting your way to a usable output.
No action layer. A second brain that can’t draft, retrieve, or execute tasks can end up giving you more work than you started with. Over time, it builds up into an excess of notes that just leads to cognitive overload.
Documenting tasks isn’t the problem. The problem is that the documents and conversations are scattered across a hundred different apps, and nothing reads across them.
What’s missing is the layer on top: something that can pull from all of it and actually do the work.
Dig deeper: How to turn Claude Code into your SEO command center




  
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How Claude Code changes the equation
Most general-purpose AI assistants are stuck behind a chat window. They can answer questions, but they can’t reach into your file system, remember what you said last week, or do anything outside their own UI.
Claude Code (Anthropic’s developer-focused agent) ticks four boxes that, together, change how you can operate day to day:
Native file system access: It reads and writes inside an actual project folder, accessing your local files like any directory.
Persistent, structured memory: It remembers across sessions through Markdown files I curate.
MCP integrations into the tools you already use: The Model Context Protocol lets it plug directly into Gmail, Slack, Google Drive, HubSpot, and Scoro. No data migration. No rebuilding workflows around it.
An action layer: It drafts internal docs, prepares analysis, and handles the repeatable parts of my workflow.
The last one is the most useful: moving from something that just stores information to something that acts on it. This is where I can save most of my time (and you can, too).
The four layers of an AI second brain
The architecture of my second brain is organized into four layers, stacked on top of each other.
1. Memory 
This is contained in a small set of plain Markdown files. One describes me and what I work on. One holds running facts worth keeping, such as pricing decisions, client preferences, and retainer structures. One defines the personality I want it to take when working with me.
These load automatically every session, so I never re-explain context.
The interesting bit is that the memory grows by itself. Every conversation gets dumped into a daily log. Once a day, a small job runs over that log and decides what’s worth promoting into long-term memory.
Throwaway chat doesn’t make the cut, and over six months, this has built a surprisingly accurate model of how I work and what each client needs.
2. Search
The long-term memory file stays small on purpose. So every daily log gets indexed into a local database.
When I ask something specific like, ‘What did we agree with this client on internal linking back in February?’ it pulls the actual conversation and context to review.
3. Skills
A skill is a focused capability I’ve defined once and can invoke by name: draft a client brief, build a sales proposal, reply to an email in my voice, summarize a discovery call into a scope of work.
Each skill is small, single-purpose, and inherits the memory layer underneath.
For example, the ‘draft email’ skill isn’t generic because it knows my tone, the recipient, and what we last discussed, by training on all the data it reviews day to day.
I deliberately avoided one giant do-everything agent because what I really wanted was an advanced assistant that can work on small, composable skills that improve each day to help support me while I work on the bigger decisions.
4. A heartbeat
Every hour during the working day, a small process pulls a snapshot across my tools, such as new emails, calendar shifts, Slack threads I’m in, and HubSpot pipeline movement, and decides whether any of it needs me.
If it does, I get a Slack ping with a one-line summary and, where appropriate, a starting draft for me to work from.
Dig deeper: How a ‘client brain’ gives AI the context SEO work needs



Where it pays back hours every week
Here’s where the time actually comes back:
Faster context-gathering for client work
When a client emails for a status update, the second brain has already pulled together every Fireflies transcript, Slack thread, and HubSpot deal note for that account.
What used to be 30 minutes of searching before I write back is now 30 seconds of context, so I can spend the time on the reply itself, not the digging.
Faster data analysis
Whether it’s analytics, rank-tracker data, or Search Console information, the second brain can pull what I need with context for review.
Discovery to scope
New retainers used to take days of back-and-forth. Now the second brain reads the discovery transcript, the email thread, and historic data on similar engagements, and it produces a scope I can pressure-test rather than build from scratch.
All of it adds up to working more efficiently and giving clients better service because the important stuff isn’t slipping through.
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The guardrails that make this work
Setting up a tool this powerful needs guardrails. Otherwise, you end up with an agent doing things you didn’t ask for.
Read-only by default
Every integration starts read-only. So for Slack, calendar, and Gmail, it can see and draft, but nothing goes out without me.
Write access gets added one tool at a time (and only once I trust the outputs from that tool). An agent with broad write access is one prompt injection away from doing something you’ll regret, and that risk compounds the more tools you wire in.
Memory hygiene matters
The temptation is to dump everything into the tool. Don’t.
Long-term memory should hold things that change how the agent acts: client pricing, ongoing decisions, and ways of working.
Trust the draft, verify the action
Read every email it drafts before it sends. Read every brief before it lands with a client.
The point isn’t to remove yourself from the work. It’s to give you a head start based on what it knows, while you still use your own expertise to finesse the final decisions.
Dig deeper: How to train Claude to sound like your brand



How to build your own second brain
You don’t need my exact stack. Use the tools you’re most comfortable with, along with this process.
Pick the four or five places real decisions live for you. Email, calendar, one messaging tool, your CRM, and your task tool. That’s enough.
Add a transcript layer. Calls are where the most context gets lost in agency work.
Build memory first. A ‘this is me’ file plus a daily log that gets distilled. Don’t move on until talking to it feels like talking to someone who knows your business.
Add skills one at a time. Pick the most repetitive thing you do and build a skill for it.
Add the heartbeat last. Once retrieval and skills both work, give it a schedule. Start with notifications only. Add write access slowly.

  
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This is a second brain, but don’t let it replace your actual one
The goal of a second brain isn’t to replace your existing one. It’s to make sure you can be as efficient as possible with your day-to-day work, providing more value for your team and clients.
The tools to build this didn’t exist 18 months ago. They do now, and the time it takes to set up is paid back within a fortnight.
Dig deeper: How to build custom SEO reports with Claude Code and Google Search Console

See how memory, search, MCP integrations, and AI skills work together to reduce context-switching and keep client work moving.

How to build a Claude Code-powered second brain for agency work

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/06/DSA-to-AI-Max-delayes.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/DSA-to-AI-Max-delayes.png 1672w, https://searchengineland.com/wp-content/seloads/2026/06/DSA-to-AI-Max-delayes-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/DSA-to-AI-Max-delayes-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/DSA-to-AI-Max-delayes-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>The extended DSA transition timeline gives advertisers more time to test AI powered options &#038; manage the transition at a slower pace.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/06/DSA-to-AI-Max-delayes.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/DSA-to-AI-Max-delayes.png 1672w, https://searchengineland.com/wp-content/seloads/2026/06/DSA-to-AI-Max-delayes-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/DSA-to-AI-Max-delayes-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/DSA-to-AI-Max-delayes-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p>Google is pushing back the automatic migration of Dynamic Search Ads (DSA) to AI Max for Search campaigns by five months and restoring advertisers&#8217; ability to create new DSA campaigns.</p>



<p>The move gives advertisers more time to test AI-powered alternatives and manage the transition away from one of Google&#8217;s longest-running search campaign types on their own terms.</p>



<p><strong>What&#8217;s changing</strong>. <a href="https://searchengineland.com/google-retire-dynamic-search-ads-ai-max-474262">Google&#8217;s automatic upgrade of DSA campaigns to AI Max</a> — or Search campaigns using broad match and Smart Bidding — has been postponed from September 2026 to February 2027.</p>



<p>The company is also restoring the ability to create new DSA campaigns beginning June 15th, reversing an earlier phase-out step.</p>



<p><strong>Why we care.</strong> Google is giving advertisers nearly six extra months more to evaluate AI Max and other AI-powered search formats before being forced off DSAs. That additional time allows teams to run side-by-side tests, benchmark performance and refine migration strategies rather than relying on an automatic transition.</p>



<p><strong>What prompted the change</strong>. According to Google Ads Liaison Ginny Marvin, the extension comes in response to advertiser feedback requesting more time to prepare for the transition.</p>



<ul class="wp-block-list">
<li>&#8220;We&#8217;ve heard your feedback loud and clear: you need more time to transition from Dynamic Search Ads (DSA) to AI Max,&#8221; Marvin wrote on LinkedIn, adding that Google wanted to avoid interfering with advertisers&#8217; Q4 planning and execution.</li>
</ul>



<p><strong>What advertisers should do</strong>. Google is encouraging advertisers to audit existing DSA campaigns, run side-by-side experiments against AI Max for Search and use voluntary migration tools before the February 2027 deadline. Manual upgrades provide greater oversight and control while preserving historical reporting and campaign learnings.</p>



<p>Marvin also noted that automatically created assets and the campaign-level broad match setting will still transition to AI Max as scheduled in September 2026.</p>



<p><strong>Default settings change. </strong>While Google is extending the DSA timeline, it is continuing to make AI Max a central part of its Search strategy. <a href="https://www.linkedin.com/posts/ginnymarvin_googleads-aimax-share-7471238060689690624-ZNXW/?utm_source=share&amp;utm_medium=member_desktop&amp;rcm=ACoAAAJTV04Bw5M-1qqJtnImkpsEOFw9bHvfSSs">Marvin revealed that AI Max is now the default setting when creating new Search campaigns</a>, citing testing that showed campaigns achieved their first conversion faster during the first two weeks after launch.</p>



<p><strong>Timeline:</strong>



<ul class="wp-block-list">
<li><strong>June 2026:</strong> DSA campaign creation returns.</li>



<li><strong>June 2026 – January 2027:</strong> Extended testing and voluntary migration period.</li>



<li><strong>January 2027:</strong> New DSA creation ends.</li>



<li><strong>February 2027:</strong> Automatic migration begins for remaining campaigns.</li>
</ul>



<p><strong>The bottom line</strong>. <a href="https://ads-developers.googleblog.com/2026/06/dynamic-search-ads-dsa-automigration.html">Google is giving advertisers a longer runway to transition from Dynamic Search Ads</a> to AI-powered search campaigns while maintaining access to DSA creation and testing capabilities in the meantime.</p>


Google is pushing back the automatic migration of Dynamic Search Ads (DSA) to AI Max for Search campaigns by five months and restoring advertisers’ ability to create new DSA campaigns.
The move gives advertisers more time to test AI-powered alternatives and manage the transition away from one of Google’s longest-running search campaign types on their own terms.
What’s changing. Google’s automatic upgrade of DSA campaigns to AI Max — or Search campaigns using broad match and Smart Bidding — has been postponed from September 2026 to February 2027.
The company is also restoring the ability to create new DSA campaigns beginning June 15th, reversing an earlier phase-out step.
Why we care. Google is giving advertisers nearly six extra months more to evaluate AI Max and other AI-powered search formats before being forced off DSAs. That additional time allows teams to run side-by-side tests, benchmark performance and refine migration strategies rather than relying on an automatic transition.
What prompted the change. According to Google Ads Liaison Ginny Marvin, the extension comes in response to advertiser feedback requesting more time to prepare for the transition.
“We’ve heard your feedback loud and clear: you need more time to transition from Dynamic Search Ads (DSA) to AI Max,” Marvin wrote on LinkedIn, adding that Google wanted to avoid interfering with advertisers’ Q4 planning and execution.
What advertisers should do. Google is encouraging advertisers to audit existing DSA campaigns, run side-by-side experiments against AI Max for Search and use voluntary migration tools before the February 2027 deadline. Manual upgrades provide greater oversight and control while preserving historical reporting and campaign learnings.
Marvin also noted that automatically created assets and the campaign-level broad match setting will still transition to AI Max as scheduled in September 2026.
Default settings change. While Google is extending the DSA timeline, it is continuing to make AI Max a central part of its Search strategy. Marvin revealed that AI Max is now the default setting when creating new Search campaigns, citing testing that showed campaigns achieved their first conversion faster during the first two weeks after launch.
Timeline:




June 2026: DSA campaign creation returns.
June 2026 – January 2027: Extended testing and voluntary migration period.
January 2027: New DSA creation ends.
February 2027: Automatic migration begins for remaining campaigns.
The bottom line. Google is giving advertisers a longer runway to transition from Dynamic Search Ads to AI-powered search campaigns while maintaining access to DSA creation and testing capabilities in the meantime.

The extended DSA transition timeline gives advertisers more time to test AI powered options & manage the transition at a slower pace.

Google delays Dynamic Search Ads migration to AI Max

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/Why-attribution-and-impact-are-no-longer-the-same-thing-in-PPC.png" class="attachment-large size-large wp-post-image" alt="Why attribution and impact are no longer the same thing in PPC" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Why-attribution-and-impact-are-no-longer-the-same-thing-in-PPC.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/Why-attribution-and-impact-are-no-longer-the-same-thing-in-PPC-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Why-attribution-and-impact-are-no-longer-the-same-thing-in-PPC-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Why-attribution-and-impact-are-no-longer-the-same-thing-in-PPC-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Platform reports tell part of the story. Incrementality, CRM data, and broader measurement can reveal marketing's true impact.


PPC attribution has never been perfect, but AI is widening the gap between what influences a conversion and what gets credit for it.
Someone might discover a product on social media, watch a YouTube review, read opinions on Reddit, ask an AI-powered search tool to compare options, and then return days later through a branded Google search ad.
The PPC report will show one conversion from branded search. Technically, that may be accurate. Strategically, it’s incomplete.
AI is changing where people discover brands, how they research purchases, and how advertising platforms decide who sees an ad. At the same time, you have less visibility into the decisions those platforms make on your behalf.
As a result, you can’t afford to treat platform attribution as business truth.
AI is changing where the journey begins 
The traditional search journey is already being disrupted before an advertiser receives a measurable click.
Responsive’s 2025 “Inside the Buyer’s Mind” research found that generative AI had overtaken traditional search for one-quarter of B2B buyers, with nearly two-thirds using AI as much as or more than search when researching vendors.
The shift is even more pronounced in technology. The same research found that 80% of technology buyers use generative AI at least as much as traditional search for vendor research. More than half use LLM assistants as a leading source for discovering new vendors.
If a buyer is using AI to create a shortlist before visiting a search engine or company website, visibility during that research phase matters.
If your brand isn’t included in the initial AI-generated answer, you may not even make it into the consideration set.
Google’s own search experience is accelerating this change. At Google I/O in May, the company announced that AI Overviews had reached more than 2.5 billion monthly active users. AI Mode had surpassed a billion monthly active users.
Pew Research Center found that users clicked a traditional search result in just 8% of visits when an AI summary appeared, compared with 15% of visits without an AI summary.
The important point isn’t simply that clicks are declining.
Visibility and influence can exist without a website session. A person may read an AI Overview, remember a brand name, and search for it later. They may use ChatGPT, Gemini, or Perplexity to compare suppliers before returning directly to a website or clicking a branded ad.
The discovery touchpoint influences the conversion, but the lower-funnel interaction receives the credit.
Dig deeper: What 4 AI search experiments reveal about attribution and buying decisions




  
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Branded search often receives credit for demand generated elsewhere
Branded search is a clear example of attribution confused with impact.
A branded search campaign will often produce efficient cost-per-acquisition figures, high conversion rates, and an impressive return on ad spend. It reaches people who already know what they want and are actively looking for the business.
That makes branded search valuable. But it doesn’t mean the campaign created the demand.
The user may be searching for the brand because they encountered it in an AI-generated comparison, watched a creator review on YouTube, saw several paid social ads, or read a discussion on Reddit.
The branded ad captures existing intent.
This is why PPC reporting needs to distinguish between demand capture and demand creation.
Branded search, remarketing, and some lower-funnel Performance Max activity may appear to be the most effective campaigns in an account because they reach people who are already close to converting.
The more useful question isn’t whether those campaigns delivered conversions, but how many of those conversions would have happened if the campaigns hadn’t existed.
That’s the difference between attribution and incrementality.
AI-driven discovery creates a measurement blind spot
We’re already seeing the implications of this shift in client data. One client whose traffic was referred directly from AI platforms converted at 8.31% across the year, compared with 2.93% for organic search traffic.
The AI audience was far smaller: 565 measurable visits compared with approximately 17,000 organic visits. But 47 of those AI-referred visitors converted.
This comes with an important caveat. It’s one client example, not an industry benchmark. The measurable visits also represent only the people who clicked through from an AI platform. We don’t know how many users encountered the brand within an AI-generated answer and later returned through another channel.
That’s exactly the attribution problem.
Visible AI traffic may represent only a small portion of the overall journey. Some of the resulting demand may later appear as direct traffic, organic brand traffic, or branded paid search conversions.
In another example, we saw AI-referred visits increase by approximately 150% month over month for an industrial machinery client. This made sense when we considered the purchase journey. Buyers were asking highly technical questions, comparing features, and researching which type of equipment was most appropriate for a specific application.
AI search is particularly useful when buyers want a detailed comparison distilled into a clear answer.
This creates both an opportunity and a reporting challenge. The final paid search click may be only the last visible step in a much longer research process.
Ads are becoming part of AI-generated search journeys
With ads now integrated into AI-powered results, the relationship between AI search and paid media has become even more complicated.
Google states that ads can appear above, below, or within AI Overviews. Existing Search, Shopping, and Performance Max campaigns may appear within them when the query and the AI Overview indicate relevant commercial intent.
The example Google provides is revealing: A user might ask why their pool has turned green and how to clean it. The query isn’t explicitly transactional, but Google may serve an ad for a pool vacuum based on the context of the AI-generated response.
That means ads can appear earlier in the research journey, alongside more complex queries that don’t resemble a conventional commercial keyword.
You have limited visibility into this.
Google states that advertisers can’t target AI Overview placements directly, can’t opt out of appearing within AI Overviews, and don’t receive segmented reporting for ads served within them.
This is significant. You’re encouraged to participate in a new search experience, but there’s no clear way to isolate its performance.
Google is also testing new AI-powered ad formats in Search, including Conversational Discovery ads, Highlighted Answers, AI-powered Shopping ads, and a Business Agent for Leads.
These formats may create new opportunities to reach customers while they are actively researching. They may also make it harder to answer a deceptively simple question: What exactly caused the conversion?
Platform automation can make attribution look better while making analysis harder
The attribution challenge isn’t limited to AI search. It’s also happening inside ad platforms.
In Meta, broad Advantage+ audiences often outperform tightly defined demographic audiences on surface-level metrics such as reach, impressions, and click-through rate.
That sounds positive. Sometimes it is. When targeting becomes broader and the platform dynamically adjusts creative, messaging, and delivery, it becomes harder to understand exactly why an ad performed well.
Was it the creative? The headline? The audience? The product? The offer? The placement?
In some cases, conversion volume may increase, but reporting granularity doesn’t.
This matters because the next optimization decision depends on understanding what worked. A strong message could be reflected on the landing page. A specific customer pain point could inform the next creative brief. A high-performing audience insight could shape the broader marketing strategy.
When the platform retains those insights, you’re left feeding more creative variations into the system without fully understanding which variables are driving performance.
This becomes particularly risky when the platform’s metrics aren’t aligned with business outcomes.
A campaign can deliver excellent reach, a strong click-through rate, and a low cost per click while still generating poor-quality leads or low-value purchases.
It’s like feeding an absolute monster with money and creative assets without always receiving the information needed to make better decisions.
That might sound dramatic, but it reflects a real concern for those managing limited budgets.
Automation can help with delivery. It shouldn’t remove the need for scrutiny.
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Poor-quality traffic can affect future optimization
This is one reason you should be cautious about adopting broad targeting or broad match simply because a platform recommends it.
Broad targeting can work well in the right account, particularly when the platform has a strong volume of high-quality conversion data.
It can also bring in low-quality traffic. That affects more than the immediate campaign report.
Poor-quality traffic can influence website audiences, remarketing pools, conversion signals, and future automated bidding decisions. If an account is optimizing toward the wrong actions or feeding low-quality leads back into the platform, the system may become better at generating more of the wrong outcome.
This is where lead quality matters. A campaign generating 30 form submissions and two genuine opportunities may be less valuable than a campaign generating 12 inquiries and six qualified opportunities.
The first campaign may look stronger in Google Ads or Meta Ads. The second campaign may be more valuable to the business.
PPC teams need access to CRM outcomes, not just lead volume.
Automation also creates a new layer of reporting risk
Increased automation means PPC teams must be vigilant about conversion actions and assets used by each platform.
During our podcast, I shared an example where an account suddenly showed approximately 50,000 additional conversions within a matter of days. Google had introduced Google-hosted local engagement actions into the account reporting.
Without closely checking the conversion setup, the report would have painted an extremely misleading picture of performance.
There have also been situations where Meta surfaced an outdated promotion from a website and used it within an ad, even though it wasn’t the offer the client wanted to promote.
These aren’t arguments against automation.
They’re arguments against assuming platform defaults are automatically aligned with the advertiser’s objectives.
Conversion settings, automated assets, product selections, and campaign recommendations need regular human review.
Upper-funnel campaigns influence lower-funnel conversions
The limitations of attribution become particularly important when businesses assess upper-funnel activity.
Google and WARC analyzed long-term media investment studies from European brands and found that returns generated within the first four months were equal to the returns generated across the following 20 months.
The same analysis found that average short-term profit ROI increased from $2.50 for each $1.34 invested to $5.50 when sustained effects were included.
You shouldn’t apply these figures directly to every account. A local lead gen campaign will behave differently from an established ecommerce brand. The principle still matters.
If a business reduces video, paid social, or awareness activity because last-click ROAS looks weak, it may improve dashboard efficiency in the short term while quietly reducing future demand.
Branded search volumes may then decline several weeks or months later. Remarketing pools may shrink. Lower-funnel campaigns may deteriorate, even though nothing material changed within those campaigns.
Dig deeper: How to measure paid social’s impact on PPC



What PPC teams should report in 2026 
A single ROAS figure is no longer enough. PPC reporting needs to combine platform attribution with broader business indicators and structured experimentation.
1. Separate demand creation from demand capture
Don’t assess every campaign against the same expectations.
Branded search and remarketing are designed to capture existing intent.
Paid social, YouTube, demand gen, and upper-funnel campaigns are more likely to influence awareness, consideration, and future searches.
For demand capture campaigns, focus on efficiency, coverage, conversion rate, and marginal value.
For demand creation campaigns, include new customer growth, assisted conversions, branded search trends, direct traffic, audience growth, and lift testing where available.
2. Review attribution paths, not just final clicks
GA4’s key event attribution paths report helps you understand which channels initiate, assist, and close conversions. It also shows metrics, including days to conversion and touchpoints to conversion.
This is a useful starting point for understanding the broader journey.
Review:
Touchpoints to conversion.
Days to conversion.
Revenue from multi-touch journeys.
Early-stage and late-stage interactions.
New and returning customer performance.
Differences between high-value and low-value conversions.
A campaign with a lower last-click ROAS may still play an important role in bringing new customers into the funnel.
3. Import deeper CRM outcomes
Lead volume alone isn’t enough. Where possible, feed qualified leads, opportunities, and completed sales back into the ad platforms.
Google recommends enhanced conversions for leads as an upgraded form of offline conversion import. It uses hashed first-party customer data and supports more accurate reporting, engaged-view conversions, and cross-device conversions.
The platform can only optimize toward the signals it receives.
If every form submission is treated as equally valuable, the bidding system has no reason to prioritize the leads most likely to become customers.
4. Monitor the metrics sitting outside the PPC dashboard
AI-driven discovery means you need to look beyond directly attributed conversions.
Track:
Branded search volume.
Direct traffic.
Organic brand traffic.
AI-referred sessions.
AI-referred conversion rates.
New customer acquisition.
Returning visitor conversion rates.
Assisted conversions.
CRM lead quality.
Revenue by customer type.
These figures won’t prove causation individually. Together, they provide context that a last-click report can’t.
5. Test incrementality rather than assuming
Where account eligibility and budgets allow, use controlled testing.
Google describes its Conversion Lift as an incrementality tool that measures purchases, visits, and other conversions directly driven by exposure to ads. It compares users who saw the ads against a control group who did not.
Run practical tests independently:
Pause branded search activity in selected regions while retaining coverage elsewhere.
Compare similar geographic areas with different upper-funnel investment levels.
Test remarketing holdouts.
Monitor branded search demand before, during, and after video campaigns.
Compare new customer acquisition rates rather than relying on blended ROAS.
No test is perfect. Controlled experiments answer a more useful question than a platform dashboard: What changed because the advertising existed?
6. Add regular human checks to automated accounts
Automation should reduce repetitive work. It shouldn’t remove accountability.
Review:
Conversion actions included in bidding and reporting.
Automated assets and extensions.
AI-generated headlines and descriptions.
Product selections.
Promotions.
Search terms.
Lead quality.
Audience quality.
Website content that platforms scrape.
The platforms are changing quickly. A setup that was appropriate last month may not behave exactly the same way today.
Dig deeper: Why your B2B PPC metrics may be lying to you




  
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Stop searching for one perfect attribution model
There’s no single PPC attribution model that can fully explain a fragmented, AI-influenced customer journey.
Last-click reporting is too narrow. Platform attribution is useful but partial.
Data-driven attribution is more sophisticated, but it’s still limited by the interactions each platform can observe.
The answer isn’t to abandon attribution. It’s to stop treating attribution as proof of causation.
In 2026, PPC teams need to report performance using multiple layers of evidence: platform data, analytics paths, CRM outcomes, new customer trends, branded search demand, AI-referred traffic, assisted conversions, and controlled testing.
The most useful question is no longer, “Which channel received credit for this conversion?”
Instead, ask: “What would have happened if this activity hadn’t existed?”

Platform reports tell part of the story. Incrementality, CRM data, and broader measurement can reveal marketing's true impact.

Why attribution and impact are no longer the same thing in PPC

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/How-AI-prompt-patterns-vary-by-industry-and-shape-search-visibility.png" class="attachment-large size-large wp-post-image" alt="How AI prompt patterns vary by industry and shape search visibility" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/How-AI-prompt-patterns-vary-by-industry-and-shape-search-visibility.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/How-AI-prompt-patterns-vary-by-industry-and-shape-search-visibility-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/How-AI-prompt-patterns-vary-by-industry-and-shape-search-visibility-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/How-AI-prompt-patterns-vary-by-industry-and-shape-search-visibility-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>From symptom-based questions to software comparisons, see how user prompts influence what AI systems choose to surface.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/06/How-AI-prompt-patterns-vary-by-industry-and-shape-search-visibility.png" class="attachment-large size-large wp-post-image" alt="How AI prompt patterns vary by industry and shape search visibility" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/How-AI-prompt-patterns-vary-by-industry-and-shape-search-visibility.png 1920w, https://searchengineland.com/wp-content/seloads/2026/06/How-AI-prompt-patterns-vary-by-industry-and-shape-search-visibility-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/How-AI-prompt-patterns-vary-by-industry-and-shape-search-visibility-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/How-AI-prompt-patterns-vary-by-industry-and-shape-search-visibility-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>For more than two decades, <a href="https://searchengineland.com/guide/what-is-seo">SEO</a> was built on keywords. But as generative AI, Google’s AI Overviews, and conversational engines like ChatGPT and Perplexity reshape how people find information, prompts are becoming the new unit of search.</p>



<p>If you don&#8217;t understand the prompts your audience feeds into large language models (LLMs), your content won&#8217;t be retrieved to answer them. Here&#8217;s how prompt patterns differ across industries and what they mean for search visibility.</p>



<h2 class="wp-block-heading" id="h-how-prompts-differ-by-vertical">How prompts differ by vertical</h2>



<p>An LLM&#8217;s response is highly dependent on context. Because users seek vastly different outcomes across industries, their prompt structures naturally evolve into distinct, predictable patterns. You must map your content to these vertical-specific frameworks.</p>



<h3 class="wp-block-heading" id="h-healthcare-symptom-driven-and-cautious-language">Healthcare: Symptom-driven and cautious language</h3>



<ul class="wp-block-list">
<li>In healthcare, users treat AI assistants as a preliminary, highly personalized triage tool. Rather than searching for a broad keyword like &#8220;chronic fatigue,&#8221; they enter highly detailed, narrative-style prompts.</li>



<li><strong>The prompt pattern:</strong> Healthcare prompts are characterized by extensive personal context, real-time symptom mapping, and risk-averse, conditional constraints. Users frequently ask AI to evaluate a list of symptoms while accounting for safety parameters, age, or potential drug interactions.</li>



<li><strong>Anatomy of a healthcare prompt:</strong> Healthcare prompts often look something like this: &#8220;I&#8217;m a 45-year-old female experiencing sudden joint pain in my wrists and a mild rash after starting [Medication X] last week. What are the potential side effects, and at what point should I seek urgent care versus waiting for a doctor&#8217;s appointment?&#8221;</li>



<li><strong>The content shift:</strong> To achieve visibility here, your content can&#8217;t just list medical definitions. It must adopt a structure that mirrors the patient&#8217;s treatment-discovery mindset.</li>



<li><strong>The action:</strong> Lean heavily on clear, highly structured FAQ formats, explicit risk-factor callouts, and conversational headers that address specific symptom combinations.</li>
</ul>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/industries-adapting-answer-driven-search-464913">How industries are adapting to answer-driven search</a></em></strong>



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<h3 class="wp-block-heading" id="h-b2b-comparison-heavy-and-roi-driven">B2B: Comparison-heavy and ROI-driven</h3>



<ul class="wp-block-list">
<li>B2B buyers use generative AI to bypass traditional top-of-funnel marketing collateral. They use prompts to synthesize market research, build business cases, and compare software vendors.</li>



<li><strong>The prompt pattern:</strong> B2B prompts are highly analytical, objective, and deeply concerned with financial justification, implementation timelines, and feature parity. They frequently request information in table or matrix format that can be presented directly to decision-makers.</li>



<li><strong>Anatomy of a B2B prompt:</strong> These prompts often look something like this: &#8220;Compare enterprise CRM &#8216;Brand A&#8217; and &#8216;Brand B&#8217; for a mid-market manufacturing company with 500 users. Provide a breakdown of implementation times, hidden API costs, and estimated ROI over a three-year period. Format the response as a comparison table.&#8221;</li>



<li><strong>The content shift:</strong> If your B2B site relies entirely on gated, vague PDFs, you&#8217;ll be invisible to LLMs.</li>



<li><strong>The action:</strong> To win the B2B prompt pull, you must publish transparent, data-dense comparison pages. Include hard statistics, direct pricing realities, API limitations, and explicit ROI calculators. The more tabular and structured your technical data, the easier it is for an LLM to extract and inject into a user&#8217;s comparison table.</li>
</ul>



<h3 class="wp-block-heading" id="h-ecommerce-intentional-clusters-of-best-cheap-and-reviews">Ecommerce: Intentional clusters of ‘best,’ ‘cheap,’ and ‘reviews’</h3>



<p>Ecommerce search in conversational engines behaves like an interactive, highly personalized shopper. Recent data shows that nearly <a href="https://searchengineland.com/llm-nudges-ai-driven-journeys-474062">45% of LLM follow-up</a> &#8220;nudges&#8221; — the next steps LLMs offer users — are budget- or deal-related, meaning the engine itself actively steers users toward pricing and comparison variables.</p>



<ul class="wp-block-list">
<li><strong>The prompt pattern:</strong> Ecommerce prompts cluster highly specific intent markers into a single request. Users routinely combine qualitative parameters (&#8220;best reviewed&#8221;) with strict financial constraints (&#8220;cheap&#8221; or &#8220;under $X&#8221;) and highly specific situational context.</li>



<li><strong>Anatomy of an ecommerce prompt:</strong> An ecommerce prompt might look something like this: &#8220;What are the best-reviewed running shoes for overpronators that cost under $150? Remove any brands with known wear-and-tear issues mentioned in user reviews.&#8221;</li>



<li><strong>The content shift:</strong> Traditional keyword optimization would target &#8220;cheap running shoes.&#8221; Prompt optimization, however, requires you to supply the semantic depth an LLM needs to validate its recommendations.</li>



<li><strong>The action:</strong> To make strides in ecommerce, optimize your Merchant Center feeds with rich conversational attributes, ensure user reviews highlighting specific use cases (such as &#8220;for overpronators&#8221;) are crawlable, and create content that explicitly links product specifications to consumer value tiers.</li>
</ul>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/ai-era-seo-regulated-industries-467168">3 pillars of AI-era SEO for regulated industries</a></em></strong>



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<h2 class="wp-block-heading" id="h-why-prompt-structure-impacts-your-search-visibility">Why prompt structure impacts your search visibility</h2>



<p>Understanding these vertical prompt variations is only half the battle. To improve your brand&#8217;s visibility in LLMs, you also need to understand why the structure of a user&#8217;s prompt directly influences whether your website receives a citation.</p>



<figure class="wp-block-table"><table class="has-fixed-layout"><tbody><tr><td><strong>Prompt structural element</strong></td><td><strong>Impact on LLM retrieval</strong></td><td><strong>How to optimize your content</strong></td></tr><tr><td><strong>Contextual constraints </strong>(such as &#8220;under $150&#8221; or &#8220;for a 45-year-old&#8221;)</td><td>LLMs filter out any source data that can’t explicitly confirm it meets the user&#8217;s criteria.</td><td>Use precise schema markup and hard data points instead of vague adjectives. State exact dimensions, prices, and demographic indicators.</td></tr><tr><td><strong>Formatting requests </strong>(such as &#8220;Format as a table&#8221; or &#8220;Give me a pros/cons list&#8221;)</td><td>Engines favor source text that is already organized logically and can be easily refactored into the requested output.</td><td>Structure content using clean HTML tables, bulleted lists, and clear H2 and H3 headings that mirror these logical layouts.</td></tr><tr><td><strong>Sequential / follow-up prompts</strong> (Multi-turn conversations)</td><td>The search session evolves. A user&#8217;s first prompt establishes the topic, and then their second and third prompts refine it with specific &#8220;why&#8221; or &#8220;how&#8221; questions.</td><td>Build comprehensive content clusters. Don’t just answer &#8220;What is product X?&#8221; Instead, anticipate the follow-up prompt by detailing &#8220;How does X integrate with Y?&#8221; on the same page.</td></tr></tbody></table></figure>



<h2 class="wp-block-heading" id="h-the-power-of-reasoning-lift-and-direct-citations">The power of ‘reasoning lift’ and direct citations</h2>



<p>Optimizing content for fluency, embedding direct citations, and including hard statistics can increase a website&#8217;s visibility in LLM responses by <a href="https://www.getfancy.ai/article-princeton-geo-decoded" target="_blank" rel="noopener">up to 40%</a>, according to joint research from Princeton University and the Allen Institute for AI.&nbsp;</p>



<p>Tracking Google’s AI Overviews reveals a staggering reality: <a href="https://ahrefs.com/blog/ai-search-overlap/" target="_blank" rel="noopener">more than 80%</a> of the links provided in conversational AI answers come from domains that don’t even rank in the top 10 of traditional, organic desktop search results, per an Ahrefs study.&nbsp;</p>



<p>What does this tell us? LLMs aren&#8217;t looking at your legacy backlink profile to determine authority. Instead, they&#8217;re evaluating your content&#8217;s semantic depth and structural readiness. If a user prompts the engine with a complex, industry-specific question, it will favor the website that provides a direct, highly structured, and verifiable answer to that exact prompt pattern.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/prompt-research-seo-geo-strategy-471399">Prompt research: The next layer of SEO and GEO strategy</a></em></strong>



<h2 class="wp-block-heading" id="h-operationalizing-prompt-research">Operationalizing prompt research</h2>



<p>Shifting your mental model from keyword volume to prompt patterns will be one of the defining SEO challenges of the late 2020s. To ensure your brand remains visible as conversational search scales, your marketing workflow must evolve in a few key ways.</p>



<ul class="wp-block-list">
<li><strong>Stop tracking isolated keywords: </strong>Instead of relying solely on keyword research, start discovering and clustering conversational prompt data from search logs, customer service transcripts, and AI search behavior proxies.</li>



<li><strong>Audit for LLM readability: </strong>Ensure your technical architecture includes modern standards, such as an <a href="https://searchengineland.com/llms-txt-proposed-standard-453676">llms.txt file,</a> alongside clean, schema-backed data that allows AI crawlers to parse your specifications instantly.</li>



<li><strong>Write for the follow-up: </strong>Build your content strategy around the entire trajectory of a conversation, not just the initial query. If you optimize only for the user&#8217;s first query, a competitor that optimized for the inevitable follow-up prompt may win the final recommendation.</li>
</ul>



<p>As conversational search evolves, understanding prompt patterns will become increasingly important for maintaining visibility. The brands that align their content with how people interact with AI systems will be better positioned to earn retrieval and citations.</p>

For more than two decades, SEO was built on keywords. But as generative AI, Google’s AI Overviews, and conversational engines like ChatGPT and Perplexity reshape how people find information, prompts are becoming the new unit of search.
If you don’t understand the prompts your audience feeds into large language models (LLMs), your content won’t be retrieved to answer them. Here’s how prompt patterns differ across industries and what they mean for search visibility.
How prompts differ by vertical
An LLM’s response is highly dependent on context. Because users seek vastly different outcomes across industries, their prompt structures naturally evolve into distinct, predictable patterns. You must map your content to these vertical-specific frameworks.
Healthcare: Symptom-driven and cautious language
In healthcare, users treat AI assistants as a preliminary, highly personalized triage tool. Rather than searching for a broad keyword like “chronic fatigue,” they enter highly detailed, narrative-style prompts.
The prompt pattern: Healthcare prompts are characterized by extensive personal context, real-time symptom mapping, and risk-averse, conditional constraints. Users frequently ask AI to evaluate a list of symptoms while accounting for safety parameters, age, or potential drug interactions.
Anatomy of a healthcare prompt: Healthcare prompts often look something like this: “I’m a 45-year-old female experiencing sudden joint pain in my wrists and a mild rash after starting [Medication X] last week. What are the potential side effects, and at what point should I seek urgent care versus waiting for a doctor’s appointment?”
The content shift: To achieve visibility here, your content can’t just list medical definitions. It must adopt a structure that mirrors the patient’s treatment-discovery mindset.
The action: Lean heavily on clear, highly structured FAQ formats, explicit risk-factor callouts, and conversational headers that address specific symptom combinations.
Dig deeper: How industries are adapting to answer-driven search




  
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B2B: Comparison-heavy and ROI-driven
B2B buyers use generative AI to bypass traditional top-of-funnel marketing collateral. They use prompts to synthesize market research, build business cases, and compare software vendors.
The prompt pattern: B2B prompts are highly analytical, objective, and deeply concerned with financial justification, implementation timelines, and feature parity. They frequently request information in table or matrix format that can be presented directly to decision-makers.
Anatomy of a B2B prompt: These prompts often look something like this: “Compare enterprise CRM ‘Brand A’ and ‘Brand B’ for a mid-market manufacturing company with 500 users. Provide a breakdown of implementation times, hidden API costs, and estimated ROI over a three-year period. Format the response as a comparison table.”
The content shift: If your B2B site relies entirely on gated, vague PDFs, you’ll be invisible to LLMs.
The action: To win the B2B prompt pull, you must publish transparent, data-dense comparison pages. Include hard statistics, direct pricing realities, API limitations, and explicit ROI calculators. The more tabular and structured your technical data, the easier it is for an LLM to extract and inject into a user’s comparison table.
Ecommerce: Intentional clusters of ‘best,’ ‘cheap,’ and ‘reviews’
Ecommerce search in conversational engines behaves like an interactive, highly personalized shopper. Recent data shows that nearly 45% of LLM follow-up “nudges” — the next steps LLMs offer users — are budget- or deal-related, meaning the engine itself actively steers users toward pricing and comparison variables.
The prompt pattern: Ecommerce prompts cluster highly specific intent markers into a single request. Users routinely combine qualitative parameters (“best reviewed”) with strict financial constraints (“cheap” or “under $X”) and highly specific situational context.
Anatomy of an ecommerce prompt: An ecommerce prompt might look something like this: “What are the best-reviewed running shoes for overpronators that cost under $150? Remove any brands with known wear-and-tear issues mentioned in user reviews.”
The content shift: Traditional keyword optimization would target “cheap running shoes.” Prompt optimization, however, requires you to supply the semantic depth an LLM needs to validate its recommendations.
The action: To make strides in ecommerce, optimize your Merchant Center feeds with rich conversational attributes, ensure user reviews highlighting specific use cases (such as “for overpronators”) are crawlable, and create content that explicitly links product specifications to consumer value tiers.
Dig deeper: 3 pillars of AI-era SEO for regulated industries




	
	
Get the newsletter search marketers rely on.
 
				
				
			
See terms.
		
Why prompt structure impacts your search visibility
Understanding these vertical prompt variations is only half the battle. To improve your brand’s visibility in LLMs, you also need to understand why the structure of a user’s prompt directly influences whether your website receives a citation.

Prompt structural elementImpact on LLM retrievalHow to optimize your content
Contextual constraints (such as “under $150” or “for a 45-year-old”)LLMs filter out any source data that can’t explicitly confirm it meets the user’s criteria.Use precise schema markup and hard data points instead of vague adjectives. State exact dimensions, prices, and demographic indicators.
Formatting requests (such as “Format as a table” or “Give me a pros/cons list”)Engines favor source text that is already organized logically and can be easily refactored into the requested output.Structure content using clean HTML tables, bulleted lists, and clear H2 and H3 headings that mirror these logical layouts.
Sequential / follow-up prompts (Multi-turn conversations)The search session evolves. A user’s first prompt establishes the topic, and then their second and third prompts refine it with specific “why” or “how” questions.Build comprehensive content clusters. Don’t just answer “What is product X?” Instead, anticipate the follow-up prompt by detailing “How does X integrate with Y?” on the same page.





The power of ‘reasoning lift’ and direct citations
Optimizing content for fluency, embedding direct citations, and including hard statistics can increase a website’s visibility in LLM responses by up to 40%, according to joint research from Princeton University and the Allen Institute for AI. 
Tracking Google’s AI Overviews reveals a staggering reality: more than 80% of the links provided in conversational AI answers come from domains that don’t even rank in the top 10 of traditional, organic desktop search results, per an Ahrefs study. 
What does this tell us? LLMs aren’t looking at your legacy backlink profile to determine authority. Instead, they’re evaluating your content’s semantic depth and structural readiness. If a user prompts the engine with a complex, industry-specific question, it will favor the website that provides a direct, highly structured, and verifiable answer to that exact prompt pattern.
Dig deeper: Prompt research: The next layer of SEO and GEO strategy



Operationalizing prompt research
Shifting your mental model from keyword volume to prompt patterns will be one of the defining SEO challenges of the late 2020s. To ensure your brand remains visible as conversational search scales, your marketing workflow must evolve in a few key ways.
Stop tracking isolated keywords: Instead of relying solely on keyword research, start discovering and clustering conversational prompt data from search logs, customer service transcripts, and AI search behavior proxies.
Audit for LLM readability: Ensure your technical architecture includes modern standards, such as an llms.txt file, alongside clean, schema-backed data that allows AI crawlers to parse your specifications instantly.
Write for the follow-up: Build your content strategy around the entire trajectory of a conversation, not just the initial query. If you optimize only for the user’s first query, a competitor that optimized for the inevitable follow-up prompt may win the final recommendation.
As conversational search evolves, understanding prompt patterns will become increasingly important for maintaining visibility. The brands that align their content with how people interact with AI systems will be better positioned to earn retrieval and citations.

From symptom-based questions to software comparisons, see how user prompts influence what AI systems choose to surface.

How AI prompt patterns vary by industry and shape search visibility

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/12/instagram-your-algorithm.png" class="attachment-large size-large wp-post-image" alt="Instagram Your Algorithm" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/12/instagram-your-algorithm.png 1920w, https://searchengineland.com/wp-content/seloads/2025/12/instagram-your-algorithm-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/12/instagram-your-algorithm-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Topic controls give users more say in what they see, pushing brands to create content tied to clear audience interests.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2025/12/instagram-your-algorithm.png" class="attachment-large size-large wp-post-image" alt="Instagram Your Algorithm" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2025/12/instagram-your-algorithm.png 1920w, https://searchengineland.com/wp-content/seloads/2025/12/instagram-your-algorithm-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2025/12/instagram-your-algorithm-1536x864.png 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Instagram expanded its Your Algorithm controls to the main feed. This update gives Instagram users a way to manage the topics that influence recommendations across Feed, Reels, and Explore.</p>



<p><strong>About Your Algorithm.</strong> The Your Algorithm feature lets people view the topics Instagram associates with their interests, remove topics they don&#8217;t want, and add topics they want to see more often. <a href="https://searchengineland.com/instagram-your-algorithm-465987">Instagram introduced Your Algorithm for Reels in December</a> and has since expanded topic controls to more recommendation surfaces.</p>



<p><strong>Feed joins Reels and Explore</strong>. The update brings topic-level controls to Instagram’s main feed, where recommended posts from accounts users don’t follow now make up a larger share of the experience.</p>



<ul class="wp-block-list">
<li>Users start with a list Instagram generates from their activity. </li>



<li>Changes to that list help the system adjust future recommendations.</li>
</ul>



<p><strong>More user control</strong>. Instagram head Adam Mosseri said the change addresses the lack of control users often feel in recommendation-driven feeds.</p>



<ul class="wp-block-list">
<li>“The system learns from what you tap, watch, and share, but you don’t really get to tell it what you want,” Mosseri wrote.</li>



<li>Large language models now allow Instagram to describe content clusters in plain language, giving users a clearer way to shape what the system believes they like, Mosseri added.</li>
</ul>



<p><strong>Interest media. </strong><a href="https://garyvee.substack.com/p/social-media-is-dead-interest-media" target="_blank" rel="noopener">Gary Vaynerchuk described this shift</a> from follower-based feeds (social media) to interest-based discovery (interest media). Platforms now prioritize content based on engagement rather than who users follow. Instagram’s update makes that model more transparent by showing users the interests behind their recommendations.</p>



<p><strong>Why we care</strong>. Instagram discovery increasingly prioritizes matching user interests over follower relationships. Your content must signal clear topics and audience intent because those signals help determine where recommendations appear.</p>



<p><strong>More controls are planned</strong>. Topics are just the beginning. Mosseri said Instagram is developing controls for people, moods, content types, and other signals.</p>


Instagram expanded its Your Algorithm controls to the main feed. This update gives Instagram users a way to manage the topics that influence recommendations across Feed, Reels, and Explore.
About Your Algorithm. The Your Algorithm feature lets people view the topics Instagram associates with their interests, remove topics they don’t want, and add topics they want to see more often. Instagram introduced Your Algorithm for Reels in December and has since expanded topic controls to more recommendation surfaces.
Feed joins Reels and Explore. The update brings topic-level controls to Instagram’s main feed, where recommended posts from accounts users don’t follow now make up a larger share of the experience.
Users start with a list Instagram generates from their activity. 
Changes to that list help the system adjust future recommendations.
More user control. Instagram head Adam Mosseri said the change addresses the lack of control users often feel in recommendation-driven feeds.
“The system learns from what you tap, watch, and share, but you don’t really get to tell it what you want,” Mosseri wrote.
Large language models now allow Instagram to describe content clusters in plain language, giving users a clearer way to shape what the system believes they like, Mosseri added.
Interest media. Gary Vaynerchuk described this shift from follower-based feeds (social media) to interest-based discovery (interest media). Platforms now prioritize content based on engagement rather than who users follow. Instagram’s update makes that model more transparent by showing users the interests behind their recommendations.
Why we care. Instagram discovery increasingly prioritizes matching user interests over follower relationships. Your content must signal clear topics and audience intent because those signals help determine where recommendations appear.
More controls are planned. Topics are just the beginning. Mosseri said Instagram is developing controls for people, moods, content types, and other signals.

Topic controls give users more say in what they see, pushing brands to create content tied to clear audience interests.

Instagram now lets users tell the algorithm what they want

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/06/Walmart_Google-DV360.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Walmart_Google-DV360.png 1672w, https://searchengineland.com/wp-content/seloads/2026/06/Walmart_Google-DV360-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Walmart_Google-DV360-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Walmart_Google-DV360-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Google and Walmart team up to let advertisers target Walmart shoppers on YouTube and measure the sales within DV360.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/06/Walmart_Google-DV360.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Walmart_Google-DV360.png 1672w, https://searchengineland.com/wp-content/seloads/2026/06/Walmart_Google-DV360-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Walmart_Google-DV360-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Walmart_Google-DV360-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p>Google and Walmart are partnering to bring Walmart Connect audience targeting and sales measurement into Display &amp; Video 360, starting with YouTube campaigns.</p>



<p>The integration gives advertisers access to Walmart&#8217;s shopper data while allowing them to measure whether YouTube ads drive actual sales at the retailer.</p>



<p><strong>What&#8217;s happening</strong>. Brands can now activate Walmart Connect audiences within Display &amp; Video 360 and use those insights to reach shoppers across YouTube.</p>



<p>The partnership also enables closed-loop measurement, allowing advertisers to connect ad exposure with purchases made at Walmart.</p>



<p><strong>Why we care. </strong>The partnership combines Walmart&#8217;s valuable first-party shopper data with YouTube&#8217;s massive reach, allowing brands to target consumers based on actual retail behavior rather than inferred interests alone.</p>



<p>More importantly, it closes a longstanding measurement gap. Instead of simply tracking views or clicks, advertisers can see whether their YouTube campaigns drove sales at Walmart, making it easier to connect media spend to business outcomes and justify investment in video advertising.</p>



<p><strong>The big picture</strong>. Retail media networks are increasingly extending beyond their owned properties, bringing shopper data and measurement capabilities to broader digital advertising channels where brands are investing more of their budgets.</p>



<p><strong>Between the lines.</strong> The deal reflects Walmart Connect&#8217;s push to make its audience and measurement tools available across more advertising platforms. Last year, Walmart ended the exclusivity arrangement tied to its partnership with The Trade Desk, opening the door to additional ad tech integrations.</p>



<p><strong>What advertisers get</strong>. Access to Walmart&#8217;s audience insights, reach to the retailer&#8217;s 150 million weekly U.S. customers through YouTube campaigns, and sales attribution tied to Walmart purchases — all within the Display &amp; Video 360 workflow.</p>



<p><strong>What&#8217;s next</strong>. The rollout is beginning with YouTube campaigns, with Google and Walmart planning to expand the integration to additional inventory over time.</p>



<p><strong>The bottom line</strong>. The partnership brings retail media data, media activation and sales measurement closer together, giving advertisers a more direct way to connect YouTube campaigns with in-store and online purchasing behavior at Walmart.</p>



<p><strong>Dig deeper.</strong>



<ul class="wp-block-list">
<li><a href="https://blog.google/products/marketingplatform/360/walmart-connect/">We’re bringing Walmart Connect to Display &amp; Video 360.</a></li>



<li><a href="https://www.adweek.com/commerce/walmart-challenges-amazon-with-google-deal/">Walmart Challenges Amazon With New Google Deal</a></li>
</ul>


Google and Walmart are partnering to bring Walmart Connect audience targeting and sales measurement into Display & Video 360, starting with YouTube campaigns.
The integration gives advertisers access to Walmart’s shopper data while allowing them to measure whether YouTube ads drive actual sales at the retailer.
What’s happening. Brands can now activate Walmart Connect audiences within Display & Video 360 and use those insights to reach shoppers across YouTube.
The partnership also enables closed-loop measurement, allowing advertisers to connect ad exposure with purchases made at Walmart.
Why we care. The partnership combines Walmart’s valuable first-party shopper data with YouTube’s massive reach, allowing brands to target consumers based on actual retail behavior rather than inferred interests alone.
More importantly, it closes a longstanding measurement gap. Instead of simply tracking views or clicks, advertisers can see whether their YouTube campaigns drove sales at Walmart, making it easier to connect media spend to business outcomes and justify investment in video advertising.
The big picture. Retail media networks are increasingly extending beyond their owned properties, bringing shopper data and measurement capabilities to broader digital advertising channels where brands are investing more of their budgets.
Between the lines. The deal reflects Walmart Connect’s push to make its audience and measurement tools available across more advertising platforms. Last year, Walmart ended the exclusivity arrangement tied to its partnership with The Trade Desk, opening the door to additional ad tech integrations.
What advertisers get. Access to Walmart’s audience insights, reach to the retailer’s 150 million weekly U.S. customers through YouTube campaigns, and sales attribution tied to Walmart purchases — all within the Display & Video 360 workflow.
What’s next. The rollout is beginning with YouTube campaigns, with Google and Walmart planning to expand the integration to additional inventory over time.
The bottom line. The partnership brings retail media data, media activation and sales measurement closer together, giving advertisers a more direct way to connect YouTube campaigns with in-store and online purchasing behavior at Walmart.
Dig deeper.




We’re bringing Walmart Connect to Display & Video 360.
Walmart Challenges Amazon With New Google Deal

Google and Walmart team up to let advertisers target Walmart shoppers on YouTube and measure the sales within DV360.

Walmart Connect audiences come to Google Display & Video 360

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/06/Amazon-Alexa-shopping-ads.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Amazon-Alexa-shopping-ads.png 1672w, https://searchengineland.com/wp-content/seloads/2026/06/Amazon-Alexa-shopping-ads-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Amazon-Alexa-shopping-ads-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Amazon-Alexa-shopping-ads-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>Amazon is weaving advertising into AI-powered shopping conversations, giving brands new ways to reach consumers.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/06/Amazon-Alexa-shopping-ads.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Amazon-Alexa-shopping-ads.png 1672w, https://searchengineland.com/wp-content/seloads/2026/06/Amazon-Alexa-shopping-ads-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/Amazon-Alexa-shopping-ads-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/Amazon-Alexa-shopping-ads-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p>Amazon is making the case that conversational and agentic shopping aren&#8217;t future concepts — they&#8217;re already reshaping how consumers discover, compare and buy products.</p>



<p>As AI assistants become shopping destinations, advertisers have a new opportunity to reach consumers at the moment intent is expressed, rather than relying on traditional search queries or passive browsing.</p>



<p><strong>What&#8217;s happening</strong>. Amazon has combined its AI shopping assistant Rufus with Alexa+ to create Alexa for Shopping, a unified experience that helps customers research products, compare options, track prices, build carts and automate purchases.</p>



<p>The company says advertising is a key part of that experience, with sponsored products, sponsored brands and conversational ad formats integrated directly into shopping conversations.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1430" height="1008" src="https://searchengineland.com/wp-content/seloads/2026/06/Amazon-brand_nws_Picture_bodyy1._TTW_.jpg" alt="" class="wp-image-479906" srcset="https://searchengineland.com/wp-content/seloads/2026/06/Amazon-brand_nws_Picture_bodyy1._TTW_.jpg 1430w, https://searchengineland.com/wp-content/seloads/2026/06/Amazon-brand_nws_Picture_bodyy1._TTW_-768x541.jpg 768w" sizes="(max-width: 1430px) 100vw, 1430px" /></figure>



<p><strong>What advertisers get</strong>. Existing sponsored ads campaigns are automatically eligible to appear in Alexa for Shopping, while conversational ad formats can help brands engage customers throughout the buying journey. Amazon also highlighted closed-loop measurement, first-party data signals and AI-powered campaign tools designed to simplify campaign creation and optimization.</p>



<p><strong>Why we care.</strong> The integration of ads into Alexa for Shopping gives advertisers access to intent-rich conversations at multiple points along the customer journey, from product discovery to purchase.</p>



<p>The update also highlights how commerce and advertising are converging inside AI assistants, potentially shortening the path from product discovery to conversion while providing advertisers with closed-loop measurement from impression to purchase.</p>



<p><strong>By the numbers</strong>. More than 300 million customers used Rufus in 2025, according to Amazon. The company also says nearly 20% of shoppers who interact with a Sponsored Brands prompt continue the conversation about that brand, while prompts drive a 6% increase in conversions.</p>



<p><strong>Between the lines.</strong> Amazon&#8217;s pitch to advertisers is that conversational AI creates richer intent signals than traditional advertising environments. Instead of inferring what a customer wants from clicks or searches, Alexa can respond to explicit requests, preferences and purchase goals shared in natural language.</p>



<p><strong>The bottom line</strong>. As shopping becomes more conversational, <a href="https://advertising.amazon.com/library/news/agentic-shopping-advertising">Amazon is positioning advertising within the same interfaces consumers use to research products, compare options and complete purchases</a>.</p>






Amazon is making the case that conversational and agentic shopping aren’t future concepts — they’re already reshaping how consumers discover, compare and buy products.
As AI assistants become shopping destinations, advertisers have a new opportunity to reach consumers at the moment intent is expressed, rather than relying on traditional search queries or passive browsing.
What’s happening. Amazon has combined its AI shopping assistant Rufus with Alexa+ to create Alexa for Shopping, a unified experience that helps customers research products, compare options, track prices, build carts and automate purchases.
The company says advertising is a key part of that experience, with sponsored products, sponsored brands and conversational ad formats integrated directly into shopping conversations.




What advertisers get. Existing sponsored ads campaigns are automatically eligible to appear in Alexa for Shopping, while conversational ad formats can help brands engage customers throughout the buying journey. Amazon also highlighted closed-loop measurement, first-party data signals and AI-powered campaign tools designed to simplify campaign creation and optimization.
Why we care. The integration of ads into Alexa for Shopping gives advertisers access to intent-rich conversations at multiple points along the customer journey, from product discovery to purchase.
The update also highlights how commerce and advertising are converging inside AI assistants, potentially shortening the path from product discovery to conversion while providing advertisers with closed-loop measurement from impression to purchase.
By the numbers. More than 300 million customers used Rufus in 2025, according to Amazon. The company also says nearly 20% of shoppers who interact with a Sponsored Brands prompt continue the conversation about that brand, while prompts drive a 6% increase in conversions.
Between the lines. Amazon’s pitch to advertisers is that conversational AI creates richer intent signals than traditional advertising environments. Instead of inferring what a customer wants from clicks or searches, Alexa can respond to explicit requests, preferences and purchase goals shared in natural language.
The bottom line. As shopping becomes more conversational, Amazon is positioning advertising within the same interfaces consumers use to research products, compare options and complete purchases.

Amazon is weaving advertising into AI-powered shopping conversations, giving brands new ways to reach consumers.

Amazon turns Alexa into a shopping agent — and an advertising platform

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/06/ChatGPT-ads-product-feeds.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/ChatGPT-ads-product-feeds.png 1672w, https://searchengineland.com/wp-content/seloads/2026/06/ChatGPT-ads-product-feeds-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/ChatGPT-ads-product-feeds-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/ChatGPT-ads-product-feeds-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>OpenAI is allowing retail advertisers connect shoppers with relevant products without manually building ads for each item.

<div><img width="1672" height="941" src="https://searchengineland.com/wp-content/seloads/2026/06/ChatGPT-ads-product-feeds.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/06/ChatGPT-ads-product-feeds.png 1672w, https://searchengineland.com/wp-content/seloads/2026/06/ChatGPT-ads-product-feeds-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/06/ChatGPT-ads-product-feeds-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/06/ChatGPT-ads-product-feeds-1200x675.png 1200w" sizes="(max-width: 1672px) 100vw, 1672px" /></div>
<p>OpenAI has introduced a new Ads Manager beta feature that allows retail advertisers to upload product feeds and automatically create ads from individual catalog items.</p>



<p>The update gives brands a way to scale advertising campaigns inside ChatGPT while serving products that are more relevant to users during purchase-focused conversations.</p>



<p><strong>What&#8217;s happening</strong>. Advertisers can now upload their full product catalogs and generate ads directly from feed data rather than building campaigns item by item.</p>



<p>According to OpenAI, feed-based ads have been among the strongest-performing formats in its Ads Beta so far.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="999" height="963" src="https://searchengineland.com/wp-content/seloads/2026/06/ChatGPT-Porduct-feeds.jpeg" alt="" class="wp-image-479901" srcset="https://searchengineland.com/wp-content/seloads/2026/06/ChatGPT-Porduct-feeds.jpeg 999w, https://searchengineland.com/wp-content/seloads/2026/06/ChatGPT-Porduct-feeds-768x740.jpeg 768w" sizes="(max-width: 999px) 100vw, 999px" /></figure>



<p><strong>Why we care. </strong>OpenAI bringing product feed ads to ChatGPT gives retailers a scalable way to match catalog inventory with high-intent conversations and potentially improve ad performance.</p>



<p><strong>Between the lines</strong>. The move mirrors a strategy long used by Google and Meta, where dynamic product feeds allow advertisers to match inventory with user intent at scale.</p>



<p><strong>What&#8217;s next</strong>. Retail advertisers in the beta can review feed requirements and begin creating campaigns directly from uploaded product catalogs.</p>



<p><strong>The bottom line</strong>. OpenAI is expanding its advertising toolkit with a format designed to make ChatGPT ads more scalable, automated and performance-driven for retailers.</p>



<p><strong>First spotted. </strong>This update was shared by Founder of JXT Group Menachem Ani, who shared the email he got from OpenAI on <a href="https://x.com/MenachemAni/status/2064778385113375047">X</a>.</p>


OpenAI has introduced a new Ads Manager beta feature that allows retail advertisers to upload product feeds and automatically create ads from individual catalog items.
The update gives brands a way to scale advertising campaigns inside ChatGPT while serving products that are more relevant to users during purchase-focused conversations.
What’s happening. Advertisers can now upload their full product catalogs and generate ads directly from feed data rather than building campaigns item by item.
According to OpenAI, feed-based ads have been among the strongest-performing formats in its Ads Beta so far.




Why we care. OpenAI bringing product feed ads to ChatGPT gives retailers a scalable way to match catalog inventory with high-intent conversations and potentially improve ad performance.
Between the lines. The move mirrors a strategy long used by Google and Meta, where dynamic product feeds allow advertisers to match inventory with user intent at scale.
What’s next. Retail advertisers in the beta can review feed requirements and begin creating campaigns directly from uploaded product catalogs.
The bottom line. OpenAI is expanding its advertising toolkit with a format designed to make ChatGPT ads more scalable, automated and performance-driven for retailers.
First spotted. This update was shared by Founder of JXT Group Menachem Ani, who shared the email he got from OpenAI on X.

OpenAI is allowing retail advertisers connect shoppers with relevant products without manually building ads for each item.

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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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