Why Microsoft’s AI Ad Strategy Deserves More Attention From PPC Managers via @sejournal, @brookeosmundson

Microsoft announced a wave of AI updates this week, and most of the coverage will likely focus on the individual launches. New targeting options, diagnostics, commerce tools, Copilot enhancements, and campaign features will naturally get the headlines.

What stood out to me was the broader vision behind them.

Microsoft is not just talking about better ads. They’re talking about a different internet, where businesses need to be relevant to both people and AI systems helping shape decisions.

In their announcement this week, AI agents are becoming the fastest-growing audience. The company says automated traffic is growing 8x faster than human traffic, AI-driven sessions nearly tripled in 2025, and agentic browser traffic is up roughly 8,000% year over year. Those visitors don’t browse the way people do. They evaluate, select, and act. If a brand’s data is weak, incomplete, or untrusted, they move on.

That changes what modern performance marketing may require. Visibility inside AI answers, stronger product data, better measurement, faster diagnostics, audience precision, and clearer control over automation all start to matter more in that environment.

Google is pushing many of these same themes in its own way, especially around product feeds, automation, and AI-assisted search experiences. But Microsoft’s recent announcements offer a distinct perspective on where advertiser value may come from as discovery and buying behavior continue to shift.

Because underneath the product updates is a bigger question for PPC teams: how do you compete when the next valuable audience may not always be human?

Microsoft Is Selling A Different AI Future

Most platform announcements focus on what a new feature does. Microsoft spent more time explaining why advertiser behavior may need to change.

Their framework centered on three parallel realities:

• People still searching on their own (the Human web)
• People using AI to compare options (the LLM web)
• AI systems taking action on behalf of users (the Agentic web)

What they’re saying beyond these parallels is that customer journeys are less linear and are finally being recognized as such.

For years, many PPC teams optimized around the click because the click was the clearest measurable moment. Someone searched, clicked, landed, and converted. That model still matters, but it no longer explains every influence that leads to a sale.

If an AI assistant narrows the shortlist before a search happens, the brand has already won or lost ground. If a shopping assistant compares shipping speed, loyalty perks, and product availability in seconds, the decision may be shaped before the landing page visit. If an agent eventually completes more transactions directly, structured data and transaction readiness become part of media performance.

That is why this announcement deserves more attention than a standard product roundup. Microsoft is describing a future where paid media performance depends on more than media settings.

Why This Matters For PPC Managers

Many advertisers are still operating with a channel mindset. Additionally, these channels likely sit within different teams in an organization (Search, SEO, CRM data, Analytics, etc.)

That separation becomes harder to sustain and sustains friction if buying journeys are influenced by connected systems rather than isolated clicks.

This is where the role of PPC teams can start to expand and/or evolve.

Strong practitioners still need campaign skills – that’s never going to change. They also need to spot when the real constraint sits outside the account, bring the right teams together, and push improvements that create better inputs for the platform.

Having these skills become your advantage as a PPC marketer down the road when campaign management and optimization become automated, but that’s a subject for another day.

How Microsoft’s AI Vision Takes A Different Approach

Google remains the largest force in paid search. It also continues to launch strong AI updates across bidding, creative, search experiences, and campaign management. This is not about Google falling behind.

What stood out to me was where Microsoft placed its focus.

A lot of AI discussion still centers on better ads, faster automation, or the next big interface. Microsoft spent more time talking about how buying behavior is changing and what advertisers may need to do differently.

Their view suggests the audience is no longer only the customer.

It can also be the AI system helping compare products, narrow options, recommend brands, or complete tasks on someone’s behalf.

That is where I think Microsoft’s message becomes more interesting than a standard product launch. They are pushing marketers to think beyond clicks and impressions and pay closer attention to how decisions are being shaped before a traditional ad interaction ever happens.

If that shift continues, many teams will realize they were optimizing the final step of the journey while missing the earlier moments that influenced the outcome.

AI Visibility In Microsoft Clarity Is Their Competitive Advantage

If I had to choose the most useful announcement for marketers, I would put AI Visibility in Microsoft Clarity near the top of the list.

Why? Because it speaks to a blind spot many businesses may already have.

A lot of performance reporting has been built around clicks, visits, and conversions that happen in trackable sessions. As AI tools start summarizing answers, citing brands, and influencing decisions before someone reaches a site, that model becomes less complete.

Some brands may already be winning attention in those moments. Others may be losing ground. Many likely cannot see either clearly today.

That is what makes this update so interesting.

Microsoft is giving businesses a way to understand how AI systems discover, cite, and surface their content. You do not need to advertise on Microsoft for that to matter. SEO teams, content teams, e-commerce leaders, and paid media teams all have a reason to care about how their brand appears in AI-driven experiences.

My bigger view is that tools like this will eventually become normal. Right now, Microsoft is one of the first major platforms speaking clearly about the problem and trying to give marketers something actionable to measure.

Audience Generation Could Be More Useful Than It Sounds

Audience Generation may sound like another setup feature, but I think it deserves more attention than that.

Microsoft describes it as an AI-powered audience assistant where advertisers can describe an ideal customer in natural language and receive recommended targeting settings. That can include demographics, locations, in-market signals, and dynamically generated audiences.

What interests me most is how this could improve strategic thinking, not just save time during campaign creation.

Many advertisers already know their obvious audience. But strong audience strategy often depends on ideas a team does not think to test.

For example, an advertiser may know they want “young professionals interested in fitness.” They may not think about adjacent areas where those consumers spend time, neighborhoods with stronger purchase intent, seasonal behaviors tied to events, or combinations of signals that reveal higher-value segments.

That is where a tool like this can become valuable.

Used thoughtfully, it can help marketers find new angles to test, challenge stale audience assumptions, and build stronger targeting plans than they may have created manually.

How Microsoft Is Turning That AI Vision Into Practical Tools

A broader vision only matters if it shows up in tools advertisers can actually use.

That is where Microsoft’s recent updates become more interesting.

Explainability Is Part Of The Product

One of the more useful launches was performance shift root-cause analysis inside the Microsoft Advertising Platform.

When results move sharply, most marketers don’t need another dashboard. They need to know what changed and clear “why”. Without the why, marketers can’t identify how to improve campaigns or pivot strategy.

Getting to that answer faster can save hours of manual work. It can also help teams act with more confidence instead of making reactive changes.

Google is thinking in a similar direction. Its Ads Advisor experience is also designed to help advertisers ask questions, surface insights, and understand account performance faster.

The opportunity for marketers is not choosing one assistant over another. It is using these tools to reduce analysis time and spend more time on better decisions.

Guardrails Still Matter

Microsoft also emphasized brand exclusions, term exclusions, and messaging constraints tied to AI-powered products like AI Max.

It mimics where Google has gone with their AI Max direction and broader advertiser controls across automated products.

That matters because many advertisers are not operating in a world where they can simply turn everything on and hope for the best. Legal review, brand standards, regulated categories, stakeholder approvals, and internal risk tolerance all shape how new tools get adopted.

That is why control features deserve more attention than they usually get. They are often what make adoption possible in the first place.

Product Data Continues To Be Bigger Than Shopping Campaigns

One of the clearest signals from both Microsoft and Google right now is that product data is starting to matter far beyond traditional Shopping campaigns.

Clean titles, accurate availability, pricing consistency, strong attributes, shipping details, and trustworthy structured data can now influence how products are surfaced across search experiences, AI recommendations, comparison journeys, and agent-assisted buying flows.

That is exactly why I wrote last week that Google’s product feed strategy points to the future of retail discovery. Product data is no longer just supporting Shopping campaigns. It is becoming part of how platforms understand inventory, evaluate relevance, and decide what gets shown in newer discovery environments.

Microsoft’s recent announcements point to the same shift through a different lens. Google is emphasizing Merchant Center and commerce surfaces. Microsoft is emphasizing agentic commerce, Copilot experiences, and AI visibility.

Feed health is becoming a growth issue, not just an operations issue – something that both Google and Microsoft are telling the industry.

What Advertisers Are Saying

Navah Hopkins, the Microsoft Ads Liaison, took to LinkedIn to share her thoughts on these updates. She highlighted diagnostics, clearer explanations, and the idea that marketers should decide what they own, what they share with AI, and what they delegate. That framing reflects how adoption actually happens inside businesses. Teams rarely hand over everything at once. They test where trust has been earned.

She also pointed to Microsoft Clarity as an increasingly valuable source of behavioral insight as AI-driven experiences grow, which I completely agree with.

Mark Creusen added his thoughts to her post:

The owning and sharing bit always pops for me. Way easier to chill about AI when you just mark out what’s “yours” and what you’re happy to throw to the bots instead of trying to wrangle it all. Otherwise teams just end up dragging each other to burnout mountain.

Frederick Vallaeys focused on another risk: invisibility. In his write-up after Microsoft’s partner event, he argued that many businesses may be unprepared for AI-driven discovery and cited Microsoft’s discussion around sites still blocking AI agents through robots.txt. He also highlighted strong early commerce statistics shared at the event, including higher purchase likelihood after Copilot interactions and conversion lifts tied to Brand Agents.

What This Means For Your Campaigns

The bigger lesson from Microsoft’s updates is that campaign performance may increasingly be shaped by factors that sit outside the traditional campaign build. That includes how your products are structured, how clean your measurement setup is, how well your audiences reflect real buying behavior, and whether your brand is visible in AI-assisted discovery moments before a search click ever happens.

Below are a few areas worth reviewing that can help shape a broader operating mindset:

• Product data quality: If your feeds are incomplete, outdated, or inconsistent, the risk may extend beyond Shopping campaigns. Product titles, availability, pricing, shipping details, and attributes can influence how platforms understand and surface your inventory across emerging discovery experiences.
• Measurement health: Now is a good time to audit conversion actions, tag coverage, offline imports, and attribution settings. As journeys become less direct, weak measurement creates larger blind spots and poorer optimization inputs.
• Audience strategy: Many accounts still rely on narrow audience assumptions or static segments. Revisit whether your current targeting reflects how customers actually behave today. There may be untapped value in layered signals, geographic nuance, seasonal behaviors, or adjacent intent patterns.
• Search term coverage: If AI tools help users refine decisions earlier, the searches that remain may become more specific, comparative, or action-oriented. Review whether your keyword strategy and ad copy are aligned to that shift in intent.
• Platform diversification: Secondary channels can become valuable learning environments before they become major budget lines. Even modest investment in Microsoft Ads can help teams test new audience models, automation controls, and reporting approaches that may influence broader strategy later.

Looking Ahead

Microsoft’s biggest advantage may not be trying to out-Google Google.

It may be continuing to invest where it already has a credible edge: advertiser workflow tools, B2B audience intelligence through LinkedIn, clearer visibility into AI-driven discovery, and commerce experiences built for a world where assistants help shape decisions.

That is a different lane, and it could be a valuable one for marketers if Microsoft keeps executing.

The next year will likely tell us whether these announcements were a strong signal of where the platform is headed or simply another round of product updates.

Which of Microsoft’s new AI features, if any, would you seriously consider testing in your own campaigns?

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Featured Image: Juan Roballo/Shutterstock

Google’s Product Feed Strategy Points To The Future Of Retail Discovery via @sejournal, @brookeosmundson

For years, many advertisers treated product feeds as a channel task tied mainly to Shopping campaigns.

If you were running Shopping ads, feed optimization likely got attention. If you weren’t, it often slipped behind priorities for the PPC campaigns you were running.

Now, that approach is starting to show its age.

Google’s recent Ads Decoded podcast episode suggests that mindset may need to change. Product data was discussed in connection with free listings, AI-powered search experiences, YouTube formats, Lens, virtual try-on, and newer e-commerce surfaces still evolving.

That reflects a much broader role than many advertisers have historically assigned to their feed.

Google appears to be positioning product data as a larger part of how products are discovered across its platforms, not just how Shopping campaigns perform.

Advertisers who still view Merchant Center as a side task may be underestimating how much visibility now starts with product data.

The more interesting question is what that shift tells us about where Google wants retail advertising to go next.

Merchant Center Is Starting To Look Like Retail Infrastructure

What stood out most in the podcast was how broadly Google described the role of Merchant Center data.

Nadja Bissinger, General Product Manager of Retail on YouTube, described Merchant Center feeds as the “backbone that powers organic and ads experiences,” adding that merchants should submit the most robust product data possible to increase discoverability.

That is a wider role than many advertisers have traditionally associated with Merchant Center.

Google said in a 2025 retail insights piece that people shop across Google more than 1 billion times per day. It also highlighted Search, YouTube, Maps, and visual discovery as key parts of modern shopping journeys. That helps explain why reusable product data is becoming more valuable than channel-specific assets alone.

Google also said Google Lens now sees more than 20 billion visual searches per month, and 1 in 4 Lens searches carry commercial intent. That is another signal that structured product data is becoming more important outside traditional Shopping ads.

For years, many brands viewed Merchant Center as a necessary setup for Shopping campaigns. Google now appears to be positioning it as a core input for how products are surfaced across its platforms.

That should change how feed work is prioritized internally.

Feed optimization is no longer just a PPC responsibility. It can influence:

• Organic visibility
• Merchandising strategy
• Creative presentation
• Promotions
• How products appear in newer AI-led experiences.

For larger organizations, that may require closer coordination between paid media, SEO, e-commerce, merchandising, and product teams.

For smaller brands, it may be as simple as giving feed quality the same level of attention already given to ad copy, landing pages, and campaign structure.

Many advertisers still treat feed work as cleanup work. That mindset is becoming expensive as product data plays a larger role in who gets seen across Google.

Why Is Google Pushing Product Data So Hard Right Now?

Google’s direction here makes sense when you look at where its retail products are heading.

The company wants more e-commerce activity to happen across Search, YouTube, Maps, AI experiences, and future agentic tools. To support that expansion, it needs merchant data that is accurate, structured, and easy to reuse across different surfaces (as Google refers to them as).

Google has financial reasons to expand e-commerce activity beyond traditional ad clicks. In their 2025 Q4 Earnings Release, they reported a 17% growth in Google Search, and YouTube revenue across ads and subscriptions over $60 billion.

A strong feed helps Google understand:

• What a product is
• Who it is for
• What makes it different
• Where it is available
• What it costs
• How the product should be presented

That matters even more as retail experiences, paid or organic, become more visual, more personalized, and more automated.

Traditional search ads leaned heavily on keywords, headlines, and landing pages. Newer e-commerce formats can also depend on product images, attributes, ratings, promotions, availability, shipping details, and other feed inputs that help match products to user intent.

Better data can lead to better experiences for users. It can also create more places where merchants can appear across Google’s properties.

Google is building more e-commerce surfaces, and product data is the fuel behind them. Advertisers who ignore that may keep optimizing campaigns while missing the larger shift happening around them.

Is Google Prepping For A More Strategic Shift?

From my perspective, there is a larger strategic shift behind Google’s product data push.

I don’t see this as a routine push for better feeds or cleaner campaign inputs. I see Google working to become more of a growth engine for advertisers, with a role that reaches beyond media buying and campaign delivery.

That expansion is moving into areas that shape business performance, including merchandising, product discovery, pricing visibility, local commerce, measurement, and newer purchase-ready experiences.

Google is not only trying to improve how ads run. It appears to be building a deeper position in how products are surfaced, how demand is created, how buying decisions are influenced, and how performance is measured.

My view is that the more Google becomes embedded across those moments, the more connected it becomes to broader business growth rather than media performance alone.

Why Many Advertisers Are Still Measuring Feed Value Wrong

One reason feed optimization still gets deprioritized is simple: many teams are using an outdated scorecard.

Google cited a 33% conversion uplift for advertisers using Demand Gen with product feeds during the podcast discussion. Even if results vary by account, it is another sign that feed quality is being tied to campaign types beyond classic Shopping ads.

If the main question is whether Shopping ROAS improved last week, it becomes easy to undervalue the broader impact of stronger product data.

That measurement approach came from a time when feeds were more closely tied to Shopping campaigns. Google is now using the same data across a much wider set of retail experiences, including discovery surfaces, visual placements, AI-led results, and other formats that do not fit neatly into one campaign report.

That creates a gap between where feed work adds value and where many teams are looking for it.

A stronger title may improve discoverability. Better imagery can increase engagement in visual placements. Accurate pricing and promotions can improve click appeal. Richer attributes can help Google better understand relevance. Availability data can support local and omnichannel visibility.

Those gains may show up across multiple touchpoints, assisted paths, and blended performance trends rather than one Shopping dashboard.

That is why some advertisers continue to underinvest in feed quality. The value is there, but their reporting model was built for an earlier version of Google.

As Google expands where products can appear, feed optimization deserves to be measured more like a visibility and growth lever, not just a Shopping maintenance task.

One of the more important quotes from the podcast came from Ginny Marvin, Google Ads Liaison, as she wrapped up the episode:

Merchants with the most structured, high quality data foundations will be positioned to win.

Winning will not come from uploading a feed once and forgetting about it for months at a time.

It comes from treating product data as an ongoing optimization just like your existing campaigns.

What Google’s AI Max Focus May Be Signaling About Search

One of the more revealing parts of the podcast was how often Search strategy was discussed through the lens of AI Max for Search, while traditional standard Search campaigns were barely mentioned.

During the episode, Firas Yaghi, Global Product Lead for Retail Solutions, talked about how advertisers should be thinking about different campaign types:

I think the role of each campaign really depends on your high level objective. Whether you’re prioritizing cross channel efficiency, granular control or hybrid approach that balances top line sales with OKRs.

He mentioned a lot around Performance Max, Demand Gen, with a little bit of AI Max for Search.

I would avoid treating that as proof that standard Search is going away. There is still clear value in campaigns built around tighter search control, brand protection, and proven high-intent terms.

At the same time, it’s hard to ignore the direction of Google’s messaging.

When Google talks about growth, expansion, and newer retail opportunities, the conversation increasingly centers on AI-assisted campaign types. We have seen similar signals elsewhere, including Google’s announcement that Dynamic Search Ads will upgrade into AI Max for Search and that AI Max represents the next step for search expansion.

My read is that standard Search remains important, but it is no longer the only story Google wants advertisers thinking about.

The company appears to be steering incremental growth toward campaign types that rely on broader matching, stronger inputs, automation, and first-party signals.

I think that Search strategies built around legacy structures will become less competitive over time. I’m not confident enough yet to say that standard Search campaigns will go away completely in the near future, but the increasing signals around keyword-less technology has me thinking more changes for Search campaigns are bound to happen.

What This Means For Your Campaigns

The bigger risk for PPC managers is assuming the teams responsible for merchandising or product data already understand how much feed quality can affect campaign performance.

In many organizations, merchandising, e-commerce, product, or development teams control what goes into Merchant Center. Their priorities may be centered on inventory, pricing, site operations, or category management, not media efficiency or visibility across Google.

That is where PPC managers can add real value.

If product information is influencing how products appear across paid, organic, and AI-led surfaces, someone needs to connect those decisions to marketing outcomes. PPC managers are often in the best position to do that because they can see changes in impressions, traffic quality, conversion trends, and missed opportunities firsthand.

That may mean bringing examples into weekly meetings, showing where missing attributes are limiting reach, flagging weak imagery, highlighting pricing issues, or sharing results from tests that improved performance.

You may not own the feed, but you can help the business understand why it deserves greater priority and where better inputs can improve campaign results.

Put More Focus On Inputs That Can Scale Performance

Many teams spend valuable time on small bid changes, minor budget moves, or endless rounds of creative tweaks while core product data remains incomplete or outdated.

Those tasks still have value, but the upside is often limited when the underlying product information is weak.

If titles are thin, images are poor, attributes are missing, or product details are outdated, fixing those gaps may create more value than another round of minor account adjustments.

Add Feed Health To Regular Performance Reviews

Most reporting cycles focus on spend, ROAS, CPA, and conversion volume.

Those metrics are important, but they do not always show whether product data is helping or limiting visibility.

Feed health deserves a place in regular reviews. Look at disapprovals, missing fields, image quality, pricing accuracy, promotional coverage, and product-level gaps with the same discipline used for media metrics.

Broaden How You Test For Growth

Many retail accounts still treat Search, Shopping, YouTube, and newer campaign types as separate lanes.

Google’s recent direction suggests those lines are becoming less rigid.

Growth testing should include where products can appear across newer surfaces, how feeds support Demand Gen and AI-led placements, and whether stronger product data can unlock reach that existing campaigns are not capturing today.

Treat Better Product Data As A Competitive Advantage

Some advertisers will wait until these newer placements are fully mature before investing seriously in feed quality.

While that delay may be costly for them, your proactiveness can pay off significantly.

What PPC Professionals Are Saying

Recent LinkedIn discussions suggest many practitioners are viewing feed quality as a larger performance lever.

Comments from the podcast episode have been overall positive and has many marketers agreeing that feed management needs to be routine.

Zhao Hanbo commented:

Really interesting to see how something that used to feel mostly like ad ops plumbing is now becoming core infra for AI commerce.

Sophie Westall had similar sentiments, stating that “feed quality is quickly becoming a core part of overall media strategy, not just a hygiene task.”

In a recent LinkedIn post, Menachem Ani said that by fixing a product feed, “campaigns start working harder without touching a single bid.”

More marketers appear to be focusing less on isolated settings and more on the quality of the data – regardless if they’re running paid campaigns or not.

What Comes Next For Retail Marketers

Some advertisers will hear Google’s renewed focus on product data and assume it mainly matters for brands running Shopping campaigns.

That interpretation misses how much wider the opportunity has become.

Google is quickly expanding how products can show up across paid placements, organic surfaces, visual experiences, and newer AI-led formats. As that happens, feed quality becomes more connected to visibility and performance than many teams have historically assumed.

In many organizations, product data still gets treated as maintenance work. It gets attention when something breaks or when Shopping results decline, then falls back down the priority list.

That approach may be harder to justify going forward.

Product data needs a larger role in planning, testing, and cross-functional discussions because it can influence far more than one campaign type.

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Featured Image: Summit Art Creations/Shutterstock

Google’s Push For Data Strength Is Really A Push For Better Bidding via @sejournal, @brookeosmundson

Google keeps coming back to the same message this year: your AI is only as good as the data feeding it.

That message has shown up across the Ads Decoded podcast, Data Manager updates, tagging guidance, partner integrations, and now even developer-focused content like the Ads DevCast podcast. It seems to reflect a broader shift in how Google expects campaigns to be built and optimized.

The issue is not that advertisers lack data. Most accounts have plenty of it. The problem is how that data has been structured, selected, and fed into bidding systems over time.

As Google leans further into AI-driven optimization, that gap becomes more visible for advertisers who don’t have a sound conversion setup. Campaign performance is increasingly tied to how clearly the system understands what success looks like.

Why Google Is Pushing Advertisers To Rethink Conversion Strategy

For years, many advertisers treated conversion tracking as something to expand, not refine over and over again.

If a platform made it easy to track an action, it got added. If a CRM could send something back, it got imported. If a new conversion type became available, it often made its way into the account without much resistance.

On paper, that sounds like a more complete dataset. The more data, the better – right?

In reality, it’s created a lot of noise for machines to learn what truly matters.

Campaigns are often optimized toward a mix of actions that did not share the same level of intent, value, or timing.

Some signals are high quality but might have low volume due to a delay in sales cycle activity. Others may be immediate but loosely tied to actual business outcomes. Many accounts end up blending all of them together under a single bidding strategy for the sake of measuring everything.

That worked well enough when automation was less dependent on precise inputs.

It becomes a bigger problem when bidding systems are expected to make decisions based on patterns in that data.

Where Most Conversion Setups Break Down

In one of the recent Ads Decoded podcast episodes, Google’s recent guidance around lead generation makes it clear what they are trying to correct. The focus is on mapping the full customer journey and identifying the conversion point that provides a usable signal for bidding.

That means looking at three things at the same time:

1. How predictive the action is of real business value
2. How frequently it occurs
3. How quickly it happens after the initial interaction

Many advertisers still default to the deepest possible conversion, assuming that optimizing toward the final sale will produce the best outcome across every campaign.

The issue isn’t that particular goal itself, but more how usable that signal is for the system in a higher-funnel campaign. And this is where many conversion strategies start to fall apart.

If that action happens infrequently or takes weeks to materialize, it limits how much the bidding system can learn from it. The result is often slower optimization, higher volatility, and less efficient scaling.

On the other end, optimizing toward early-stage actions without considering quality can inflate volume without improving actual outcomes.

Selecting the right signal requires matching the conversion to the role the campaign plays and ensuring that signal is both meaningful and usable for bidding.

That shift requires more intentional decision-making than many accounts have historically applied to conversion setup. It also introduces a level of discipline that many advertisers have not needed when automation was less dependent on signal quality.

Why Is Google Putting So Much Weight On Data Strength?

Google is not being subtle about the Data Strength push. It’s showing up in product updates, integrations, tagging changes, and even in the way Google is speaking to both advertisers and developers.

Part of the reason is practical. Advertisers have lost visibility into many of the signals they used to rely on. Privacy changes, browser restrictions, and platform limitations have made measurement less complete than it used to be.

At the same time, Google’s bidding systems are being asked to do more with less. That puts more pressure on the signals that are still available.

This is where Data Strength comes in. Google is trying to make those signals more reliable, easier to connect, and more useful for optimization. Data Manager, tag gateway, and partner integrations all support that goal.

The expansion of integrations with platforms like HubSpot, Zapier, and Cloudflare also supports this effort. Instead of relying on custom implementations, advertisers can connect the systems where their data already exists with less effort.

This improves consistency in how data flows into bidding systems.

It also reinforces Google’s broader goal of making its automation more effective in a lower-signal environment.

Does This Point To A Broader Role For Google?

I also think there is a bigger shift underneath this push.

Google is moving closer to the systems where business outcomes actually happen, not just where ads are served. Connecting CRM data, offline conversions, and audience signals allows Google’s platforms to better understand what a “good” customer looks like beyond the initial click or form fill.

That can absolutely help advertisers improve performance.

At the same time, it positions Google as more than just an ads platform. It becomes more integrated into how businesses measure performance, define value, and connect marketing efforts back to real outcomes

Where Does Server-Side Tagging Fit In With This?

There has been a lot of confusion around server-side tagging and how it relates to what Google is promoting today.

They are related, but they aren’t the same thing.

Google tag gateway focuses on how the Google tag is delivered and how requests are routed through first-party infrastructure. It is a way to make existing tagging setups more resilient and aligned with privacy expectations.

Server-side tagging is a broader architectural approach. It shifts data processing from the browser to a server environment that the advertiser controls. This can improve site performance, provide more control over data handling, and support more advanced use cases across multiple platforms.

In practical terms, tag gateway is often a more accessible first step for advertisers looking to improve data reliability without a full infrastructure overhaul.

Server-side tagging is a larger investment and tends to be more relevant for organizations with more complex data requirements or stricter governance needs.

The two approaches can work together, and Google documentation often recommends combining them for a more durable setup.

A Thoughtful Approach To Data Strength

The increased focus on Data Strength is directionally positive, but it does not remove the need for careful decision-making.

Simplifying setup does not automatically lead to better outcomes. If conversion actions are poorly defined or not aligned with campaign intent, connecting them more efficiently will not improve performance.

If you’re a marketer who isn’t directly involved with setting up conversions, it may be worthwhile to meet with your Analytics teams. Create a list of must-have conversion events or actions you need to track for campaigns (online and/or offline), and cross-check that list with what’s currently set up.

There is also a governance component to consider. As tagging becomes more automated and data collection expands, teams need to understand what is being captured, how it is being used, and how it aligns with internal policies.

Google has noted that expanded automatic event collection may result in additional data being sent to its systems, which should be reviewed as part of implementation.

Another consideration is how platform-specific improvements fit into a broader measurement strategy.

Google’s push around Data Strength is primarily focused on improving performance within its own arena. That is valuable, but it should be complemented by broader measurement approaches when making budget and channel decisions.

This is where initiatives like Meridian come into play. Google has positioned Meridian as an open-source marketing mix modeling solution to help advertisers evaluate performance across channels and connect those insights to budget planning.

How Google Is Reinforcing Data Strength Across The Industry

One of the more interesting aspects of this push is how consistently it’s showing up across different mediums.

Product updates are only one piece of it.

Google is also investing in education and communication around Data Strength, using formats that reach both marketers and developers. Ads Decoded continues to focus on practical campaign strategies, including how to map the customer journey and select the right conversion signals.

At the same time, newer initiatives like Ads DevCast are aimed at a more technical audience, with episodes focused on topics like the Data Manager API and data integration workflows. The goal seems to be to meet teams where they are, whether they are responsible for campaign strategy or the underlying implementation.

The Data Manager API itself reinforces this direction. Google is shifting workflows like Customer Match into a system designed specifically for data connectivity, privacy controls, and more consistent ingestion of first-party data.

That combination of product changes, partnerships, and education signals a coordinated effort to strengthen how data is collected, connected, and used across the entire advertising atmosphere.

What Advertisers Are Saying About The Data Strength Conversation

The discussion around Data Strength and lead quality have sparked a lot of needed conversations between Google and advertisers.

In reaction to the Ads Decoded episode “Beyond the Form Fill“, many advertisers are happy that B2B businesses are getting the attention they’ve been asking for. Melissa Mackey praised the episode, stating that “All lead gen advertisers should go listen.” A few marketers noted the need to improve or purge the amount of bot leads they see in their B2B campaigns, including Robert Peck.

Google also did a series of posts and interviews with experts on the importance of data strength. All seemed to have similar sentiment and this is where I started seeing more and more advertisers connect the dots.

Adrija Bose commented on a discussion with Kamal Janardhan, Senior PM Director at Google, and Jeff Sauer, CEO of MeasureU:

What strikes me most is the framing of AI as the engine, not the strategy. Too many leaders conflate the two, expecting AI to compensate for weak signals. This post nails why high-quality data is non-negotiable for meaningful outcomes.

Jonathan Reed also showed his support on the renewed focus of data strength, stating that while it’s a full-time job for his team, they’ve seen “seeing dramatic increases in conversions, and dramatic decreases in cost!”

What Does This Mean For Your Campaigns?

This shift will show up pretty quickly once you look at how your campaigns are actually set up.

A lot of accounts still treat conversion tracking as something to build once and leave alone. But if the signals feeding your campaigns don’t match the intent behind the queries you’re targeting, it becomes harder for bidding to do its job well.

That usually shows up in ways you’ve probably already seen, where performance feels inconsistent and scaling becomes more difficult. Even small changes can create overly volatile swings.

None of that is coming from one setting or one campaign. It is usually a reflection of how the system is learning from the data it is given.

That is why this push toward Data Strength matters so much.

It forces a closer look at which signals are actually being used for optimization, how reliable they are, and whether they reflect real business outcomes.

In some cases, that means connecting better data from your CRM. In others, it is fixing how your tags are set up or how conversions are being defined in the first place.

As Google continues to lean into this direction, the gap will likely grow between accounts that are intentional about their data and those that aren’t.

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Google Adds Scenario Planner, Performance Max Updates, And Veo – PPC Pulse via @sejournal, @brookeosmundson

Welcome to this week’s PPC Pulse.

This week’s updates focus on Performance Max visibility improvements, new budget planning tools in Google Analytics, and generative video now built directly into Google Ads.

Here’s what was announced this week and why they matter for your campaigns.

Google Adds More Visibility and Control To Performance Max

Google rolled out several updates to Performance Max aimed at two ongoing gaps: control and reporting.

Advertisers can now exclude first-party customer lists. This gives teams running acquisition-focused campaigns a cleaner way to avoid spending on existing users.

On the reporting side, Google added:

• Budget report
• Expanded audience insights, including demographic breakdowns
• Placement reporting segmented by network

Why This Matters For Advertisers

Audience exclusions help reduce overlap between prospecting and retention, assuming your customer lists are accurate. The reporting updates are more practical. Advertisers get better visibility into spend pacing, who campaigns are reaching, and where ads are showing.

For teams already using Performance Max, this improves day-to-day oversight. It does not turn it into a fully controllable campaign type.

What PPC Professionals Are Saying

Anthony Simonetti is “very excited for more insight” for PMax campaigns, while the company Optifeed shared its support for the update by saying “Love seeing PMax get more transparent!”

Google Analytics Introduces Scenario Planner and Projections

Google Analytics launched two new tools as part of its cross-channel budgeting feature:

• Scenario Planner for building forward-looking budget models
• Projections for tracking whether live campaigns are pacing toward goals

Both tools use historical data to estimate conversions, revenue, and spend across channels, including non-Google platforms if cost data is imported.

Right now access is limited due to it being a beta feature. Advertisers need at least one year of data across multiple channels, as well as a few other eligibility requirements.

Why This Matters For Advertisers

Planning and performance have traditionally lived in separate places. These tools bring them closer together, especially to those marketers who manage more than just Google Ads.

Advertisers can now model budgets and monitor pacing in the same platform used for reporting. That can help teams managing multiple channels make faster adjustments during a campaign.

The tradeoff is reliability. Outputs depend entirely on data quality and historical consistency. For many accounts, that will limit how actionable these projections actually are.

Veo Brings AI Video Creation Into Google Ads

Google introduced Veo, its generative video model, inside Asset Studio in Google Ads.

Advertisers can start by uploading just three static images and generate short-form videos, then package them into ads for formats like Demand Gen.

Each uploaded image can generate a video by Veo that’s up to 10 seconds long.

Google is positioning this around speed and creative variation, and can be used in conjunction with the rollout of Nano Banana Pro. The goal is to make it easier to produce multiple video assets without traditional production.

Why This Matters For Advertisers

Creative production has been a bottleneck for many teams, especially for video.

Veo lowers that barrier immensely for brands. Advertisers can generate variations faster and test more creative without additional resources.

The bigger shift is volume. Google continues to push toward having multiple creative variations in-market at all times. This gives advertisers another way to keep up with that expectation, even if the output still needs review and refinement.

What PPC Professionals Are Saying

This got a lot of traction from advertisers, including 70 comments and over 340 reposts from its LinkedIn announcement.

André Felizol shared:

The key here will be the brands that could create something different. With AI facilitating the creation of videos based on images, everything will be similar. So, the companies that will invest more in creativity with different and creative approaches to show their products will win in the long run.

Brooke Hess is “looking forward to testing” for her agency’s clients while Thomas Eccel has already dug in and created a live demo test of Veo 3.

Personally, I’m excited to test it out after being introduced to the first version of Veo at the 2025 Google Marketing Live event last year:

Theme of the Week: More Ways To Plan, Steer, And Build

This week’s updates all support a more hands-on role for advertisers.

Google added more steering and reporting inside Performance Max, more planning functionality inside Analytics, and more creative production tools inside Google Ads.

Advertisers are getting more ways to shape performance instead of just reacting to it after the fact.

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Featured Image: Djile/Shutterstock; Paulo Bobita/Search Engine Journal

Google Ads Creative Tools Expand, Microsoft Simplifies Bidding – PPC Pulse via @sejournal, @brookeosmundson

Welcome to this week’s PPC Pulse. Updates focus on expanding creative tools in Google Ads and updates to bidding strategies in Microsoft Ads.

The newest version of Nano Banana Pro is now available to advertisers in Google Ads. In a separate creative update, marketers spotted an expansion to Google’s Creative Toolkit in the platform. Lastly, Microsoft Ads made changes to some of their automated bid strategies to streamline setup.

Here’s what happened this week and why it matters for advertisers.

Nano Banana Pro Version Now Available in Google Ads

While Nano Banana Pro was originally introduced back in November 2025, advertisers were alerted via email this week that its newest version is now available for free in Google Ads.

Now that it’s in Google Ads, advertisers can do all of these things in one platform:

• Generate new visuals using prompts
• Edit existing assets conversationally
• Create multi-product scenes
• Produce more detailed, photo-realistic imagery

Here’s a peek at what it looks like once you navigate to Asset Studio in Google Ads.

Why This Matters For Advertisers

Embedding Nano Banana Pro directly into Google Ads removes a lot of potential friction between create generation and campaign execution.

This means that for advertisers who have more creative control, creative becomes part of the optimization loop, not a completely separate workflow. Instead of planning creative updates in batches like a traditional process, advertisers can generate and test assets in response to performance changes.

Additionally, cost is not a barrier to entry. Making this available for free inside Google Ads lowers the threshold for advertisers who may not have been able to invest in external creative tools or AI platforms.

Lastly, creative volume can quickly scale. This is something that I’ve experienced personally working with my Google rep this quarter. They seem to be pushing creative volume across the board.

When the tool becomes easier to generate assets, most accounts will naturally start testing more variations.

However, brands still need to check the outputs of these AI-generated assets to make sure they adhere to any brand guidelines, product accuracy, and compliance requirements.

Google Expands Creative Toolkit Inside Google Ads

In another possible related creative update, Bia Camargo took to LinkedIn to share an update she got in Google Ads about creative assets.

In her post, the Google notification says: “More rich media available for your Google Ads. In addition to Google-owned images, Google-owned rich media (including photos, videos, icons, 3D assets, text and more) will be available for use in Google Ads.”

It looks like the goal is to allow advertisers to build and assemble more creative directly inside the platform rather than relying entirely on external tools. Whether this is completely tied to the launch of Nano Banana Pro in Google Ads is unclear.

Why This Matters For Advertisers

This update continues Google’s push to bring more of the campaign workflow into Google Ads.

For advertisers, this can reduce the time between identifying a creative gap and launching new variations.

It can also help smaller teams or advertisers without dedicated design resources produce a broader set of assets.

What PPC Professionals Are Saying

Most comments were in favor of this move. Brian Lasonde called this a “genuine win” while Virgil Brewster commented “How cool is that? Bring on the toolbox.”

Bryan Shue had an interesting take around the influence of creative production in the platform:

This feels like a bigger shift than just creative convenience. Once production moves inside the ad platform, the system gains more influence over the signals entering the campaign from the start. Faster testing is the obvious upside, but it also means the line between creative development and platform optimization keeps getting thinner.

Microsoft Ads Simplifies Automated Bidding Setup

This week, Microsoft Advertising introduced an update to how automated bidding is structured for new campaigns.

Target CPA (tCPA) and Target ROAS (tROAS) are now available as optional target settings within conversion-focused bid strategies:

• Choose Maximize Conversions and optionally set a tCPA
• Choose Conversion Value and optionally set a tROAS

Microsoft confirmed that existing campaigns using tCPA or tROAS remain unchanged, and portfolio bid strategies are unaffected.

Microsoft has positioned this as a simplification of bidding setup rather than a change to how the strategies perform.

It was originally announced last year, but this week’s rollout makes it global to all advertisers.

Why This Matters For Advertisers

This change does not alter how campaigns optimize, but it does change how decisions are made during setup.

The choice of bid strategy is now more implied. Instead of selecting between multiple strategies, advertisers are guided into a smaller set of options with targets layered in.

That shifts the focus toward how targets are set and adjusted over time.

For advertisers managing performance closely, this reinforces the importance of:

• Setting realistic CPA or ROAS targets based on actual performance
• Allowing enough time for campaigns to stabilize before adjusting targets
• Avoiding overly aggressive constraints early in the campaign lifecycle

Theme Of The Week: Less Friction In Setup, More Responsibility In Execution

This week’s updates focus on two different parts of campaign setup, but both change how much effort is required to move from idea to launch.

Google expanded what advertisers can do inside the platform by adding more built-in creative assets and making Nano Banana Pro accessible directly in Google Ads.

Microsoft simplified how bidding is applied in new campaigns by restructuring how targets are set.

Both are meant to reduce friction, but from an execution standpoint, it requires more upfront thought and attention from advertisers.

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Featured Image: Gorodenkoff/Shutterstock; Paulo Bobita/Search Engine Journal

New Meridian Tool, Performance Max Learning Path – PPC Pulse via @sejournal, @brookeosmundson

Welcome to this week’s PPC Pulse, where this week’s focus is on scenario-based planning in both Google and Microsoft platforms.

Google introduced a new Scenario Planner within Meridian, giving marketers the ability to model budget allocation shifts before committing spend. Microsoft launched a scenario-based Performance Max learning path designed to walk advertisers through practical campaign situations.

Both updates point to a growing emphasis on improving decisions before campaigns go live.

Here’s what happened this week and why it matters for advertisers.

Google Introduces Scenario Planner For Meridian

Google announced a new Scenario Planner within Meridian, its Marketing Mix Modeling platform. The tool allows marketers to test budget allocation scenarios and forecast potential outcomes using Meridian’s modeled insights.

Instead of waiting for quarterly MMM reports or static insights, advertisers can now simulate how shifting spend across channels might impact performance metrics like revenue, conversions, or return on investment.

According to Google, the goal is to make MMM insights more accessible and actionable for marketers who need to defend budgets and make planning decisions in real time. It also reiterated that coding isn’t required to use this tool.

It looks to be a promising planning tool built for higher-level strategy conversations between advertisers and key decision-makers.

Why This Matters For Advertisers

Marketing Mix Modeling has traditionally been handled at a higher level of the organization. It tends to show up in quarterly reviews, annual planning decks, or conversations led by finance and analytics teams. Most PPC managers are not sitting inside MMM tools on a weekly basis.

What makes this update notable is that Google is moving those insights closer to the teams actually managing budgets day to day.

PPC marketers are being asked more frequently to justify budget increases or reallocations with something stronger than last-click performance.

A tool like this could influence how those conversations happen. Instead of pointing only to recent return on ad spend (ROAS) trends, teams may start leaning more on modeled projections and incremental impact estimates when proposing changes.

What PPC Professionals Are Saying

Ginny Marvin, Ads Liaison for Google, shared the announcement on LinkedIn. Here’s what she emphasized about the Scenario Planner:

“No technical MMM experience needed to go from ‘what happened?’ to ‘what’s next?’”

Advertisers like Ivan Walker are “very excited!” about the update, while others like Ashley V. are curious about hearing feedback from others who have started using it.

Microsoft Launches Scenario-Based Performance Max Learning Path

Along the same lines of planning, Microsoft Advertising announced a new Performance Max learning path within its Learning Lab.

Unlike standard certification modules, this path walks advertisers through real-world scenarios designed to build hands-on expertise. The training focuses on practical decision-making across campaign setup, optimization, and troubleshooting.

I appreciate how Microsoft is positioning – that Performance Max success requires understanding, context, and strategy instead of focusing solely on what settings to toggle.

The learning path is designed to help advertisers think through situations they are likely to encounter in live accounts. For example, how to approach budget allocation, how to evaluate asset performance, and how to troubleshoot underperformance.

Why This Matters For Advertisers

Performance Max is not new at this point. Most advertisers have at least tested it, and many are running it at scale. What has changed is the level of thinking required to run it well.

There is still a misconception that PMax runs on its own once you flip it on. In reality, outcomes are heavily influenced by how campaigns are structured, what signals are being fed into the system, and how clearly conversion goals are defined.

Microsoft is leaning into the idea that automation does not remove the need for strategy. It shifts where strategy shows up. Instead of spending time adjusting bids manually, advertisers are spending time making decisions around inputs, segmentation, creative quality, and measurement alignment.

For agencies and in-house teams, scenario-based training could be useful for onboarding or leveling up junior team members. It provides context around the types of situations teams actually encounter, rather than just explaining what each setting does.

Theme Of The Week: Planning Before Spending

Both updates this week center around the same idea, which is trying to improve the quality of decisions before money is spent.

Google is giving marketers a way to test budget allocation scenarios before shifting spend to other platforms. Microsoft is walking advertisers through realistic campaign situations before they are live in their accounts.

While many industry updates focus on optimizations after campaigns are running, these ones focus on the earlier stage. How confident are you in the structure? How confident are you in the allocation? How confident are you in the assumptions behind the strategy?

Especially with budgets under tighter scrutiny than ever, and automation handling much more of campaign execution, the planning phase definitely carries more weight than it used to.

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Featured Image: Kansuda2 Kaewwannarat/Shutterstock; Paulo Bobita/Search Engine Journal

Shopping Ads Testing In AI Mode, Microsoft’s AI Search Guide & Keyword Strategy Shift – PPC Pulse via @sejournal, @brookeosmundson

Welcome to this week’s PPC Pulse: updates all revolve around how AI is being woven directly into search monetization and campaign structure.

Google is testing Shopping ads inside AI Mode conversations. Microsoft published a practical guide on how AI search surfaces brands. The Google Ads Decoded podcast made it clear that keywords are no longer the strategic starting point for campaign structure.

Here’s what happened this week and why it matters for advertisers.

Google Testing Shopping Ads Inside AI Mode

In a blog post from Google this week, Vidhya Srinivasan, vice president/general manager of Ads & Commerce at Google, confirmed they’re testing a new ad format in AI Mode.

Specifically, it’s a Shopping ad format that recommends products based on a user’s query within AI Mode.

In addition to this announcement, Google also said it’s testing similar formats in other verticals beyond retail, such as the travel category.

What may be the most interesting part of the announcement was the framing of ads. Srinivasan stated:

“We aren’t just bringing ads to AI experiences in Search; we are reinventing what an ad is.”

This could signal a shift in the existing ad formats in Google Ads or the possibility of adding in new formats down the road.

Why This Matters For Advertisers

This feels less like a “new placement” and more about how Search monetization is changing.

In AI Mode, the user journey is compressed. People are not scanning a page of results in the same way. They’re asking, refining, comparing, and making decisions inside a conversation.

That matters because it changes what “being present” looks like. It’s not only about ranking, or even being the first paid result. It’s about being one of the options the AI experience is willing to put in front of someone while they’re comparing.

For Shopping advertisers, this puts more pressure on feed strength. If AI Mode is assembling recommendations based on product attributes, availability, pricing, and retailer options, your data has to be clean enough to compete in that environment.

It also raises a practical question that I think a lot of teams are going to feel quickly. If AI Mode surfaces fewer visible commercial options than a traditional results page, those slots get more competitive. Winning may depend more on eligibility and relevance than on brute force bidding.

What PPC Professionals Are Saying

The initial reaction across PPC LinkedIn has mostly been “this was inevitable,” with people focusing on how this might work operationally.

Thomas Eccel, founder/managing director at AdSea Innovations, shared the announcement and called out that this format will be eligible through existing Shopping and Performance Max setups, which is the part advertisers will care about first. Are you going to need a net-new campaign type, or is this a distribution expansion of what already exists?

In a post shared by Andrew Lolk, founder of SavvyRevenue, comments around how Google is going about monetizing AI Mode vs. other players like ChatGPT and Claude were discussed.

Martin GroBe, head of SEA and Programmatic Display at Suchmeisterei GmbH, stated:

“Google has the advantage of having established AI Mode as a version of Gemini that users perceive as an evolution of Google Search and therefore accept advertising quite naturally. This allows Google to conduct monetization tests in AI Mode without negatively impacting the user experience of ‘Pure Gemini.’ Regarding Pure Gemini, Google can sit back and watch how successful Claude/ChatGPT are with their ad strategies – and then start monetization with the winning strategy.”

A lot of the chatter is less “cool new thing” and more “OK, what’s the eligibility, and what data do we need to tighten up now.” Others were questioning what attribution will look like if these ads are being shown in a discovery-first phase.

Microsoft Releases AI Search Playbook For Marketers

Microsoft Advertising published an updated edition of its AI search playbook, positioned as a practical guide for how AI-powered search and assistants are reshaping discovery.

Microsoft’s angle in this focuses more on being understood, trusted, and surfaced inside of AI-generated answers and less on simply ranking links.

It also directly addresses the overlap and difference between SEO and what it calls generative engine optimization, along with guidance on creating clearer, more structured content that AI systems can interpret confidently.

Why This Matters For Advertisers

On the surface, this looks like an SEO conversation. But paid teams should care for two reasons.

First, Microsoft is putting a flag in the ground that AI-driven discovery is not theoretical. It is treating it as the current operating environment, and it wants marketers to adjust how they show up.

Second, the “structure” theme is the part that connects directly to paid performance. In AI experiences, brands do not get pulled into answers because the copy is clever. They get pulled in because information is clear, consistent, and easy for machines to interpret.

Even if you live in Microsoft Ads or Google Ads all day, this should sound familiar. The industry keeps moving toward fewer manual levers, and more dependence on clean inputs. Content quality, feed quality, and landing page clarity are part of those inputs.

This guide is basically Microsoft saying: if you want visibility in AI discovery, you need to treat your information architecture like performance infrastructure.

What Professionals Are Saying

The response to Microsoft’s playbook has been positive, mostly because it aims to explain the mechanics without turning it into hype.

International SEO Consultant Aleyda Solis, who contributed to the guide (along with other professionals), praised Microsoft for “leading the way” and sharing practical resources for search marketers they can actually use.

Navah Hopkins, Microsoft Ads liaison, also shared the update with her take on why it’s useful for paid media folks, including topics like budget focus, landing page insights, and communication styles.

That theme of “finally, someone wrote this down in plain language” shows up in a lot of the reactions.

“Keywords Are A Means To An End” In Ads Decoded Podcast

In the latest Ads Decoded episode focusing on Search campaign structure, Google made a direct point that will land differently depending on how long you’ve been in this industry.

This week’s guest was Brandon Ervin, director of Product Management for Search Ads at Google. He and the host, Ginny Marvin (Google Ads liaison), discussed multiple topics, including account structure and the role of keywords now.

Ervin stated that the role of keywords in 2026 was that “keywords are a means to an end” and not the end itself, and that advertisers should start with the business goal and go-to-market approach first. Keywords become a thematic layer that supports that strategy.

They also discussed the ongoing shift toward semantic matching, why exact match still has a role for tighter control, and how query matching continues to evolve with frequent backend improvements.

Ginny Marvin also shared the episode on LinkedIn, framing it around modern Search structures and the role of the keyword in today’s environment.

Why This Matters For Advertisers

This topic matters because it is essentially Google validating what many advertisers have had to learn the hard way.
For years, the gold standard was granularity. Tight ad groups. Tight keyword lists. Maximum control.

And to be fair, that approach worked for a long time. I was firmly in that camp. SKAG structures made sense in the era they were built for. Broad match felt like an unnecessary gamble. Campaign consolidation felt like you were asking for wasted spend.

But the reality is the system changed. User behavior changed. And the way Google interprets intent changed.

So when Google says “keywords are a means to an end,” the real message is: stop treating keyword architecture as the strategy. Treat it as one layer of a strategy that starts with business outcomes, messaging, and intent.

It also reframes how people should think about search terms that “don’t look right” at first glance. Sometimes, those queries are noise. Sometimes, they are a discovery behavior that your account can either learn from, or completely miss because you filtered too aggressively.

I don’t think this means everyone should throw structure out the window. But it does mean segmentation should have a job. If two ad groups have the same intent, same landing page, and same creative approach, splitting them may just be creating artificial walls that the system has to work around.

What PPC Professionals Are Saying

The PPC conversation around this topic tends to split into two camps.

One group hears “keywords are a means to an end” and translates it as “Google wants us to have less control.” The other group hears it and says, “finally, this is how the system has been behaving anyway.”

The comments on Ginny Marvin’s post about the episode reflect that interest, especially around modern structure decisions and what still deserves separation in 2026.

Brad Geddes, co-founder of Adalysis, thanked Ervin and Marvin for their candidness, stating:

“I suspected that Google was using conversion data from across the account for bidding and other optimization, but I could never get anyone to confirm this. You finally confirmed it, so now I can confidently say this is true 🙂 TY.”

Alexandr Stambari, performance marketing specialist, showed support for the message overall, but expressed a concern about segmentation. He stated:

“However, there’s one point that concerns me slightly: moving too far away from segmentation can reduce control. In highly competitive niches (e-commerce, B2B lead generation), segmentation by intent, margin, and query type still plays an important role. Full consolidation without deep analytics can average out performance and hide growth opportunities.”

Marvin responded to his comment and reiterated that Ervin makes it clear that “advertisers should use segmentation where it makes sense and ground their analysis and their structure in their business goals.”

It’s also notable that Google is choosing to have this conversation in public, in a format designed for marketers, not engineers. That tells you it expects more advertisers to be wrestling with restructuring decisions this year.

Theme Of The Week: Tightening AI Search Infrastructure

This week’s updates all reinforce the same underlying shift. AI is not adding a new layer to Search. It is exposing whether your existing structure holds up.

Google is testing Shopping ads inside AI Mode, which means product visibility depends on how well your data can compete inside a summarized answer. Microsoft is explaining how brands are surfaced in AI responses, and structured, trustworthy inputs are central to that process. Google is also reminding advertisers that keywords are simply one input. The real foundation is business intent.

When discovery happens inside generated answers and fewer placements carry more weight, structure stops being a preference. It becomes performance leverage.

If your feeds are clean, your content is clear, and your campaigns are aligned to real intent, that leverage works in your favor. If not, AI environments tend to surface the gaps quickly.

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Featured Image: beast01/Shutterstock