Web Design and Development San Diego

Google rolls out new AI safety features in Ads Advisor

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What 23 tests reveal about AI Max performance in Google Ads

Google is adding three new “agentic” safety features to Ads Advisor, its AI assistant inside Google Ads, aimed at reducing manual work while tightening security and compliance.

As campaigns grow more complex, advertisers are spending more time fixing policy issues, managing access, and handling certifications. Google’s pitch: let AI handle the heavy lifting so marketers can focus on performance.

What’s new. The update introduces proactive troubleshooting, always-on security monitoring, and instant certifications — all powered by AI and Gemini capabilities.

Zoom in:

  • Ads Advisor can now flag and help resolve policy violations automatically, even before advertisers notice them.
  • It monitors accounts 24/7, surfacing risks like suspicious domains or inactive users through a new security dashboard.
  • Certifications that once took weeks can now be granted instantly or submitted with a single click.

How it works. Instead of waiting for user prompts, Ads Advisor scans accounts and websites proactively, suggests fixes, and confirms resolution before appeals are submitted. On the security side, it continuously evaluates account health and recommends improvements, while new passkey support reduces reliance on passwords.

Why we care. Tasks that used to take hours — fixing policy issues, monitoring account security, and handling certifications — can now be done proactively by Ads Advisor, reducing delays and aims to reduce risks. The result is faster campaign execution, fewer disruptions, and less manual overhead.

What to watch. These features are rolling out in the coming months to English-language accounts, with more languages expected later.

Bottom line. Google is turning Ads Advisor into a hands-on operator, not just a helper — aiming to make ad accounts safer, faster, and far less manual to manage.

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Web Design and Development San Diego

How to build a YouTube analytics report in Data Studio

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How to build a YouTube analytics report in Data Studio

Creating video content takes time and budget, so understanding how it performs is critical.

YouTube’s native analytics in YouTube Studio are robust, but they’re locked behind account access. That can make reporting difficult — especially when you need to share data or don’t have direct login access.

Moving that data into Google Data Studio (formerly Looker Studio) makes it easier to analyze and distribute.

With Data Studio, you can:

  • Pull YouTube data into reports you already use.
  • Schedule automated updates for stakeholders.
  • Customize dashboards around the metrics that matter.
  • Track performance without relying on backend access.

Here’s how to pull your YouTube analytics into a Data Studio report.

Using a template or starting from scratch

You have two options when setting up a YouTube report in Data Studio.

  • If you want something quick and easy, you can use Google’s YouTube Analytics template from their template gallery. It’s a great place to start because it provides a clean, well-designed report with foundational metrics and puts you in a good position to understand which metrics are available. But know that this template has problems you’ll need to fix, which I’ll discuss below.
  • The other option is to create a report from scratch, which is a great choice if you already have a report you want to add a new YouTube Analytics page to, or if you just want to learn how to use Data Studio.

The information below will help you do both.

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If you’re not the YouTube account owner

If you’re setting up this report for a client, or if you’re not the owner of the YouTube account, you’re going to run into an issue where the YouTube account doesn’t show up as a usable source in Data Studio. Here’s how to get around it:

  • Go to YouTube Studio settings > Permissions, and give Manager permissions to the account email that you’re using in Data Studio.
  • Get the Channel ID from the channel’s YouTube URL.
  • Add a YouTube connector to Data Studio, go to Advanced, and paste the Channel ID.

You should now have access to that YouTube account.

Using the Data Studio YouTube Analytics template

From the Data Studio home page, click Templates > Template Gallery. Under the category dropdown, click on YouTube Analytics.

Clicking this will create a brand new Data Studio report that’s mostly ready to use. It loads up with sample data from the Google Analytics YouTube channel. Click the button at the top that says “Use my own data.”

The first time you set up a report, you need to authorize access to your data. Click the Authorize button.

Choose the Google Account connected to your YouTube channel, and then you’ll see any connected channels in the dropdown at the top of the page.

You’ll notice that the data doesn’t change when you select a site here. That’s because this dropdown is connected only to the other dropdowns next to it, not any of the charts on the page.

To update everything else on the page, click the Edit and Share button.

If this is the first time using Data Studio, you’ll also need to do some basic account setup.

Then click the Edit button at the top of the page.

Now you’ll need to add your YouTube channel as a source. Click the Add data button and then search for the YouTube Analytics connector.

If the Google Account is the owner of the YouTube account you connected to this Data Studio report, it’ll show up in the Channel section as an option. 

Your main YouTube channel will be in the My Channel tab, and other channels are in the All Channels tab, as shown below. 

If you don’t own the channel, see the section above to connect other channels that you don’t own, but have access to.

Now you’ll be able to change the data source on any charts on the page. Simply click a chart, and you’ll see the data sources available to you in the right Properties panel.

You can change the source of all of the charts on the page by selecting a chart, right-clicking on it, going to the Select menu, and then choosing “Charts with this data source on page” and then choosing your data source in the Properties sidebar.

You’re mostly done, but as mentioned earlier, there are some errors in this report that you’ll need to fix. The charts at the bottom of the report are using the wrong metrics.

I don’t know why Google hasn’t updated this template. It’s been like this for a long time, so I don’t know if they ever will. In the meantime, you’ll need to update the following.

Change:

  • Likes from “Average Watch Time” to “Video Likes Added”
  • Subscriptions from “Video Link” to “User Subscriptions Added”
  • Dislikes from “Average View Percentage” to “Video Dislikes Added”

The charts in the Comments section are correct, so you don’t need to change anything there.

Click on each of the charts highlighted above, one by one, and change the metric in the Properties sidebar.

And now the report is finished and ready to use. Click the View button at the top of the page to view the report in a view-only format.

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Copying a template into an existing report

Data Studio doesn’t support the ability to add or import templates into an existing report, but you can copy a page from one report to another. Follow the steps above to create a report using the YouTube Analytics Channel template, then copy it into another report.

To do that, go into Edit mode, select all (Ctrl+A or Cmd+A), and copy all (Ctrl+C or Cmd+C). Then, in your existing report, create a new page, and paste everything you’ve copied into the page (Ctrl+V or Cmd+V), or right-click on the page and select Paste.

All of the charts will likely come in broken, but you can easily update them using the tip mentioned earlier – right-clicking a chart, choosing Charts with this data source on page, and then choosing the correct source in the Properties sidebar.

Customizing your report

The YouTube template in Data Studio has most of what you need, but you can add much more.

There are some metrics you simply can’t get in Data Studio that you’d find in the official YouTube Analytics backend, such as revenue, how viewers find your videos, watch behavior, popular viewing times, device types, genders, and retention, so there are some big limitations, but there’s still plenty to work with.

To add more charts to your report, you’ll need to create more space at the bottom. In the menu, click on Page > Current page settings.

In the Style tab of the Current Page Settings sidebar, set the canvas size to something like 3,000 pixels. This will give you plenty of space to work with, and you can always shorten or lengthen it as needed later.

Now you can add many types of charts with a wide range of dimensions and metrics.

You can add multiple metrics to graphs to get the data you need for better analysis. You can also rename headers to clean them up, and make them look less cluttered.

You can pull in quite a lot of data. Here’s what’s currently offered:

Using Data Studio for ongoing YouTube reporting

Setting up a Data Studio report for YouTube is a great way to track your top-level metrics, and can be especially useful for monthly client reporting. It takes siloed, hard-to-share data from YouTube, and puts it into a clean, automated, centralized tool for easier decision-making.

You can also set up scheduling so that Data Studio sends automated PDF exports to your email.

That’s it. As you can see, it’s fairly simple to set up, but you can also add more advanced customizations to track many other KPIs.

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Web Design and Development San Diego

Why IBM says every brand now needs a GEO playbook

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GEO playbook

Search has changed, and brands need to catch up fast, according to IBM’s Alexis Zamkow (global lead of Marketing Transformation solutions) and Sandhya Ranganathan Iyer (associate partner – AI), speaking yesterday at Adobe Summit.

AI tools don’t just help people search. They answer questions, compare products, and recommend brands. In many cases, users never even visit a website.

That means if your brand isn’t part of the AI-generated answer, you may not be part of the decision.

To keep up, brands need more than new tactics. They need a system — a GEO (Generative Engine Optimization) playbook. Here’s a recap of their presentation, Adapt or Disappear: How Brands Win with AI-Powered Search.

The AI shift: You’re marketing to machines

AI agents now sit between you and your customer.

They take a complex market and simplify it. They decide what information to show. And they often speak on your behalf.

  • “These machines are disintermediating the brand experience,” Zamkow said.

At the same time:

  • Consumers are using AI for research and decisions
  • Businesses are adopting it even faster
  • Many searches now end without a click

Zamkow said an estimated 75% of search visibility could shift to AI agents in the next two years.

That’s why visibility today depends on being part of the answer itself.

The GEO playbook: 12 components every brand needs

To respond, the speakers outlined a 12-part playbook. It spans content, technology, and operations.

1. Strategic content foundations

Your content must tell one clear story — everywhere.

That includes your website, PR, social, and third-party mentions. If each channel says something different, AI won’t trust your brand.

For example, if your site highlights premium quality, but reviews focus on low price, that mixed message weakens your authority.

Consistency builds trust for people and machines.

2. Retrieval-grade passage standards

AI doesn’t rank webpages. It extracts answers. So your content must be easy to extract.

Good content looks like:

  • Clear questions and answers.
  • Short, focused sections.
  • Direct language.

For example, instead of a long paragraph, write:

  • Question: What are the best running shoes for beginners?
  • Answer: A short, clear response

This makes it easier for AI to reuse your content in answers.

3. Technical foundations

Even great content won’t work if AI can’t read it.

Machines rely on:

  • Clean HTML (not just visual design)
  • Structured data (schema, metadata)
  • Pages that load content directly

One example from the session: a beautiful website appeared to AI as “a headline and a blank page.”

If your content isn’t readable, it won’t be used.

4. On-site search + genAI search alignment

Start with your own site.

If your internal search — especially AI-powered search — works well, you’re already ahead.

Think of it this way: If your own system can’t find answers on your site, external AI tools won’t either

Strong internal search helps train your content for external visibility.

5. AI search citation qualification model

In GEO, the goal isn’t just to be mentioned. It’s to be cited.

  • Mentions mean you show up.
  • Citations mean AI trusts you.

AI looks for signals like:

  • Clear expertise.
  • Consistent messaging.
  • Agreement across sources.

Zamkow called citations the “holy grail” of visibility.

6. Extraction optimization

AI tools pull content from many places and combine it.

To be included, your content must be:

  • Easy to extract.
  • Clearly structured.
  • Rich in context.

If your content is hard to break apart, AI will skip it and use something else.

7. Real estate: third-party strategy

Your website is no longer your main source of visibility.

  • 85% of mentions come from external domains.
  • Third-party content drives most citations.

That includes:

  • Reddit
  • Social media
  • Reviews and forums
  • Media coverage

This means your PR and social teams are now critical to search success.

Your brand lives across the internet — not just on your site.

8. Measurement, KPIs, and reporting

Old metrics don’t tell the full story anymore.

Instead of just tracking clicks, you need to track:

  • How often AI mentions your brand.
  • Where you’re cited.
  • Which platforms show your content.

The key question changes from “Did we get traffic?” to “Did AI recommend us?”

9. SOPs (standard operating procedures)

Consistency doesn’t happen by accident. Teams need clear rules for:

  • How content is written.
  • How it is structured.
  • How it is published.

Without SOPs, different teams will create different formats. That confuses AI and weakens your visibility.

10. Prompting best practices

Search is now conversational.

While people still type keywords, they are increasingly describing their needs using more conversational language. For example:

  • Old search: “running shoes”
  • New search: “I’m training for a marathon. What shoes should I buy?”

Your content needs to match these types of questions.

That means thinking like the user — and writing like the answer.

11. Change management

This shift affects the whole organization.

Marketing, IT, PR, and product teams all play a role.

That means:

  • Training teams on new workflows.
  • Aligning goals and KPIs.
  • Breaking down silos.

This is bigger than just a marketing update. It’s a company-wide change.

12. Governance + versioning

GEO is never finished.

AI systems change constantly. Competitors update content. Rankings shift fast.

To keep up, brands need:

  • Ongoing monitoring.
  • Regular content updates.
  • Clear ownership of changes.

If your content becomes outdated, you can quickly lose your position in AI answers.

From SEO tactics to GEO systems

The GEO playbook reflects a larger change in how marketing works:

  • From keywords to prompts.
  • From links to citations.
  • From websites to ecosystems.
  • From traffic to answer eligibility.
  • From campaigns to continuous content.

The focus has shifted to building a system that consistently feeds AI the right information.

This is now a leadership issue

This shift is already reaching the top of the organization.

In one example, a product leader asked why their brand didn’t show up in an AI recommendation. The issue quickly escalated beyond marketing.

  • “This is not a problem for your SEO team,” Zamkow said. “This is at the CEO level.”

As AI becomes the front door to discovery, every leader will care about visibility.

Adapt or disappear

AI is already shaping how people discover and choose brands.

Consumers trust it. Businesses are using it. And it’s growing fast.

Brands that build and follow a clear GEO playbook — across all 12 components — will stay visible.

Everyone else risks being left out of the answer.

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Web Design and Development San Diego

SEO reporting outgrew Data Studio — here’s what comes next

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SEO reporting outgrew Data Studio — here’s what comes next

Picture this: Your company relies on Data Studio for SEO reporting. 

It’s right before your next big meeting when you’re planning to present results… but Data Studio has an outage (again) and suddenly you have nothing to show. 

That’s embarrassing. And it happens more than it should.

It wasn’t even a year ago that I touted the benefits of Looker Studio (now Data Studio) for SEO reporting. Now the platform feels archaic compared to the agentic coding tools available today.  

Here’s how rigid SEO dashboards like those produced in Data Studio are holding you back and why code-driven SEO reporting is the only way to remain efficient and competitive.

The problem with Data Studio 

In the not-too-distant past, Data Studio was considered one of the best ways to customize SEO reporting. 

But things have evolved, and with new technology at our fingertips, Data Studio’s flaws are only becoming more pronounced. 

Here are some common issues that you may recognize when generating reports using Data Studio.

It’s easy to explode your dataset, and then everything breaks

You assume Data Studio can handle massive “Google-scale” data, but it’s buggy. For example, there are low limits on rows and fields, and even adding a few dimensions or joining multiple data sources can break the report at the worst times. 

You’re manually clicking through a slow interface

Every change in Data Studio requires manual updates. You’re clicking, refreshing and waiting to see whether it worked. That makes iteration painfully slow. Even with added AI features, they only address a small part of the report development workflow.

Relatedly, debugging reports is a nightmare

Whereas agents can simply scan files with code-based reports, in Data Studio, a user has to laboriously click around the interface. 

The API is weak

Like a lot of Google services, Data Studio isn’t built as an API-first platform. This is something Google got institutionally wrong decades ago. Not being able to manage the platform using external tools creates bottlenecks.

Despite its recent rebrand, Data Studio hasn’t become any more relevant — not with the technologies that are now in play for SEO reporting.

But it’s not just Data Studio. Really, what SEO teams are up against is the rigidity of any dashboard-based reporting tool. Now all that is changing.

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What’s changed: AI, APIs, and coding 

The shift away from rigid SEO dashboards is now possible because large language models are becoming more capable of generating reliable code, and APIs are accessible across many platforms.

This has led to the rise of AI-driven coding tools, including Claude Code, OpenAI Codex, and Gemini CLI.

At a high level, it works like this: You describe what you want in your SEO report, and they handle the heavy lifting. 

These tools are “agentic” because they can execute multi-step workflows like pulling data, transforming it, analyzing it, and then generating reports with minimal intervention.

You don’t need advanced coding skills to use them, but a basic understanding of data structures and APIs will make the process effective.

In practice, the entire reporting workflow can be done programmatically from start to finish.

They generate code that connects directly to data sources through APIs, removing the need to rely on dashboard connectors or preconfigured data pipelines.  

From there, they can analyze the data and create full reports. This can happen in minutes as you become more familiar with the tools.

While each of the tools I mentioned has different strengths (for instance, some are better at reasoning, others at speed or integrations), they essentially do the same thing: transform SEO reporting from a manual, rigid process into something with endless possibilities. 

The power of this technology is hard to overstate. 

Why AI coding tools are better for SEO teams

AI coding tools are removing the roadblocks between data, development, and reporting for SEO teams. 

Faster SEO reporting and analysis

Speed is the most obvious advantage. 

Agentic coding assistants are enabling SEOs to create reports that previously required support from developers.  

In many cases, tasks that previously took days can be done in hours and tasks that took hours can be done in minutes. 

You can see this improvement even in small interactions.

For example, when data is processed directly in the browser (instead of re-querying a dashboard), it makes filtering, sorting, and slicing data significantly faster. 

Instead of waiting for a dashboard to refresh after every change, you can interact with the data in real time.

That’s just one way these technologies make you more agile.

Flexible and custom reporting workflows

Instead of having to work in predefined templates and a fixed structure, you can build the report for exactly what the situation requires. 

Plus, every major data visualization and plotting library is available on demand in any programming language. 

If you feel like one approach isn’t capturing the whole story in your SEO report, you can switch or combine multiple frameworks in the same output. 

From rankings and traffic trends to keyword clusters or content performance, you can apply nearly any chart. 

The examples below come from Observable Plot, created by data visualization expert Mike Bostock, but many other charting libraries are available.

While setup and onboarding take some initial effort, these tools are accessible to most roles on the team and immediately become more efficient than traditional reporting.

Transparent data constraints

Data limitations are clearer, too. 

For example, when you’re working with browser-based charting libraries, you have a better feel for how many rows you’re handling and what the system can realistically process. 

And when you do hit a limit, you understand exactly what’s happening and how to adjust. This helps prevent misleading or incomplete reporting. 

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Real-world SEO reporting applications

What are some practical ways you can use these agentic coding assistants to run SEO reporting? 

Pre-meeting reports

Before client meetings, you can pull data from Google Search Console and GA4 via APIs, then have it cleaned and segmented programmatically and generate a notebook, dashboard or slide deck in a single workflow.

Technical SEO analysis

Say you need to analyze crawl data or log files. Instead of exporting, filtering, and then visualizing the data manually, you could get the raw data, process it with code, and generate custom visualizations tailored to the exact problem you’re trying to solve.

Ad hoc stakeholder requests

Once data connections are established, last-minute reporting requests no longer have to mean staying up late to pull data and build reports. The next time someone asks for something like “non-brand CTR trends by device over the last 90 days,” you can produce this data with much less effort. 

Really, if you can imagine it, you can do it with these agentic coding assistants. As a result, SEO teams can do more proactive analyses.

What this means for agencies and in-house teams

AI is impacting all knowledge workers, not just SEOs. 

By now, many have seen the viral article “Something Big Is Happening” by Matt Shumer, which paints a startling picture about the future of AI-powered work and adopting an “adapt or die” mentality.  

Research is beginning to show how these types of technologies are impacting productivity. 

One study by Stanford and MIT researchers found that access to AI tools in the workplace increases productivity by at least 14% on average, with a 34% increase for low-skilled workers. 

The bottom line is that anything that can be generated with code is going to be eaten by these CLI tools and agents, because they’re just so much faster. 

Businesses are catching on. Up to 64% of businesses now generate a majority of their code with AI assistance, according to a Business Insider report, and high-adoption teams are producing nearly double the output. 

For SEO teams, they’re experiencing faster reporting cycles, more iterative analyses, and the ability to handle more complex data.

AI coding assistants are also helping analysts become builders. Non-technical users can build and iterate in ways that were previously out of reach.

Ultimately, this shift is becoming table stakes. The SEO teams that integrate these tools into their workflows will move faster and produce better results. 

The competitive advantage is going to those who adopt these technologies first.

Where to begin, though? Consider piloting a small project:

  • Start with one repeatable reporting workflow.
  • Connect a data source like Google Search Console via API.
  • Test and refine a single report before expanding to other use cases.

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The future of SEO reporting is agentic and code-driven

Traditional SEO reporting tools are quickly becoming a bottleneck. 

AI coding assistants are helping SEO teams respond to any type of reporting without the added friction, while delivering faster, better insights. 

The companies that adapt will gain the advantage in SEO execution. Start by replacing one recurring report with a code-driven workflow and build from there.

Read more at Read More

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Microsoft launches AI Max and new ad tools for the “agentic web” era

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Microsoft (Credit: Shutterstock)

Microsoft is rolling out a suite of updates across Microsoft Advertising to help brands stay visible — not just to people, but to AI agents increasingly making decisions on their behalf.

What’s new. The update spans measurement, commerce, and media, with new tools designed to help advertisers show up in AI-driven experiences and transactions.

On the ads side. Microsoft is introducing AI Max for Search campaigns, which expands query matching and personalizes ad delivery across AI surfaces like Copilot and Bing. It’s also launching “Offer Highlights,” new ad formats that surface key selling points — like free shipping — directly within AI conversations.

Zoom in:

  • Expanded AI Visibility in Microsoft Clarity shows how brands appear in AI-generated answers, including which content gets cited and where competitors outperform.
  • New Universal Commerce Protocol support in Microsoft Merchant Center structures product data so AI agents can discover and transact on it more easily.
  • Copilot Checkout enhancements enable purchases directly inside Microsoft Copilot, reducing friction from discovery to sale.

Also notable. A new AI-powered audience generation tool lets advertisers describe their ideal customer in plain language, with the system building targeting segments automatically.

Why we care. Microsoft is changin how visibility works in Microsoft Advertising — shifting from clicks and rankings to being selected by AI systems. Tools like AI Max, AI Visibility, and Offer Highlights help brands show up in AI-driven decisions, not just search results. As AI agents take a bigger role in discovery and transactions, advertisers who adapt early will have a clear advantage.

Between the lines. This is a shift from optimizing for clicks to optimizing for selection — ensuring your brand is chosen by AI systems, not just seen by users.

What to watch. Early data suggests AI-driven traffic is growing far faster than human traffic, signaling where future demand may concentrate.

Bottom line. Microsoft is preparing advertisers for a world where winning means being understood — and trusted — by AI agents, not just ranking in search results.

Dig deeper. Win Across All Three Eras of the Web

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How to measure Demand Gen creative impact with asset uplift tests

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How to measure Demand Gen creative impact with asset uplift tests

Demand Gen campaigns have high visibility across YouTube, Discover, and Gmail. However, they pose a key challenge: the “attribution illusion.” You’ll often question whether reported conversions in the platform are truly incremental or if these users would’ve converted through search either way.

That’s why in November, Google launched asset uplift experiments, giving you the ability to measure the impact of Demand Gen creative through an A/B split test. This means you can replace assumptions with a clearer view of what’s actually driving incremental results.

Relying too heavily on creative instinct or default reporting can lead you down an inefficient path and divert valuable creative resources toward poor-performing assets. Using Google’s A/B testing capabilities helps you isolate the impact of individual assets and avoid that outcome.

Why attribution doesn’t equal incrementality

If a user views a Demand Gen ad on YouTube and doesn’t click but then searches for the brand and converts, Google may attribute partial or full credit to the Demand Gen campaign and creative. This attribution more so reflects correlation rather than causation.

Accurate measurement and the scientific method show the need to understand the scenario in which the creative isn’t shown. By withholding the test assets from a segment of the target audience, it’s possible to establish a baseline. 

The difference in conversion rates or any primary KPI between the treatment group — those who were exposed to the ad — and the control group — those who weren’t exposed — shows the actual incremental lift the creative is driving.

Dig deeper: Why incrementality is the only metric that proves marketing’s real impact

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What you need before testing creative uplift

One common mistake is launching experiments without enough data to reach statistical significance. To avoid inconclusive or invalid results, make sure your campaign meets these prerequisites before setting up the test.

Conversion volume 

Google recommends having at least 50 conversions across treatment and control arms during the experiment to measure lift accurately. If your primary conversion doesn’t receive this volume, consider optimizing the test around high-intent micro-conversion actions, such as “Add to Cart.”

Budget minimums

Experiments should run with continuous, uninterrupted spending. If your Demand Gen campaign is limited by budget and stops early each day, the control group data will be skewed. 

The campaign must have a sufficient budget to run for at least four weeks, or until a statistically significant result is achieved.

Creative isolation

Test only one new variable at a time. To determine if a specific video asset drives uplift, keep all other campaign elements, such as audience, bidding, and standard image assets, unchanged.

Dig deeper: Why Demand Gen is the most underrated campaign type in Google Ads

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How to run an asset uplift test in Google Ads

Setting up a creative uplift test is now more streamlined within Google Ads. To build a valid experiment, follow these steps.

1. Define a clear hypothesis

Every valid scientific test begins with a clear hypothesis. Avoid running tests without a defined objective. For example:

  • Bad hypothesis: “Let’s see if our new video works.”
  • Good hypothesis: “Adding user-generated content (UGC) to our Demand Gen asset group will drive a 10% incremental lift in ‘purchase’ conversions compared to standard static image carousels.”

Navigate to the Experiments interface

Log in to your Google Ads account and navigate to the left menu. Select Campaigns > Experiments. Click the plus (+) button to create a new experiment, choose Asset tests provided by you, and make it a Demand Gen campaign experiment.

Configure a 50/50 split

Google will prompt you to define your split. To set up statistically sound results, use a 50/50 cookie-based split. 

This ensures both control and treatment groups have equal historical data and algorithmic weighting, and prevents users from ending up in both arms of the test. Assign your existing campaign as the control, and the duplicated campaign with new assets as the treatment.

Lock your variables

Once the experiment begins, you must practice extreme discipline. Don’t change audiences or targeting, and avoid drastic bid and budget changes. 

Any adjustment made to either campaign during the testing window will introduce noise and could invalidate the statistical significance of your results.

Set the duration

Run the experiment for at least four weeks. 

  • Week 1 serves as a learning period while the algorithm adjusts to the audience split, new creative, and bid model learning (especially if leveraging smart bidding). 
  • Weeks 2 to 4 provide actionable performance data. 

For longer conversion cycles, such as B2B SaaS, consider extending the test to six or eight weeks.

Dig deeper: What it takes to make demand gen work for B2B and ecommerce

What your experiment results actually mean

When the experiment concludes, review results in the Experiments dashboard, where a report showing the performance of each arm and its confidence interval across metrics is available. Interpret the outcomes as follows to validate your hypothesis made earlier.

Outcome 1: Positive lift (statistically significant)

If the treatment group shows a positive lift with 95% confidence, your creative asset has been proven to drive incremental conversions. 

From there, you can calculate incremental cost per acquisition (iCPA) by dividing the treatment group’s total ad spend by the incremental conversions above the control arm. 

Use this iCPA as your benchmark for scaling the campaign going forward.

Outcome 2: Negative lift

Occasionally, a new creative asset may suppress performance. It may be too disruptive, or the video may have a high skip rate, causing the algorithm to reduce delivery to high-intent users. Pause the treatment asset immediately. This allows you to let data guide your budget decisions vs. preference.

Outcome 3: Inconclusive result

If the difference between groups is negligible and the system cannot confidently attribute conversions to the ad after four weeks and adequate conversion volume, consider extending the test for two more weeks to collect additional data. 

If results are still inconclusive, it could be that creatives are too similar. Test a significantly different creative asset, as small changes rarely produce a statistically significant lift in Demand Gen.

Prove creative impact with incrementality testing

Creative is a key remaining lever and differentiator you can pull to drive performance. Producing high-quality video or UGC is just the first step in this world, where creative bandwidth and impact must be proven as a driver of results. 

Demand Gen is a powerful tool for visual storytelling, but justifying its budget to stakeholders requires rigorous, scientific evidence of its impact. Asset uplift experiments enable just that. Begin your first holdout test, establish a baseline, and let data guide your creative decisions and roadmap.

Dig deeper: The Google Ads Demand Gen playbook

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Web Design and Development San Diego

groas introduces a fully autonomous approach to Google Ads management by groas

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groas distributed AI agent network managing Google Ads campaigns across multiple screens.

For 20 years, Google Ads management has followed the same basic model: you log in, review performance, make changes, and hope they work before the next check-in. 

Agencies, freelancers, and in-house teams all work this way, even as the tools have changed. Spreadsheets gave way to scripts, and scripts gave way to automated bidding, but the core loop never changed — someone still had to sit in the account.

groas aims to change that model by introducing a system designed to automate campaign execution end-to-end.

Our company announced today it has developed a fully end-to-end autonomous system that’s designed to match or exceed PPC performance benchmarks observed in internal testing. It’s designed to operate without routine manual approvals or constant dashboard monitoring.

From campaign creation through bid management, ad copy generation, keyword expansion, negative keyword pruning, budget allocation, and dynamic landing page deployment — along with everything else you can do in the Google Ads console and beyond — the entire workflow now runs autonomously, 24/7. 

The system runs on a distributed network of specialized AI agents that handle different parts of campaign management and communicate in real time.

We didn’t start here. 

A year ago, groas launched as a lightweight product that surfaced optimization recommendations for you to review and implement. The same model most PPC products still follow. 

By the founder’s own admission, it was a fairly unremarkable v1. But what it lacked in sophistication, it made up for in something more valuable: real data from large volumes of real campaigns at scale.

Hundreds of early customers across the world signed up and connected their Google Ads accounts, representing a wide range of ad spend levels, campaign structures, and conversion goals.

These weren’t a narrow slice of one vertical. They spanned dozens of industries and niches — from local service businesses spending a few thousand a month to large agencies managing seven-figure monthly budgets across full client portfolios.

That diversity became the most important asset groas built. 

The custom-trained, fine-tuned models that now power the system were shaped by this breadth — not a static dataset or simulation, but live campaigns with real money on the line across every industry and budget tier. 

Without that base of early adopters, what groas is today couldn’t exist. The training data that enables autonomous management came from actively managing real dollars across real campaigns, learning what worked and what didn’t in conditions no synthetic environment could replicate.

David Pourquery, founder and CEO of groas, said:

“We kept seeing the same pattern. We’d surface a recommendation that would clearly improve performance, and it would sit there for days or weeks because the account manager was busy, or the client needed to approve it, or someone was on vacation. The insight had a shelf life, and by the time it got implemented, the data had moved on. So we stopped recommending and started doing.”

That realization drove a complete six-month rebuild. The result is a system of interconnected AI agents, each specialized in a different part of campaign management, collectively processing over 100,000 data points per hour per campaign. 

The network handles a wide range of tasks typically performed inside the Google Ads console without the limits of working hours, cognitive load, or the tradeoffs that come with managing multiple accounts. The system automates most day-to-day campaign management tasks that would typically require manual input. If you wouldn’t have time to do it, the agents would.

From day one, groas built dynamic landing pages into the system, deployed and continuously A/B tested to find winning combinations of messaging, layout, and calls to action for every campaign. groas deploys them with a single line of JavaScript on your existing site — no developer resources, no new hosting, no CMS changes. The system tests and iterates 24/7, designed to improve conversion rates through continuous testing.

There’s a full undo capability for each agent action, but the point is you don’t need to regularly check into groas or Google Ads. Weekly reports are emailed, summarizing what was done, while a dedicated human PPC account manager oversees everything groas does around the clock.

Onboarding is fully hands-off. After sign-up, your groas account manager learns your business, audits your existing Google Ads accounts, and delivers a detailed action plan within 24 hours. From there, they implement everything across groas and Google Ads with zero work on your side.

In less than a year since shifting to full autonomy, groas now manages eight figures in monthly ad spend across its client base. Every account came through organic discovery or direct referrals — the company hasn’t spent anything on paid acquisition to date.

The client base has consolidated around two profiles:

  • Businesses moving away from agency relationships where results haven’t kept pace with cost. These are companies paying $5,000 to $15,000 per month and looking for more consistent performance and transparency. groas provides an alternative by automating day-to-day execution while reducing management overhead.
  • Agencies. This is now the larger segment. Agencies plug groas into their clients’ accounts behind the scenes, bundle the cost into your existing fees, and let the agent network handle day-to-day execution while their teams focus on strategy, creative direction, and client relationships. The implementation runs behind the scenes within agency workflows. groas turns a labor-intensive, low-margin service into something that scales without added headcount. groas offers a 30% lifetime recurring commission for referrals, but most of you choose to pay for it yourselves and keep the margin.

Google’s automation — from Performance Max to AI Max to broad match expansion — has pushed the industry toward more black-box control for years. Many advertisers feel they are losing visibility into what’s actually happening inside their campaigns. Meanwhile, agencies and recommendation-based products still run the old loop: review, recommend, wait for approval, implement, repeat.

groas occupies a category that didn’t exist. Instead of helping you manage campaigns better or relying on Google’s automation, it removes you from the execution loop while keeping you in the strategic loop through a dedicated account manager.

The PPC industry has spent two decades debating how much to automate. groas is the first to answer “everything” and back it up with eight figures in managed spend. 

The growth points to something the industry has been circling for years without arriving at. The bottleneck in Google Ads performance has often been the limits of manual execution — constrained by time, attention, and the volume of data modern campaigns generate.

groas didn’t build a better recommendation engine — it reduced the need for traditional recommendation-based workflows.

groas starts at $999 per month for up to $15,000 in managed ad spend, scaling to $6,999 per month for up to $150,000. No contracts, lock-ins, or setup fees. The only requirement is at least $2,000 per month in Google Ads spend — below that, there isn’t enough data for the agents to optimize effectively.

Learn more about how groas works at groas.ai.

Watch this video on YouTube

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Web Design and Development San Diego

Yelp launches AI-powered Assistant to streamline local search and bookings

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yelp

Yelp is rolling out its most significant AI update yet, centered on a new conversational “Yelp Assistant” designed to move users from searching to actually booking, ordering, and scheduling — all in one flow.

What’s new. Yelp Assistant sits at the center of the update, acting as a chatbot that can answer complex queries, recommend businesses, and complete actions like reservations or appointments without leaving the app.

Zoom in. The assistant pulls from Yelp’s massive base of user reviews and photos to generate tailored recommendations, explain why a business fits, and let users refine results conversationally. It can then take the next step — booking a table, ordering food, or requesting a quote — directly within the same interaction.

What else is new. Yelp is expanding integrations with platforms like Vagaro, Zocdoc, and Calendly to streamline bookings across categories like beauty, healthcare, and home services, while deepening delivery ties with DoorDash.

Also notable. An upgraded “Menu Vision” feature uses AI and visual overlays to show dishes, reviews, and photos in real time when scanning a menu, helping users decide what to order faster.

Why we care. Yelp is shifting from a discovery platform to a transaction-driven experience powered by AI. With Yelp Assistant handling recommendations and bookings in one flow, visibility alone may not be enough — businesses will need to be optimized for conversion within the platform. The update also signals more competition for high-intent users as Yelp tightens control over the path from search to purchase.

Between the lines. Yelp is leaning into AI not just for discovery, but for conversion — turning intent into transactions without sending users elsewhere.

What’s next. The assistant is live on iOS and Android with broader expansion across categories and desktop coming later this year.

Bottom line. Yelp wants to own the full local journey — from “where should I go?” to “it’s booked.”

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Meta is on track to overtake Google in global ad revenue for the first time

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Inside Meta’s AI-driven advertising system: How Andromeda and GEM work together

A major shift is underway in digital advertising: Meta Platforms is projected to generate more ad revenue than Google in 2026, signaling how marketers are increasingly favoring automated, performance-driven platforms.

Driving the news. According to Emarketer, Meta is expected to bring in $243.46 billion in global ad revenue this year, narrowly topping Google’s projected $239.54 billion.

  • Meta is forecast to capture 26.8% of global ad spend.
  • Google is projected to take 26.4%.
  • It would be the first time Google has lost the top spot in digital ad revenue.

Why we care. Meta’s growth suggests brands are getting more value from automated, performance-focused tools, which could influence how they split budgets between Meta and Google. It’s also a reminder that platform dynamics are changing fast, so media strategies need to stay flexible.

Catch up quick: Google has long dominated digital advertising through Search ads, Display ads across the web, and YouTube.

But its core ad business is growing more slowly than in previous years.

Meanwhile, Meta has benefited from AI-powered ad automation, stronger performance measurement tools, and continued scale across Facebook, Instagram, and WhatsApp.

Why Meta is winning now. Advertisers are increasingly prioritizing platforms that can deliver both reach and measurable return.

Meta’s advantage has been its ability to automate creative and targeting faster, optimize campaigns with less manual input, and make it easier for brands to prove ROI.

That’s especially appealing in a tighter economic environment where marketers are under pressure to do more with less.

Yes, but. Google is still enormous — and still growing.

Its search business remains one of the most profitable ad engines in the world, and YouTube continues to attract brand budgets. But the company faces more pressure from, AI search disruption, antitrust scrutiny, and slowing growth in traditional search advertising.

The bottom line. Meta passing Google in ad revenue would mark more than a symbolic milestone — it reflects a broader power shift toward platforms that make advertising easier to automate, measure, and scale.

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Google Ads advertisers report wave of unexplained ad disapprovals

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Google Local Services Ads vs. Search Ads- Which drives better local leads?

A growing number of advertisers say their Google Ads campaigns were suddenly hit with mass disapprovals tied to DNS and 500 server errors — even when their sites appeared to be working normally. The issue is raising fresh concerns about platform reliability and the risk of sudden performance disruptions.

Driving the news. PPC advertisers began flagging widespread problems this week across Google Ads accounts, with multiple agency leaders saying clients were affected at the same time.

  • Managing Director at Cornerhouse Media, Ryan Berry, said more than 1,500 ads were disapproved in a single account around 1:30 p.m. UTC.
  • Others said they received overnight emails warning that ads had been disapproved.

Why we care. Sudden mass disapprovals can instantly pause traffic, leads, and revenue — even if nothing is actually wrong with their website. If Google’s systems are incorrectly flagging DNS or server errors, brands could lose performance and spend valuable time troubleshooting an issue they didn’t cause. It also highlights the need for closer monitoring and faster escalation when platform glitches happen.

What advertisers are seeing:

  • DNS errors, even when internal IT teams found no website issue.
  • HTTP 500 errors, despite landing pages loading normally.
  • Repeated disapprovals across multiple accounts.

Google Ads trainer, Charlotte Osborne said she saw two separate cases this week — one tied to a DNS error and another to a 500 error — with no issues found on the client side.

Google Advertising specialist Joshua Barr said he received “lots of emails overnight” about disapproved ads and has been dealing with similar problems for weeks.

Several Paid Search experts also said they were seeing the same issue across accounts.

What’s likely happening. Google’s ad review systems use automated crawlers to test landing pages. If Googlebot encounters temporary server issues, DNS lookup failures, redirects, or timeout errors, ads can be automatically disapproved under the platform’s “destination not working” policy.

That means advertisers can be penalized even if:

  • their site is live for users,
  • the issue is temporary,
  • or the problem is on Google’s crawler side.

What to do now:

  • Check Google Ads policy manager for exact disapproval reasons.
  • Test landing pages using multiple locations and devices.
  • Review DNS uptime, redirects, and CDN/firewall settings.
  • Submit appeals for clearly incorrect disapprovals.
  • Document account-level impacts in case the issue proves platform-wide.

The bottom line. For advertisers, this is a reminder that campaign performance can be derailed by platform glitches as much as by strategy — and when Google’s systems misfire, spend and leads can disappear fast.

First spotted. The errors were first spotted by Ryan Berry in the UK and Founder Anthony Higman also spotted issues in the US.

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