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Category: News

Streamline Google Ads with Tag Manager Controls Built-In
Have you ever wished for a simpler way to manage your Google Ads tags? Well, it seems Google might just be offering a solution soon. They’re pulling the Google Tag Manager interface directly into Google Ads, which could make tracking and tag management far easier.

What’s happening. Recently, in Google Ads, I noticed a new “Manage” option within the Data Manager section. This feature opens Tag Manager controls without the need to leave the platform.

The update came to light thanks to Marthijn Hoiting and Adriaan Dekker. They shared screenshots revealing elements of Tag Manager seamlessly embedded within the Google Ads interface.

Why this matters. If you’ve ever grappled with tag setup and troubleshooting, you know how it often involves juggling multiple tools and navigating technical handoffs.

With Tag Manager now integrated into Google Ads, the process could become less complicated, especially for smaller teams or advertisers without dedicated developers at their side.

Zoom in. When exploring inside the Data Manager interface, you will find connected data sources, including Tag Manager, which allows you to handle management actions right within Google Ads.

This suggests a move by Google towards a more unified measurement workflow, streamlining tagging, data connections, and campaign setup.

Between the lines. This change aligns with Google’s broader objective of simplifying measurement and enhancing data accuracy, a goal that has become critical amidst privacy transformations and signal loss.

It’s also part of Google’s effort to make tagging more accessible without requiring extensive technical setups.

What to watch:
- Will the full Tag Manager functionality be fully embedded or remain partial?
- How will this update impact workflows between marketers and developers?
- Will this new method become the standard for managing tags among advertisers?
Bottom line. Google is subtly narrowing the gap between campaign setup and measurement, positioning tagging closer to the actual management of ads.

First seen. This interesting development was initially reported by Adrian Dekker on LinkedIn, crediting Marthijn Hoiting, a Data and Analytics specialist, for the discovery.

Inspired by this post on Search Engine Land.
May 8, 2026
Unlock More with Microsoft’s Customizable Conversion Metrics

As someone exploring the ins and outs of Microsoft Advertising, I’ve discovered an update that’s sure to enhance our campaign analysis. Microsoft is now allowing us to customize columns with all conversion metrics, providing us with deeper insights and aligning reports with our unique business goals.

What does this mean for us? Well, according to Navah Hopkins, our go-to expert at Microsoft, we can now build custom metrics by leveraging the full spectrum of conversion data available in the platform. This means we can track all conversions and primary conversions, enabling us to tailor our reporting to meet our specific objectives more closely.

Please note the new image showcasing Microsoft’s enhanced custom columns feature. It’s a visual reminder of how these updates can transform our analytical capabilities.

Why am I excited about this? Because the standard reporting often doesn’t mirror how we truly measure success. By giving us the tools to expand custom columns, Microsoft allows us to define metrics that truly matter—be they lead quality, revenue, or a combination of conversion actions.

This flexibility is crucial for managing a variety of conversion types or navigating complex marketing funnels. Now, I can create custom columns, using ratios and metric combinations such as cost per qualified lead or conversion rates focused on primary goals.

Moreover, I appreciate that the revenue and ROAS calculations will now reflect the values that align with my conversion goals, providing more accurate insights directly linked to business outcomes.

What does this change imply for us in a broader sense? It represents a shift toward a more flexible and advertiser-defined measurement approach, instead of relying solely on standardized platform metrics.

This update highlights the ongoing demand for improved reporting customization as campaigns become increasingly automated and intricate.

So, what should we keep an eye on? I’ll be observing how advertisers like us utilize these custom metrics to guide optimization decisions, whether consistency in reporting improves across teams, and if similar flexibilities will roll out in other areas of the platform.

Bottom line? With Microsoft giving us more control over how we measure success, custom columns are evolving into a vital asset for campaign analysis. Read more about this update here.

Inspired by this post on Search Engine Land.

May 7, 2026
Navigating AI Max vs DSA: Advertisers Seek More Control
I’ve noticed that advertisers, including myself, are expressing concerns about AI Max’s limited control over landing pages compared to the older Dynamic Search Ads (DSA), especially as Google acknowledges some existing gaps in this area.

During a recent discussion on LinkedIn, digital marketing expert, Gabriele Benedetti, pointed out that AI Max doesn’t offer the same URL-based targeting controls that DSA campaigns did. This is a significant issue for those of us who depend on detailed URL targeting for effective campaigns.

To give more context, DSA allowed us to fine-tune campaigns to align with website architecture using categories, URL paths, and page rules. Unfortunately, AI Max doesn’t yet offer that detailed level of control.

For advertisers like me, managing large or structured sites, maintaining campaign structures that reflect site architecture is crucial. Losing detailed control over where users land could impact the user experience, relevance, and conversion rates.

This situation underscores a larger conflict within Google Ads: balancing automation with our need for control.

In response, Google Ads Liaison Ginny Marvin assured us that AI Max does support some URL-based controls that include:
- URL rules and combinations
- Page feeds with custom labels
- URL inclusions at ad group level and exclusions at campaign level
Nevertheless, she admitted that not all DSA targeting rules, like “page contains” conditions, are supported yet.

Reading between the lines, it seems Google isn’t taking away control entirely but rather redefining how it operates. Instead of elaborate rule-building, we’re being encouraged to use structured inputs, such as page feeds and labels, which AI Max can interpret.

For those of us transitioning from DSA to AI Max, there’s a transition phase where existing URL rules will persist, albeit with limitations. Unsupported rules will remain active as read-only—functional but uneditable.

This setup, however, is merely a stopgap and not a permanent solution.

Looking forward, Google plans to further enhance controls, including introducing content and title-based exclusions at the account level later this year. This would add to the “inventory-aware” capabilities of AI Max, which already automatically excludes out-of-stock items.

The takeaway is clear: AI Max is evolving, yet it doesn’t fully replace DSA’s granular control, and this has been a point of contention for advertisers like me.

If you’re keen on diving deeper into the discussion, you can check the full conversation on LinkedIn.

Inspired by this post on Search Engine Land.
May 7, 2026
How AI is Revolutionizing Microsoft’s Search Indexing

I recently came across an intriguing blog post by Microsoft Bing that delves into how AI is transforming the traditional concept of search indexing into something far more sophisticated. Bing has been focusing on enhancing factual accuracy, attribution, and confidence levels before AI-driven answers are generated.

The transition from page ranking to supporting AI-generated answers is reshaping how search engines operate. According to Bing’s latest insights, AI requires a more complex indexing system compared to the conventional web searches we’re used to.

Traditional Search vs. Grounding Systems

Microsoft highlighted a key difference: while traditional searches allow users the opportunity to self-correct, AI systems must derive more substantial evidence since they generate definitive answers.

Grounding systems focus on verifiable facts with transparent sourcing, crafting combined answers where errors could compound through different reasoning steps.

They shared this illustrative table:

What Sets Them Apart

Traditional algorithms optimize for relevance. In contrast, AI grounding evaluates whether information is correct, recent, well-sourced, and comprehensive enough to support an answer. It also considers whether the essence of a page endures through transformations and chunking.

Stale Content Concerns

Microsoft pointed out that outdated content poses a unique risk to AI-generated answers. Unlike traditional ranking, outdated information can lead to inaccurate AI results.

Handling Contradictions

In traditional search, a hierarchy can be established by ranking sources for users to choose trusted information. Grounding systems, however, must identify conflicting data and deliberate their consolidation into a singular response.

The Complexity of Retrieval

Unlike a one-time query in traditional search, AI systems might fetch information multiple times, refining previous results, and re-evaluating confidence before shaping an answer.

Measuring Indexing Quality

While the quality of conventional search indexes centers on ranking performance, grounding systems require assessment of factual accuracy, source integrity, freshness, and conflict recognition. Microsoft notes the ongoing journey in refining these measurements.

Complementing, Not Replacing Search

Grounding isn’t intended to replace search. Rather, it supplements existing systems with a focus on evidence quality and attribution, determining if AI should refrain from responding when necessary.

Why This Matters

For decades, search indexes have guided users to relevant web pages. Today, AI grounding is about ensuring the data it uses stands the test of reliability. This evolution demands that brands and publishers focus on creating data AI can leverage with greater certainty.

For More Insights read the detailed blog post, Evolving Role of the Index: From Ranking Pages to Supporting Answers.

Inspired by this post on Search Engine Land.

May 6, 2026
Discover Unified Conversion Data with Google Analytics API
In my latest venture into Google Analytics, I’ve discovered exciting news. Google is enhancing its Analytics Data API by adding cross-channel conversion reporting. Although it’s still in the alpha phase, developers like myself now have programmatic access to both paid and organic conversion data in a unified view.

What’s happening. Currently in alpha, this new feature lets users pull conversion data across various channels through the API, mirroring data from the Conversion performance report in the Analytics interface.

For developers, this means we can now capture the same insights without the need for manual reporting, making the process smoother and more efficient.

Why it matters. In a world where digital measurement is increasingly complex, having a unified view of performance across both paid and organic channels is crucial. This feature empowers teams to automate their reporting processes, seamlessly integrate data into existing systems, and build advanced analysis workflows.

It’s a game-changer for businesses juggling multiple platforms, helping to centralize performance data for better strategic decisions.

The caveat. Not every Google Analytics property has access to this feature yet. Google is actively working to broaden availability, so it’s wise to connect with support teams to verify eligibility.

What to watch:
- The transition from alpha to wide availability of the feature.
- How advertisers leverage this API access to create customized attribution models.
- Potential addition of more reporting capabilities to the Data API.
Bottom line. Google’s integration of cross-channel conversion data into the API equips advertisers and developers like me with more control over how we access, analyze, and act on performance data. You can find more information about this update here.

Inspired by this post on Search Engine Land.
May 6, 2026
Explore Google’s New AI Search Link & Citation Updates

Have you noticed a change in how Google displays links and citations in its AI search features? I recently learned about five key updates that aim to enhance our experience with AI Mode and AI Overviews.

According to Hema Budaraju, VP, Product Management at Google, these upgrades are designed to help us connect with authentic voices and access valuable information across the web. She detailed these updates in a recent article.

Let’s dive into the updates rolling out:

(1) Suggested angles at the end of AI responses. Google now suggests further reading options at the end of AI responses. These link to unique articles or analyses that deepen our understanding of the topic. It’s like having a roadmap to satisfy our curiosity!

Here’s a preview of this feature:

(2) Easier access to your news subscriptions. With this update, Google displays links from our news subscriptions prominently. This means I can quickly access content I trust, maximizing the value of my subscriptions. During Google’s early tests, these subscription links significantly boosted click-through rates.

If you’re a publisher, check out the documentation to enable this feature.

Here’s what this looks like in action:

(3) Social media and online discussions now include creator details. When AI features cite social media, Google includes not only the website’s name but also the creator’s name, handle, and community name. This transparency helps me spot firsthand sources at a glance.

Here’s a glimpse of how this plays out:

(4) More links, next to relevant text. Google is increasing the number of links shown directly within AI responses, strategically placing them next to relevant text. This makes it tempting for me to explore these sources further.

Here’s what it looks like:

(5) Hover over inline links for a quick look. Now when I hover over an inline link in Google’s AI features, I get a sneak peek of the website. This could just be the nudge I need to click through and explore further. I remember seeing Google test this back in February and thought it was a brilliant idea.

Here’s an example of the feature:

Why this matters. Google is committed to ongoing testing and refinements, ensuring these features serve us better. I truly believe these changes will promote more engagement with the cited pages, presenting an exciting step forward for both users and the web ecosystem. The real question is, will they meet my expectations?

Inspired by this post on Search Engine Land.

May 6, 2026
Unlocking New Potential: ChatGPT’s Self-Serve Ads Revolution
I’ve witnessed firsthand how ChatGPT ads are evolving with self-serve buying options, enhanced measurement features, and a vision to create a scalable advertising platform.

OpenAI is stepping up its game with the ChatGPT ads platform by introducing self-serve buying, CPC bidding, and improved measurement methods to invite more advertisers into its ecosystem.

What’s happening. The ChatGPT ads initiative is shifting from a limited pilot to a broader rollout, providing businesses new methods to purchase and manage their campaigns. Advertisers can now access inventory through agency and tech partners or directly via the new beta Ads Manager, which is currently rolling out in the U.S.

This marks a significant move from a controlled test phase to a promising, scalable ad platform.

Why we care. In the past, access to ChatGPT ads was restricted and costly, limiting it to major advertisers. These updates are lowering the entry barriers, allowing SMBs, startups, and diverse brands to experiment with this channel.

By introducing CPC bidding, ChatGPT aligns more closely with established performance platforms, enabling advertisers to optimize for actions rather than just impressions.

Self-serve Ads Manager. With the new Ads Manager, advertisers gain direct control over campaigns, including budgeting, bidding, creative uploads, and performance tracking.

Even though it’s still in beta, it demonstrates OpenAI’s commitment to building a full-service ad platform, beyond a mere partner-led ecosystem.

Between the lines. This approach is not new. Typically, platforms start with high-touch, partner-led campaigns before transitioning to self-serve tools that enhance scalability. ChatGPT is entering this second phase.

CPC bidding arrives. Originally, ChatGPT ads were sold on a CPM basis. The inclusion of CPC enables advertisers to align expenditures with user actions—a critical evolution for performance marketers.

The nature of ChatGPT queries—often exploratory, comparative, and decision-driven—means that clicks could become an effective indicator of user intent.

Measurement catches up. OpenAI is also introducing pixel-based tracking and a Conversions API, allowing advertisers to measure actions like purchases, sign-ups, and leads.

Notably, this data is aggregated, ensuring no access to individual conversations, emphasizing OpenAI’s commitment to privacy.

Why this is a big deal. Measurement was a major gap in early ChatGPT ads. Without it, justifying ad spend was challenging for advertisers. These updates help bridge that gap, making optimization more feasible.

The ecosystem grows. OpenAI is expanding its network by partnering with agencies like WPP and Publicis Groupe, along with tech platforms such as Criteo and Adobe.

This allows advertisers to buy ChatGPT ads through tools and workflows they are already familiar with.

What to watch:
- How quickly self-serve adoption scales
- Whether CPC performance holds as competition increases
- How measurement evolves to match advertiser expectations
Bottom line. ChatGPT ads are transitioning from an experiment to a platform—and with self-serve tools, CPC bidding, and enhanced measurement, OpenAI is laying the foundation for expansive growth.

Inspired by this post on Search Engine Land.
May 5, 2026
Google’s UCP Checkout Revolutionizes Search Shopping

I find it fascinating that Google’s Universal Commerce Protocol (UCP), which was initially limited to AI Mode, is now expanding into regular search results. It’s not just a fleeting trend; some retailers have already begun integrating this technology into their listing pages, making our online shopping experience even more intuitive.

Earlier this year, Google rolled out UCP for AI-agents to facilitate direct purchases from search results. It first launched exclusively within Google’s AI Mode but now, we’re seeing it implemented in Google’s main search results for retailers who support UCP.

Discovering what the UCP checkout looks like was made easier thanks to a post by Brodie Clark. He shared a screenshot showing how Wayfair’s listings on Google Search now feature a UCP-powered ‘Buy’ button. This button is a game-changer because it allows purchases directly from Google’s interface without navigating to Wayfair’s website.

The UCP protocol is paving the way for seamless transactions by establishing a common language for AI agents and commerce systems. No longer do we have to worry about bespoke integrations across different platforms.

Collaboratively developed with big names like Shopify, Etsy, Wayfair, and Target, UCP aligns with existing standards, such as Agent2Agent and Agent Payments Protocols, creating a more cohesive digital commerce space.

What really excites me is the potential for profit growth for retailers who embrace this technology. Although Wayfair might miss out on direct site traffic for specific searches, their affiliation with Google through UCP can still result in conversions.

While it’s clear that not everyone will bypass the traditional shopping journey, as many of us still prefer exploring products on the retailer’s site, the option to ‘Buy’ directly adds a layer of convenience. It’s definitely something worth monitoring as its prevalence in search results increases.

Inspired by this post on Search Engine Land.

May 5, 2026
Google’s New Tools to Enhance Measurement in Advertising
When I heard that Google is unveiling new measurement tools, I was eager to see how these could empower advertisers to connect data more effectively, prove their impact, and make smarter decisions.

Google’s latest tools are designed to give advertisers a better grasp of performance across increasingly complex customer journeys. As AI evolves in transforming campaigns, creative strategies, and targeting, Google is offering updates in data integration, experimentation, and media mix modeling. This helps us, as marketers, convert fragmented signals into actionable insights.

The reason why this matters to me is that while automation has simplified campaign management, understanding what truly works has become more complex. These updates aim to facilitate data connections, provide proof of what’s driving results, and enable smarter budget decisions across various channels. As AI manages more execution, robust measurement becomes crucial for performance and growth differentiation.

Data is the foundation here. Google’s expansion of its Data Manager offers a clearer view of data flow across platforms like BigQuery, HubSpot, and Shopify. A new map-based interface will allow us to visualize connections between data sources and address gaps in tracking or configuration. Additionally, updates to the Google tag are designed to simplify setups, enabling advertisers like me to enhance existing tags without additional coding.

The overall goal is to unify signals and improve data quality, which directly influences campaign performance. Google recognizes that advertisers often face more challenges in data setup and integration than in executing campaigns themselves. By streamlining tagging and data flows, Google aims to eliminate one of the biggest hurdles to effective AI adoption.

Introducing Meridian GeoX, Google provides a new geo-experimentation tool to measure incremental impact across regions. Built on an open-source framework, GeoX integrates with Google’s broader Marketing Mix Model, Meridian, offering a more robust way to validate performance — particularly when presenting results to finance teams.

This signifies a shift from merely correlating data to focusing on causal measurement.

As changes in privacy reduce visibility and make attribution more complex, we’re under pressure to prove impact. Tools like GeoX aim to offer that “ground truth” which many attribution models struggle to provide.

To simplify complex Marketing Mix Models (MMMs), Google is launching Meridian Studio, a Google Cloud-powered platform. This helps teams like mine to build, customize, and scale models more efficiently, focusing on making MMMs less resource-intensive and more accessible for enterprise teams handling large datasets.

What I’m keeping an eye on:
- Whether simplified tools will encourage wider adoption of MMMs among advertisers
- The effectiveness of GeoX in proving incremental impact
- If improved data visibility will lead to better campaign performance
In summary, Google is strategically shifting focus: in our AI-driven world, it is better measurement — and not just better automation — that will dictate success.

Inspired by this post on Search Engine Land.
May 5, 2026
Explore Google’s Innovative Web Bot Auth for Authentic Bot Verification
Have you ever wondered how Google is ensuring the authenticity of AI bots? I recently stumbled upon Google’s latest experimental method, Web Bot Auth, which aims to address exactly that. This project is currently in a limited testing phase, specifically for AI agents hosted on Google’s infrastructure, but it could be expanded in the future.

In Google’s new help document, they clarify that Web Bot Auth is a “new cryptographic protocol that helps websites validate that bots are authentic.” This innovative approach is designed to automate the authentication of AI Agent bots, distinguishing between genuine and fraudulent bots.

Limited test phase: Google’s team mentions they are “testing the protocol with some AI agents hosted on Google infrastructure.” It’s important to note that not all Google user agents are currently using Web Bot Auth, and the company isn’t signing every bot request with this protocol just yet.

During this gradual rollout, Google advises us to keep using IP addresses, reverse DNS, and user-agent strings alongside Web Bot Auth, as not all traffic is currently signed.

What is Web Bot Auth? Defined as “an experimental cryptographic protocol used to authenticate requests sent by bots,” this method moves away from self-reported headers and IP addresses. Instead, it allows agents to sign their requests cryptographically.

According to Google, Web Bot Auth offers several benefits:
- Future-proofing: Supporting a trusted environment where agent providers and websites can mutually verify access.
- Cryptographic certainty: Transitioning from easily falsified headers to a verified identity, separate from IP addresses.
- Better observability: Gaining clear insights into agent interactions with your content.
Why this matters to us: As AI agents continue to proliferate online, managing access to our sites becomes increasingly complex. This new authentication method could effectively distinguish credible AI agents from deceptive ones, ensuring the right entities access our data.

Since Web Bot Auth is still “experimental,” I’ll be keeping an eye on its development. It might just transform how we manage AI bot access in the future.

Inspired by this post on Search Engine Land.
May 5, 2026

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