We are excited to announce Query groups, a powerful Search Console Insights feature that groups
similar search queries. One of the challenges when analyzing search performance data is that there
are many different ways to write the same query: you might see a dozen different variations for a
single user question – including common misspellings, slightly different phrasing, and different languages.
Query groups solve this problem by grouping similar queries.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-27 07:00:002025-10-27 07:00:00Introducing Query groups in Search Console Insights
Nearly 90% of businesses are worried about losing organic visibility as AI transforms how people find information, according to a new survey by Ann Smarty.
Why we care. The shift from search results to AI-generated answers seems to be happening faster than many expected, threatening the foundation of how companies are found online and drive sales. AI is changing the customer journey and forcing an SEO evolution.
By the numbers. Most prefer to keep the “SEO” label – with “SEO for AI” (49%) and “GEO” (41%) emerging as leading terms for this new discipline.
87.8% of businesses said they’re worried about their online findability in the AI era.
85.7% are already investing or plan to invest in AI/LLM optimization.
61.2% plan to increase their SEO budgets due to AI.
Brand over clicks. Three in four businesses (75.5%) said their top priority is brand visibility in AI-generated answers – even when there’s no link back to their site.
Just 14.3% prioritize being cited as a source (which could drive traffic).
A small group said they need both.
Top concerns. “Not being able to get my business found online” ranked as the biggest fear, followed by the total loss of organic search and loss of traffic attribution.
About the survey. Smarty surveyed 300+ in-house marketers and business owners, mostly from medium and enterprise companies, with nearly half representing ecommerce brands.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-24 17:10:572025-10-24 17:10:5790% of businesses fear losing SEO visibility as AI reshapes search
What Is AI Optimization (And Why You Should Care)?
AI optimization is the process of making your website accessible and understandable to AI-powered search tools. Like ChatGPT, Claude, Gemini, Perplexity, Google AI Overview, and Bing Copilot.
Some call it “AI search optimization.” Others “AI content optimization.”
Terminologies vary, but they’re all about the same thing:
Make your site easy for large language models (LLMs) to find, understand, and reference in their answers.
It’s not a brand-new strategy. It’s built on the core SEO principles.
Only now, you’re optimizing for tools that pull, summarize, and use your information — not just rank.
But why is AI optimization so important now?
AI tools are expected to drive more traffic than traditional search engines by 2028.
And here’s the kicker:
This traffic pool is only getting bigger.
Over 700 million people use ChatGPT every week. Millions more use Perplexity, Gemini, and other AI platforms.
Google’s AI Mode already has more than 100 million monthly active users. And that’s just in the US and India.
As it rolls out globally, adoption will only grow.
AI search optimization helps you be visible to these users.
It ensures your site appears in AI-powered search results, increasing your chances of getting referral traffic and finding new customers.
How AI Search Works
LLMs find relevant content across the web based on users’ prompts, then combines it into one comprehensive answer with source links.
There are three broad steps:
1. Understanding Your Prompt
First, AI interprets what you’re asking.
Some platforms (and specific models) may even expand or tweak your query for better results.
For instance, if I search “best sneakers,” ChatGPT’s o3 model searches for more specific phrases like “best running shoes 2025.”
2. Retrieval
Next, the AI platform searches for information in real time.
Different platforms use different sources (Google’s index, Bing, curated databases, etc.). But they all work the same way.
They gather relevant content from across the web for your expanded query.
3. Synthesis
Finally, AI decides which sources to include.
How?
The exact criteria aren’t public. But these factors seem to matter the most:
Authority: Recognized brands (entities it knows) and established experts
Structure: Clear, scannable content with direct answers
Context: Content that covers topics semantically (related concepts, not just keyword matches)
The most relevant sources get cited. The rest get ignored.
Which means ranking well isn’t enough. Your content also needs to be properly structured for AI systems.
I Analyzed 10 Queries Across Multiple AI Search Platforms: Here’s What I Found
Before we move forward to discuss how to optimize for AI search, I wanted to understand three things:
Do different AI platforms cite different types of content?
Which domains consistently appear across platforms?
Does multi-platform presence actually matter for AI visibility?
So I ran a simple experiment.
I searched 10 queries across ChatGPT 5, Claude Sonnet 4, Perplexity (Sonar model), Gemini 2.5 Flash, and Google’s AI Mode — a mix of commercial, informational, local, and trending topics.
And I found some interesting insights.
How Each Platform Chooses Sources
Platforms
Citation Behavior
ChatGPT
Acts like a community aggregator. Mixes Reddit discussions with Wikipedia and review sites.
Claude
Prefers recent, authoritative sources. Zero Reddit citations. Focuses on 2024-2025 content
Perplexity
Most diverse. Balances buying guides, YouTube reviews, and some Reddit.
Gemini
Relies mostly on training data. And since there’s no option to turn on web search, you can’t get it to cite sources for most of your queries.
Google AI Mode
Pulls from beyond top search results. 50% of citations weren’t on page one of Google.
The “Citation Core” Effect
Certain domains have achieved what we call the “citation core” status.
Citation core (n.): A small group of sites and brands that every major AI search tool trusts, cites, and uses as default sources.
Wikipedia showed up 16 times. Mayo Clinic owned health queries. RTINGS controlled electronics reviews.
These sites have become AI’s default sources.
What This Means for Brand Sites
One pattern jumped out: Official brand websites were underrepresented.
In my test, they made up around ~10% of all citations.
But that doesn’t mean your site doesn’t matter for informational or educational queries.
It means most sites aren’t yet AI-friendly. And that’s the opportunity.
When your site is structured, detailed, and optimized, it becomes one of the few brand-owned sources AI can actually cite for product specs, features, case studies, and stats. Information third-party sites can’t provide.
Think of it like this: Your website gives you the authoritative base layer. Off-site presence just amplifies it.
These findings aren’t surprising. But they reinforce what we’ve suspected all along.
In fact, a lot of what we do here at Backlinko aligns with these patterns:
Google’s guideline says good SEO is good AI optimization.
Their official guidelines mostly rehash standard SEO practices, with a few AI-specific points. Like using preview controls and ensuring structured data matches visible content.
But the foundation to make your site AI search-ready starts with three teams working in sync:
Developers: They make your site technically accessible to AI crawlers
SEOs: They structure content so AI can extract and understand it
Content teams: They create information worth extracting
Most companies treat these as separate projects.
That’s a mistake.
Leigh McKenzie, Head of SEO at Backlinko, explains why:
“Ranking in Google doesn’t guarantee you’ll show up in AI tools. SEO is still table stakes. But generative engines don’t just lift the top results. They scan at a semantic level, fan queries out into dozens of variants, and stitch together answers from multiple sources.”
You’ll need a coordinated effort to execute.
Let’s look at exactly what each team needs to do for effective AI search optimization.
Note: Most traditional SEO practices work for AI optimization too.
I’m not covering the basics here, like using sitemaps and including metadata. You should already be doing those.
Instead, I’m focusing on factors that specifically impact AI search visibility. These are insights based on my own experience, analyzing what’s working across different sites, and comparing notes with other SEOs.
Want the complete list?
I’ve created an AI Search Engine Optimization Checklist that covers everything — the well-known tactics, the experimental ones, and the “can’t hurt to try” optimizations that might give you an edge.
Developer Tasks
Understanding how to optimize for AI search starts with your developers. Because they control whether AI can actually access and understand your content.
No access means no citations.
Here’s what they need to check:
1. Make Your Site Accessible to AI Crawlers
AI crawlers need permission to access your site through your robots.txt file.
If you block them, your content won’t appear in AI search results.
Here are the main AI crawlers:
GPTBot (OpenAI/ChatGPT)
Google-Extended (Google’s AI Overview)
Claude-Web (Anthropic/Claude)
PerplexityBot (Perplexity)
To check if you’re blocking them, go to yoursite.com/robots.txt.
Look for any lines that say “Disallow” next to these crawler names.
If you find them blocked (or want to make sure they’re allowed), add these lines to your robots.txt:
Your developers handled the technical requirements. AI can now access your site.
But access doesn’t guarantee visibility in AI results.
Your SEO team controls how AI discovers, understands, and prioritizes your content.
Here’s what they need to control in your AI SEO strategy:
7. Structure Pages for Fragment-Friendly Indexing
AI pulls specific fragments from your pages — sentences and paragraphs it can use in responses.
Your page structure affects how easily AI can extract these fragments.
Start with a clean heading hierarchy.
Proper H2s and H3s help AI (and your readers) understand where one idea ends and another begins.
Go a step further by breaking big topics into unique subsections.
Instead of one giant guide to “healthy recipes,” create separate sections for “healthy breakfast recipes,” “healthy lunch recipes,” and “healthy dinner recipes.”
That way, you match the variations people actually search for.
Pro tip: Don’t bury your best insights in long paragraphs.
Use callouts (like this one)
Add short lists and bullets
Drop quick tables for comparisons
That’s how you turn raw text into structured fragments AI can actually use.
When your content is structured this way, every section becomes a potential answer.
8. Build Topic Clusters That Signal Full Coverage
Internal linking creates topical connections across your site.
When you link related pages together, you’re building topic clusters that show comprehensive coverage.
This is standard SEO practice that also helps AI discovery.
Create pillar pages for your main topics. These are comprehensive guides that link out to all related content.
For “project management,” your pillar would link to:
Task automation guide
Team collaboration tools
Workflow optimization
Resource planning
Each supporting page links back to the pillar and to other relevant pages in the cluster.
This helps both users and AI understand page relationships.
The cluster structure accomplishes two things:
First, it improves crawl efficiency. AI finds your hub and immediately discovers all related content through the links.
Second, it demonstrates topical depth. Organized clusters show comprehensive coverage better than scattered pages.
This structural approach helps organize your site architecture to showcase expertise through strategic internal linking.
9. Add Schema Markup to Label Your Content
When AI crawls your page, it sees text.
But it doesn’t know (without natural language processing) if that text is a recipe, a review, or a how-to guide.
Schema explicitly labels each element of the page.
It makes data more structured and easier to understand.
There are several types of schema markups.
I’ve found the FAQ schema particularly effective for AI search visibility.
Here’s how it looks:
json{
"@type": "FAQPage",
"mainEntity": [{
"@type": "Question",
"name": "What is churn rate?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Churn rate is the percentage of customers who cancel during a specific period."
}
}]
}
This markup tells AI exactly where to find questions and answers on your page.
The Q&A format matches how AI structures many of its responses, making your content easy to process.
Depending on the content management system (CMS) you’re using, you can add schema using plugins, add-ons, or manually.
For instance, WordPress has several good plugins.
After implementation, you can test it at validator.schema.org to ensure it’s working properly.
Note: Schema is just one type of metadata. Others include title tags, meta descriptions, and Open Graph tags.
Keeping them accurate and consistent may help AI platforms interpret your content correctly.
You can check your metadata using browser dev tools or SEO extensions, like SEO META in 1 CLICK.
10. Add Detailed Content to Category and Product Pages
Most category pages are just product grids. That’s a missed opportunity for AI search optimization.
The same goes for individual product pages with just specs and a buy button.
These pages get tons of commercial searches.
But they lack substantial content.
So, AI has limited information to work with when answering product queries.
You want to add buyer-focused information directly on these pages, like this:
They can cover:
Feature comparison tables
Common questions with clear answers
Use cases and industry applications
Technical specifications that matter
For product pages, go beyond basic descriptions.
Include materials, dimensions, compatibility, warranties, reviews — whatever matters to your buyers.
For example, GlassesUSA.com has several details on its product pages than just product specifications.
They include information that AI can use when answering specific questions.
Similarly, for category pages, add content that helps buyers choose.
What’s the difference between options? What should they consider? Which product fits which need?
Eyewear retailer Frames Direct does this well.
It has detailed content at the end of its category pages.
The key is putting this information directly on the page. Not hiding it behind tabs or “read more” buttons.
When someone asks AI about products in your category, you want substance it can quote. Not just a grid of images it can’t interpret.
11. Track Where AI Mentions Your Brand (and Where It Doesn’t)
You need to know where AI is mentioning your brand and where it isn’t.
Because if competitors appear in AI results and you don’t, they’re capturing the traffic you should be getting.
You can try checking this manually.
Run your target queries (e.g., “nutrition tracking app 2025”) across different AI platforms.
Scan the answers. And see if your brand shows up.
But that’s slow. And you’ll only catch a small slice of what’s happening.
It tracks how often your brand appears in AI-generated answers across various platforms like ChatGPT, Google AI Mode, and Google AI Overview. (In the “Visibility Overview” report.)
You can see exactly which topics and prompts your brand appears for.
And which prompts your competitors appear for, but you don’t. (In the “Competitor Research” report.)
For instance, if you find that AI cites competitors for “Cats and Feline Care” but skips your brand, that’s a clear signal to create or optimize a page targeting that exact query.
You also get strategic recommendations. So you can spot gaps, fix weak content, and double down where you’re already winning. (In the “Brand Performance” reports.)
With a tool like AI SEO Toolkit, you’re not guessing about your AI search visibility.
You’re improving based on real AI visibility data.
12. Optimize for Natural Language Prompts, Not Just Keywords
But they ask AI, “What’s the warmest jacket for Chicago winters under $300?”
Your content needs to match these natural language patterns.
Start by identifying how people actually phrase questions in your industry.
Use the AI SEO Toolkit to find high-value prompts in your industry.
Go to the “Narrative Drivers” report.
And scroll down to the “All Questions” section to see which prompts mention your brand and where competitors appear instead.
Document these prompt patterns.
Share them with your content team to create pages that answer these specific questions — not just target the base keyword.
The goal isn’t abandoning keywords.
It’s expanding from “winter jacket” pages to content that answers “warmest jacket for Chicago winters under $300.”
Content Tasks
Your site is technically ready. Your SEO is taken care of.
Now your content team needs to create valuable information and build presence across the web.
Here’s how to optimize content for AI search:
13. Publish Original Content with Data, Examples, and Insights
Generic blog posts restating common knowledge rarely perform well in AI search results.
But content with fresh angles and concrete examples does.
At Backlinko, we focus on publishing content that provides unique value through examples, original research, and exclusive insights.
Like this article:
And even if we’re talking about a common topic (e.g., organic traffic), we add fresh examples.
So how do you make your content stand out?
Run small studies or polls to produce original data. Even simple numbers can set your content apart.
Use screenshots, case studies, and workflows from real projects.
Back up your points with stats and cite credible sources.
Add expert quotes to strengthen authority.
Test tools or strategies yourself, and share the actual results.
AI systems look for concrete details they can pull into answers.
The more unique evidence, examples, and voices you add, the better.
14. Embed Your Brand Name in Context-Inclusive Copy
Context-inclusive copy means writing sentences that make sense on their own.
Each line should carry enough detail that an AI system understands it without needing the surrounding text.
But take that a step further.
Don’t just make your sentences self-contained.
Embed your brand name inside them so when AI reuses a fragment, your company is part of the answer.
Instead of: “Our tool helped increase conversions by 25%”
Write: “[Product] helped [client] increase checkout completions by 25%”
The second version keeps your brand attached to the insight when AI extracts it.
So how do you do this in practice?
With data: Tie your brand name directly to research findings or surveys you publish
With comparisons: Mention your brand alongside alternatives, so it’s always part of the conversation
With tutorials: Show steps using your product or service in real workflows
With results: Attach your brand name to case studies and examples
Here’s an example from Semrush, using their brand name vs. “we”:
The goal is simple:
Every quotable fragment should carry both context and your brand name.
That way, when AI pulls it into an answer, your company is mentioned too.
15. Create Pages for Every Use Case, Feature, and Integration
Specific pages are more likely to appear in AI responses than generic ones.
So, don’t bundle all features on one page.
Create dedicated pages for each major feature, use case, and integration.
Here’s an example of JustCall doing it right with unique pages for each of its main features and use cases:
The strategy is simple: match how people actually search.
For instance, someone looking for “Slack integration” wants a page about that specific integration. Not a features page where Slack is item #12 in a list.
Structure these pages to answer real questions, like:
What problem does this solve?
Who typically uses it?
How does it actually work?
What specific outcomes can they expect?
Get granular with your targeting. Instead of broad topics, focus on specific scenarios.
For example:
→ Ecommerce sites can create pages for each product application
→ Service businesses can detail each service variation
→ Publishers can target specific reader scenarios
The depth of coverage signals expertise while giving AI exact matches for detailed queries.
This specificity is what makes AI content optimization work. You’re creating exactly what AI systems need to cite
16. Expand Your Reach Through Non-Owned Channels
AI engines lean heavily on third-party sources. Which means your brand needs to show up in places you don’t fully control.
This goes beyond your on-site efforts.
But it’s still part of the bigger AI visibility play. And your content team can drive it by publishing externally and fueling PR.
Take this example: when I search “best duffel bags for men 2025” in Claude, it references an Outdoor Gear Lab roundup of top bags.
If you sell duffels, you’d want to be in that article.
There are two ways to expand your presence on non-owned channels.
One is publishing on other sites yourself — guest posts, bylined articles, or original research placed on authority blogs and industry outlets.
These extend your reach, position you as an expert, and increase your AI search visibility.
You’ll find guest post opportunities in several well-known sites. Like Fast Company here, which has an authority score of 67.
The other way to build visibility is getting featured by others.
Think reviews, roundups, and product comparisons that highlight your solution.
This usually involves working closely with your PR team.
But the content team fuels those opportunities with the data, case studies, and assets that make the pitch worth covering.
Either way, the goal of this AI content strategy is the same: substantive coverage.
A one-line mention usually isn’t enough. You need full features, detailed reviews, or exclusive insights that stand out.
Because the more credible coverage you earn (whether you wrote it or someone else did), the more evidence AI has to pull into its answers.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-22 14:05:192025-10-22 14:05:19AI Optimization: How to Rank in AI Search (+ Checklist)
The message from this month’s SEO Update is clear: AI and data accuracy are reshaping how we plan, optimize, and measure SEO. This is not just a slate of updates, but a signal to rethink impressions, content creation, and tooling so you stay effective. Chris Scott, Yoast’s Senior Marketing Manager, hosted the session. Alex Moss and Carolyn Shelby shared deep dives on AI trends, Google updates, and Yoast product news.
Data and rankings in flux
A key shift centers on data. Google removed the num=100 parameter, which changed how much ranking data shows up per page in Google Search Console. The result isn’t a sudden performance drop; it’s a correction. Impressions can look lower because the data is being cleaned up, and that matters more than the raw numbers. Paid search data stays solid, since ads rely on precise counting for financial reasons.
AI content and media: use it, don’t rely on it
Sora 2 can generate short videos from text prompts, providing handy visuals to accompany blog posts. Use AI visuals to complement your core messaging, not to replace it. In e-commerce, Walmart, WooCommerce, and Shopify are testing AI-enabled shopping features. Don’t rush a full switch before major buying events.
Local SEO and engines beyond Google
Bing’s Business Manager now has a refreshed UI focused on local listings, signaling a push into local search. Diversifying beyond Google can reveal new AI-powered opportunities. It’s about testing where AI-driven search and shopping perform best, not moving budgets blindly.
AI mode and how people behave
Research into AI-dominant sessions shows a distinct pattern: users linger 50 to 80 seconds on AI-generated text, and clicks tend to be transactional. Intent patterns shift, too. Now, comparisons lead to review sites, decisive purchases land on product pages, and local tasks point to maps and assets.
Meta descriptions and AI generation
Google tested AI-generated descriptions for threads lacking meta content, but meta descriptions aren’t obsolete. Best practice is to lean on Yoast’s default meta templates (like %excerpt%) as a reliable fallback. Write with an inverted pyramid in mind, which puts key information first, so AI can extract it cleanly. Keep a fallback description in Yoast SEO so automation stays under your control.
AI in everyday workflows
ChatGPT updates push toward more human-to-human interactions, and tools like Slack can summarize threads and search discussions by meaning, not just keywords. Growth in AI usage feels steadier now; some younger users opt for other AI tools.
Insights from Microsoft and Google
The core rules haven’t changed: concise, unique, value-packed content wins. Shorter, focused writing works best for AI synthesis; trim fluff and sharpen clarity. The message is simple because clarity beats complexity, especially as AI becomes more central to how content is consumed.
Yoast product updates to watch
The Yoast SEO AI+ bundle adds AI Brand Insights to track mentions and citations in AI outputs, and pronoun support has been added to schema markup for inclusivity. If you’re tracking AI relevance beyond traditional signals, this bundle can be a smart addition.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-22 13:11:592025-10-22 13:11:59A recap of the October 2025 SEO Update by Yoast
Yelp just unveiled its 2025 Fall Product Release, a sweeping AI-driven update that turns the local discovery platform into a more conversational, visual, and intelligent experience.
Driving the news: Yelp’s rollout includes over 35 new AI-powered features, headlined by:
Yelp Assistant, an upgraded chatbot that instantly answers customer questions about restaurants, shops, or attractions—citing reviews and photos.
Menu Vision, which lets users scan menus to see photos, reviews, and dish details in real time.
Yelp Host and Yelp Receptionist, AI-powered call solutions that handle reservations, collect leads, and answer questions with natural, customizable voices.
Natural language and voice search, allowing users to search conversationally (“best vegan sushi near me”) for smarter, more relevant results.
Popular Offerings, which highlights a business’s most-mentioned services, products, or experiences.
Why we care. Yelp’s new AI tools make it easier to capture and convert high-intent customers at the moment of discovery. With features like Yelp Assistant, AI-powered call handling, and natural language search, businesses can respond instantly, stay visible in smarter search results, and never miss a lead. The update turns Yelp from a review site into an always-on customer engagement platform—giving advertisers more efficient ways to connect, communicate, and close.
What’s next. Yelp plans to make its AI assistant the primary interface for discovery and transactions in 2026, merging instant answers, booking, and customer messaging into one seamless experience.
The bottom line. Yelp’s latest AI release gives brands smarter tools to engage customers in real time—turning everyday search and service interactions into instant connections.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/10/Screenshot-2025-10-21-at-19.18.04-kjKpB5.png?fit=776%2C1386&ssl=11386776http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-21 18:25:342025-10-21 18:25:34Yelp’s new tools help brands connect faster and engage customers in real time
OpenAI announced the launch of its first web browser, which they named ChatGPT Atlas. Atlas is currently available on Mac only right now and has all the features you would expect from an AI browser. But the most surprising part is that its built-in search features seem to be powered by Google and not Microsoft Bing, its early partner and one of its largest investors.
How to download Atlas. If you are on a Mac, you can download ChatGPT Atlas at chatgpt.com/atlas. From there, the web browser will download to your computer, you double click on the installer and then drag the application to your application folder.
What Atlas does. It is a web browser, first and foremost. You can go directly to web pages and browse them, but as you do that, there is ChatGPT available on the sidebar, like other AI powered web browsers. You can ask ChatGPT questions, you can have it re-write your content in Gmail and other tabs, offers personalization and memory, plus it will help you complete tasks, code and even shop using agentic features.
Search in Atlas. The interesting thing is that when you search in ChatGPT Atlas, it gives you a ChatGPT like response but also adds search vertical tabs to the top, like you have in other search engines. Like web, images, videos, news and more. Then when you go to those tabs, there is a link at the top of each set of search results to Google.
Here are screenshots:
More details. ChatGPT Atlas is launching worldwide on macOS today to Free, Plus, Pro, and Go users. Atlas is also available in beta for Business, and if enabled by their plan administrator, for Enterprise and Edu users. Experiences for Windows, iOS, and Android are coming soon.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/10/chatgpt-atlas-search-web-1-58f6Gh.png?fit=1862%2C1302&ssl=113021862http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-21 17:58:422025-10-21 17:58:42OpenAI launches a web browser – ChatGPT Atlas
Google Merchant Center is rolling out a new Issue Details Page (IDP) to help advertisers more easily diagnose and resolve account or product-level problems.
How it works:
Located under the “Needs attention” tab, the page provides a consolidated overview of current issues.
It surfaces recommended actions, business impact metrics, and sample affected products — giving merchants a clearer sense of what to fix first.
Why we care. Until now, identifying and fixing issues in Merchant Center often required navigating multiple sections and reports. The new Issue Details Page (IDP) in Google Merchant Center gives advertisers a single place to view and fix account or product issues.
It highlights the problem’s impact, recommends actions, and shows affected products, helping advertisers resolve issues faster and keep listings active. In short, it saves time, improves visibility, and helps prevent lost sales.
The big picture. The update is part of Google’s broader push to improve Merchant Center usability ahead of the holiday shopping season, when product accuracy and uptime are critical for advertisers.
The bottom line.Google’s new IDP could save advertisers time and guesswork by putting all issue diagnostics and solutions in one place.
First seen. The newly released help doc was spotted by PPC News Feed founder, Hana Kobzová
Google Ads appears to be testing an automatic assignment of New Customer Value within New Customer Acquisition (NCA) campaigns — and it’s doing so without advertisers’ explicit consent.
The change, first spotted by performance marketer Bilal Yasin, has led to unexpected reporting shifts and frustration among advertisers.
“Without any heads-up, and without it being in the change history, a new customer value has suddenly been applied to a customer,” Yasin wrote on LinkedIn. “It was set to 200 DKK… One thing is that Google has assigned a value, but another is that I can’t remove it again!”
Why we care. Advertisers rely on New Customer Value settings to determine how campaigns optimize toward acquiring new users. When Google sets those values automatically, it can distort revenue reporting and campaign efficiency metrics.
Yasin noted several issues:
Google doesn’t know the true lifetime value of a new customer.
Many conversions are still classified as “unknown,” further clouding data.
What they’re saying. Google Ads Liaison Ginny Marvin confirmed the behavior is part of an experiment.
“This guidance is part of an experiment aimed at helping advertisers use settings that will improve results—specifically, to increase new customer ratios,” Marvin wrote.
She added that when the New Customer Value is too low—or not set—it can hinder campaign optimization.
What’s next. Google says it plans to roll out new customer reporting for all purchase conversion campaigns “in the next couple of quarters.”
The bottom line. While Google frames the test as a way to improve campaign performance, advertisers are raising alarms over transparency — especially when automatic value assignments alter reported revenue without clear notice or control.
It’s true that YouTube Ads perform very well for ecommerce advertising aimed at consumers. But YouTube can also help drive B2B leads.
You might be scratching your head and saying, “But I’ve tried YouTube for B2B. It doesn’t convert.” And you would be right.
YouTube Ads for B2B rarely convert directly into leads. Complex products with long sales cycles are not going to sell themselves in one video.
But YouTube campaigns definitely have a positive influence on B2B lead generation – we’ve seen it across nearly all of our B2B clients.
Here are two case studies, featuring very different advertisers, that show how YouTube Ads can be used to increase B2B conversions.
Case study 1: Enterprise B2B SaaS advertiser
One of our enterprise B2B SaaS clients offers multiple business solutions.
Paid search is a strong lead source for most of them, but two struggled to convert – traffic was steady, yet the cost per lead was high.
When we dug in, we found that users weren’t aware of these solutions or how they addressed specific business needs. The landing page content wasn’t persuasive enough.
We tested YouTube video campaigns that clearly explained each solution’s value. The impact was undeniable.
Comparing search performance from the quarter before video to the quarter during, we saw key metrics – CTR, CPC, cost per lead, and conversion rate – all improve.
Here, CTR improved significantly with the video live, which indicates that users had a better understanding of the solution after seeing the video.
This led to a lower CPC, which, combined with a slightly improved conversion rate, lowered cost per lead by 30%.
With the second solution, the results were even more dramatic.
For this solution, front-end metrics actually got worse: CTR declined, and CPC increased.
Search competition in this space was stiffer during the “after” period, which pushed CPCs up.
However, the campaigns still saw a 25% decrease in cost per lead, and conversion rates more than doubled.
In this instance, the video campaigns really helped explain how the solution can benefit users, which directly translated into better conversion rates from search.
For the first five months of 2025, this advertiser ran a small YouTube video campaign intended to drive consideration.
We had hoped the video would directly drive a few leads, and ran it on a Maximize Conversions bid strategy, but it never generated a single lead.
At the same time, CPLs across the entire account were rising, so in early June, we decided to pause YouTube and use the budget on campaigns that were directly driving leads.
That turned out to be a mistake.
CPLs on brand search campaigns rose by 47% when we stopped video.
This is a business without much seasonality, and brand is usually less impacted by seasonality anyway, so at first, we were puzzled. Then we decided to relaunch video.
Voila! Brand search CPLs returned to their previous levels.
We suspected the video campaigns were contributing to the success of the brand campaigns, so we decided to try adding a Demand Gen campaign to the mix.
Brand CPLs decreased by 47%.
Not only were we able to return brand search CPLs to their original levels, but we were also able to cut them nearly in half when combined with YouTube and Demand Gen campaigns.
During the whole nine-month period, YouTube and Demand Gen campaigns only generated two conversions directly. However, the positive impact on brand search performance was indisputable.
It’s important to stress here that we made other optimizations during the test periods for both clients, so the improvements in search are probably not 100% attributable to the addition of the video campaigns.
However, in the case of the enterprise client, the improvements for the solutions that ran video outpaced performance across the rest of the account.
And the fact that two very different advertisers saw correlated improvements in search performance lends further credence to the theory that video played an important role.
Even though these two cases involved very different clients, here are the key practices that made both video campaigns successful:
Use custom segments made up of high-performing search keywords. Don’t use broad targeting or in-market audiences unless you have a very large awareness budget.
If you have first-party audiences and want to run Demand Gen, use them for a lookalike audience. Otherwise, custom segments of strong search keywords work best.
Make your geo-targeting spot-on. Don’t waste spend on irrelevant regions. For the local B2B client, we carefully selected areas of the city that best met their needs. For the enterprise client, even though they wanted to reach a global audience, we took care with which countries we targeted.
Use short videos – no more than 15-30 seconds – and include your brand name and logo in the first few seconds.
Choose a Target CPV bid strategy. We were able to get CPV below $0.01, which got our message in front of as many users in the target audience as possible.
The more videos, the better. If you have 3, 4, 5, or more videos, use them. Even slight variations help minimize video fatigue and grab attention.
You don’t need huge budgets for this to work – in both cases, we spent less than 5% of the client’s total budget on video.
With the right targeting, you can keep costs very reasonable – and the campaigns pay for themselves in lower CPLs in search.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/10/Enterprise-B2B-SaaS-advertiser-Solution-1-yzX8m1.jpg?fit=375%2C131&ssl=1131375http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-10-21 13:00:002025-10-21 13:00:00How to use YouTube Ads to drive B2B conversions
Marketing mix modeling (MMM) is having a moment in marketing measurement.
As privacy regulations limit user-level tracking, marketers are turning to it for reliable, cross-channel measurement. (We love it at my agency – MMM analyses often lead to smarter budget allocation with significant downstream impact.)
But as adoption grows, so do execution errors and misconceptions about what MMM can and can’t do.
Despite its strategic potential, it’s often misused, misinterpreted, or oversold – leading to costly mistakes and credibility loss from unrealistic expectations.
MMM isn’t a black box. To produce meaningful insights, it demands context, strategy, iteration, and strong data.
Context is especially critical. Without it, MMM becomes what I call a mathematical echo chamber – no external inputs and little connection to reality.
This article breaks down how to approach MMM correctly, avoid common pitfalls, and turn your analysis into real business value.
Execution errors
Too often, teams fixate on the modeling technique and overlook the broader system – data quality, assumptions, and stakeholder context.
There are plenty of possible mistakes, but the ones I see most often are:
Using inconsistent, incomplete, or unvalidated spend and performance data.
Assuming immediate or linear responses to media spend, which oversimplifies reality.
Interpreting statistical relationships as proof of impact without experimentation.
Using MMM for daily campaign decisions despite its strategic design and lagging granularity.
Building models that are over-optimized in-sample but fail in the real world.
If you make any of these, your MMM efforts will be muddled and ineffective, and you will not get much buy-in for the initiative going forward.
Faulty expectations vs. reality
When run properly, MMM can offer highly valuable insights, but only within its appropriate use case.
With good modeling and inputs, you can:
Reallocate budgets based on marginal ROI and saturation.
Forecast sales impact from various budget scenarios.
Set spending caps to avoid diminishing returns.
Show long-term contributions of brand versus performance channels.
Track media effectiveness over time and support cross-functional alignment.
What you cannot expect MMM to do:
Optimize daily media buying decisions.
Attribute at the user or creative level.
Replace lift tests or experimentation (which are a necessary complement to MMM).
In other words, treat MMM as a strategic GPS that needs other inputs to work well, not a tactical turn-by-turn navigation tool.
Misreadings of output
You can give three marketers the same MMM output, and they might have three very different interpretations of what it means and what to do next.
We’ve got a handy chart of the ways people misread the data (and how to fix those mistakes):
The misinterpretation I’d like to spend a bit of time on here is the correlation/causation dynamic.
Marketers need to understand that MMM is essentially a fancy correlation analysis that needs to be supplemented by incrementality testing, such as geo lift testing, to establish causation.
MMM does involve coding, but it’s a lot more than that.
It’s a cross-functional discipline involving data science, marketing, finance, and strategy.
To get it right, you need:
1. Clean, longitudinal data
One note before I dive into the data elements you need to run MMM: data density is critical.
For businesses without a huge pool of revenue-generating events (think of big SaaS platforms or car dealerships advertising online), use strategic proxy metrics that happen earlier in the purchase journey and provide strong predictors of revenue generation.
With that in mind, here’s the data needed (or recommended) for your model:
Weekly data across 2–3 years.
Media spend by channel and campaign. (Region is recommended.)
Control variables (all recommended): Promos, pricing, and competitors.
Note: seasonality is baked into the model for Meta’s Robyn, one of my favorite MMM options.
2. Advanced modeling techniques
Adstock/lag functions to reflect delayed impact.
Saturation models (e.g., Hill curves) for diminishing returns.
Regularization or Bayesian priors to stabilize estimates.
3. Validation and iteration
Running an MMM analysis once and taking the results at face value is never going to get you the best possible insights.
If you’re serious about adopting MMM, prepare to include the following in your process:
I highly recommend running analyses more than once and using different methods/platforms to identify commonalities and differences.
In the visual comparing Robyn and Meridian’s output from a recent client analysis, both models attributed similar influence across most channels – a good sign that helps validate the model.
But there’s a wrinkle: for channel 0, Meridian showed much higher organic influence and a slight bump in paid.
That suggests we need additional testing before moving to action items.
4. Stakeholder engagement
Even with top-tier MMM analyses, how you communicate the findings – and what they enable – is critical to getting buy-in from clients or management.
Before you start, align with stakeholders on KPIs, ROI definitions, and model assumptions to prevent surprises or misunderstandings later.
When you share results, include uncertainty ranges and clear action items that flow directly from your data.
If you can’t answer the inevitable “So what?” question, you’re not ready to present your findings.
Better MMM becomes a competitive edge
Overall, the shift away from user-based tracking is healthy for the marketing industry.
Initiatives like incrementality testing and MMM are finally getting their due as core parts of campaign analysis.
As major platforms level the optimization playing field with automation, running these analyses more effectively than your competitors is one way to drive differentiated growth.
