How To Optimize PPC Accounts With Less Search Term Visibility – Ask A PPC via @sejournal, @navahf
This month’s Ask A PPC tackles a very real concern: Search term visibility keeps shrinking on some platforms while other platforms continue to maintain more transparency. Advertisers still need to understand, diagnose, and optimize campaigns even when they do not have full search term signals.
“With search terms increasingly unavailable, what technical signals should advertisers prioritize to diagnose smart bidding?”
We’ll walk through three techniques advertisers can use on any platform, then unpack how Microsoft Advertising’s search term visibility can strengthen optimization across other channels.
Quick disclosure: I work for Microsoft, and I’ve written this as objectively and platform-agnostically as possible.
Technique 1: Use Behavioral Analytics To Understand User Quality
Losing access to individual search terms does not mean you lose access to meaningful insight. You can check traffic segments to see if they deliver value:
Times: Certain times will be more prone to impulse purchases vs considered investments. If your product/service sees a large amount of non-converting activity during a time that doesn’t align with the conventional customer journey, you can make educated guesses about removing those times.
Audiences: By including observation audiences, you can get a sense of how much traffic fits into desired demographics. These insights can be used to understand the intent behind the traffic.
Devices: If you know you do better with phone calls and the lion-share of your traffic is desktop, there may be a need to apply device-specific rules to direct traffic in a more profitable way.
Locations: Identifying locations with higher CPCs and lower conversion rates is the easiest way to ensure budget is scaling in the right way.
Then, you can start to understand whether those sources deserve more investment.
For example, if you see a high uptick in conversions at 7 a.m. on mobile devices, but they tend to be lower value than the conversions that happen at 4 p.m. on desktop, you might make some educated guesses that the former group were impulse shopping, while the latter have put more thought into engaging with you. The high-volume conversion group might also be on their way to work and not processing as clearly which brand they gave their information to.
That said, avoid snap decisions. Some interactions help start the conversion journey even if they do not complete the conversion action until later. This is why signals from offline conversions and view-through conversions matter so much.
Both Behavioral Analytics And CRM/Shop Data Can Help Fill In Gaps When Search Terms Are Missing
Behavioral analytics tools, including Microsoft Clarity, can help you understand how different humans and AI systems engage based on the times, platforms, and audience cohorts they come from. These insights can show whether your landing page gets in the way.
For example, if you see a high uptick of traffic coming to your site with no conversions, check to see if they’re trying to convert and can’t. Behavioral analytics tools can show you “rage clicks” as well as whether people are confused by page content.
They also can highlight potential CRM integration issues. If you are watching users complete conversion actions multiple times, but not seeing their info in your CRM, you are inviting false positives in the conversion tracking.
Technique 2: Open Yourself Up To Zero-Click Value Actions
The AI era is changing how people discover, evaluate, and act. People may complete valuable actions without clicking to a website. Search term visibility may shrink because of privacy mechanics, but limited visibility into the full query or conversation does not mean no value occurred.
Grounding queries and citation counts can begin to provide useful information. For example, if AI visibility reports show a lot of grounding queries, review whether those queries are contextually relevant to your brand.
For example, if you see that a grounding query is pulling you into financial advice topics and you’re a general business consultant, you may want to adapt your landing pages, search themes, and keywords to focus on “consultant.” While “advisor” is a close variant of “consultant,” financial topics tend to have higher CPCs and won’t be relevant to general business consulting. This is also where term exclusions and message constraints can be helpful, so AI tooling can provide a clearer picture into what product/service you offer.
You may also decide to remove unnecessary JavaScript from pages you want AI systems to engage with to reduce any potential blocks stopping crawlers from accessing the page.
If AI systems cannot engage with a page, we remove ourselves from opportunities to connect with customers before those customers even look for us. AI can only put forward what it can understand, take in, and parse as useful.
Technique 3: Use Offline Conversions And Human Data To Validate Platform Claims
This technique takes more work, but it gives you useful insight into campaign performance, or where AI-powered bidding, struggles when it does not receive clean data. Give bidding systems data that is as accurate and useful as possible while also using directionally correct conversion values when campaigns need to hit certain thresholds.
Conversion values and conversion value rules usually communicate value more effectively than bid adjustments. That matters because when we try to mitigate waste, we should use the technology that aligns with how platforms work rather than forcing newer models to operate through legacy controls.
Bid adjustments and bid caps can force auctions in ways that reflect older campaign management logic. Conversion values and conversion value rules offer a stronger path forward for mitigating waste in modern automated systems.
A Note On Search Term Visibility
This month’s question assumes a Google-first world. Microsoft Advertising takes a different approach: Advertisers can see search terms for traffic that resulted in a click. That transparency matters because advertisers should be able to understand how their budgets get spent.
With transparency comes search terms that invite scrutiny. Some may become useful negatives, while others may challenge biases in how you think about search behavior and how some queries drive valuable performance you may not have expected. People’s search habits change, and in a world where AI plays a larger role, bot-type queries will not always equal waste. Sometimes they represent genuine, useful queries.
One helpful way to explore this is to review the AI crawler bot report in Microsoft Clarity and look at the ratio of AI crawler bots. That context can help you understand whether AI engagement connects to meaningful behavior.
3 Ways To Use Microsoft Search Term Visibility Across Other Channels
1. Identify Winners
Because Microsoft Advertising shows search terms that result in clicks, you gain more transparency into what deserves focus. That insight can inform better search themes for Performance Max campaigns, stronger keyword targets for traditional search campaigns, and content ideas worth building around.
Do note that match types behave the same between Microsoft and Google.
2. Find Potential Negatives
If a query idea clearly does not make sense on Microsoft Advertising, there is a good chance it also does not make sense on Google or other keyword-targeted platforms. Microsoft requires negative keywords to use phrase or exact match, but you can still identify root words and apply them as negatives in the match types available on other platforms.
This added confidence can make Microsoft Advertising useful as a source of search terms that inform broader account hygiene.
3. Pair Behavioral Analytics With Conversion Tracking
Search term visibility helps you identify winners and losers, but the real insight and optimization happens when you understand what users do after they complete those searches. If a query brings people to your site but conversions do not register, you may have a conversion tracking issue rather than a query quality issue.
The reverse can also happen, if conversions increase but the queries tied to those conversions do not look quite right. Data exclusions can help in both Google and Microsoft when you need to account for tracking or data quality issues.
Behavioral analytics paired with search term visibility gives you one of the strongest ways to understand why people search the way they search and what influences them after they land on your website
Final Takeaway
Search term visibility may be shrinking on some channels, but it still exists on others. Rather than assuming visibility has disappeared entirely, advertisers can adapt their perspective and use multiple channels to capture different forms of insight.
Google and Microsoft do reach unique users, so the data will not map perfectly one-to-one. Still, if a search does not make sense for your brand and you can see it on one network even when you cannot see it on another, you gain value by identifying that idea once instead of paying for it repeatedly.
More Resources:
Featured Image: Paulo Bobita/Search Engine Journal
Google Ads Expands Travel Campaigns To Things To Do And Events via @sejournal, @brookeosmundson
Google Ads is expanding its Search campaigns for Travel beta to two additional verticals: Things to Do and Events.
Google announced the update in a post on X and LinkedIn on July 8, 2026. Advertisers selling attractions, tours, and event tickets can now access the campaign type through an open beta, although availability remains limited.
The expansion gives eligible advertisers another campaign option to test alongside existing Search ands Performance Max campaigns.
What’s Changing With Search Campaigns For Travel
Search campaigns for Travel is a Google Ads campaign type designed for travel-related advertisers. It first appeared as Google expanded AI-powered campaign types across Travel and Shopping earlier this year.
With this update, the beta now extends beyond traditional travel categories to include Things to Do and Events. That means advertisers promoting attractions, guided tours, and event tickets may now be eligible to use the campaign type.
The announcement came as part of a five-post thread. At the time of writing, Google has not shared complete details around eligibility, supported features, or geographic availability.
Why This Matters
Attractions, tours, and event tickets share many of the same characteristics as hotel or airline bookings. Availability changes frequently, pricing can fluctuate, and purchase decisions are often tied to specific dates or locations. Those are all areas where Google’s newer campaign types have increasingly relied on automation to determine which searches are most likely to convert.
Until now, advertisers in these verticals have generally relied on standard Search campaigns or Performance Max. Expanding the Travel campaign type suggests Google sees these businesses as a natural fit for a more specialized campaign format.
The update also aligns with Google’s broader push toward AI-driven campaign management. Over the past year, the company has introduced AI Max across additional campaign types while continuing to consolidate older campaign formats.
While Google hasn’t explained why it selected these verticals, they fit naturally alongside other travel-related advertisers already using the campaign type.
What Advertisers Should Do
If you’re eligible for the beta, treat it as a controlled test rather than a replacement for your existing campaigns.
Review your current Search and Performance Max performance, set aside a limited testing budget, and compare booking or ticket sales against your existing campaign mix. Since Google has not released complete feature details, it’s also worth setting expectations internally that functionality may continue to evolve throughout the beta.
What’s Still Unknown
Google has not yet confirmed which bidding strategies, assets, reporting capabilities, or feed requirements will be available for the Things to Do and Events verticals. It also remains unclear how these campaigns will interact with existing AI Max for Travel functionality.
We’ll update this article as Google shares additional details about eligibility, features, and availability.
Google Clarifies Smart Bidding Update After Advertiser Concerns via @sejournal, @brookeosmundson
Google is clarifying its Smart Bidding update after advertisers questioned how budget-limited campaigns will behave beginning August 17.
The original announcement around Smart Bidding changes was June 22. The update essentially changes how Target CPA and Target ROAS campaigns behave when they’re limited by budget.
Today, many budget-limited campaigns outperform their bidding targets. Smart Bidding often enters only the auctions most likely to convert efficiently, producing stronger-than-expected CPA or ROAS.
Google says that wasn’t the intended behavior.
Instead, Smart Bidding will optimize more closely toward the Target CPA or Target ROAS advertisers actually set. Campaigns that currently outperform those targets may move closer to them after the update.
The announcement immediately raised questions across the PPC industry. Advertisers wanted to know why Google would reduce efficiency in campaigns that were already exceeding expectations.
Google’s follow-up comments answer many of those questions. They also explain why the company believes the change will make campaign scaling more predictable.
Historically, those campaigns often outperformed their bidding targets. A campaign with a $50 Target CPA, for example, might consistently generate conversions at $35.
Beginning August 17, Google will optimize those campaigns more closely toward the Target CPA or Target ROAS advertisers set. The company says this should create more predictable performance when advertisers adjust campaign budgets.
Google also clarified several points after announcing the update:
Budgets will not automatically increase
Google won’t automatically change Target CPA or Target ROAS settings
Advertisers who want to maintain current performance may need to lower their bidding targets before the rollout
Google is rolling out account notifications and a Bid Target Adjustment Tool to identify affected campaigns
Those clarifications addressed some of the initial confusion. They also sparked a broader discussion about how the update could affect campaign performance in practice.
The Biggest Concern: Is Google Becoming Less Efficient?
One question surfaced repeatedly as advertisers discussed the update: Is Google making Smart Bidding less efficient?
Kirk Williams summed up that concern in a LinkedIn post.
He wrote:
…How and why will the system stop trying to be as efficient as possible… Does that mean smart bidding when limited by budget will no longer be trying to find better auctions?… So does that mean they’re building the system to literally choose to be dumber when limited by budget?
Williams questioned why Google would move campaigns closer to their stated targets if Smart Bidding could already deliver stronger performance.
Mike Ryan offered one of the most detailed explanations in the comments.
Ryan argued that Google isn’t making Smart Bidding less intelligent. Instead, he believes the system has become too conservative in budget-limited campaigns.
According to Ryan, Smart Bidding has favored exploitation over exploration. Rather than entering more auctions that still satisfy an advertiser’s target, the system has focused on the safest opportunities. That produced stronger-than-expected efficiency. It also meant campaigns didn’t consistently optimize toward the Target CPA or Target ROAS advertisers actually set.
Ryan believes the updated system will follow those bidding targets more closely. That may reduce the overperformance many advertisers have seen in budget-limited campaigns, but it also aligns with Google’s stated goal of making bidding targets behave more predictably.
Predictable Scaling vs. Peak Efficiency
Aaron Levy focused on a different part of the update: campaign scaling.
He described a campaign with an $8 CPA and a $12 Target CPA. If an advertiser doubled the budget today, the CPA might unexpectedly climb to $16 instead of remaining near the target.
Levy believes the update should make that behavior more predictable. Rather than introducing large swings in efficiency, Smart Bidding should continue optimizing toward the advertiser’s Target CPA as budgets change.
Kirk Williams questioned whether that tradeoff benefits advertisers. If Smart Bidding can already outperform a target, he argued, some advertisers may prefer that extra efficiency over more predictable budget increases.
Google has consistently framed the update around predictability. They say campaigns should optimize toward the targets advertisers actually set, making budget changes easier to manage and forecast.
Whether advertisers agree with that tradeoff will likely depend on how their campaigns perform after the rollout.
Google Clarifies Several Misconceptions
Google Ads Liaison Ginny Marvin responded directly to several concerns advertisers raised after the announcement.
One of the biggest misconceptions was that Google was encouraging advertisers to simply spend more money.
To be clear, this won’t result in campaign spend changes… Our guidance for those with budget-constrained campaigns currently over-performing on their target is to ensure the targets are in line with your goals.
She also emphasized that advertisers will only spend more if they choose to raise their campaign budgets. The update itself does not change campaign budgets or automatically adjust bidding targets.
Jack Carr raised a similar concern, arguing that budget constraints have historically acted as an efficiency lever and that Google’s recommendation effectively removes that advantage.
Our advice is not to ‘let the system spend more money’… this change won’t result in spend changes on a campaign already budget constrained.
She also explained why Google is making the change.
Performance has often fluctuated unexpectedly… especially with budget changes. That’s not been a great experience for advertisers & made it challenging to scale campaigns with confidence.
According to Google, the backend update will make Smart Bidding optimize more consistently toward the Target CPA or Target ROAS advertisers actually set, even when campaigns are limited by budget.
Kristen Kelleher questioned whether the change would simply push campaigns into lower-quality traffic.
Marvin pushed back on that assumption as well.
The system sets bids to find as many conversions as possible at the ROAS/CPA target you set… With this update, advertisers can also expect this same behavior in budget-constrained campaigns with targets.
She added that advertisers who want to maintain today’s stronger-than-target performance should consider updating their Target CPA or Target ROAS before the rollout.
Google’s position has remained consistent throughout the discussion. The company says the update changes how closely Smart Bidding follows bidding targets. It doesn’t change campaign budgets or automatically modify campaign settings.
What This Means For Advertisers
Not every advertiser will need to make changes before August 17.
Campaigns already hitting their intended Target CPA or Target ROAS may continue operating much as they do today. The biggest impact will likely fall on budget-limited campaigns that have consistently outperformed their bidding targets.
For example, if a campaign has averaged a $20 CPA against a $35 Target CPA, Google says advertisers should consider whether $20 is now the more appropriate target. Leaving the original target unchanged could allow performance to move closer to $35 after the update.
Before the rollout, review any budget-limited campaigns that consistently outperform their Target CPA or Target ROAS. Compare current performance against your configured targets and decide whether those targets still reflect your business goals.
The update also changes how advertisers should think about bidding controls. Many advertisers have treated limited budgets as an efficiency lever because campaigns often outperformed their targets. Google has made it clear that budgets and bidding targets serve different purposes. Budgets control spend. Target CPA and Target ROAS control efficiency.
If Google’s explanation plays out as expected, advertisers who keep bidding targets aligned with actual performance should see fewer surprises when adjusting campaign budgets after August 17.
What Happens Next
Google has explained how Smart Bidding should behave after August 17. The remaining question is how closely those expectations match real-world campaign performance.
Advertisers with budget-limited Target CPA or Target ROAS campaigns will likely be watching those accounts closely after the rollout. Campaigns that have consistently outperformed their bidding targets may provide the clearest indication of how much the update changes day-to-day performance.
Google has also encouraged advertisers to review bidding targets before the rollout if current performance already aligns with their business goals. As more accounts transition to the updated bidding behavior, advertisers should have a better understanding of how the change affects campaign efficiency and budget management in practice.
Google Tests ‘Strongest Match’ Labels On Search Ads via @sejournal, @brookeosmundson
Google is testing a new Search ads label that could give certain advertisers a visible endorsement directly within search results.
In a LinkedIn post, Google Ads Liaison Ginny Marvin announced a limited U.S. experiment that adds a “Strongest match” or “Strong match” label to select Search ads.
According to Marvin, the labels are intended to help users quickly identify the most relevant information for their query while helping advertisers connect with high-intent audiences.
The experiment is currently rolling out to a small percentage of users in the United States.
Marvin said the designation relies on existing ad quality and relevance signals that Google already uses to evaluate Search ads.
While the announcement itself was relatively brief, it immediately sparked questions from advertisers about how the label is determined, whether it could influence click behavior, and what it might signal about the future direction of Search.
Google Hasn’t Explained What Qualifies As A “Strongest Match”
Google’s announcement answered what the label is intended to do, but not how advertisers qualify for it.
According to Marvin, the designation is based on existing quality and relevance signals. Beyond that, Google has not shared any details about how the label is determined.
As a result, advertisers still don’t know:
Which signals are used to determine the label
How those signals are weighted
Whether the designation is based on the query, keyword, ad, landing page, or a combination of factors
Whether multiple advertisers can receive the label in the same auction
Whether the label is tied to ad position
The lack of detail quickly became one of the main discussion points following the announcement.
Several advertisers questioned whether the designation reflects the same systems Google already uses to evaluate ad relevance or whether the experiment introduces an additional layer of evaluation.
Others questioned whether bid strength plays any role.
Google’s description suggests the label is intended to reflect relevance rather than spend. However, the company has not explained how those determinations are made.
Until Google shares more information, advertisers are left with a label that appears meaningful but lacks a clear definition.
Advertisers Are Asking For More Transparency
Advertisers quickly focused on a different question: how Google determines which ads receive the label.
Several commenters asked whether “Strongest match” reflects the same relevance systems Google already uses or whether additional factors are involved.
Terry Hogan questioned whether the designation is truly based on relevance or whether bid strength contributes to the decision.
Kristen Kelleher asked a popular question, based on the amount of likes she got:
What components make up the scoring underneath the match label? Is this based on the keyword based quality score, ad relevance, landing page exp or is it only based on the ad itself?
So far, Google has not provided additional detail.
Other comments asked measurement questions around the label testing.
Craig Graham asked: “Are there plans for any kind of advertiser-side reporting for this if the experiment rolls out more broadly?”
That visibility could become important if the designation influences click behavior. Advertisers will likely want to know when their ads receive the label and whether it impacts performance.
Questions also accumulated in Marvin’s LinkedIn post about how the label will appear within search results.
Bernt Muurling asked whether the strongest match will always be the first result shown.
If the label only appears on the top-ranked ad, it reinforces Google’s existing ranking decisions. If it can appear elsewhere on the page, it introduces a new signal that users may evaluate alongside ad position.
Justin Windschitl pointed to what may be the biggest challenge for the experiment:
Interested what the criteria are for labeling “match types” and if there can be flaws with the labeling, hurting businesses. On the flip side, if it’s buttoned up, it could be very beneficial for filtering best matches and more effective ad spend!
If the designation is occasionally inaccurate, advertisers may question whether Google is effectively endorsing one business over another.
Could This Become A Public Relevance Signal?
The experiment stands out because it could make Google’s assessment of relevance visible to users.
Advertisers have always known that Google evaluates factors such as ad relevance, landing page experience, expected click-through rate, and other quality signals when determining which ads appear and where they rank. Those evaluations largely happen behind the scenes.
A “Strongest match” label would move part of Google’s relevance evaluation from Google Ads into the user experience itself.
That may seem like a small change, but it introduces a new dynamic into the search experience.
Users already see ad position. A visible label gives them another signal to evaluate.
That is one reason several advertisers immediately questioned how the designation is determined and whether it could influence click behavior.
It also raises questions about whether the label becomes a competitive advantage of its own.
If users begin viewing the designation as a recommendation from Google, advertisers who receive the label could benefit beyond the visibility that comes with ranking well in the auction.
Whether that happens will likely depend on how often the label appears and whether users respond to it.
For now, Google has positioned the experiment as a way to help users identify relevant information more quickly. The broader question is whether advertisers and users eventually view the designation as a relevance signal, a recommendation, or something in between.
Why Google May Be Testing This Now
While Google hasn’t shared the reasoning behind the experiment, the test arrives as Search continues to evolve beyond a traditional list of links.
Google already makes relevance decisions every time an auction takes place. The difference is that those decisions typically remain behind the scenes.
This experiment tests what happens when part of that evaluation becomes visible to users.
Google already makes relevance decisions every time an auction takes place. This experiment tests whether those assessments should be visible to users.
Like many Search tests, the feature may never move beyond experimentation. If it does, it could mark another step toward Google making more of its relevance decisions visible within the Search experience itself.
What This Means For Advertisers
At this point, advertisers should view the label as an experiment rather than a new optimization opportunity.
Google hasn’t introduced any controls, reporting, or guidance around how advertisers qualify for the designation.
For now, the announcement is getting a lot of attention because it introduces a new user-facing signal in Search ads. Whether that signal influences click behavior or campaign performance is unclear until Google provides more information.
We’ll continue watching the rollout and update this story if Google shares additional details about qualification criteria, reporting, or broader availability.
Featured image: Kues / Shutterstock, Phone image courtesy of Google
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
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.
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.
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?
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.
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.
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.
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.
Customer-in-the-loop (CITL): Assets are generated based on inputs like a website URL or a user prompt. The advertiser always has a choice as to whether or not they want to include these assets in their campaigns.
Dynamic composition: Ads are composed at serving time in different formats based on existing groups of assets, with performant winners selected and scaled (i.e., how Performance Max works). May or may not include AI-generated assets based on customer preferences.
Auto-generated: New assets or ads are generated after a campaign is launched based on inputs like URLs, search queries, or existing videos to improve performance. These assets are not reviewed and approved by advertisers before serving, but can generally be viewed and controlled in reporting.
These performance gains aren’t new; AI ads have been meeting or exceeding human creative as early as 2018.
Three text ads: one made by a human, the others autogenerated (Image from author, April 2026)Results of three ads from a logistics company over 30 days (Image from author, April 2026)
That performance edge comes from two core advantages.
First, auto-generated creative is highly adaptable. It can flex across formats and placements in ways that would be time-consuming or impractical for humans to manage manually.
Second, it is bias-free in its willingness to apply the creative most likely to perform for humans searching in a profitable way, rather than the semantic syntax we think will succeed.
This article is not about declaring auto-generated creative right or wrong. There is no universal answer. Whether leaning into it makes sense will always depend on business constraints, brand rules, and personal comfort levels.
What we are going to do is walk through a practical framework you can use to decide whether auto-generated creative is worth testing for your business, and how to use platform tools to better understand how well your site and messaging are being interpreted by AI systems.
Before we get into it, an important disclosure. I am a Microsoft Advertising employee. The guidance here is intended to be platform-agnostic, but I will reference a few Microsoft-specific tools that are free to use and particularly helpful for understanding how your site is being interpreted by machines and humans alike.
The Case For Using Auto-Generated Creative
The number one reason to consider auto-generated creative is simple: time savings.
At its core, auto-generated creative takes your existing assets and adapts them to meet the formatting and placement needs of different inventory. Instead of building bespoke creative for every surface, you allow the system to reassemble what you already have in ways that let you reach more people with less manual effort.
The inputs for auto-generated creative typically come from your website, your existing ads, and, in some cases, proven concepts that are broadly applicable across advertisers. You can also apply brand style guides to ensure fonts, colors, and creative (including tone of voice) are compliant with brand standards.
Because auto-generated creative allows advertisers to be eligible for more placements (with Ad Rank determining the ad shown), it naturally has access to more impressions. More impressions create more opportunities to win auctions, which can translate into incremental volume that would have been difficult to capture using tightly controlled, manually built assets alone.
Auto-generated creative does not have to be all-or-nothing. There is also a hybrid approach where humans partner with AI systems. That can mean using in-platform tools from Google or Microsoft, or external AI tools, to help generate ideas, headlines, or variations that are then reviewed, approved, and manually uploaded.
Some advertisers draw a distinction between AI-assisted ideation and auto-generated creative. In practice, if you are using AI at any point to help create or shape ad messaging, there is already an element of automation in the process.
The Case Against Using Auto-Generated Creative
There are absolutely valid reasons to opt out.
The most pressing is brand compliance. If your organization requires explicit approval for every piece of creative before spend can occur, allowing systems to dynamically generate variations may simply not be permissible.
That said, many platforms provide preview tools that show examples of how creative may appear.
Image from author, April 2026
If you are willing to explore those previews and lean into tools like brand kits that enforce fonts, colors, and tone, it may be possible to secure internal approval where it previously felt impossible.
Another reason advertisers shy away from auto-generated creative is reliance on proven assets with no tolerance for variation. Sometimes budget approval is contingent on using specific creative that has already demonstrated performance, and there is no room to test alternatives.
Image from author, April 2026
It is worth noting, however, that auto-generated creative already relies heavily on your existing assets. If the primary concern is avoiding untested messaging, allowing your site content and proven ads to inform the system can help mitigate that risk.
Bonus Tip: Using Auto-Generated Creative To Understand How AI Sees You
One of the most underrated benefits of campaigns like Performance Max, Dynamic Search Ads, and other feed or keywordless-based formats is that they reveal how well platforms understand your site and landing pages.
Image from author, April 2026
If you strongly disagree with the creative shown in previews for AI Max, Performance Max, or similar formats, that is a warning sign. Running budget to those pages risks confusing users if the system’s interpretation does not align with your intended messaging.
These tools can function as diagnostic instruments, not just delivery mechanisms.
Image from author, April 2026
You can go a step further by pairing them with behavioral analysis tools like Microsoft Clarity, which shows how users actually interact with your site. When creative interpretation and user behavior do not line up, the issue is often not the ads, but the underlying content.
Another advantage of modern campaign creation tools is their built-in AI editing capabilities. Even if you never allow auto-generated creative to go live, you can still use these tools to explore tone shifts, rewrites, and messaging ideas that inform your manual creative work.
Image from author, April 2026
There are many use cases for these systems beyond automation alone. Insight generation is one of the most valuable.
Final Takeaways
At its core, the decision to lean into auto-generated creative comes down to whether your brand is allowed to test.
If the answer is yes, there is little downside to experimenting. Auto-generated creative is largely built from your existing assets, and poor results are often a signal that your landing pages or messaging need refinement anyway.
If the answer is no, whether due to brand compliance, limited testing bandwidth, or the need to lock spend behind proven creative, it is entirely reasonable to opt out.
Used thoughtfully, it can save time, unlock scale, and surface insights about how your brand is understood by machines and users alike. Used blindly, it can create risk. The goal is not blind trust, but informed experimentation.
Hope you found this helpful, and I’ll see you next month for another edition of Ask the PPC.
More Resources:
Featured Image: Paulo Bobita/Search Engine Journal
Google Is Replacing Dynamic Search Ads With AI Max via @sejournal, @brookeosmundson
Google just announced the deprecation of Dynamic Search Ads (DSA) and is officially moving its legacy capabilities into AI Max.
Starting in September, eligible campaigns using Dynamic Search Ads (DSA), automatically created assets (ACA), and campaign-level broad match settings will automatically upgrade to AI Max.
While advertisers have speculated about this change for months, the update is now official.
If you’re running Dynamic Search Ads, automatically created assets (ACA), and/or campaign-level broad match settings, keep reading to understand how your campaigns will be affected.
DSA Features Migrating Into AI Max
Beginning in September, advertisers will no longer be able to create new DSA campaigns through Google Ads, Google Ads Editor, or the Google Ads API. Existing eligible campaigns will be migrated automatically.
Google positions AI Max as the next generation of DSA.
Historically, DSA helped advertisers capture additional search demand beyond their keyword lists by using website content to generate headlines and choose landing pages. That made it useful for large sites, inventory-heavy businesses, and advertisers looking for broader query coverage.
AI Max keeps that concept but adds more signals and controls.
According to Google, AI Max combines advertiser assets, landing page content, and broader intent signals to help match ads to more relevant queries. It also adds controls such as:
Brand controls
Location controls
Text guidelines
Search term matching
Text customization
Final URL expansion
Image credit: Google, April 2026
Google says campaigns using the full AI Max feature suite see an average of 7% more conversions or conversion value at a similar CPA or ROAS compared with using search term matching alone.
Google is also splitting the transition into two phases.
Phase 1: Voluntary Upgrades
Google announced that upgrade tools for existing DSA users are rolling out this week.
DSA advertisers will receive tools to move historical settings and data into new standard ad groups. ACA and campaign-level broad match users may see in-platform prompts to upgrade to AI Max.
Phase 2: Automatic Upgrades
Starting in September, remaining eligible campaigns with legacy settings will be upgraded automatically.
Google says all eligible upgrades are expected to finish by the end of September.
It’s important to note how legacy settings will be automatically migrated over to AI Max settings:
DSA users will have all three AI Max features enabled by default (search term matching, text customization, final URL expansion)
ACA users will have two AI Max features enabled by default (search term matching and text customization)
Campaign-level broad match users will have just search term matching enabled by default
What Advertisers Can Do To Prepare For The AI Max Transition
If you still rely on Dynamic Search Ads, now is the time to review where those campaigns sit in your account and how much value they drive.
Some advertisers use DSA as a core growth lever. Others use it as a low-maintenance catch-all for incremental growth. Your next steps may differ depending on that role.
#1. Review Your DSA Performance Now
Before the automatic upgrades begin, pull recent performance data for your DSA campaigns.
Look at conversions, assisted conversions, search terms, landing pages, and efficiency metrics. That baseline will help you judge whether performance changes after migration are positive, neutral, or negative.
#2. Upgrade On Your Timeline Before Automatic Upgrades
Google is encouraging advertisers to move early, and there is a practical reason for that.
A voluntary upgrade gives you more control over settings, structure, and testing than waiting for an automatic migration.
If DSA is important to your business, it makes sense to evaluate the upgrade before September.
#3. Test AI Max Impact
Google recommends using one-click experiments because they give advertisers a cleaner way to compare performance before making a full rollout decision. While I haven’t tried this yet, I will be testing it myself in the coming months.
Even if AI Max improves results on average, averages do not guarantee results in every account. Lead generation, e-commerce, local services, and B2B advertisers may all see different outcomes.
Run controlled tests where possible and compare against your existing baseline.
#4. Lean Into Additional Controls
Many advertisers asked for more steering options in search automation, and Google has listened to our feedback. AI Max includes more controls than legacy DSA.
Spend time understanding brand settings, location controls, and text guidance. Those inputs may matter as much as the automation itself.
#5. Watch Search Match and Landing Page Quality
Once you’ve migrated your DSAs to AI Max, watch closely for the search terms your campaigns are now matching with. How does it compare to past DSA performance?
You’ll also want to pay attention to the landing pages used (if final URL expansion is turned 0n), lead quality, and conversion paths.
Looking Ahead
Dynamic Search Ads have helped advertisers scale beyond their current keyword lists for years. Now, Google is folding that capability into its broader AI Max framework.
The clearest next step is to review where DSA is still active in your account and decide whether to migrate on your own timeline or wait for the automatic upgrade.
The real focus should be protecting performance during the transition and understanding where AI Max improves results, or where it needs tighter management control.
How To Measure PPC Performance When AI Controls The Auction via @sejournal, @brookeosmundson
For most of the history of paid search, performance measurement followed a clear cause-and-effect relationship.
Advertisers controlled the inputs inside their campaigns like bid strategies, keyword and campaign structure, ad copy, and landing pages. All these factors contributed to conversion performance in some shape or form.
When performance changed, the explanation was usually traceable. For example, a new keyword theme improved conversion rates. Or, a bidding strategy increased efficiency.
That simple cause-and-effect framework is breaking down in real time, and has been for a while.
Over the past several months, Google has accelerated its transition toward AI-driven campaign types like Performance Max, Demand Gen, or assets inside those like AI Max or AI-driven ad creative components.
Not only do these change how campaigns are set up and managed, but they also change how performance must be measured.
Advertisers increasingly receive conversions from queries they did not explicitly target, from creative assets that are automatically assembled, and from placements distributed across multiple channels. In this environment, measuring performance by analyzing individual campaign inputs becomes less useful.
The real challenge is understanding how automated systems generate outcomes.
This article provides a measurement framework for that reality. It explains what has changed in advertising platforms, how PPC teams can evaluate performance when automation controls more of the auction, and how practitioners can communicate results clearly to leadership.
The Current Measurement Crisis In PPC
Right now, most discussions about AI in PPC tend to focus on automation features like campaign types, targeting capabilities, ad creative development, and bid strategy expansion.
But, there’s a deeper shift happening in measurement but not talked about as much.
Automation introduces a larger set of variables influencing each auction. When the platforms make targeting, bidding, placement decisions (and more) dynamically, isolating the impact of individual campaign inputs becomes difficult.
Recent platform updates have not only changed how campaigns are managed, but also how performance should be interpreted. The connection between action and outcome is less direct, and in many cases, partially obscured.
Several platform developments illustrate why traditional measurement methods are becoming less reliable.
AI Max Expands Queries Beyond Keyword Lists
In my opinion, AI Max represents Google’s most aggressive step toward intent-driven matching.
Instead of relying solely on advertiser-defined keywords, AI systems evaluate contextual signals, user behavior patterns, and historical performance data to match ads with queries that may not exist in the account.
Not only that, but AI Max goes beyond search terms. It also has the ability to change your ad assets for more tailored messaging when Google deems appropriate.
For PPC managers, this introduces a structural shift in how to measure performance. Conversions may originate from queries that were never explicitly targeted.
And we knew that something like this was coming. Back in 2023, Google first publicly used the word “keywordless” in communications when talking about Search and Performance Max.
Source: Mike Ryan, X.com, March 2026
For example, a retailer who bids on “trail running shoes” may now appear for search terms like:
“best shoes for rocky terrain running”
“ultra marathon footwear”
“durable hiking running hybrids”
These queries reflect the same intent, but they don’t map cleanly back to the original keyword strategy.
Instead of trying to force these queries into keyword-level reporting, try analyzing performance by grouping into intent clusters. By evaluating conversion rate and revenue at the category level, teams can maintain strategic clarity even as query matching expands.
Google Ads already does a decent job of this in the Insights tab within the platform. They have a “Search terms insights” report that groups queries into “Search category,” where you can see conversions and search volume.
Screenshot by author, March 2026
Performance Max Distributes Spend Across Multiple Channels
Performance Max can further complicate measurement by distributing budget across Search, YouTube, Display, Discover, Gmail, and Maps.
Up until last year, there was little-to-no transparency in how spend was allocated across those channels. Back in April 2025, Google launched the long-awaited feature of channel reporting to the PMax campaign type. It now shows channel-level reporting, better search terms data, and expanded asset performance metrics.
For example, say you have a $40,000 monthly PMax campaign budget and see this channel breakdown:
Channel
Spend
Conversions
Search
$18,500
310
YouTube
$10,200
82
Display
$7,100
45
Discover
$4,200
28
If Search drives the majority of conversions, but YouTube consumes a large portion of spend, PPC marketers could try the following:
Test separating out branded search outside of PMax.
Refine asset groups to improve search alignment.
Run controlled experiments comparing PMax vs. Search.
Measurement becomes an exercise in interpreting how the system allocates spend rather than controlling each placement.
Ads Are Beginning To Appear Inside AI Conversations
Conversational search introduces an entirely new layer of complexity into PPC measurement.
Google is now testing shopping results embedded directly within AI Mode, allowing users to compare products without leaving the interface.
Google isn’t the only one doing this. ChatGPT announced on Jan. 16, 2026, that it would begin testing ads for its Free and Go users in the United States.
No matter which platform is running or testing ads in AI conversations, it’s clear that the measurement gap hasn’t been solved, and leaves many PPC managers with unanswered questions.
In my own recent search, I came across ads at the end of an AI Mode thread when I searched “noise cancelling headphones”:
So, if I were to click on one of those sponsored ads but convert at a later time, that attribution is unclear right now. Will my conversion be measured from the AI recommendation, the product listing click, or a later branded search?
These journeys challenge traditional attribution models, which were built around linear click paths rather than multi-step AI interactions.
Why Traditional PPC Metrics Are No Longer Enough
Many PPC reporting dashboards still rely on communicating metrics like impressions, clicks, conversion rate, and return on ad spend.
While some of those metrics remain useful, they no longer tell the full user story when bringing in automated and AI-driven environments.
These three shifts explain why.
1. Attribution Windows Are Expanding
AI-assisted search increases both the length and complexity of user journeys.
Research from Google and Boston Consulting Group show that “4S behaviors” (streaming, scrolling, searching, and shopping) have completely reshaped how users discover and engage with brands.
When AI introduces product recommendations earlier in a user’s journey, the time between initial interaction and conversion often grows. This could be because that user is still at the beginning of their research phase. Just because you’re introducing a product earlier, does not mean that they’ll be ready to purchase it any earlier.
So, what can marketers do about that gap now? Here are a few helpful tips to better understand how users are engaging with your business:
Review conversion lag reports in Google Ads.
Analyze time-to-conversion in GA4. Are there any differences or shifts in the last three, six, or nine months?
Extend attribution windows to 60-90 days where appropriate.
This ensures automated systems receive more accurate feedback on what (and when they) drive conversions.
Organic Search Is Losing Click Share
Search results now include everything from AI Overviews, scrollable shopping modules at the top, and expanded ad placements across all devices.
This reduces organic traffic even more and shifts more demand capture towards paid media.
From a measurement standpoint, PPC should be evaluated alongside organic performance when possible.
Tracking blended search revenue provides a more accurate view of total search performance, rather than isolating paid channels.
AI Systems Optimize For Outcomes Rather Than Inputs
Traditional PPC management focused on inputs like keywords, bids, and ad copy to influence performance directly.
AI systems work differently. Instead of optimizing individual levers, they evaluate large sets of signals in real-time to determine which combinations are most likely to drive conversions.
This changes what measurement needs to do. Instead of asking which specific keyword or bid strategy adjustment improved performance, marketers need to evaluate whether the platform is producing the right business outcomes.
As platforms take over more of the execution, measurement has to focus less on the mechanics and more on whether automation is driving profitable, meaningful results.
The New Measurement Stack For AI-Driven PPC
If AI is now controlling more of the auction, then PPC teams need a different way to evaluate performance.
The old measurement stack was built around visibility into campaign inputs. You could look at keyword performance, search terms, ad copy, device segmentation, and bid adjustments to understand what was working. That model starts to fall apart when automation is making many of those decisions on your behalf.
The replacement becomes a new measurement stack that advertisers should look at in these four layers:
Profitability.
Incrementality.
Blended acquisition efficiency.
First-party conversion quality.
Together, these give marketers a more accurate picture of whether automation is actually helping the business grow.
Start With Profit, Not Just ROAS
ROAS still has value, but it should no longer be treated as the primary success metric in highly automated campaigns.
The problem is that AI-driven systems are often very good at capturing demand that already exists. That can make campaign efficiency look strong on paper, even if the business is not gaining much incremental value.
A campaign with a 700% ROAS may still be underperforming if it is primarily driving low-margin products, repeat purchasers, or orders that would have happened anyway.
That is why profitability should sit at the top of the measurement stack.
Instead of asking, “Did this campaign generate enough revenue?” marketers should be asking, “Did this campaign generate profitable revenue?”
For ecommerce brands, this could mean incorporating:
Contribution margin.
Product margin by category.
Average order profitability.
New customer revenue vs. returning customer revenue.
A simple starting point is to compare campaign revenue against both ad spend and cost of goods sold.
For lead gen advertisers, the same principle applies, just different incorporations:
Qualified lead rate.
Sales acceptance rate.
Close rate by campaign.
Revenue per opportunity.
If AI is optimizing toward cheap conversions that never turn into revenue, the system is learning the wrong lesson.
Add Incrementality To Separate Demand Capture From Demand Creation
The second layer of the stack is incrementality. This is where many PPC measurement frameworks still fall short.
Automation can be highly effective at finding conversions, but that does not automatically mean it is generating new business. In many cases, AI systems are simply getting better at intercepting users who were already on their way to converting.
If your campaign is mostly capturing existing demand, performance may look strong inside the ad platform while actual business lift remains modest.
This is why incrementality testing has become much more important in the AI era.
For PPC teams, this means at least part of measurement should be designed to answer: “Would this conversion have happened without the ad?”
You don’t need an enterprise-level media mix modeling to get started. A few practical approaches include:
Geo holdout tests. Pause or reduce spend in a small set of markets while maintaining normal activity elsewhere.
Use Google incrementality testing. Google reduced the minimum of testing incrementality in its platform to just $5,000, making it more affordable for many advertisers.
Branded search suppression tests. In select markets or windows, test the impact of reducing branded spend where brand demand is already strong.
Answering this question does not mean automation is bad. It means PPC teams need a better way to distinguish between platform efficiency and true business lift.
Use Blended CAC To Measure Search More Realistically
The third layer of the new measurement stack is blended acquisition efficiency.
As AI Overviews, AI Mode, and other search changes continue to reduce traditional organic click opportunities, PPC should not be measured in a vacuum.
That is especially true for brands where paid and organic search are increasingly working together to capture the same demand.
A campaign may appear less efficient in-platform while still playing a critical role in maintaining total search visibility and revenue.
That is where blended customer acquisition cost (CAC) becomes useful.
Blended CAC looks at total acquisition spend across relevant channels and divides it by the total number of new customers acquired.
The formula for this is simple:
Total acquisition spend ÷ total new customers = blended CAC
This gives leadership a much more realistic picture of what it actually costs to grow the business.
It also helps PPC managers explain why paid search may need to carry more weight when organic search visibility declines due to AI-driven search features.
In other words, this metric helps move the conversation away from “Did Google Ads hit target ROAS?” and toward “What is it costing us to acquire a customer across modern search systems?”
Make First-Party Conversion Quality The Foundation
The final layer of the stack is first-party data quality. This is the part many advertisers still underestimate.
As platforms automate more of the targeting, bidding, and matching logic, the quality of the signals you send back becomes even more important. If the platform is deciding who to show ads to and which conversions to optimize toward, your job is to make sure it is learning from the right outcomes.
That means not all conversions should be treated equally.
If a lead form completion, low-value purchase, repeat customer order, and high-margin new customer sale are all fed back into the system the same way, automation will optimize toward volume, not value.
For PPC teams, that means the measurement stack should include a serious review of conversion quality inputs, including:
Offline conversion imports.
CRM-based revenue mapping.
New vs. returning customer segmentation.
Lead quality or opportunity-stage imports.
Customer lifetime value indicators where available.
This is where measurement and optimization start to overlap.
If the wrong conversions are being measured, the wrong outcomes will be optimized.
That is why first-party data is not just a reporting issue. It is the foundation of the entire AI-era measurement stack.
What To Show Your CMO Or Clients
One of the most difficult aspects of managing automated campaigns is explaining performance to leadership teams.
Executives often expect reporting frameworks built around the mechanics of traditional campaign management. In automated environments, those indicators tell only a small part of the story.
A more effective reporting structure focuses on three layers that connect advertising performance to business outcomes.
The first layer should always focus on the metrics that leadership teams care about most. Revenue growth, contribution margin, and customer acquisition cost provide a direct connection between marketing activity and company performance. These indicators allow executives to evaluate marketing investments in the same framework they use to evaluate other business decisions.
Instead of presenting keyword-level reports, PPC leaders should begin with a clear summary of how paid media contributed to revenue and profit during the reporting period. If revenue increased by 18% quarter over quarter while customer acquisition costs remained stable, that outcome provides a far more meaningful signal than any individual campaign metric.
The second layer of reporting should explain how paid media contributes to the broader acquisition ecosystem. As AI-driven search experiences reshape the visibility of organic results, paid media often carries a larger share of the responsibility for capturing demand.
Blended customer acquisition cost provides an effective way to communicate this relationship. By combining marketing spend across channels and dividing it by the total number of new customers acquired, organizations gain a clearer understanding of the overall efficiency of their acquisition strategy.
This approach also helps executives understand how paid search interacts with organic search, social advertising, and other marketing channels. Rather than evaluating PPC in isolation, leadership can see how the entire acquisition system performs.
The final layer of reporting should focus on experimentation and strategic insights. Automated systems constantly evolve, and the best way to evaluate them is through structured experimentation.
Reports should include summaries of campaign experiments, including:
The hypotheses tested.
The metrics evaluated.
The outcomes observed.
For example, if enabling AI-driven query expansion increased conversion volume while maintaining acceptable acquisition costs, that result provides valuable guidance for future campaign structure decisions.
Equally important is identifying metrics that are becoming less relevant.
Keyword-level performance reports, average ad position, and manual bid adjustments were once central components of PPC reporting. In automated campaign environments, those metrics often provide little strategic value. Continuing to emphasize them can distract leadership from the outcomes that truly matter.
Effective reporting in the AI era should emphasize growth, profitability, and strategic learning rather than operational mechanics.
Measurement Gaps That Still Exist
Despite improvements in automation and reporting transparency, several emerging advertising experiences remain difficult to measure.
One example is the growing presence of personalized offers within AI-driven shopping experiences. Google’s Direct Offers feature allows retailers to surface dynamic discounts during AI-generated shopping recommendations. While the feature may influence purchase decisions, advertisers currently have limited visibility into how frequently those offers appear or how strongly they influence conversion behavior.
Without that visibility, marketers cannot easily determine whether the discounts are generating incremental revenue or simply reducing margins on purchases that would have occurred anyway.
Another emerging measurement challenge involves conversational commerce. Google has begun exploring “agentic commerce” systems where AI assistants help users research and purchase products across multiple retailers.
In these environments, the user journey may involve several conversational prompts before a purchase occurs. The traditional concept of an ad impression or click may become less meaningful when AI systems guide the user through a multi-step research process.
As these experiences evolve, marketers will need new attribution models capable of evaluating influence across conversational journeys rather than isolated interactions.
These developments highlight the importance of ongoing experimentation and advocacy from advertisers. Measurement frameworks will need to evolve alongside the platforms themselves.
The Future Of PPC Measurement
Automation has changed the mechanics of paid advertising, but it has not eliminated the need for strategic oversight.
If anything, the role of human expertise has become more important.
AI systems are extremely effective at executing campaigns across large datasets and complex auctions. What they cannot do on their own is define the business outcomes that matter most or interpret performance within the broader context of organizational growth.
The most effective PPC teams are adapting to this reality. Instead of focusing exclusively on the mechanics of campaign management, they are investing more effort in defining profitability metrics, designing incrementality tests, and building reporting frameworks that connect advertising performance to business outcomes.
Measurement in the AI era will look different from the measurement frameworks that defined the early years of paid search. The focus will shift away from controlling individual campaign inputs and toward understanding how automated systems generate value for the business.
For PPC practitioners and marketing leaders alike, that shift represents the next stage in the evolution of paid media strategy.
