<div><img width="1600" height="900" src="https://searchengineland.com/wp-content/seloads/2026/05/PLTP-Image-13.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" fetchpriority="high" srcset="https://searchengineland.com/wp-content/seloads/2026/05/PLTP-Image-13.png 1600w, https://searchengineland.com/wp-content/seloads/2026/05/PLTP-Image-13-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/PLTP-Image-13-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/PLTP-Image-13-1200x675.png 1200w" sizes="(max-width: 1600px) 100vw, 1600px" /></div>Dean Kadi shared a real-world PPC story about the consequences of ignoring performance data and overriding a winning ad strategy.

<div><img width="1600" height="900" src="https://searchengineland.com/wp-content/seloads/2026/05/PLTP-Image-13.png" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/PLTP-Image-13.png 1600w, https://searchengineland.com/wp-content/seloads/2026/05/PLTP-Image-13-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/PLTP-Image-13-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/PLTP-Image-13-1200x675.png 1200w" sizes="(max-width: 1600px) 100vw, 1600px" /></div>
<figure class="wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio"><div class="wp-block-embed__wrapper">
<p><a href="https://www.youtube.com/embed/EB14i8NCEuE?feature=oembed" rel="noopener noreferrer">Watch this video on YouTube</a>
</div></figure>



<p>In a recent PPC Live podcast conversation, Dean Kadi, Head of Paid Growth at One Link Media, shared a real-world agency experience where a client insisted on replacing high-performing Meta ads with heavily branded creative — despite clear evidence that the existing strategy was delivering strong results. The discussion highlights the tension agencies often face between expertise and client preference, while offering valuable lessons on communication, testing, tracking, and why data should always lead decision-making in PPC.</p>



<h2 class="wp-block-heading" id="h-the-campaign-was-performing-exceptionally-well">The campaign was performing exceptionally well</h2>



<p>Dean Kadi and his team at One Link Media built a highly successful Meta advertising strategy for premium woodworking brand Rubio Monocoat using user-generated content (UGC). By testing multiple creators, hooks, formats, and messaging angles, they improved the account’s ROAS from around 2.1x to consistently between 3x and 4x. Their testing revealed that the biggest purchase driver was not the product’s variety of colours, but the fact that customers only needed one coat of product, saving significant time and effort.</p>



<h2 class="wp-block-heading" id="h-the-client-wanted-to-pause-all-winning-ads">The client wanted to pause all winning ads</h2>



<p>Despite strong performance, the client unexpectedly requested that all successful UGC ads be paused in favour of heavily branded static and video creatives. The new ads looked polished but failed to feel native to the Meta platform, which is often critical for engagement and conversion. The decision wasn’t based on performance issues but on the client’s preference for more traditional branding.</p>



<h2 class="wp-block-heading" id="h-the-dangerous-assumption-behind-the-new-strategy">The dangerous assumption behind the new strategy</h2>



<p>The client based their new creative direction on a survey suggesting customers liked the brand’s colour range, assuming this was the primary reason people purchased. However, the agency’s testing data had already proven otherwise. This highlights a common mistake in marketing where internal assumptions or isolated feedback override broader performance data and real-world customer behaviour.</p>



<h2 class="wp-block-heading" id="h-we-d-prefer-this-to-be-a-winner">“We’d prefer this to be a winner”</h2>



<p>One of the most telling moments in the discussion came when the client admitted they simply wanted the new creative approach to succeed. Dean pointed out that paid media doesn’t work based on preference or hope — audiences decide what resonates. No matter how strongly stakeholders feel about a campaign direction, performance data ultimately determines success.</p>



<h2 class="wp-block-heading" id="h-what-agencies-should-do-in-situations-like-this">What agencies should do in situations like this</h2>



<p>Dean advised agencies to stay calm, professional, and evidence-led when disagreements with clients arise. Rather than arguing emotionally, marketers should clearly communicate risks, explain their reasoning, and document recommendations in writing. By maintaining professionalism and allowing the data to speak for itself, agencies can protect relationships while still standing behind their expertise.</p>



<h2 class="wp-block-heading" id="h-the-results-tanked-exactly-as-expected">The results tanked — exactly as expected</h2>



<p>The new branded creatives quickly underperformed, with rising acquisition costs and declining efficiency across Meta campaigns. Although the agency continued testing audiences and optimisation strategies, the core issue remained the creative itself. After approximately eight weeks of poor results, it became clear that the client’s new direction was not working.</p>



<h2 class="wp-block-heading" id="h-returning-to-ugc-restored-performance">Returning to UGC restored performance</h2>



<p>Once the client agreed to reintroduce the original UGC ads, campaign performance improved rapidly within just a couple of weeks. The return of native-looking content and proven messaging angles restored the account’s efficiency and validated the agency’s original strategy. Interestingly, Google Ads performance remained relatively stable because those campaigns relied more heavily on branded search activity.</p>



<h2 class="wp-block-heading" id="h-the-bigger-lesson-let-data-tell-the-story">The bigger lesson: let data tell the story</h2>



<p>Dean’s biggest takeaway was that agencies should rely on data rather than emotion when navigating difficult client situations. Sometimes clients need to see underperformance firsthand before accepting recommendations. By consistently presenting clear reporting and measurable outcomes, marketers can use evidence to guide conversations and rebuild trust.</p>



<h2 class="wp-block-heading" id="h-common-ppc-mistakes-agencies-still-encounter">Common PPC mistakes agencies still encounter</h2>



<p>Beyond this client story, Dean highlighted poor tracking setup as one of the most common mistakes still seen in PPC accounts today. Missing server-side tracking, incorrect event configurations, and weak conversion tracking setups can severely impact optimisation and reporting. Even the strongest campaigns struggle if the underlying data infrastructure is flawed.</p>



<h2 class="wp-block-heading" id="h-ai-won-t-fix-a-bad-strategy">AI won’t fix a bad strategy</h2>



<p>Dean also warned against overreliance on AI tools in marketing. While AI can improve efficiency and speed up workflows, it cannot compensate for weak strategy or poor thinking. Marketers still need to critically evaluate outputs, refine prompts, and apply human judgment, because clients ultimately hold people accountable — not AI systems.</p>



<h2 class="wp-block-heading" id="h-final-thoughts">Final thoughts</h2>



<p>This story serves as a reminder that successful PPC campaigns rely on testing, data, and strategic discipline rather than internal opinions or branding preferences alone. Agencies must balance professionalism with confidence in their expertise, document their recommendations carefully, and trust performance metrics over assumptions. In the end, audiences decide what works — and the data almost always reveals the truth.</p>


Watch this video on YouTube




In a recent PPC Live podcast conversation, Dean Kadi, Head of Paid Growth at One Link Media, shared a real-world agency experience where a client insisted on replacing high-performing Meta ads with heavily branded creative — despite clear evidence that the existing strategy was delivering strong results. The discussion highlights the tension agencies often face between expertise and client preference, while offering valuable lessons on communication, testing, tracking, and why data should always lead decision-making in PPC.
The campaign was performing exceptionally well
Dean Kadi and his team at One Link Media built a highly successful Meta advertising strategy for premium woodworking brand Rubio Monocoat using user-generated content (UGC). By testing multiple creators, hooks, formats, and messaging angles, they improved the account’s ROAS from around 2.1x to consistently between 3x and 4x. Their testing revealed that the biggest purchase driver was not the product’s variety of colours, but the fact that customers only needed one coat of product, saving significant time and effort.
The client wanted to pause all winning ads
Despite strong performance, the client unexpectedly requested that all successful UGC ads be paused in favour of heavily branded static and video creatives. The new ads looked polished but failed to feel native to the Meta platform, which is often critical for engagement and conversion. The decision wasn’t based on performance issues but on the client’s preference for more traditional branding.
The dangerous assumption behind the new strategy
The client based their new creative direction on a survey suggesting customers liked the brand’s colour range, assuming this was the primary reason people purchased. However, the agency’s testing data had already proven otherwise. This highlights a common mistake in marketing where internal assumptions or isolated feedback override broader performance data and real-world customer behaviour.
“We’d prefer this to be a winner”
One of the most telling moments in the discussion came when the client admitted they simply wanted the new creative approach to succeed. Dean pointed out that paid media doesn’t work based on preference or hope — audiences decide what resonates. No matter how strongly stakeholders feel about a campaign direction, performance data ultimately determines success.
What agencies should do in situations like this
Dean advised agencies to stay calm, professional, and evidence-led when disagreements with clients arise. Rather than arguing emotionally, marketers should clearly communicate risks, explain their reasoning, and document recommendations in writing. By maintaining professionalism and allowing the data to speak for itself, agencies can protect relationships while still standing behind their expertise.
The results tanked — exactly as expected
The new branded creatives quickly underperformed, with rising acquisition costs and declining efficiency across Meta campaigns. Although the agency continued testing audiences and optimisation strategies, the core issue remained the creative itself. After approximately eight weeks of poor results, it became clear that the client’s new direction was not working.
Returning to UGC restored performance
Once the client agreed to reintroduce the original UGC ads, campaign performance improved rapidly within just a couple of weeks. The return of native-looking content and proven messaging angles restored the account’s efficiency and validated the agency’s original strategy. Interestingly, Google Ads performance remained relatively stable because those campaigns relied more heavily on branded search activity.
The bigger lesson: let data tell the story
Dean’s biggest takeaway was that agencies should rely on data rather than emotion when navigating difficult client situations. Sometimes clients need to see underperformance firsthand before accepting recommendations. By consistently presenting clear reporting and measurable outcomes, marketers can use evidence to guide conversations and rebuild trust.
Common PPC mistakes agencies still encounter
Beyond this client story, Dean highlighted poor tracking setup as one of the most common mistakes still seen in PPC accounts today. Missing server-side tracking, incorrect event configurations, and weak conversion tracking setups can severely impact optimisation and reporting. Even the strongest campaigns struggle if the underlying data infrastructure is flawed.
AI won’t fix a bad strategy
Dean also warned against overreliance on AI tools in marketing. While AI can improve efficiency and speed up workflows, it cannot compensate for weak strategy or poor thinking. Marketers still need to critically evaluate outputs, refine prompts, and apply human judgment, because clients ultimately hold people accountable — not AI systems.
Final thoughts
This story serves as a reminder that successful PPC campaigns rely on testing, data, and strategic discipline rather than internal opinions or branding preferences alone. Agencies must balance professionalism with confidence in their expertise, document their recommendations carefully, and trust performance metrics over assumptions. In the end, audiences decide what works — and the data almost always reveals the truth.

Dean Kadi shared a real-world PPC story about the consequences of ignoring performance data and overriding a winning ad strategy.

Dean Kadi talks clients ignoring performance data

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-800x450.jpg" class="attachment-large size-large wp-post-image" alt="Search marketing jobs" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-800x450.jpg 800w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-600x338.jpg 600w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-200x113.jpg 200w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-150x84.jpg 150w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing-550x310.jpg 550w, https://searchengineland.com/wp-content/seloads/2021/10/hiring-in-search-marketing.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>Land your next job in SEO or PPC. These brands and agencies are hiring to fill open search marketing positions right now.

Looking to take the next step in your search marketing career? 
Below, you will find the latest SEO, PPC, and digital marketing jobs at brands and agencies. We also include positions from previous weeks that are still open.
Newest SEO Jobs 
(Provided to Search Engine Land by SEOjobs.com) 



Account Manager
May 13, 2026 
We’re Hiring: Account Manager @ Masse (x2)   Location: Fully Remote Schedule: Monday-Friday, 9am-5pm ET (or PT, if preferred) – You do not need to be based in the US, but we do require US hours. Employment: Full-time Contract Start Date: Immediate   Masse is a 40-person SEO agency that has scaled entirely through word-of-mouth, […]

SEO Manager (AI Search)
May 12, 2026 
SEO Manager (AI Search) We’re hiring a hungry, detail-obsessed operator to help execute and manage SEO + AI search campaigns for fast-growing companies. This is not a traditional SEO role. You’ll be working directly on cutting-edge AI search (ChatGPT, Perplexity, Gemini) while helping drive execution across multiple client projects. If you’re organized, sharp, and want […]

SEO Manager – Cardiff – £50,000
May 10, 2026 
SEO Manager Job Description Location Cardiff, Wales Salary £50,000 per year We are looking for an experienced and driven SEO Manager to join our growing digital marketing team in Cardiff. This is a fantastic opportunity for someone who genuinely understands search engine optimisation beyond the usual buzzwords and recycled LinkedIn nonsense. We want someone who […]

Digital Program Manager (Online Marketing, Non-profit, Fundraising)
May 8, 2026 
Full-time Description Are you a talented digital fundraiser who is passionate about progressive causes? Do you thrive working in a fast-paced environment? Are you excited about collaborating with a team to build something greater than what can be built alone? Avalon is a full-service direct marketing fundraising consulting agency, and we are looking for a […]

SEO Specialist – Remote (must be located in the US)
May 5, 2026 
Do you love nerding out on SEO and working with clients? If your friends & family are sick of hearing about your latest search rankings, then we’re your kind of people and you will love this job. $80k in year 1 + potential bonuses You will get an absolute masterclass on SEO and working with […]

Senior Product Manager (SEO)
May 4, 2026 
At NerdWallet, we’re on a mission to bring clarity to all of life’s financial decisions and every great mission needs a team of exceptional Nerds. We’ve built an inclusive, flexible, and candid culture where you’re empowered to grow, take smart risks, and be unapologetically yourself (cape optional). Whether remote or in-office, we support how you […]

Digital Marketing Manager (SEO/SEM)
April 28, 2026 
Job Description Attention: Kapitus is aware that individuals posing as recruiters may be communicating with job seekers about supposed positions with Kapitus. Kapitus has received reports that the content and method of communication can vary, but messages may contain requests for payment (e.g., fees for equipment or training) and/or for sensitive financial information. Kapitus will […]

SEO Account Manager
April 24, 2026 
Remote (Canada-wide) · Full-time · $75,000–$90,000 CAD About Webserv Webserv is a digital marketing agency that helps mission-driven businesses — particularly in behavioral health — grow through SEO, paid media, and conversion-focused web strategy. We’re a tight-knit team that values curiosity, ownership, and the kind of work that actually moves the needle for our clients. […]

Sr. SEO/AEO Manager (B2B SaaS – US or Canada Based)
April 21, 2026 
The Basics: Growth Plays is hiring a Senior SEO/AEO Manager based in the US, Canada or LATAM, to support and manage ongoing customer engagements and relationships. You’ll act as the main point of contact for your clients, and focus on building relationships and trust while driving strategy-aligned growth for the long term. This role is […]

Intermediate Search Specialist
April 17, 2026 
We are seeking an intermediate-level SEO Specialist for Hive Digital, a cutting-edge and award-winning agency that prides itself on helping change the world for the better. We offer a highly collaborative team that works together to deliver the best possible outcomes for our clients in a fast-paced, fun work environment. Are you ready to bring […]

Newest PPC and paid media jobs
(Provided to Search Engine Land by PPCjobs.com) 







Growth Marketing Associate
May 15, 2026 
About the Role We’re looking for a contract-to-hire Growth Marketing Associate to help drive measurable growth across our digital channels. This role combines creative social media content, the analytical rigor of performance marketing, and the project management muscle to run a real event end-to-end. You’ll own a meaningful slice of the funnel — producing the […]

Sr. Product Manager, Programmatic Advertising
May 15, 2026 
FreeWheel, a Comcast company, provides comprehensive ad platforms for publishers, advertisers, and media buyers. Powered by premium video content, robust data, and advanced technology, we’re making it easier for buyers and sellers to transact across all screens, data types, and sales channels. As a global company, we have offices in nine countries and can insert […]

Growth Marketing Senior Associate: Digital Acquisition & AI
May 15, 2026 
DraftKings is hiring a Growth Marketing Senior Associate in New York, NY. This role involves developing and executing digital marketing campaigns across various channels to drive customer acquisition and engagement. Ideal candidates have at least 2 years of digital marketing experience, a relevant Bachelor's Degree, and skills in optimizing campaigns, A/B testing, and performance analysis. […]

Growth Marketing Manager — Home Rebuilds & Real Estate
May 15, 2026 
A construction and rebuilding firm in California seeks a Marketing Manager to drive lead generation efforts central to helping homeowners rebuild. You will design and optimize multi-channel campaigns aimed directly at victims of the Eaton Fire, blending aggressive marketing with empathetic communication. The ideal candidate has a proven background in generating high-ticket B2C leads, specializes […]

Strategic Growth Marketing Account Lead
May 15, 2026 
A leading technology firm in North Bethesda seeks an Associate Account Manager to enhance client relationships through strategic marketing solutions. The successful candidate will provide insights and develop client-focused strategies in a collaborative environment. To thrive, candidates should possess 1-3 years of digital marketing experience, excellent communication skills, and some familiarity with SEO and marketing […]

Other roles you may be interested in
Senior Search Marketing Manager, Acadaca (Remote)
Salary: $75,000 – $85,000
Strategize & Execute: Develop, launch, and manage comprehensive paid search, display, shopping, pmax, demand gen, youtube and ctv campaigns from conception to optimization, ensuring alignment with client business objectives.
Performance Optimization: Proactively manage bids, budgets, and targeting to achieve and exceed key performance indicators (KPIs) such as Revenue and ROAS.
Senior Manager, SEO/AEO, ActiveCampaign (Remote)
Salary: $140,500 – $193,200
Identify opportunities for technical improvements across the ActiveCampaign website, prioritize them based on their potential business impact, and collaborate with cross-functional stakeholders to implement them.
Pioneer LLM optimization and Answer Engine Optimization (AEO) by developing content strategies that ensure ActiveCampaign is the authoritative source material used by LLMs. 
SEO Marketing Manager, Care.com (Hybrid, Dallas, TX)
Salary: $85,000 – $95,000
Organic Growth: Build and execute the SEO roadmap across technical, content, and off-page. Own the numbers: traffic, rankings, conversions. No handoffs, no excuses.
AI-Optimized Search (AIO): Define and drive CARE.com’s strategy for visibility in AI-generated results — Google AI Overviews, ChatGPT, Perplexity, and whatever comes next. Optimize entity coverage, content structure, and schema to ensure we’re the answer, not just a result.
Manager, SEO, KINESSO (Hyrid, New York, NY)
Salary: $90,000 – $95,000
Manage senior analysts and help analysts grow into the next level of their career.
Translate clients’ business goals and marketing objectives into successful search engine optimization strategies.
Senior Marketing Manager, Vanguard Renewables (Remote)
Salary: $120,000 – $182,000
Work closely with CMO and RNG team to develop and execute a strategic marketing roadmap aligned with business priorities.
Serve as the primary marketing liaison for RNG team, acting as the connective tissue between the Marketing and Commercial groups.
SEO Manager, Veracity Insurance Solutions, LLC, (Remote)
Salary: $100,000 – $135,000
Lead, coach, and develop a high-performing team of SEO Specialists
Set clear expectations, quality standards, workflows, and growth paths across the team
Marketing, Social Media & PR Manager, PARTNERS Staffing (Fort Myers, FL)
Salary: $75,000 – $85,000
Develop and execute integrated marketing campaigns for shows, content releases, events, and brand initiatives
Identify target audiences and create strategies to grow reach and engagement
Senior Paid Media Manager, Brightly Media Lab (Remote)
Salary: $70,000 – $100,000
Directly build, manage, and optimize campaigns within Google Ads, Microsoft Ads, and Facebook Ads (Meta).
Serve as the lead point of contact for your book of clients, taking full ownership of their success and growth.
Marketing Specialist, The Bradford group (Hybrid, The Greater Chicago area)
Salary: $60,000 – $62,000
Launch and manage paid social campaigns primarily on Meta platforms.
Oversee daily budgets and performance optimizations against revenue and ROI goals, using data-driven insights to continuously improve results.
Paid Search Specialist, Maui Jim Sunglasses (Peoria, IL)
Salary: $65,000 – $70,000
Plan, set up, and manage paid search, display, and shopping campaigns on Google Ads.
Manage and optimize advertising budgets to achieve revenue and efficiency targets.
Note: We update this post weekly. So make sure to bookmark this page and check back.

Land your next job in SEO or PPC. These brands and agencies are hiring to fill open search marketing positions right now.

The latest jobs in search marketing

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/05/google-ai-optimization-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/google-ai-optimization-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/05/google-ai-optimization-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/google-ai-optimization-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>This new guide talks about the differences, if any, between SEO, AEO, GEO and so on and touches on commodity content, AI agents and much more.

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/05/google-ai-optimization-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/google-ai-optimization-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/05/google-ai-optimization-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/google-ai-optimization-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Google has published a new guide on how to optimize for Google&#8217;s generative AI features, like AI Mode, AI Overviews and more. A lot of this content is put together from previous communication from Google but all in a neat new help document named <a href="https://developers.google.com/search/docs/fundamentals/ai-optimization-guide">Optimizing your website for generative AI features on Google Search</a>.</p>



<p><strong>What&#8217;s in the document. </strong>The document covers the following topics:</p>



<ul class="wp-block-list">
<li>SEO is relevant for generative AI search, follow Google&#8217;s best practices for SEO.</li>



<li>Create valuable, non-commodity content for your audience
<ul class="wp-block-list">
<li>Provide a unique point of view</li>



<li>Create non-commodity content that&#8217;s helpful, reliable and people-first</li>



<li>Organize content in a way that helps your readers</li>



<li>Add high-quality images and video</li>



<li>Focus on what your users want, and avoid overdoing it</li>



<li>Make sure your AI tools you use follow Google&#8217;s guidelines and best practices</li>
</ul>
</li>



<li>Build and maintain a clear technical structure
<ul class="wp-block-list">
<li>Meet the search technical requirements</li>



<li>Follow crawling best practices</li>



<li>When it comes to semantic HTML, focus on human readability, not code</li>



<li>JavaScript should follow Google&#8217;s best practices as well</li>



<li>Provide a good page experience</li>



<li>Reduce duplicate content</li>
</ul>
</li>



<li>Optimize your local business and ecommerce details</li>



<li>Mythbusting &#8211; what you don&#8217;t need to do
<ul class="wp-block-list">
<li>You don&#8217;t need LLMS.txt files</li>



<li>You don&#8217;t need other special markup</li>



<li>You don&#8217;t need to &#8220;Chunk&#8221; content</li>



<li>You don&#8217;t need to rewrite content for AI systems</li>



<li>You don&#8217;t need to seek inauthentic mentions</li>



<li>You shouldn&#8217;t overfocus on structured data</li>
</ul>
</li>



<li>Explore agentic experiences</li>



<li>Next steps</li>
</ul>



<p><strong>Why we care. </strong>This document sums up a lot of the advice Google has posted on its blogs, videos, spoken about at in-person events and more. It is a good document to see how Google thinks site owners should optimize their sites for AI-generative engines.</p>



<p>Check out the <a href="https://developers.google.com/search/docs/fundamentals/ai-optimization-guide">document over here</a>.</p>


Google has published a new guide on how to optimize for Google’s generative AI features, like AI Mode, AI Overviews and more. A lot of this content is put together from previous communication from Google but all in a neat new help document named Optimizing your website for generative AI features on Google Search.
What’s in the document. The document covers the following topics:
SEO is relevant for generative AI search, follow Google’s best practices for SEO.
Create valuable, non-commodity content for your audience

Provide a unique point of view
Create non-commodity content that’s helpful, reliable and people-first
Organize content in a way that helps your readers
Add high-quality images and video
Focus on what your users want, and avoid overdoing it
Make sure your AI tools you use follow Google’s guidelines and best practices
Build and maintain a clear technical structure

Meet the search technical requirements
Follow crawling best practices
When it comes to semantic HTML, focus on human readability, not code
JavaScript should follow Google’s best practices as well
Provide a good page experience
Reduce duplicate content
Optimize your local business and ecommerce details
Mythbusting – what you don’t need to do

You don’t need LLMS.txt files
You don’t need other special markup
You don’t need to “Chunk” content
You don’t need to rewrite content for AI systems
You don’t need to seek inauthentic mentions
You shouldn’t overfocus on structured data
Explore agentic experiences
Next steps
Why we care. This document sums up a lot of the advice Google has posted on its blogs, videos, spoken about at in-person events and more. It is a good document to see how Google thinks site owners should optimize their sites for AI-generative engines.
Check out the document over here.

This new guide talks about the differences, if any, between SEO, AEO, GEO and so on and touches on commodity content, AI agents and much more.

Google publishes guide on optimizing for generative AI features

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-800x450.jpg" class="attachment-large size-large wp-post-image" alt="Google Ads" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-800x450.jpg 800w, https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-600x338.jpg 600w, https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-200x113.jpg 200w, https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-150x84.jpg 150w, https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>Google is pushing developers to move offline conversion imports from the Google Ads API to the Data Manager API by June.

<div><img width="800" height="450" src="https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-800x450.jpg" class="attachment-large size-large wp-post-image" alt="Google Ads" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-800x450.jpg 800w, https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-600x338.jpg 600w, https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-200x113.jpg 200w, https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-768x432.jpg 768w, https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-1536x864.jpg 1536w, https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957-150x84.jpg 150w, https://searchengineland.com/wp-content/seloads/2023/04/Shutterstock_1382216957.jpg 1920w" sizes="(max-width: 800px) 100vw, 800px" /></div>
<p>Google is phasing out offline conversion imports through the Google Ads API for some developers starting June 15th.</p>



<p>Advertisers and martech providers that rely on offline conversion imports, including enhanced conversions for leads, will need to migrate workflows to the Data Manager API to avoid disruptions.</p>



<p id="h-what-s-happening"><strong>Details. </strong>Google notified developers that offline conversion imports using the <code>UploadClickConversions</code> request will stop working after June 15, for accounts that have not used the functionality in the last 180 days.</p>



<p>The change applies to offline conversion imports and enhanced conversions for leads. Other Google Ads API operations will continue functioning normally.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="733" height="1472" src="https://searchengineland.com/wp-content/seloads/2026/05/Offline-conversion-imports.png" alt="" class="wp-image-477670"/></figure>



<p>Google says affected developers should move those workflows to the Data Manager API going forward.</p>



<p><strong>Why we care.</strong> Offline conversion imports are critical for measuring leads, sales, and other actions that happen outside the web journey. If integrations are not migrated in time, conversion data could stop flowing into Google Ads, which may impact reporting, attribution, and automated bidding performance. The update also signals Google’s continued shift toward centralized, AI-driven data infrastructure tied more closely to first-party data and automation.</p>



<p id="h-the-bigger-picture"><strong>The bigger picture.</strong> The move is part of Google’s broader push toward centralized data ingestion and automated measurement infrastructure.</p>



<p>Google describes the Data Manager API as a unified ingestion system for sending advertiser data into Google Ads, including Customer Match and conversion imports. The company also says the newer API offers a better developer experience and additional functionality not available in the existing workflow.</p>



<p id="h-between-the-lines"><strong>Between the lines.</strong> As attribution becomes more privacy-focused and increasingly dependent on first-party data, Google continues consolidating advertiser tools into fewer systems that are more tightly connected to automation and AI-driven campaign products.</p>



<p>For developers and platforms, the migration may require updating integrations, rebuilding import processes, and testing new ingestion workflows ahead of the deadline.</p>



<p id="h-what-s-next"><strong>What’s next. </strong>Developers can continue using the Google Ads API for all non-offline conversion operations.</p>



<p>However, any workflows involving offline conversion imports should be migrated to the Data Manager API before June 15th, when the existing functionality begins failing for affected accounts.</p>



<p><strong>First spotted. </strong>This update was spotted by PPC Specialist Arpan Banerjee who shared the comms he received on <a href="https://www.linkedin.com/posts/arpan301_googleads-ppc-sem-share-7461049492927176704-h9MQ?utm_source=social_share_send&amp;utm_medium=member_desktop_web&amp;rcm=ACoAAAJTV04Bw5M-1qqJtnImkpsEOFw9bHvfSSs">LinkedIn.</a>


Google is phasing out offline conversion imports through the Google Ads API for some developers starting June 15th.
Advertisers and martech providers that rely on offline conversion imports, including enhanced conversions for leads, will need to migrate workflows to the Data Manager API to avoid disruptions.
Details. Google notified developers that offline conversion imports using the UploadClickConversions request will stop working after June 15, for accounts that have not used the functionality in the last 180 days.
The change applies to offline conversion imports and enhanced conversions for leads. Other Google Ads API operations will continue functioning normally.




Google says affected developers should move those workflows to the Data Manager API going forward.
Why we care. Offline conversion imports are critical for measuring leads, sales, and other actions that happen outside the web journey. If integrations are not migrated in time, conversion data could stop flowing into Google Ads, which may impact reporting, attribution, and automated bidding performance. The update also signals Google’s continued shift toward centralized, AI-driven data infrastructure tied more closely to first-party data and automation.
The bigger picture. The move is part of Google’s broader push toward centralized data ingestion and automated measurement infrastructure.
Google describes the Data Manager API as a unified ingestion system for sending advertiser data into Google Ads, including Customer Match and conversion imports. The company also says the newer API offers a better developer experience and additional functionality not available in the existing workflow.
Between the lines. As attribution becomes more privacy-focused and increasingly dependent on first-party data, Google continues consolidating advertiser tools into fewer systems that are more tightly connected to automation and AI-driven campaign products.
For developers and platforms, the migration may require updating integrations, rebuilding import processes, and testing new ingestion workflows ahead of the deadline.
What’s next. Developers can continue using the Google Ads API for all non-offline conversion operations.
However, any workflows involving offline conversion imports should be migrated to the Data Manager API before June 15th, when the existing functionality begins failing for affected accounts.
First spotted. This update was spotted by PPC Specialist Arpan Banerjee who shared the comms he received on LinkedIn.

Google is pushing developers to move offline conversion imports from the Google Ads API to the Data Manager API by June.

Google is moving offline conversion imports out of the Google Ads API

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/How-custom-visuals-boosted-organic-traffic.png" class="attachment-large size-large wp-post-image" alt="How custom visuals boosted organic traffic" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/How-custom-visuals-boosted-organic-traffic.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/How-custom-visuals-boosted-organic-traffic-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/How-custom-visuals-boosted-organic-traffic-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/How-custom-visuals-boosted-organic-traffic-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>A six-month test across 47 articles revealed which visual assets improved SEO performance and which ones failed to move the needle.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/How-custom-visuals-boosted-organic-traffic.png" class="attachment-large size-large wp-post-image" alt="How custom visuals boosted organic traffic" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/How-custom-visuals-boosted-organic-traffic.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/How-custom-visuals-boosted-organic-traffic-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/How-custom-visuals-boosted-organic-traffic-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/How-custom-visuals-boosted-organic-traffic-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>We spent six months testing custom visual assets across 47 articles on a client&#8217;s high-traffic accounting education website to find out which design investments actually improved SEO performance.</p>



<p>The experiment included featured images, infographics, and video across both new and existing content. Some visual assets consistently increased organic traffic. Others turned out to be a poor use of time and budget despite requiring far more production effort.</p>



<p>The goal wasn’t to prove that any image helps. Basic visuals are already table stakes. Instead, the focus was on the ROI of bespoke design and which visual assets consistently improved organic traffic.</p>



<p>The clearest winner was infographics. Across the articles tested, infographic additions produced an average 110% increase in organic traffic.</p>



<p>The broader takeaway was just as important: Custom visuals worked best on pages that already had search demand. Design amplified strong content, but it didn’t revive declining pages or create interest where none existed.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="754" height="912" src="https://searchengineland.com/wp-content/seloads/2026/05/An-article-pre-left-and-post-publishing-right-of-our-custom-design-assets.png" alt="An article pre- (left) and post-publishing (right) of our custom design assets" class="wp-image-477642"/><figcaption class="wp-element-caption"><em><em>An article pre- (left) and post-publishing (right) of our custom design assets</em></em></figcaption></figure>
</div>


<h2 class="wp-block-heading" id="h-the-47-page-custom-design-experiment-s-structure">The 47-page custom design experiment’s structure</h2>



<p>The experiment tested featured images, infographics, and video across two content groups:</p>



<ul class="wp-block-list">
<li><strong>Custom featured images on existing pages:</strong> The first group included existing content that needed a refresh. Each page received only a custom featured image.
<ul class="wp-block-list">
<li><strong>Total:</strong> 41 articles</li>
</ul>
</li>



<li><strong>New content with three types of design assets:</strong> The second group included brand-new articles published on the website, with featured images, infographics, and videos added at different stages.
<ul class="wp-block-list">
<li><strong>Total:</strong> Six articles</li>
</ul>
</li>
</ul>



<p>The second group also made it possible to test whether visual elements performed differently depending on when they were added. Some articles launched with only a featured image. Infographics were added later, followed by video on a smaller subset of articles.</p>



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<h2 class="wp-block-heading" id="h-how-the-project-s-success-was-measured">How the project’s success was measured</h2>



<p>Success was measured using monthly page visits, specifically the change in organic traffic before and after each design element was added.</p>



<p>Two time periods were compared:</p>



<ul class="wp-block-list">
<li>The month before implementation.</li>



<li>The average of the implementation month and the following month to create a more transparent comparison. (For example, April versus the average of May and June).</li>
</ul>



<p>This approach created a cleaner picture because some designs went live toward the end of the month.</p>



<h2 class="wp-block-heading" id="h-phase-1-testing-custom-featured-images-on-39-existing-pages-13-organic-traffic-nbsp">Phase 1: Testing custom featured images on 39 existing pages (+13% organic traffic)&nbsp;</h2>



<p>The experiment started with content that already had organic traffic, making it easier to compare performance before and after adding custom designs. Custom featured images were added to 39 existing articles.</p>



<p>Results across all 39 pages showed a 13% average increase in organic traffic. Some pages saw much larger jumps in monthly visits:</p>



<ul class="wp-block-list">
<li>QuickBooks ProAdvisor Academy: +379%</li>



<li>The CAS page: +100%</li>



<li>Build a CAS team: +73%</li>



<li>The IES product launch: +60%</li>



<li>ProAdvisor certification: +58%</li>



<li>Financial storytelling: +46%</li>



<li>Pricing strategy: +42%</li>
</ul>



<p>The takeaway was clear: Pages with existing demand responded best. Design amplified visibility where interest already existed.</p>



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<h2 class="wp-block-heading" id="h-phase-2-testing-custom-designs-on-brand-new-articles">Phase 2: Testing custom designs on brand-new articles</h2>



<p>The second phase focused on six newly created articles published on the website.&nbsp;</p>



<p>Because these articles were brand new, there was no pre-design baseline to measure against since the content and visuals were published together or layered in over time.&nbsp;</p>



<p>Performance could still be analyzed based on how the assets were layered. Across these articles, 63% of all design additions had a positive impact on organic traffic.</p>



<p>Here’s how each design asset affected the new articles.</p>



<h3 class="wp-block-heading" id="h-custom-featured-images">Custom featured images</h3>



<p>Every new article launched with a custom featured image, so there was no way to measure pre-image versus post-image performance.</p>



<p>Featured images appeared to function as a baseline performance enhancer. They weren’t a growth hack, but they did support credibility and improve presentation early on.</p>



<h3 class="wp-block-heading" id="h-custom-infographics-were-the-clear-winner-110-nbsp">Custom infographics were the clear winner (+110%)&nbsp;</h3>



<p>Five out of six infographic additions resulted in a strong positive traffic lift, with an average increase of 110%.</p>



<p>Infographics were the most reliable asset type across the entire experiment:</p>



<ul class="wp-block-list">
<li>Five out of six infographics increased organic traffic.</li>



<li>110% average increase in page visits. </li>
</ul>



<p>Infographics worked especially well because they:&nbsp;</p>



<ul class="wp-block-list">
<li>Improved clarity.</li>



<li>Broke up dense educational content.</li>



<li>Increased time on page.</li>



<li>Encouraged sharing.</li>
</ul>



<p>If there was one asset type to prioritize for new content, especially evergreen content, it was infographics.</p>



<p>Here’s an example of a custom infographic created for the accounting blog:&nbsp;</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="901" height="507" src="https://searchengineland.com/wp-content/seloads/2026/05/Challenges-of-AI-in-accounting.png" alt="Challenges of AI in accounting" class="wp-image-477641" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Challenges-of-AI-in-accounting.png 901w, https://searchengineland.com/wp-content/seloads/2026/05/Challenges-of-AI-in-accounting-768x432.png 768w" sizes="(max-width: 901px) 100vw, 901px" /></figure>
</div>


<h3 class="wp-block-heading" id="h-video-had-the-biggest-potential">Video had the biggest potential</h3>



<p>Video was the most resource-intensive lever tested, so it was used selectively.</p>



<ul class="wp-block-list">
<li>One video: +292% increase in organic page visits</li>



<li>Two videos: Minimal impact on on-site traffic despite approximately 500 YouTube views each</li>
</ul>



<p>The standout example was the AI financial modeling video, which ranked in Google’s video results and reached the first page of YouTube for “AI financial modeling.”</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="723" height="1141" src="https://searchengineland.com/wp-content/seloads/2026/05/Video-had-the-biggest-potential.png" alt="Video had the biggest potential" class="wp-image-477643"/></figure>
</div>

<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1592" height="365" src="https://searchengineland.com/wp-content/seloads/2026/05/YouTube-video-on-AI-financial-modeling.png" alt="YouTube video on AI financial modeling" class="wp-image-477644" srcset="https://searchengineland.com/wp-content/seloads/2026/05/YouTube-video-on-AI-financial-modeling.png 1592w, https://searchengineland.com/wp-content/seloads/2026/05/YouTube-video-on-AI-financial-modeling-768x176.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/YouTube-video-on-AI-financial-modeling-1536x352.png 1536w" sizes="(max-width: 1592px) 100vw, 1592px" /></figure>
</div>


<p>This revealed something important: Video created a second visibility layer beyond traditional organic listings, but the outcome wasn’t guaranteed.</p>



<p>Video worked best when:</p>



<ul class="wp-block-list">
<li> The topic had strong search demand.</li>



<li>There was clear alignment between search intent and video consumption.</li>
</ul>



<h2 class="wp-block-heading" id="h-key-takeaways-from-all-47-pages">Key takeaways from all 47 pages</h2>



<p>Several patterns stood out across both phases of the experiment:</p>



<ul class="wp-block-list">
<li><strong>Infographics were the most reliable growth tool:</strong> They consistently delivered the strongest organic traffic gains.</li>



<li><strong>Layering visuals amplified organic performance:</strong> Combining featured images, infographics, and video improved performance where demand already existed.</li>



<li><strong>Evergreen educational content performed best:</strong> How-to guides and advisory-focused pages responded particularly well to design additions.</li>



<li><strong>Custom design worked best on content that already had attention:</strong> Adding custom visuals to pages with existing traffic made strong pages even stronger.</li>
</ul>



<p>This context matters more than ever. With Google AI Overviews now reducing organic search clicks by <a href="https://searchengineland.com/google-ai-overviews-cut-search-clicks-report-471497">as much as 42%</a>, static articles alone are no longer enough to hold traffic.</p>



<p>That’s why interactive calculators built with <a href="https://searchengineland.com/vibe-coding-search-marketing-workflows-466661">vibe coding</a> have become part of the strategy for the same client. These tools give users a reason to engage, return, and convert in ways AI-generated answers can’t replicate. Every calculator landing page launches with a custom featured image designed to match the brand’s visual identity.</p>



<h2 class="wp-block-heading" id="h-where-design-failed-to-move-the-needle">Where design failed to move the needle</h2>



<p>What didn’t work was just as important as what did:</p>



<ul class="wp-block-list">
<li>Design didn’t revive declining content.</li>



<li>If search demand was decreasing, visuals alone couldn’t reverse the trend.</li>



<li>Overinvesting in low-demand topics was a mistake because no visual asset can manufacture interest where none exists.</li>
</ul>



<p>The lesson was clear: Design supports performance. It doesn’t replace strong content and demand.</p>



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<h2 class="wp-block-heading" id="h-our-cheat-sheet-moving-forward">Our ‘cheat sheet’ moving forward</h2>



<p>The results made our next steps clear.</p>



<figure class="wp-block-table"><table><tbody><tr><td><strong>For new content</strong></td><td><strong>For existing content</strong></td></tr><tr><td><strong>Mandatory featured images:</strong> All new content must launch with a custom hero image to establish early credibility.</td><td><strong>Target high-performing pages:</strong> Prioritize design enhancements for pages that already have established organic traffic.</td></tr><tr><td><strong>Prioritize infographics:</strong> These are the primary growth lever and should be the first choice for evergreen educational content.</td><td><strong>Lead with infographics:</strong> Use infographics on existing high-traffic pages to achieve an even higher return on investment.</td></tr><tr><td><strong>Layer video selectively:</strong> Only invest in video for high-demand, high-impact topics where video consumption intent is clear.</td><td><strong>Pause on declining pages:</strong> Avoid adding visuals to declining pages until underlying content issues are resolved first.</td></tr></tbody></table></figure>


<div class="wp-block-image">
<figure class="aligncenter size-full"><img decoding="async" width="1210" height="792" src="https://searchengineland.com/wp-content/seloads/2026/05/Our-‘cheat-sheet-moving-forward.png" alt="Our ‘cheat sheet’ moving forward" class="wp-image-477645" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Our-‘cheat-sheet-moving-forward.png 1210w, https://searchengineland.com/wp-content/seloads/2026/05/Our-‘cheat-sheet-moving-forward-768x503.png 768w" sizes="(max-width: 1210px) 100vw, 1210px" /></figure>
</div>


<p><strong>For new content</strong> </p>



<ul class="wp-block-list">
<li>Start with a custom featured image. This is non-negotiable for establishing early credibility.</li>



<li>Prioritize infographics. They proved to be the strongest growth lever for evergreen educational content.</li>



<li>Add video selectively for high-demand, high-impact topics where video consumption intent is clear.</li>
</ul>



<p><strong>For existing content</strong> </p>



<ul class="wp-block-list">
<li>Focus design enhancements on pages that already have established organic traffic.</li>



<li>Lead with infographics on high-performing pages to maximize return on investment.</li>



<li>Avoid adding visuals to declining pages until underlying content issues are resolved.</li>
</ul>



<p>The biggest takeaway from the experiment was simple: Custom visuals work best where demand already exists and can amplify it further.</p>

We spent six months testing custom visual assets across 47 articles on a client’s high-traffic accounting education website to find out which design investments actually improved SEO performance.
The experiment included featured images, infographics, and video across both new and existing content. Some visual assets consistently increased organic traffic. Others turned out to be a poor use of time and budget despite requiring far more production effort.
The goal wasn’t to prove that any image helps. Basic visuals are already table stakes. Instead, the focus was on the ROI of bespoke design and which visual assets consistently improved organic traffic.
The clearest winner was infographics. Across the articles tested, infographic additions produced an average 110% increase in organic traffic.
The broader takeaway was just as important: Custom visuals worked best on pages that already had search demand. Design amplified strong content, but it didn’t revive declining pages or create interest where none existed.
An article pre- (left) and post-publishing (right) of our custom design assets



The 47-page custom design experiment’s structure
The experiment tested featured images, infographics, and video across two content groups:
Custom featured images on existing pages: The first group included existing content that needed a refresh. Each page received only a custom featured image.

Total: 41 articles
New content with three types of design assets: The second group included brand-new articles published on the website, with featured images, infographics, and videos added at different stages.

Total: Six articles
The second group also made it possible to test whether visual elements performed differently depending on when they were added. Some articles launched with only a featured image. Infographics were added later, followed by video on a smaller subset of articles.
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How the project’s success was measured
Success was measured using monthly page visits, specifically the change in organic traffic before and after each design element was added.
Two time periods were compared:
The month before implementation.
The average of the implementation month and the following month to create a more transparent comparison. (For example, April versus the average of May and June).
This approach created a cleaner picture because some designs went live toward the end of the month.
Phase 1: Testing custom featured images on 39 existing pages (+13% organic traffic) 
The experiment started with content that already had organic traffic, making it easier to compare performance before and after adding custom designs. Custom featured images were added to 39 existing articles.
Results across all 39 pages showed a 13% average increase in organic traffic. Some pages saw much larger jumps in monthly visits:
QuickBooks ProAdvisor Academy: +379%
The CAS page: +100%
Build a CAS team: +73%
The IES product launch: +60%
ProAdvisor certification: +58%
Financial storytelling: +46%
Pricing strategy: +42%
The takeaway was clear: Pages with existing demand responded best. Design amplified visibility where interest already existed.
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Phase 2: Testing custom designs on brand-new articles
The second phase focused on six newly created articles published on the website. 
Because these articles were brand new, there was no pre-design baseline to measure against since the content and visuals were published together or layered in over time. 
Performance could still be analyzed based on how the assets were layered. Across these articles, 63% of all design additions had a positive impact on organic traffic.
Here’s how each design asset affected the new articles.
Custom featured images
Every new article launched with a custom featured image, so there was no way to measure pre-image versus post-image performance.
Featured images appeared to function as a baseline performance enhancer. They weren’t a growth hack, but they did support credibility and improve presentation early on.
Custom infographics were the clear winner (+110%) 
Five out of six infographic additions resulted in a strong positive traffic lift, with an average increase of 110%.
Infographics were the most reliable asset type across the entire experiment:
Five out of six infographics increased organic traffic.
110% average increase in page visits. 
Infographics worked especially well because they: 
Improved clarity.
Broke up dense educational content.
Increased time on page.
Encouraged sharing.
If there was one asset type to prioritize for new content, especially evergreen content, it was infographics.
Here’s an example of a custom infographic created for the accounting blog: 




Video had the biggest potential
Video was the most resource-intensive lever tested, so it was used selectively.
One video: +292% increase in organic page visits
Two videos: Minimal impact on on-site traffic despite approximately 500 YouTube views each
The standout example was the AI financial modeling video, which ranked in Google’s video results and reached the first page of YouTube for “AI financial modeling.”








This revealed something important: Video created a second visibility layer beyond traditional organic listings, but the outcome wasn’t guaranteed.
Video worked best when:
 The topic had strong search demand.
There was clear alignment between search intent and video consumption.
Key takeaways from all 47 pages
Several patterns stood out across both phases of the experiment:
Infographics were the most reliable growth tool: They consistently delivered the strongest organic traffic gains.
Layering visuals amplified organic performance: Combining featured images, infographics, and video improved performance where demand already existed.
Evergreen educational content performed best: How-to guides and advisory-focused pages responded particularly well to design additions.
Custom design worked best on content that already had attention: Adding custom visuals to pages with existing traffic made strong pages even stronger.
This context matters more than ever. With Google AI Overviews now reducing organic search clicks by as much as 42%, static articles alone are no longer enough to hold traffic.
That’s why interactive calculators built with vibe coding have become part of the strategy for the same client. These tools give users a reason to engage, return, and convert in ways AI-generated answers can’t replicate. Every calculator landing page launches with a custom featured image designed to match the brand’s visual identity.
Where design failed to move the needle
What didn’t work was just as important as what did:
Design didn’t revive declining content.
If search demand was decreasing, visuals alone couldn’t reverse the trend.
Overinvesting in low-demand topics was a mistake because no visual asset can manufacture interest where none exists.
The lesson was clear: Design supports performance. It doesn’t replace strong content and demand.
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Our ‘cheat sheet’ moving forward
The results made our next steps clear.

For new contentFor existing content
Mandatory featured images: All new content must launch with a custom hero image to establish early credibility.Target high-performing pages: Prioritize design enhancements for pages that already have established organic traffic.
Prioritize infographics: These are the primary growth lever and should be the first choice for evergreen educational content.Lead with infographics: Use infographics on existing high-traffic pages to achieve an even higher return on investment.
Layer video selectively: Only invest in video for high-demand, high-impact topics where video consumption intent is clear.Pause on declining pages: Avoid adding visuals to declining pages until underlying content issues are resolved first.









For new content 
Start with a custom featured image. This is non-negotiable for establishing early credibility.
Prioritize infographics. They proved to be the strongest growth lever for evergreen educational content.
Add video selectively for high-demand, high-impact topics where video consumption intent is clear.
For existing content 
Focus design enhancements on pages that already have established organic traffic.
Lead with infographics on high-performing pages to maximize return on investment.
Avoid adding visuals to declining pages until underlying content issues are resolved.
The biggest takeaway from the experiment was simple: Custom visuals work best where demand already exists and can amplify it further.

A six-month test across 47 articles revealed which visual assets improved SEO performance and which ones failed to move the needle.

How custom visuals boosted organic traffic by up to 110%

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/05/microsoft-clarity-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/microsoft-clarity-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/05/microsoft-clarity-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/microsoft-clarity-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>The Citation dashboard shows how your content is referenced in AI-generated answers across supported AI experiences.

<div><img width="1920" height="1097" src="https://searchengineland.com/wp-content/seloads/2026/05/microsoft-clarity-1920.jpg" class="attachment-large size-large wp-post-image" alt="" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/microsoft-clarity-1920.jpg 1920w, https://searchengineland.com/wp-content/seloads/2026/05/microsoft-clarity-1920-768x439.jpg 768w, https://searchengineland.com/wp-content/seloads/2026/05/microsoft-clarity-1920-1536x878.jpg 1536w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>Microsoft has rolled out the Citations dashboard within Microsoft Clarity, the analytics tool. Microsoft <a href="https://clarity.microsoft.com/blog/citations-now-generally-available/">announced</a>, &#8220;With this release, Citations in Microsoft Clarity moves into general availability with those refinements incorporated into the product.&#8221;</p>



<p>This should give you access to see how well your pages are performing within AI-experiences.</p>



<p><strong>Citations dashboard. </strong>The Citation dashboard shows how your content is referenced in AI-generated answers across supported AI experiences by summarizing and aggregating citation activity across the following areas:</p>



<ul class="wp-block-list">
<li><strong>Page citations:</strong> The total number of times pages from your domain were referenced in AI-generated answers during the selected time period, including multiple citations within the same answer. </li>



<li><strong>Share of authority:</strong> A competitive view showing the percentage of total citations attributed to your domain compared to other cited domains within the same set of queries where your domain appeared.  </li>



<li><strong>AI referral traffic:</strong> The percentage of sessions on your site originating from AI assistants during the selected time period, calculated as AI-referred sessions divided by total sessions. </li>



<li><strong>Queries: </strong>The queries used by AI systems to retrieve and evaluate your content before generating an answer, helping you understand how AI systems interpret user intent and connect it to your content. </li>



<li><strong>My cited pages:</strong> A page-level view showing which URLs from your domain were cited in AI-generated answers, along with citation counts and associated grounding queries. This helps identify which content is most frequently selected as a trusted source by AI systems. </li>



<li><strong>Trendlines:</strong> With trendlines for cited pages and queries, you can analyze how activity changes over time as content evolves and AI query patterns shift. </li>
</ul>



<p>Microsoft also said it updated Clarity to include &#8220;reporting model, query views, filtering, and pagination to improve performance across larger datasets and longer time ranges while creating a more streamlined visibility analysis experience.&#8221;</p>



<p>You can access Citations within the Dashboards then clicking on AI Visibility and then Citations.</p>



<p>For more details, see this <a href="https://learn.microsoft.com/en-us/clarity/ai-visibility/ai-citations">help document</a>.</p>



<p><strong>What it looks like. </strong>Here is a screenshot of the Citations dashboard in Microsoft Clarity:</p>



<figure class="wp-block-image size-full"><img decoding="async" width="2048" height="1280" src="https://searchengineland.com/wp-content/seloads/2026/05/Citations-v1-no-topic-3-1-scaled-1-scaled.png" alt="" class="wp-image-477667" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Citations-v1-no-topic-3-1-scaled-1-scaled.png 2048w, https://searchengineland.com/wp-content/seloads/2026/05/Citations-v1-no-topic-3-1-scaled-1-768x480.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Citations-v1-no-topic-3-1-scaled-1-1536x960.png 1536w" sizes="(max-width: 2048px) 100vw, 2048px" /></figure>



<p><strong>Why we care.</strong> As AI search becomes more popular, any insights we can get on how users are finding our content and websites through AI is useful. Clarity added the Citations report to help us do just that.  </p>



<p>Google Analytics also recently added <a href="https://searchengineland.com/google-analytics-ai-assistant-477544">AI assistant traffic reporting</a> to help with that as well.</p>



<p>And you can expect these reports to get better over time.</p>


Microsoft has rolled out the Citations dashboard within Microsoft Clarity, the analytics tool. Microsoft announced, “With this release, Citations in Microsoft Clarity moves into general availability with those refinements incorporated into the product.”
This should give you access to see how well your pages are performing within AI-experiences.
Citations dashboard. The Citation dashboard shows how your content is referenced in AI-generated answers across supported AI experiences by summarizing and aggregating citation activity across the following areas:
Page citations: The total number of times pages from your domain were referenced in AI-generated answers during the selected time period, including multiple citations within the same answer. 
Share of authority: A competitive view showing the percentage of total citations attributed to your domain compared to other cited domains within the same set of queries where your domain appeared.  
AI referral traffic: The percentage of sessions on your site originating from AI assistants during the selected time period, calculated as AI-referred sessions divided by total sessions. 
Queries: The queries used by AI systems to retrieve and evaluate your content before generating an answer, helping you understand how AI systems interpret user intent and connect it to your content. 
My cited pages: A page-level view showing which URLs from your domain were cited in AI-generated answers, along with citation counts and associated grounding queries. This helps identify which content is most frequently selected as a trusted source by AI systems. 
Trendlines: With trendlines for cited pages and queries, you can analyze how activity changes over time as content evolves and AI query patterns shift. 
Microsoft also said it updated Clarity to include “reporting model, query views, filtering, and pagination to improve performance across larger datasets and longer time ranges while creating a more streamlined visibility analysis experience.”
You can access Citations within the Dashboards then clicking on AI Visibility and then Citations.
For more details, see this help document.
What it looks like. Here is a screenshot of the Citations dashboard in Microsoft Clarity:




Why we care. As AI search becomes more popular, any insights we can get on how users are finding our content and websites through AI is useful. Clarity added the Citations report to help us do just that.  
Google Analytics also recently added AI assistant traffic reporting to help with that as well.
And you can expect these reports to get better over time.

The Citation dashboard shows how your content is referenced in AI-generated answers across supported AI experiences.

Microsoft Clarity citations dashboard rolls out

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/product-packs.png" class="attachment-large size-large wp-post-image" alt="Product packs data" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/product-packs.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/product-packs-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/product-packs-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/product-packs-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Analysis of 63,000+ merchants reveals what separates brands winning traffic from product packs versus those just showing up.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/product-packs.png" class="attachment-large size-large wp-post-image" alt="Product packs data" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/product-packs.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/product-packs-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/product-packs-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/product-packs-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p>If you’ve searched for almost any product on Google recently, you’ve seen how much the results page has changed. </p>



<p>Product packs and scrollable carousels of individual listings now appear multiple times on a single results page.</p>



<figure class="wp-block-image size-full"><img decoding="async" width="1883" height="749" src="https://searchengineland.com/wp-content/seloads/2026/05/stainless-steel-camp-stove-search-results.png" alt="" class="wp-image-477598" srcset="https://searchengineland.com/wp-content/seloads/2026/05/stainless-steel-camp-stove-search-results.png 1883w, https://searchengineland.com/wp-content/seloads/2026/05/stainless-steel-camp-stove-search-results-768x305.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/stainless-steel-camp-stove-search-results-1536x611.png 1536w" sizes="(max-width: 1883px) 100vw, 1883px" /></figure>



<p>We’re constantly evaluating data to understand how to scale ecommerce visibility.</p>



<p>We’ve tracked searches that returned 60 individual organic product listings on a single page. These are premium placements — and for a growing share of commercial searches, they’re where the purchase journey begins.</p>



<p>This shift is happening gradually enough that many brands haven’t fully recalibrated their strategy around it.</p>



<p>The data suggests it’s worth revisiting. The traffic opportunity in product packs is significant, competition is already intense, and the brands winning today are doing a few specific things differently.</p>



<p>With help from Nozzle, we analyzed data from more than 63,000 merchants across a broad set of ecommerce keywords from January 2025 through January 2026. Here’s what stood out.</p>



<h2 class="wp-block-heading" id="h-defining-success-appearances-vs-actual-traffic">Defining success: Appearances vs. actual traffic</h2>



<p>Appearing in product packs and actually winning traffic from them are two different things, and the gap can be surprisingly large, the data reveals.</p>



<ul class="wp-block-list">
<li>eBay appears in product results for 874,621 keywords in this dataset. </li>



<li>Home Depot appears for 831,699, a comparable footprint. </li>
</ul>



<p>But the estimated traffic from those appearances is dramatically different:</p>



<ul class="wp-block-list">
<li>eBay drives about 3.2 million visits from product pack results.</li>



<li>Home Depot drives nearly 28.8 million from a slightly smaller keyword footprint.</li>
</ul>



<p>The difference comes down to position quality within the pack. Home Depot’s products consistently appear in visible, above-the-fold positions where shoppers actually see and click them.</p>



<p>For eBay, many long-tail marketplace terms drive a smaller overall impact. Product packs are a critical tool for Google to drive awareness for common products and goods, often tied to head terms. Brands competing in product packs need to understand how Google uses them to drive clear purchase decisions for everyday products.</p>



<ul class="wp-block-list">
<li><strong>For marketers:</strong> When reviewing product pack performance, it’s critical to segment your data. Prioritizing categories with higher search volume and demand is essential for improving performance within product packs. That distinction gives you a much clearer view of where the real opportunity exists.</li>
</ul>



<h2 class="wp-block-heading" id="h-the-critical-gap-distinguishing-product-pack-visibility">The critical gap: Distinguishing product pack visibility  </h2>



<p>Product carousels scroll horizontally, so the first slots get far more exposure than listings hidden further back. That makes the distinction between visible and non-visible appearances a critical metric for understanding true reach.</p>



<p>Analysis of major retailers highlights this disparity:</p>



<ul class="wp-block-list">
<li>REI has 3.8 million products, yet 1.52 million of its appearances require scrolling to be seen.</li>



<li>Walmart similarly holds 1.29 million non-visible placements despite having 3.5 million unique products. </li>
</ul>



<p>Even for industry leaders, aggregate numbers can mask how much of their presence is effectively out of reach for Google shoppers. Analyzing that split is the clearest way to identify where improvements in product data and feed optimization will drive the greatest returns.</p>



<p>Product carousels often require horizontal scrolling to display all products. Listings that appear in the most visible positions without scrolling receive significantly more exposure than those placed further back in the carousel.</p>



<p>Understanding the split between visible and non-visible appearances is one of the clearest ways to identify where feed optimization and product data improvements will have the greatest impact.</p>



<ul class="wp-block-list">
<li><strong>For CMOs:</strong> If your team reports total product pack appearances as a success metric, it’s worth asking what percentage of those appearances are actually visible. That ratio is a more accurate reflection of the channel’s contribution to your business.</li>
</ul>



<h2 class="wp-block-heading" id="h-does-discounting-drive-product-pack-visibility">Does discounting drive product pack visibility?</h2>



<p>A common hypothesis is that discounted products send stronger pricing signals, and that stronger pricing signals should earn better placement in Google’s product packs. However, data across the top 10 merchants doesn&#8217;t show a clear relationship between the two.</p>



<ul class="wp-block-list">
<li>Amazon.com leads all merchants, with 49% of its catalog discounted. Its visibility rate of 72% is strong, but it ranks in the middle of the pack. </li>



<li>eBay takes the opposite approach: just 8% of its products are discounted, yet it ties for the highest visibility rate in the dataset at 81%. </li>



<li>Walmart Seller discounts only 24% of its catalog and also reaches 81% visibility. Meanwhile, Walmart discounts 27% of products but ranks near the bottom in visibility at 62%.</li>
</ul>



<figure class="wp-block-image size-full"><img decoding="async" width="1080" height="1350" src="https://searchengineland.com/wp-content/seloads/2026/05/pricing-strategy-product-pack-visibility-relationship.png" alt="" class="wp-image-477599" srcset="https://searchengineland.com/wp-content/seloads/2026/05/pricing-strategy-product-pack-visibility-relationship.png 1080w, https://searchengineland.com/wp-content/seloads/2026/05/pricing-strategy-product-pack-visibility-relationship-768x960.png 768w" sizes="(max-width: 1080px) 100vw, 1080px" /></figure>



<p>The pattern simply doesn&#8217;t hold consistently in either direction. Merchants with high discount rates aren&#8217;t consistently more visible, and merchants with low discount rates aren&#8217;t consistently penalized.</p>



<p>This tells us that discounting is likely one signal among many. It matters at the margins, but it isn&#8217;t the mechanism that determines whether a product earns a visible placement in a product pack. Feed quality, category relevance, review signals, and image standards appear to carry significantly more weight.</p>



<ul class="wp-block-list">
<li><strong>For retail teams:</strong> If your product pack strategy relies heavily on promotional pricing to win visibility, the data suggests those resources may be better spent elsewhere. Success in the current landscape depends on building presence where users make purchasing decisions and prioritizing strategic coverage over price reductions.</li>
</ul>



<h2 class="wp-block-heading" id="h-specialist-brands-are-competing-with-the-giants-and-winning">Specialist brands are competing with the giants and winning</h2>



<p>One of the most encouraging findings in this data is that product pack success is not limited to the largest retailers. Specialist brands with focused category expertise are competing extremely well against much larger players.</p>



<ul class="wp-block-list">
<li>Camp Chef appears in product results for 155,299 keywords, a fraction of the footprint of Walmart or eBay. Yet Camp Chef generates an estimated 2.6 million visits from those appearances, with above-the-fold positioning that rivals Home Depot.</li>



<li>Vertical ecommerce brands like Fellow are generating meaningful exposure as they expand into categories like high-end coffee makers, a key opportunity for brands using organic channels to drive growth.</li>
</ul>



<figure class="wp-block-image size-full"><img decoding="async" width="1852" height="1658" src="https://searchengineland.com/wp-content/seloads/2026/05/high-end-coffee-maker-search-results.png" alt="" class="wp-image-477600" srcset="https://searchengineland.com/wp-content/seloads/2026/05/high-end-coffee-maker-search-results.png 1852w, https://searchengineland.com/wp-content/seloads/2026/05/high-end-coffee-maker-search-results-768x688.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/high-end-coffee-maker-search-results-1536x1375.png 1536w" sizes="(max-width: 1852px) 100vw, 1852px" /></figure>



<ul class="wp-block-list">
<li>Fire Maple generates more than 1 million estimated visits from 185,184 keyword appearances. </li>
</ul>



<p>These brands are winning meaningful traffic in product packs against retailers many times their size because product packs reward category relevance and feed quality, not just brand scale.</p>



<p>For brands that have historically felt outmatched by larger competitors in traditional SEO, product packs represent a real opportunity to compete on a more level playing field. Strong product data, competitive pricing, quality imagery, and strong reviews can outperform a much larger retailer for the keywords that matter most within a category.</p>



<ul class="wp-block-list">
<li><strong>For agencies:</strong> This channel rewards focus and quality over scale. If a brand you support has real depth in a category, that expertise can translate directly into product pack performance in ways broader competitors struggle to match.</li>
</ul>



<h2 class="wp-block-heading" id="h-product-pack-visibility-shifts-staying-informed-matters">Product pack visibility shifts. Staying informed matters.</h2>



<p>Looking at the full dataset, one pattern is consistent across nearly every merchant: product pack visibility shifts throughout the year.</p>



<p>Brands that held strong positions throughout parts of the year saw meaningful fluctuations as Google changed how product results surfaced. Some merchants grew steadily through midyear before pulling back in Q4. Others spiked during promotional periods before returning to baseline.</p>



<p>This is simply how the channel works. Feed quality, product availability, review volume, pricing, and image standards all influence product pack placement, and Google regularly updates how these results are surfaced.</p>



<p>The brands that respond most effectively are the ones with consistent visibility into performance, so they can spot changes early and act before those shifts impact revenue.</p>



<p>Future Google I/O announcements and the deeper integration of AI through models like Gemini 3 will fundamentally reshape product packs through the rise of agentic commerce and the Universal Commerce Protocol (UCP). </p>



<p>In this environment, Google’s central challenge will be balancing paid and organic visibility. The shift is already creating a two-tiered search economy where citation in AI Overviews is becoming critical for brand recognition, influencing both organic and paid product pack performance.</p>



<h2 class="wp-block-heading" id="h-the-bigger-picture">The bigger picture</h2>



<p>Google’s product packs have evolved from a supplementary feature into a primary purchase touchpoint for commercial searches. </p>



<p>The Nozzle data in this analysis, covering more than 63,000 merchants, makes it clear that competition for this space is already intense. The leaders are pulling ahead, and the gap between brands paying close attention and those that are not is already measurable in traffic and revenue.</p>



<p>The good news is that the fundamentals of winning in this channel are within reach for most brands: strong product data, smart pricing strategy, quality creative, and consistent monitoring.</p>



<p>None of that requires a massive budget. It requires focus, the right tools, and the right questions at the right level of the organization.</p>


If you’ve searched for almost any product on Google recently, you’ve seen how much the results page has changed. 
Product packs and scrollable carousels of individual listings now appear multiple times on a single results page.




We’re constantly evaluating data to understand how to scale ecommerce visibility.
We’ve tracked searches that returned 60 individual organic product listings on a single page. These are premium placements — and for a growing share of commercial searches, they’re where the purchase journey begins.
This shift is happening gradually enough that many brands haven’t fully recalibrated their strategy around it.
The data suggests it’s worth revisiting. The traffic opportunity in product packs is significant, competition is already intense, and the brands winning today are doing a few specific things differently.
With help from Nozzle, we analyzed data from more than 63,000 merchants across a broad set of ecommerce keywords from January 2025 through January 2026. Here’s what stood out.
Defining success: Appearances vs. actual traffic
Appearing in product packs and actually winning traffic from them are two different things, and the gap can be surprisingly large, the data reveals.
eBay appears in product results for 874,621 keywords in this dataset. 
Home Depot appears for 831,699, a comparable footprint. 
But the estimated traffic from those appearances is dramatically different:
eBay drives about 3.2 million visits from product pack results.
Home Depot drives nearly 28.8 million from a slightly smaller keyword footprint.
The difference comes down to position quality within the pack. Home Depot’s products consistently appear in visible, above-the-fold positions where shoppers actually see and click them.
For eBay, many long-tail marketplace terms drive a smaller overall impact. Product packs are a critical tool for Google to drive awareness for common products and goods, often tied to head terms. Brands competing in product packs need to understand how Google uses them to drive clear purchase decisions for everyday products.
For marketers: When reviewing product pack performance, it’s critical to segment your data. Prioritizing categories with higher search volume and demand is essential for improving performance within product packs. That distinction gives you a much clearer view of where the real opportunity exists.
The critical gap: Distinguishing product pack visibility  
Product carousels scroll horizontally, so the first slots get far more exposure than listings hidden further back. That makes the distinction between visible and non-visible appearances a critical metric for understanding true reach.
Analysis of major retailers highlights this disparity:
REI has 3.8 million products, yet 1.52 million of its appearances require scrolling to be seen.
Walmart similarly holds 1.29 million non-visible placements despite having 3.5 million unique products. 
Even for industry leaders, aggregate numbers can mask how much of their presence is effectively out of reach for Google shoppers. Analyzing that split is the clearest way to identify where improvements in product data and feed optimization will drive the greatest returns.
Product carousels often require horizontal scrolling to display all products. Listings that appear in the most visible positions without scrolling receive significantly more exposure than those placed further back in the carousel.
Understanding the split between visible and non-visible appearances is one of the clearest ways to identify where feed optimization and product data improvements will have the greatest impact.
For CMOs: If your team reports total product pack appearances as a success metric, it’s worth asking what percentage of those appearances are actually visible. That ratio is a more accurate reflection of the channel’s contribution to your business.
Does discounting drive product pack visibility?
A common hypothesis is that discounted products send stronger pricing signals, and that stronger pricing signals should earn better placement in Google’s product packs. However, data across the top 10 merchants doesn’t show a clear relationship between the two.
Amazon.com leads all merchants, with 49% of its catalog discounted. Its visibility rate of 72% is strong, but it ranks in the middle of the pack. 
eBay takes the opposite approach: just 8% of its products are discounted, yet it ties for the highest visibility rate in the dataset at 81%. 
Walmart Seller discounts only 24% of its catalog and also reaches 81% visibility. Meanwhile, Walmart discounts 27% of products but ranks near the bottom in visibility at 62%.




The pattern simply doesn’t hold consistently in either direction. Merchants with high discount rates aren’t consistently more visible, and merchants with low discount rates aren’t consistently penalized.
This tells us that discounting is likely one signal among many. It matters at the margins, but it isn’t the mechanism that determines whether a product earns a visible placement in a product pack. Feed quality, category relevance, review signals, and image standards appear to carry significantly more weight.
For retail teams: If your product pack strategy relies heavily on promotional pricing to win visibility, the data suggests those resources may be better spent elsewhere. Success in the current landscape depends on building presence where users make purchasing decisions and prioritizing strategic coverage over price reductions.
Specialist brands are competing with the giants and winning
One of the most encouraging findings in this data is that product pack success is not limited to the largest retailers. Specialist brands with focused category expertise are competing extremely well against much larger players.
Camp Chef appears in product results for 155,299 keywords, a fraction of the footprint of Walmart or eBay. Yet Camp Chef generates an estimated 2.6 million visits from those appearances, with above-the-fold positioning that rivals Home Depot.
Vertical ecommerce brands like Fellow are generating meaningful exposure as they expand into categories like high-end coffee makers, a key opportunity for brands using organic channels to drive growth.





Fire Maple generates more than 1 million estimated visits from 185,184 keyword appearances. 
These brands are winning meaningful traffic in product packs against retailers many times their size because product packs reward category relevance and feed quality, not just brand scale.
For brands that have historically felt outmatched by larger competitors in traditional SEO, product packs represent a real opportunity to compete on a more level playing field. Strong product data, competitive pricing, quality imagery, and strong reviews can outperform a much larger retailer for the keywords that matter most within a category.
For agencies: This channel rewards focus and quality over scale. If a brand you support has real depth in a category, that expertise can translate directly into product pack performance in ways broader competitors struggle to match.
Product pack visibility shifts. Staying informed matters.
Looking at the full dataset, one pattern is consistent across nearly every merchant: product pack visibility shifts throughout the year.
Brands that held strong positions throughout parts of the year saw meaningful fluctuations as Google changed how product results surfaced. Some merchants grew steadily through midyear before pulling back in Q4. Others spiked during promotional periods before returning to baseline.
This is simply how the channel works. Feed quality, product availability, review volume, pricing, and image standards all influence product pack placement, and Google regularly updates how these results are surfaced.
The brands that respond most effectively are the ones with consistent visibility into performance, so they can spot changes early and act before those shifts impact revenue.
Future Google I/O announcements and the deeper integration of AI through models like Gemini 3 will fundamentally reshape product packs through the rise of agentic commerce and the Universal Commerce Protocol (UCP). 
In this environment, Google’s central challenge will be balancing paid and organic visibility. The shift is already creating a two-tiered search economy where citation in AI Overviews is becoming critical for brand recognition, influencing both organic and paid product pack performance.
The bigger picture
Google’s product packs have evolved from a supplementary feature into a primary purchase touchpoint for commercial searches. 
The Nozzle data in this analysis, covering more than 63,000 merchants, makes it clear that competition for this space is already intense. The leaders are pulling ahead, and the gap between brands paying close attention and those that are not is already measurable in traffic and revenue.
The good news is that the fundamentals of winning in this channel are within reach for most brands: strong product data, smart pricing strategy, quality creative, and consistent monitoring.
None of that requires a massive budget. It requires focus, the right tools, and the right questions at the right level of the organization.

Analysis of 63,000+ merchants reveals what separates brands winning traffic from product packs versus those just showing up.

Google’s product packs are now a primary sales channel. Here’s what the data shows

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/Why-now-is-the-time-to-prepare-for-WebMCP.png" class="attachment-large size-large wp-post-image" alt="Why now is the time to prepare for WebMCP" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/Why-now-is-the-time-to-prepare-for-WebMCP.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/Why-now-is-the-time-to-prepare-for-WebMCP-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/Why-now-is-the-time-to-prepare-for-WebMCP-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/Why-now-is-the-time-to-prepare-for-WebMCP-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>AI agents are becoming a new discovery layer, and WebMCP could become the schema markup that helps them understand and use your site.

New technologies come and go. Early in my career, I often chased shiny new things in an attempt to be on the cutting edge, but it didn’t take more than a few years to realize I was spending countless hours of my time, and my clients’ time, implementing technologies and techniques that went by the wayside. Google Authorship, anyone?
It turns out that if you simply wait for wider — but still early — adoption, learn from the first movers’ mistakes, and catch up quickly, you can avoid wasting time and create greater value for yourself and those you serve. That lesson has served me well.
And then there are those key moments where the early movers stand to not just win in the current landscape, but to shape and lead the next one. Think of the first people reading the PageRank paper and thinking, “I should build some links.” WebMCP feels like one of those moments, only bigger.
It’s not just a revolution in how search works or even in generative engine visibility. We’re at a moment where the very place discoverability occurs is changing, and who (or rather, what) is doing the discovering is changing with it.
Coming soon: Non-human engagement
While SEOs have long debated whether we should be optimizing for search engines or humans (shockingly, it’s both), that paradigm is about to be turned on its head. What happens when discovery shifts from a human to an LLM or agentic system?
This change is already underway. Whenever you visit ChatGPT with a request, it makes decisions, runs supplemental searches, asks follow-up questions, and returns conclusions. The agent is planning and deciding on your behalf, and your resulting output is shaped entirely by what it retrieves and how it interprets it.
We can even see the supplemental (fanout) queries in DevTools:




I think of this as the latest chapter in a longer story:
Discovery v1: People interacted with the world and discovered things firsthand. Experience and word of mouth were the discovery points.
Discovery v2: People started writing things down. Libraries and educational institutions became the discovery points, followed by newspapers and books.
Discovery v3: The web proliferates information and media at a scale previously unimaginable. Directories, then search engines, rose to aid discovery.
Discovery v4 (current): After about 25 years of search engines, LLMs rose and discovery moved to a blended, LLM-forward format. Light agentic capabilities are baked in to assist retrieval. People are still in the loop, but the assistant is doing more of the legwork.
Discovery v5 (on the horizon): Agentic systems move beyond being assistants in the retrieval and presentation layer and are given autonomy to act on users’ behalf. Many users will have their own agents. Companies will offer them. Google almost certainly will.
I would argue that the stage we’re entering, Discovery v5, will be the most dramatic since the shift to v2. 
Can’t you just imagine a world where basic decisions are offloaded from your brain and body, leaving you room to pursue more important things? I know I’ve seen this utopia before.




I honestly don’t see it resulting in this future, but the world we’re creating right now is fundamentally different from the one we’re in presently as marketers, and WebMCP is one of the first concrete steps in this journey. 
Dig deeper: WebMCP explained: Inside Chrome 146’s agent-ready web preview




  
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The trust ratchet only turns one way
Do you accept what you read in an AI Overview and stop your journey there more often than you did on the day it launched? Not 100% of the time, but more often than you did? You do. So do I.
For quick, low-risk queries, we’re happy to trust it. If you’re like me, as these systems have evolved and improved, you’ve started trusting them with higher-stakes information. 
Would I trust an AI Overview with tax questions or major health decisions? No. Would I trust it to remind me of the benefits of vitamin D or pull together a dinner recipe? Absolutely.
That boundary keeps moving. As it moves, so does what we’re willing to let an agent do on our behalf, not just what we’ll let it tell us.
The cost of being wrong when automating the reorder of groceries you’re running low on is small.
The benefit of an agent monitoring flight and hotel combinations for an amazing refundable deal, on your days off, within your budget, is very high.
The benefit of hopping in an autonomous vehicle with your family after work on a Friday, dinner in hand, playing a game and sleeping, and arriving at Disney World rested just in time for opening — that’s pretty compelling.
You may say you’ll never hand your autonomy to an agentic system. People said the same about search engines, smartphones, and GPS. The path usually goes: 
Skepticism (“Who would ever enter their credit card number on a website?!”)
Reluctant adoption (“Ugh, it’s an online service, and I trust the company and don’t have a choice. Alright, I’ll give them my card. But just this once.”)
Dependency (“I can’t believe I used to actually go into stores!”)
What does this have to do with WebMCP?
Here’s where it gets concrete and actionable.
MCP servers and skills files are early versions of the infrastructure that makes Discovery v5 possible, but the barrier to entry is high, and they apply only in specific contexts. 
WebMCP is different. It’s a browser-native web standard, currently published as a W3C Community Group Draft and in early preview in Chrome 146 beta as of this writing, that gives websites a structured way to expose actions directly to AI agents without scraping, guessing, or brittle automation.
This isn’t a Google-only initiative. The specification is co-authored by engineers from both Google and Microsoft, which matters. When two of the largest browser and AI platform vendors are writing the spec together, it has a different trajectory than a unilateral bet.
Right now, when an AI agent tries to take an action on your website, like filling out a form, booking an assessment, or searching your inventory, it has to figure everything out by reading your page and inferring intent.
It looks at your DOM, guesses what your fields mean, hopes the date format it picks is the one your form expects, and submits. It’s intelligent, but it’s also fragile. One UI change and the whole flow breaks.
WebMCP changes this by letting you tell the agent exactly what your site can do and how to do it. The spec defines two distinct ways to do that: one that closely maps to what you already know, and one that handles more complex, dynamic interactions.
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Declarative vs. imperative: You already know this distinction
WebMCP proposes two APIs, and the difference between them will feel familiar to anyone who’s spent time in technical SEO.
The Declarative API is the one that should make you sit up and get to work right away. The idea is straightforward.
You annotate your existing HTML forms with attributes that describe what the form does and what each field means.
The browser automatically translates that into a structured tool any agent can call. 
The form continues working exactly as before for human visitors. 
The agent gets a clean, unambiguous interface.
To be clear, the declarative API is still being formally specified, and the exact attribute names aren’t locked down yet. But the concept is settled, and demos are already running. 
Think of it the way you’d think about schema markup in its early days: the syntax evolved, but the underlying idea, annotating what already exists so machines can understand it, was clear and worth acting on.
The analogy to schema markup is almost exact. You’re not building a new system. You’re making what you already have legible to a new class of visitor. That’s a pattern SEOs understand intuitively.
The Imperative API is more mature in the spec and already available for testing. You register tools directly in JavaScript. Here’s an example for a site taking bookings for an assessment:

navigator.modelContext.registerTool({
  name: "book-assessment",
  description: "Book a free IT assessment for your business.",
  inputSchema: {
    type: "object",
    properties: {
      name: { type: "string", description: "Customer's full name" },
      city: { type: "string", description: "City for the assessment" },
      slot: { type: "string", description: "Preferred time in ISO 8601 format" }
    },
    required: ["name", "city", "slot"]
  },
  execute: async (input) => {
    // your booking logic here
    return { confirmed: true, appointmentId: "APT-001" };
  }
});





This is more powerful and flexible, the right approach for dynamic interactions, multi-step flows, or anything that can’t map cleanly to a single form. Here’s something that makes it genuinely interesting: the tools available on a page can change based on state.
A hotel booking demo from Google Chrome Labs illustrates this well. After an agent runs a search_location tool, a new filter_search_results tool appears. After selecting a hotel, start_booking becomes available. The agent’s toolset evolves as the user’s journey progresses, just as a well-designed interface guides a human through a flow.
Think of declarative as the equivalent of adding schema markup to existing content: low lift, high legibility, great starting point. An imperative is like building a fully structured data feed, It takes more effort, offers more power, and is better suited to complex or dynamic needs. Most sites should start with declarative and extend into imperative as their needs grow.
A quick note on scope: The example below uses the declarative side of WebMCP because, as we’ve discussed, that’s the easiest place for most site owners and SEOs to start. It maps naturally to existing HTML forms. Add clear machine-readable descriptions to the form and its fields, and the page becomes easier for agents to understand. 
The imperative API is more case-specific. It’s better suited to dynamic flows, multi-step interactions, custom JavaScript logic, or cases where an action does not map cleanly to a single form.
What the agent sees: Before and after
The contrast is easiest to see with something every service business already has: a booking or contact form. This form:

<form action="/contact" method="POST">
  <label for="name">Name</label>
  <input id="name" name="name" type="text" required>
  <label for="email">Email</label>
  <input id="email" name="email" type="email" required>
  <label for="city">City</label>
  <input id="city" name="city" type="text">
  <label for="message">Message</label>
  <textarea id="message" name="message" required></textarea>
  <button type="submit">Send</button>
</form>





Now here is the same form prepared for WebMCP using declarative-style annotations:

<form action="/contact" method="POST"
toolname="submitContactInquiry"
tooldescription="Submit a contact inquiry for a service business.">
<label for="name">Name</label>
<input
  id="name"
  name="name"
  type="text"
  required
  toolparamdescription="The requester's full name."
>
<label for="email">Email</label>
<input
  id="email"
  name="email"
  type="email"
  required
  toolparamdescription="A valid email address where the requester can be contacted."
>
<label for="city">City</label>
<input
  id="city"
  name="city"
  type="text"
  toolparamdescription="The city where the requester is located."
>
<label for="message">Message</label>
<textarea
  id="message"
  name="message"
  required
  toolparamdescription="The requester's question, project details, or service need."
></textarea>
<button type="submit">Send</button>
</form>





The form still works the same way for a human visitor. Nothing about the normal user experience had to change.
The difference is that an agent no longer has to guess what the form does or what each field means. The form declares its action with toolname and tooldescription, and each important input explains itself with toolparamdescription.
That’s the core idea. You’re not rebuilding the site for agents. You’re making the existing interface easier for them to understand.
And critically, this doesn’t have to mean fully automatic submission. For a contact form, you may want an agent to prepare the form and let the user review it before sending. For a low-risk action, you may eventually allow more automation. The point is that the action becomes explicit, structured, and less fragile.
The attributes proposed for forms are:
toolname: The name of the tool (in this case, a form tool).
tooldescription: The description of the tool (in this case, the description of a form).
toolautosubmit: A boolean attribute that lets the agent submit the form on the user’s behalf without requiring consent. This may not seem like it’d make sense if you’re thinking about just chatting with ChatGPT, but it suddenly makes sense if you have agents engaged in complex tasks, hooked up to your email, and tasked with completing something complex like making reservations or compiling information that requires details beyond a login or form fill.
toolparamdescription: A description of a specific parameter, so the agent is aware of the field it’s engaging with.
You can keep up with the specifics of the declarative API as it evolves in the Declarative API Explainer.
Why this matters for your sites specifically
Think about the types of queries agentic systems will handle on behalf of users in a Discovery v5 world:
“Find me an SEO consultant who understands technical SEO, doesn’t talk like a LinkedIn carousel, and has time for a call next week.”
“Compare three AI agent observability tools and tell me which one seems most likely to solve my actual problem instead of selling me a chatbot.”
“Find a contra dance near me this Friday, check whether beginners are welcome, and add it to my calendar if the band looks fun.”
Which site gets the engagement? The one the agent can interact with cleanly, confidently, and without friction. If your competitor has WebMCP-registered tools and you don’t, the agent completes the action on their site and moves on. The user may never know they had a choice.
There’s a secondary implication worth naming. Tool descriptions are the new meta descriptions. The quality of your tool name, description, and parameter definitions will directly shape whether an agent selects your tool over a competitor’s, understands what it does, and calls it correctly. 
The best practices guidance in the WebMCP documentation reads like conversion copywriting. Use clear verbs, explain the why behind options, and be specific about what each parameter means. If that sounds familiar, it should. You’ve been writing for machine readers for years. This is the next layer.

  
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The window is open, but not forever
I’ve been skeptical of early adoption my whole career. I still am, as a default. But I’ve also learned to recognize the moments that are different in kind, not just degree.




Schema markup was one. SSL was one. Mobile optimization was one. Each time, the window in which early movers earned disproportionate returns was real and finite. In each case, the people who understood the underlying shift, not just the tactic, were the ones who compounded that advantage.
WebMCP is a W3C Community Group Draft today, co-authored by Google and Microsoft, already running in Chrome 146 beta, and already integrated into Cloudflare’s infrastructure. It’s not table stakes yet. But the trajectory is clear:
The spec matures.
Browsers ship it.
Agents learn to prefer sites that expose structured tools.
The sites that haven’t caught up become invisible to that class of visitor.
The declarative approach, once finalized, means the barrier to starting will be genuinely low: annotations on your most important forms, not a new backend system. The imperative API is available for testing right now.
That’s the argument. It’s the reason I’m making it now, not in six to 12 months when everyone else is trying to catch up.

AI agents are becoming a new discovery layer, and WebMCP could become the schema markup that helps them understand and use your site.

Why now is the time to prepare for WebMCP

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/The-future-of-law-firm-SEO-depends-on-authority-not-volume.png" class="attachment-large size-large wp-post-image" alt="The future of law firm SEO depends on authority, not volume" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/The-future-of-law-firm-SEO-depends-on-authority-not-volume.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/The-future-of-law-firm-SEO-depends-on-authority-not-volume-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/The-future-of-law-firm-SEO-depends-on-authority-not-volume-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/The-future-of-law-firm-SEO-depends-on-authority-not-volume-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Technical SEO and content still matter, but sustained visibility now depends on trust, recognition, and E-E-A-T signals across the web.

<div><img width="1920" height="1080" src="https://searchengineland.com/wp-content/seloads/2026/05/The-future-of-law-firm-SEO-depends-on-authority-not-volume.png" class="attachment-large size-large wp-post-image" alt="The future of law firm SEO depends on authority, not volume" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/The-future-of-law-firm-SEO-depends-on-authority-not-volume.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/The-future-of-law-firm-SEO-depends-on-authority-not-volume-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/The-future-of-law-firm-SEO-depends-on-authority-not-volume-1536x864.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/The-future-of-law-firm-SEO-depends-on-authority-not-volume-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>
<p id="h-most-law-firms-follow-the-same-trajectory-with-seo-they-invest-in-content-build-out-service-pages-refine-their-technical-foundation-and-align-their-site-to-targeted-keywords-nbsp">Most law firms follow the same trajectory with <a href="https://searchengineland.com/guide/what-is-seo">SEO</a>. They invest in content, build out service pages, refine their technical foundation, and align their site to targeted keywords. </p>



<p id="h-early-on-the-results-are-there-but-firms-often-don-t-realize-they-ve-hit-the-ceiling-on-their-seo-strategy-they-just-feel-it-rankings-stall-growth-slows-and-the-default-response-is-almost-always-to-do-more-publish-more-content-target-more-keywords-make-more-incremental-optimizations-nbsp">Early on, the results are there. But firms often don’t realize they’ve hit the ceiling on their SEO strategy. They just feel it. Rankings stall, growth slows, and the default response is almost always to do more: Publish more content, target more keywords, make more incremental optimizations. </p>



<p id="h-however-for-firms-already-investing-in-seo-the-problem-likely-isn-t-effort-or-execution-it-s-that-their-strategy-is-missing-the-layer-that-actually-drives-sustained-visibility-nbsp">However, for firms already investing in SEO, the problem likely isn’t effort or execution. It’s that their strategy is missing the layer that actually drives sustained visibility.&nbsp;</p>



<p id="h-seo-still-is-and-always-will-be-the-foundation-but-without-authority-real-verifiable-credibility-across-the-web-it-stops-building-on-itself-and-in-a-search-environment-that-s-consistently-changing-and-being-shaped-by-ai-that-gap-becomes-more-and-more-expensive-nbsp">SEO still is (and always will be) the foundation, but without authority — real, verifiable credibility across the web — it stops building on itself. And in a search environment that’s consistently changing and being shaped by AI, that gap becomes more and more expensive.&nbsp;</p>



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<h2 class="wp-block-heading" id="h-what-real-authority-actually-looks-like">What ‘real authority’ actually looks like</h2>



<p>Authority is about how the broader web perceives your credibility. It’s often reduced to metrics such as domain authority scores, backlink counts, or content volume. While those stats are useful for tracking, reporting, and strategy, they don’t fully reflect how search engines and AI systems evaluate trustworthiness and expertise.</p>



<p>Real authority is whether your firm shows up as a recognized, trusted entity across the web. It’s about how often you’re referenced, cited, and connected to your areas of expertise beyond your own website.&nbsp;</p>



<h3 class="wp-block-heading" id="h-recognition-beyond-your-own-website">Recognition beyond your own website</h3>



<p>The strongest firms don’t just publish content. They’re referenced in places that actually matter to the audience they want to reach. This shows up in a few key ways:</p>



<ul class="wp-block-list">
<li>Mentions in legal publications, industry outlets, and news organizations.</li>



<li>Quoted expertise in third-party articles, not just bylined posts on your own site.</li>



<li>Awards, rankings, and association memberships that create verifiable connections.</li>
</ul>



<p>For example, a labor and employment firm that’s regularly cited in HR trade publications builds a fundamentally different authority profile than one publishing weekly blogs that never get referenced outside of their own website. That difference is visible to both search engines <strong>and</strong> AI systems. </p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/seo-goal-recognition-476756">SEO’s new goal in 2026: Recognition, not rankings</a></em></strong>



<h3 class="wp-block-heading" id="h-e-e-a-t-as-an-authority-framework">E-E-A-T as an authority framework</h3>



<p>Google’s <a href="https://searchengineland.com/google-e-e-a-t-guide-seo-394191">E-E-A-T</a> framework (experience, expertise, authoritativeness, trustworthiness) isn’t a checklist. It’s a lens for evaluating whether a source deserves to rank in search results.</p>



<p>For law firms, this means:&nbsp;</p>



<ul class="wp-block-list">
<li>Attorney bios with verified credentials and external publication links</li>



<li>Content authored or reviewed by practicing attorneys</li>



<li>Consistent digital footprint that connects the firm to its practice areas across multiple platforms</li>
</ul>



<p>E-E-A-T isn’t a separate strategy from SEO. It’s the credibility layer that allows your SEO efforts to perform at a higher level and sustain momentum over time.&nbsp;</p>



<h2 class="wp-block-heading" id="h-why-authority-matters-more-in-an-ai-search-landscape">Why authority matters more in an AI search landscape</h2>



<p>This shift becomes more important as search itself evolves. AI-generated results are changing how visibility is earned and where users engage with your firm.&nbsp;</p>



<p>AI systems don’t simply pull from the most optimized pages. They prioritize sources they recognize as credible, introducing a new layer of competition that isn’t tied strictly to rank. </p>



<p>Some trends that are already shaping this reality:&nbsp;</p>



<ul class="wp-block-list">
<li>In July 2025, an Ahrefs study found that only <a href="https://ahrefs.com/blog/ai-overview-citations-top-10/" target="_blank" rel="noopener">76% of URLs</a> cited in AI Overviews also appeared in the top 10 organic search results. However, in March 2026, a secondary study found that only roughly 38% of AI citations were pulled from the top 10, with the majority 62% split almost evenly from positions 11-100 and beyond the top 100 spots. </li>



<li>AI Overviews now appear in <a href="https://searchengineland.com/google-ai-overviews-drive-drop-organic-paid-ctr-464212">50%+ of searches</a>, and organic CTR has declined by 61% when they appear.</li>
</ul>



<p>This is where good SEO and GEO converge: The same authority signals that improve your organic rankings are what make AI engines trust and cite your content.&nbsp;</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/authority-ai-search-469466">The authority era: How AI is reshaping what ranks in search</a></em></strong>



<h2 class="wp-block-heading" id="h-how-to-build-authority-that-impacts-both-rankings-and-ai-visibility">How to build authority that impacts both rankings and AI visibility</h2>



<p>Authority isn’t built overnight; it compounds over time, and for most firms, it requires a shift from purely on-site optimization to a more complete view of digital visibility.</p>



<h3 class="wp-block-heading" id="h-audit-your-off-site-footprint">Audit your off-site footprint</h3>



<p>The first step is understanding where you are today. When you search for your firm or your attorneys, what appears beyond your own website?&nbsp;</p>



<p>Look specifically for:&nbsp;</p>



<ul class="wp-block-list">
<li>Mentions, links, or citations on third-party websites.</li>



<li>Presence in relevant legal or industry directories.</li>



<li>Visibility in publications tied to your practice areas.</li>
</ul>



<p>If those signals are limited, that’s often the primary gap, not anything happening on your site. Comparing your footprint to competitors can make this even clearer — are they showing up in places you’re not?</p>



<h3 class="wp-block-heading" id="h-create-citable-not-just-indexable-content">Create citable, not just indexable, content</h3>



<p>Often, law firm content is written specifically to rank, and very little is written to be referenced. To close this gap, shift the approach to:&nbsp;</p>



<ul class="wp-block-list">
<li>Develop original insights, frameworks, or perspectives that others can cite.</li>



<li>Make expertise explicit through attribution and clear positioning.</li>



<li>Structure content so both search engines and AI systems can extract key points.</li>
</ul>



<p>When content is built this way, it extends beyond your website and becomes part of a broader authority “network.” This is also where digital PR plays a crucial role in amplifying and reinforcing that visibility.&nbsp;</p>



<h3 class="wp-block-heading" id="h-build-entity-connections-systematically">Build entity connections systematically</h3>



<p>It’s very important to note that authority is reinforced through consistency. Your firm, your attorneys, and your areas of expertise need to be clearly connected across platforms: legal directories, LinkedIn, publication bylines, conference speaking engagements, etc.&nbsp;</p>



<p>Digital PR that earns editorial attention, not just backlinks, from publications your target clients and AI engines trust is also paramount to building a cohesive digital identity and authoritative foundation across the web.&nbsp;</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/links-brand-signals-seo-authority-model-475968">From links to brand signals: The new SEO authority model</a></em></strong>



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<h2 class="wp-block-heading">Spark growth through authority</h2>



<p>SEO is still the foundation of any effective law firm SEO strategy. It ensures your site is structurally sound, accessible, and aligned with how potential clients search. But if your strategy stops at optimization, it will eventually plateau.</p>



<p>Authority is what turns that foundation into something that truly compounds. It’s what separates firms that level off from those that keep growing. And increasingly, it’s what determines who gets cited, who gets surfaced, and who earns trust (in both traditional and AI search).&nbsp;</p>



<p>The firms investing in authority now aren’t just improving performance today. They’re positioning themselves to be the sources that search engines, AI platforms, and potential clients rely on moving forward.</p>



<p><strong><em>Dig deeper: <a href="https://searchengineland.com/why-law-firm-best-leads-dont-convert-472012">Why your law firm’s best leads don’t convert after research</a></em></strong>


Most law firms follow the same trajectory with SEO. They invest in content, build out service pages, refine their technical foundation, and align their site to targeted keywords. 
Early on, the results are there. But firms often don’t realize they’ve hit the ceiling on their SEO strategy. They just feel it. Rankings stall, growth slows, and the default response is almost always to do more: Publish more content, target more keywords, make more incremental optimizations. 
However, for firms already investing in SEO, the problem likely isn’t effort or execution. It’s that their strategy is missing the layer that actually drives sustained visibility. 
SEO still is (and always will be) the foundation, but without authority — real, verifiable credibility across the web — it stops building on itself. And in a search environment that’s consistently changing and being shaped by AI, that gap becomes more and more expensive. 
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What ‘real authority’ actually looks like
Authority is about how the broader web perceives your credibility. It’s often reduced to metrics such as domain authority scores, backlink counts, or content volume. While those stats are useful for tracking, reporting, and strategy, they don’t fully reflect how search engines and AI systems evaluate trustworthiness and expertise.
Real authority is whether your firm shows up as a recognized, trusted entity across the web. It’s about how often you’re referenced, cited, and connected to your areas of expertise beyond your own website. 
Recognition beyond your own website
The strongest firms don’t just publish content. They’re referenced in places that actually matter to the audience they want to reach. This shows up in a few key ways:
Mentions in legal publications, industry outlets, and news organizations.
Quoted expertise in third-party articles, not just bylined posts on your own site.
Awards, rankings, and association memberships that create verifiable connections.
For example, a labor and employment firm that’s regularly cited in HR trade publications builds a fundamentally different authority profile than one publishing weekly blogs that never get referenced outside of their own website. That difference is visible to both search engines and AI systems. 
Dig deeper: SEO’s new goal in 2026: Recognition, not rankings



E-E-A-T as an authority framework
Google’s E-E-A-T framework (experience, expertise, authoritativeness, trustworthiness) isn’t a checklist. It’s a lens for evaluating whether a source deserves to rank in search results.
For law firms, this means: 
Attorney bios with verified credentials and external publication links
Content authored or reviewed by practicing attorneys
Consistent digital footprint that connects the firm to its practice areas across multiple platforms
E-E-A-T isn’t a separate strategy from SEO. It’s the credibility layer that allows your SEO efforts to perform at a higher level and sustain momentum over time. 
Why authority matters more in an AI search landscape
This shift becomes more important as search itself evolves. AI-generated results are changing how visibility is earned and where users engage with your firm. 
AI systems don’t simply pull from the most optimized pages. They prioritize sources they recognize as credible, introducing a new layer of competition that isn’t tied strictly to rank. 
Some trends that are already shaping this reality: 
In July 2025, an Ahrefs study found that only 76% of URLs cited in AI Overviews also appeared in the top 10 organic search results. However, in March 2026, a secondary study found that only roughly 38% of AI citations were pulled from the top 10, with the majority 62% split almost evenly from positions 11-100 and beyond the top 100 spots. 
AI Overviews now appear in 50%+ of searches, and organic CTR has declined by 61% when they appear.
This is where good SEO and GEO converge: The same authority signals that improve your organic rankings are what make AI engines trust and cite your content. 
Dig deeper: The authority era: How AI is reshaping what ranks in search



How to build authority that impacts both rankings and AI visibility
Authority isn’t built overnight; it compounds over time, and for most firms, it requires a shift from purely on-site optimization to a more complete view of digital visibility.
Audit your off-site footprint
The first step is understanding where you are today. When you search for your firm or your attorneys, what appears beyond your own website? 
Look specifically for: 
Mentions, links, or citations on third-party websites.
Presence in relevant legal or industry directories.
Visibility in publications tied to your practice areas.
If those signals are limited, that’s often the primary gap, not anything happening on your site. Comparing your footprint to competitors can make this even clearer — are they showing up in places you’re not?
Create citable, not just indexable, content
Often, law firm content is written specifically to rank, and very little is written to be referenced. To close this gap, shift the approach to: 
Develop original insights, frameworks, or perspectives that others can cite.
Make expertise explicit through attribution and clear positioning.
Structure content so both search engines and AI systems can extract key points.
When content is built this way, it extends beyond your website and becomes part of a broader authority “network.” This is also where digital PR plays a crucial role in amplifying and reinforcing that visibility. 
Build entity connections systematically
It’s very important to note that authority is reinforced through consistency. Your firm, your attorneys, and your areas of expertise need to be clearly connected across platforms: legal directories, LinkedIn, publication bylines, conference speaking engagements, etc. 
Digital PR that earns editorial attention, not just backlinks, from publications your target clients and AI engines trust is also paramount to building a cohesive digital identity and authoritative foundation across the web. 
Dig deeper: From links to brand signals: The new SEO authority model




  
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Spark growth through authority
SEO is still the foundation of any effective law firm SEO strategy. It ensures your site is structurally sound, accessible, and aligned with how potential clients search. But if your strategy stops at optimization, it will eventually plateau.
Authority is what turns that foundation into something that truly compounds. It’s what separates firms that level off from those that keep growing. And increasingly, it’s what determines who gets cited, who gets surfaced, and who earns trust (in both traditional and AI search). 
The firms investing in authority now aren’t just improving performance today. They’re positioning themselves to be the sources that search engines, AI platforms, and potential clients rely on moving forward.
Dig deeper: Why your law firm’s best leads don’t convert after research

Technical SEO and content still matter, but sustained visibility now depends on trust, recognition, and E-E-A-T signals across the web.

The future of law firm SEO depends on authority, not volume

<div><img width="1920" height="1081" src="https://searchengineland.com/wp-content/seloads/2026/05/How-to-model-non-linear-SEO-seasonality-with-Prophet.png" class="attachment-large size-large wp-post-image" alt="How to model non-linear SEO seasonality with Prophet" decoding="async" srcset="https://searchengineland.com/wp-content/seloads/2026/05/How-to-model-non-linear-SEO-seasonality-with-Prophet.png 1920w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-model-non-linear-SEO-seasonality-with-Prophet-768x432.png 768w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-model-non-linear-SEO-seasonality-with-Prophet-1536x865.png 1536w, https://searchengineland.com/wp-content/seloads/2026/05/How-to-model-non-linear-SEO-seasonality-with-Prophet-1200x675.png 1200w" sizes="(max-width: 1920px) 100vw, 1920px" /></div>Traditional SEO forecasts often miss seasonality and volatility. Learn how to model non-stationary search behavior with Prophet and Python.

Forecasting SEO performance means estimating future outcomes from historical data. But search behavior rarely follows stable or linear patterns.
Seasonal demand, anomalies, SERP changes, and measurement issues can all distort your data and lead to unreliable forecasts.
That makes forecasting more complex than running linear regression, exponential smoothing, or asking an LLM to project trends from historical performance.
Here’s how to account for seasonality, detect anomalies, and build more reliable SEO forecasts in Python using models designed for non-linear search data.
SEO forecasting pays the bills, but doesn’t add much value
Decision-makers rely on forecasts to justify investments and align expectations across digital teams. Stakeholders want forward-looking estimates, finance needs revenue projections, and roadmaps require a clear view of expected returns. However, the value of forecasting has diminished today.
AI Mode and AI Overviews created a major disconnect between clicks and impressions as LLM-driven scrapers increased bot activity and inflated impression data in reporting tools.
Additionally, Google reported a logging issue affecting Search Console impression data since May 2025. As a result, many forecasts end up serving as reassurance rather than guidance. They shield decision-makers from scrutiny while failing to reflect the business’s actual operating context.
From a data analytics perspective, if search performance followed a normal distribution, you could rely on linear regression, exponential smoothing, or even a simple moving average (SMA) with confidence.
However, the average SEO forecast still relies on assumptions that don’t hold in organic search:
Stable trends.
Normal distributions.
Consistent relationships between inputs and outputs.

TechniqueDescriptionWhen to useWhen not to use
Linear regressionFits a straight line through historical data to model long-term trends and project future performance.When traffic or rankings show a consistent upward or downward trend with relatively low volatility. Useful for baseline forecasting and directional planning.When data is highly volatile, seasonal, or affected by frequent algorithm updates, migrations, or campaign spikes.
Exponential smoothingApplies weighted averages where recent data points have more influence than older ones. Can adapt to short-term changes.When recent performance is more indicative of future outcomes, such as after site changes, migrations, or content updates. Useful for short-term forecasting.When long-term trends matter more than recency, or when sharp anomalies may distort recent weighting.
Simple moving average (SMA)Averages values over a fixed window to smooth noise and highlight underlying trends.When you need to understand data direction, such as smoothing daily traffic for reporting.When forecasting future performance because predictions rely on aggregated historical averages and may miss turning points.





Today’s AI landscape forces a rethink of forecasting as search shifts toward highly volatile and probabilistic outcomes. In other words, today, a 10% increase in effort doesn’t translate into a proportional 10% increase in traffic.
Several structural factors are at play:
Long-tail traffic distribution: A small number of pages typically generate most traffic, while most pages contribute very little.
Binary user behavior: Many core SEO metrics, such as CTR, are driven by yes/no interactions (click versus no click) that diverge from normally distributed patterns.
Zero-click search impact: High rankings don’t guarantee traffic — more queries are resolved directly in the SERP, inflating visibility without corresponding clicks.
If you have to forecast, do it properly. Baseline models still have a role:
Linear regression for directional trends.
Exponential smoothing for short-term adjustments.
Moving averages for noise reduction.
There are ways to apply these techniques in Google Sheets. However, they should be treated as descriptive tools, not decision-making systems. To make forecasting useful, you need to move beyond them.

  
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Why LLMs aren’t the answer to SEO forecasting
LLMs and MCP connections only compound the inefficiencies listed above. There are two structural problems with this approach.
They assume data behaves linearly
Pre-configured prompts or skills implicitly assume the data follows a linear distribution. This is misleading because SEO data is dominated by seasonality, cyclical demand, and structural breaks. Any system that treats it as smooth or continuous will systematically misrepresent future performance.
They optimize for plausibility, not statistical accuracy
LLMs aren’t forecasting models. They’re probabilistic text generation systems. They assign probability scores to predict token sequences based on patterns observed during training. They’re trained to reward your thinking, not challenge it.
As a result, they can produce confident but ungrounded outputs that lack the business and domain context required to interpret anomalies.
No matter how well engineered the prompt is, the system can still hallucinate – not because it’s “wrong,” but because it’s optimizing for linguistic plausibility, not statistical validity.
Forecasting requires explicit handling of seasonality, non-linearity, and critical interpretation of outputs. These analytical responsibilities can’t be abstracted away through prompting alone.
LLMs can assist with workflows, accelerate analysis, and even help operationalize models. But they can’t replace the role of an analyst in framing the problem, selecting the methodology, and validating the results.
How to do an SEO forecast that accounts for seasonal effects
Asking the right questions is often the hardest part of any analysis.
SEO forecasts are often requested by enterprise stakeholders or pushed by agencies during new business pitches. This typically makes forecasting more straightforward because the research question is already defined upfront.
Either way, the subject of the analysis is usually one of the following search indicators:
Clicks (search demand).
Impressions (search visibility).
Rankings (position distribution).
CTR (SERP behavior).
For this article, we’ll use Python to forecast synthetic clicks for a fictitious website influenced by seasonal demand.
Retrieving and preprocessing seasonal fluctuations
Based on the scope of analysis, gather historical data from Google Search Console through either the API or Google BigQuery.
While a larger dataset with broader historical coverage is technically better, it may not justify the query costs in BigQuery for an SEO forecast.
Carefully assess the tradeoff between cost, resources, time, and data sampling. You might find that using an API to retrieve as much historical data as possible (e.g., via Search Analytics for Sheets) does the job.
Set up a Google Colab notebook, install the required dependencies, load your dataset with date and clicks as columns, and convert the date column into a datetime index.
Enforce daily frequency to ensure consistency across dates, and quickly fill any missing data gaps using interpolation.

#data viz
!pip install plotly
import plotly.graph_objects as go
import plotly.express as px
import matplotlib.pyplot as plt
import matplotlib.pyplot as pyplot
import seaborn as sns
from scipy.stats import boxcox
#anomaly detection
from statsmodels.tsa.stattools import adfuller
from statsmodels.tsa.seasonal import STL
#timeseries decomposition
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
#data manipulation
import pandas as pd
import numpy as np
#time series plotting
from prophet import Prophet
from statsmodels.tsa.statespace.sarimax import SARIMAX
from sklearn.metrics import mean_absolute_error, mean_squared_error
df = pd.read_excel('/content/input.xlsx')
df.columns = map(str.lower, df.columns)
df['date'] = pd.to_datetime(df['date'])
df = df.sort_values('date')
# Set index
df.set_index('date', inplace=True)
# Ensure daily frequency (important for decomposition)
df = df.asfreq('D')
# Handle missing values
df['clicks'] = df['clicks'].interpolate()
df.head()





Raw clicks line for all available date 



Does it look like a linear distribution, or can you already spot anomalies?
Data preprocessing involves standardizing and cleaning your dataset to reduce the impact of outliers on your next forecast. This step is often overlooked, yet it’s critical for improving model reliability.
To prove this, we need to assess stationarity, i.e., whether the relevant measures of central tendency, namely the mean and variance, remain stable over time.

result = adfuller(df['clicks'].dropna())
print(f"ADF Statistic: {result[0]}")
print(f"p-value: {result[1]}")





For context, the smaller the p-value (<0.05), the more confident you can be that patterns in the time series aren’t random.

ADF Statistic: -3.014113904399305
p-value: 0.06246422059834887





The p-value isn’t convincing here, meaning the series isn’t stationary (linear), and seasonality likely plays a role.
As discussed, assuming SEO data is stationary (i.e., follows a linear distribution) is a flawed heuristic.
SEO data often follows non-linear trends, so relying on simple methods that assume stable data can lead to poor forecasts. Instead, you should decompose the time series and model seasonality.
Seasonality decomposition helps separate true performance trends from recurring patterns such as weekly or monthly cycles.
To do this, we need to zoom in on granular weekly search patterns. 

#If data recorded daily, and you want to analyse weekly seasonality (period=7)
result_weekly = seasonal_decompose(df['clicks'], model='additive', period=7)
#If data recorded monthly, and you want to analyse yearly seasonality (period=12)
#result_monthly = seasonal_decompose(df['clicks'], model='additive', period=12)
# Plot the decomposition for monthly data
result_weekly.plot()
plt.title('Weekly Seasonal Decomposition')
plt.show()





STL decomposition framework 



The trend plot itself is already suggestive:
Search interest (clicks) is trending downward.
Search interest is likely affected by weekly sales cycles – look at the numerous small peaks.
Search interest likely follows seasonal demand – it ebbs and flows at certain times of year.
However, the residuals plot contains clusters of large spikes, both positive and negative, reaching up to 500,000. These represent anomalies, or outliers, that appear connected to the trend’s inflection points.
This means the model made a “mistake” when decomposing the trend line because it didn’t fully capture sudden spikes.
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Handling seasonality with SEO forecast 
To decompose and isolate seasonality, you can use several models depending on the level of complexity and flexibility you need:

ModelDescription
STL decompositionA robust technique for separating a time series into trend, seasonality, and residuals. Ideal for revealing the underlying structure in data where patterns vary over time, making it useful for anomaly detection.
SARIMAXARIMA extended to seasonal data. A statistical model that handles non-stationary data, seasonal patterns, and external independent variables such as algorithm updates.
ProphetBuilt by Meta for real-world data, it handles multiple seasonalities, missing data, and abrupt shifts. Leveraging additive models, it’s particularly suited for time series with strong seasonal patterns.
BSTSA Bayesian model that captures trend and seasonality while incorporating uncertainty. BSTS is commonly used for counterfactual estimation in causal impact analysis (“what would have happened if X never occurred?”), making it suitable for testing applications such as pre- versus post-analysis. Useful if you want to learn R.





For this article, we’re going to use STL decomposition for anomaly detection in a “wobbling” (non-stationary) time series.

# Fit STL decomposition (period=7 for weekly cycle)
stl = STL(df['clicks'], period=7, robust=True)
result = stl.fit()

# Extract residuals and flag anomalies via IQR
resid = result.resid
Q1, Q3 = resid.quantile(0.25), resid.quantile(0.75)
IQR = Q3 - Q1
anomalies = df[(resid < Q1 - 1.5 * IQR) | (resid > Q3 + 1.5 * IQR)]

# Plot
fig, ax = plt.subplots(figsize=(14, 5))
ax.plot(df.index, df['clicks'], label='Clicks', color='steelblue')
ax.scatter(anomalies.index, anomalies['clicks'], color='red', label='Anomalies', zorder=5)
ax.set_title('Click Anomalies (STL + IQR)')
ax.legend()
plt.tight_layout()
plt.show()





Weekly anomaly detection using STL decomposition



The red points are extreme values that aren’t explained by either trend or seasonality. However, detecting anomalies isn’t the same as removing them.
In non-stationary time series, variability changes over time (e.g., seasonality, trends, algorithm updates). Removing outliers outright breaks the time index and introduces artificial gaps that bias the actual seasonal impact.
A more robust approach is to replace anomalies with expected values.

df['trend'] = result.trend
df['seasonal'] = result.seasonal
df['resid'] = result.resid
# --- Define anomaly flag (based on residuals) ---
Q1, Q3 = df['resid'].quantile(0.25), df['resid'].quantile(0.75)
IQR = Q3 - Q1

df['anomaly'] = (
    (df['resid'] < Q1 - 1.5 * IQR) |
    (df['resid'] > Q3 + 1.5 * IQR)
)# --- Replace anomalies with expected value (trend + seasonal) ---
df['clean_clicks'] = df['clicks'].copy()
df.loc[df['anomaly'], 'clean_clicks'] = (
    df['trend'] + df['seasonal']
)





Because this approach preserves the time series rows, the forecasting baseline is now protected from bias and artificial gaps. You can validate this by applying STL decomposition to the cleaned time series.

result_clean = seasonal_decompose(df['clean_clicks'], model='additive', period=7)
result_clean.plot()
plt.title('Weekly Seasonal Decomposition (Cleaned Data)')
plt.show()





STL decomposition framework without anomalies



What finally stands out is that once a week (every seven observations), there’s a spike. This suggests peak search demand on Saturday or Sunday, indicating stable and consistent interest patterns.
A few scattered residuals, or anomalies, remain, but they’re rare and random, showing no clustering or drift. This confirms that outlier handling has been effective and the model fit is robust.
At this stage, the time series decomposition is clean enough and ready for forecasting.
Plotting a non-stationary SEO forecast
While you could experiment with SARIMAX or BSTS, this synthetic SEO forecast uses Prophet because it’s well-suited for handling time series with strong seasonality.
Using our anomaly-free dataset with a preserved time index, Prophet can forecast click performance over the next 90 days. To add more context, you can introduce a regressor to flag external factors such as Google core updates or measurement issues.
In this example, you can apply a flag to account for the Google Search Console logging issue that artificially inflated impressions between May 2025 and April 2026.
The code below generates a 90-day forecast and outputs a line chart, with the option to export the forecast as an .xlsx table.
Tabular output of Prophet’s 90-day click forecast from anomaly-free non-stationary timeseries.



Note that the lower and upper bounds represent the confidence interval, indicating the range within which clicks are expected to fall over the forecast horizon.

prophet_df = df[['clean_clicks']].reset_index()
prophet_df.columns = ['date', 'clicks']
prophet_df['date'] = pd.to_datetime(prophet_df['date'])
prophet_df = prophet_df.rename(columns={'date': 'ds', 'clicks': 'y'})

# ── GSC INFLATION FLAG ───────────────────
start = pd.to_datetime('2025-05-13')
end   = pd.to_datetime('2026-04-13')
prophet_df['gsc_inflation_flag'] = 0
prophet_df.loc[
    (prophet_df['ds'] >= start) & (prophet_df['ds'] <= end),
    'gsc_inflation_flag'
] = 1

model = Prophet(
    yearly_seasonality=True,
    weekly_seasonality=True,
    daily_seasonality=False
)model.add_regressor('gsc_inflation_flag')
model.fit(prophet_df)

# ── FORECAST────────────────────────────────────────────
future = model.make_future_dataframe(periods=90)

future['gsc_inflation_flag'] = 0
future.loc[
    (future['ds'] >= start) & (future['ds'] <= end),
    'gsc_inflation_flag'
] = 1
forecast = model.predict(future)

forecast_clean = forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].copy()
forecast_clean.columns = [
    'date',
    'clicks_forecast',
    'lower bound',
    'upper bound'
]
# Extract next 90 days only
forecast_90 = forecast_clean.tail(90)

# ── EXPORT OPTION ─────────────────────────────────────
EXPORT = True
if EXPORT:
    forecast_90.to_excel('seo_forecast_90_days.xlsx', index=False)
# ── PLOTLY VISUALISATION ──────────────────────────────
fig = go.Figure()
# Actuals
fig.add_trace(go.Scatter(
    x=prophet_df['ds'],
    y=prophet_df['y'],
    mode='lines',
    name='Actual (Cleaned)',
    opacity=0.6
))
# Forecast
fig.add_trace(go.Scatter(
    x=forecast_clean['date'],
    y=forecast_clean['clicks_forecast'],
    mode='lines',
    name='Forecast',
    line=dict(dash='dash')
))
# Confidence band
fig.add_trace(go.Scatter(
    x=forecast_clean['date'],
    y=forecast_clean['upper bound'],
    mode='lines',
    line=dict(width=0),
    showlegend=False
))
fig.add_trace(go.Scatter(
    x=forecast_clean['date'],
    y=forecast_clean['lower bound'],
    mode='lines',
    fill='tonexty',
    name='Confidence Interval',
    line=dict(width=0)
))
# Highlight inflation period
fig.add_vrect(
    x0=start, x1=end,
    annotation_text="GSC Inflation Period",
    annotation_position="top left",
    opacity=0.2
)fig.update_layout(
    title='SEO Forecast Adjusted for GSC Impression Inflation Bias',
    xaxis_title='Date',
    yaxis_title='Clicks'
)fig.show()





Prophet’s 90-day clicks forecast from anomaly-free non-stationary timeseries 




  
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SEO forecasting isn’t usually linear
SEO forecasting isn’t about projecting neat, linear trends – it’s about understanding messy, non-stationary data shaped by seasonality, anomalies, and external shocks.
By cleaning data properly, modeling seasonality, and accounting for real-world distortions such as SERP changes and tracking issues, forecasts become less about false certainty and more about informed direction.
While the goal isn’t perfect accuracy, a robust approach to forecasting non-stationary time series is essential for framing stakeholder expectations within a realistic range and making better decisions.

Traditional SEO forecasts often miss seasonality and volatility. Learn how to model non-stationary search behavior with Prophet and Python.

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