This month’s Ask A PPC explores a question many advertisers are starting to ask:
“Can AI Mode ads actually drive conversions, or is this just another awareness play?”
The answer to this question will vary based on how advertisers define success and what they’re comparing it against. Many accounts measure new opportunities against campaigns that have been refined for years through search query mining, bidding adjustments, landing page testing, and budget prioritization.
That type of comparison can create unrealistic expectations for any new traffic source.
In this post, we’ll look at where AI Mode may drive direct net-new conversions, where it may play more of an awareness role, and how advertisers should evaluate performance without using the wrong benchmark.
AI Mode Is Not Competing With Your Best Keywords
One of the biggest mistakes advertisers can make is comparing AI Mode traffic to their top-performing branded or bottom-funnel non-brand campaigns.
That is not the right comparison when looking at AI Max performance.
Your best campaigns are often built on years of optimization. Of course, those campaigns are efficient.
Those searches may not convert at the same rate on day one. That doesn’t mean they have no value in your campaigns.
It means you are entering net-new demand and broader intent pools.
If you feel your existing campaigns have maxed out (no pun intended) your bottom-of-funnel searches, why wouldn’t you want to expand to show up for searches that a user might be doing to find your brand?
AI Mode Can Drive Conversion, But Expect Different Economics
Can AI Mode generate conversions? Absolutely.
The real question is: at what cost, and with what expectations?
Most non-brand expansion efforts come with a higher cost per action than what advertisers are used to seeing from their core campaigns. That has historically been true. It was true with broad match expansion, Dynamic Search Ads, Performance Max, and now AI-driven placements.
If you are only willing to buy conversions at the exact same CPA as your most mature campaigns, you may shut off growth opportunities before they have a chance to develop.
That does not mean you should spend any extra or testing budgets blindly. It means understanding that incremental conversions often cost more than your historical average.
A better way to think about it is not “Does this beat my blended CPA?” but “What will my next dollar get me?”
That is the question growth-focused advertisers should be asking.
What Early AI Max Data Suggests
While AI Mode ad data is still limited, early AI Max performance data gives advertisers a useful directional signal.
In an analysis of 250+ campaigns, Mike Ryan of SMEC found AI Max delivered a 13% lift in conversion value overall, though CPA increased and return on ad spend results were less predictable across accounts.
That lines up with how many expansion products behave.
You may get more volume, and you may reach new search terms. You may increase total conversion value. But, efficiency can soften if you compare it to your most optimized traffic sources.
That does not make the channel bad. It means it needs the right job description and expectations for your overall business goals.
If someone searches broad informational questions, comparison queries, or early research topics, that click may not convert immediately. In those cases, AI Mode can function more like awareness or assisted discovery.
That shouldn’t scare advertisers. In my opinion, informational or research-based search terms are still further down the funnel than true awareness tactics like YouTube, OTT, Direct Mail, etc. Those are still creating demand where ads in searches are still capturing (or reacting) to the demand already there.
Many customer journeys are not one-click journeys. A user may discover you through an AI-assisted search, return later through branded search, then convert through email or direct traffic.
This is where marketers should also consider assisted conversions, branded search lift, remarketing incremental growth, and total account performance.
How I’d Approach Testing AI Mode
If I were evaluating AI Mode today, I would keep expectations realistic and testing structured. Start with a budget you can afford to learn with and don’t let your best campaigns carry the burden of comparison.
Segment performance where possible. Watch query quality, conversion lag, assisted paths, and total conversion volume. Most importantly, give it enough time to gather signal before making a final call.
Too many advertisers want expansion-level growth with core campaign efficiency on day one. That is rarely how growth works.
In Conclusion
AI Mode ads can drive conversions. I do not view them as awareness-only inventory.
But, I also would not expect them to perform like the most polished parts of an account that have been tuned for years.
For some advertisers, AI Mode may become a meaningful source of incremental growth. For others, it may be better suited for discovery and assisted conversions.
The opportunity is there, but advertiser expectations need to be realistic based on what AI Max is intended to do.
Cost-per-click (CPC) remains one of the most closely scrutinized metrics in digital advertising for both business owners and expert practitioners. This is understandable; it’s a tangible, easy-to-track metric that offers immediate gratification when it drops and immediate anxiety when it rises. After all, if your average CPC increases from $2 to $5, it’s natural to assume your campaign is performing worse.
However, it’s strategically wrong to evaluate your CPC in isolation. In modern Google Ads account structures, particularly those using Smart Bidding, I’ve noticed that a higher CPC is frequently a sign of account health, while a rock-bottom CPC can be a huge red flag.
We’ll explore why this paradox exists, delineate the scenarios where high CPCs signal success versus inefficiency, and use a real-life case study to illustrate the problem with focusing on CPCs – and what high-value metrics you should prioritize instead.
Why High CPCs Often Signal High Quality
If you transition from manual bidding to smart bidding strategies like maximize conversions or target ROAS, you will likely notice an immediate increase in your average CPC. It can be jarring, but this is a fundamental feature of how the algorithm operates.
Remember, cheap clicks are cheap for a reason: Your competitors didn’t want them! If you focus solely on driving down CPCs, you risk optimizing your account for the low-quality “leftover” traffic. However, when you use smart bidding, while you still pay per click, you are not optimizing for clicks; you are optimizing for the probability of a conversion, and potentially even the probable value of a conversion. This is how you align your business goals with your Google Ads campaigns’ goals, and the unintended (but necessary) side effect may be higher CPCs.
If this occurs, recognize that you are now bidding on conversion probabilities, not keywords. In the old world of manual CPC, you bid a flat rate for a keyword. In the new world, Google’s smart bidding algorithms analyze millions of data points in real-time – including device, location, time of day, operating system, browsing history, audience membership, and even the unique query itself – to assess user intent.
The algorithm is designed to bid aggressively for users who signal a high likelihood of converting. For example, if a user is searching for your specific solution, has a history of converting on similar offers, and is searching during business hours, the system will bid higher to win that auction. You are paying a premium to ensure your ad appears before the most valuable users.
Conversely, the algorithm bids down (or not at all) on users who are unlikely to convert. These might be users who frequently click ads but never buy, or users searching with low-intent informational queries. By avoiding these low-value clicks, your overall traffic volume may decrease, and/or your average cost per click may rise, because you have removed the “cheap” denominator from your equation.
The result should be expensive traffic, but traffic that actually turns into revenue.
In some industries like insurance, law, or emergency services, CPCs can reach an eye-watering $100 or $150 per click. This is simply the cost of doing business in a competitive market where a single client is worth thousands of dollars. If your Average Order Value is high, a high CPC is not a bug; it is a feature of a healthy, competitive auction, and the potential of those clicks for your business.
If High CPCs Often Indicate Quality, What Do Low CPCs Indicate?
If you are seeing CPCs under $1.00 for non-brand search campaigns, you should investigate immediately. Extremely low costs may mean you are purchasing inventory that your competitors have rejected.
Junk Inventory: Low CPCs often indicate you are inadvertently opted into the Google Display Network or Search Partners. These networks frequently drive lower-intent traffic compared to the primary Search Engine Results Page (SERP).
Broad Match or AI Max mis-matches: Cheap clicks can result from loose keyword matching, where your ads appear for irrelevant, low-competition queries. The root cause of this issue is usually a poor conversion tracking setup and/or the wrong bid strategy; you’ll want to fix the root cause of both issues
However, it is also possible that you’re lucky! I’ve seen non-brand CPCs in the $0.10 to $0.90 range, in 2026, for niches like alcohol and hair salons. Low competition and high-quality ads can mean you get to enjoy low CPCs with zero consequences. Sadly, this is usually the exception, not the rule.
Context Matters: The Non-Search Exception
It is critical to note that the logic of “High CPC = High Quality” changes significantly when you move away from Search. In non-search campaigns, you are interrupting users rather than capturing active intent, so the metrics behave differently.
Display & Demand Gen: On the GDN, “good” metrics are often misleading. A high CTR (usually over 1%) is usually a sign of accidental clicks or bot activity. While CPCs here are generally low, extremely low costs (pennies) typically signal placement on low-quality sites. This is why prioritizing the higher quality inventory on Demand Gen, like Discover and Gmail, is often worth it, even with slightly higher CPCs than Display.
Video (YouTube): High CPCs on Video are meaningless because the primary goal is views, not clicks. You should be optimizing for cost per view (CPV) or cost per reach (CPM), not CPC.
Performance Max: Since PMax blends all of these networks, CPC serves as even less of a diagnostic tool. A very low average CPC ($0.10-$0.50) can suggest the campaign is leaning heavily on Display/Video inventory. A higher CPC can indicate it is successfully winning auctions in Search and Shopping. Your Channel Performance Report will be a more useful optimization tool than looking at blended CPC.
The Counter-Argument: When High CPCs Are A Red Flag
While high CPCs can indicate quality, they are not a free pass to ignore your costs altogether. There are specific scenarios where a high CPC is still a warning sign of inefficiency. This is where your judgment as a skilled practitioner needs to come in:
1. Your Quality Score Is Low
If your Quality Score is low (specifically 5 or below), then you are overpaying for your clicks to compensate.
The Fix: Check your keyword report, add the Quality Score columns, and see which component is the most “Below Average”: Expected CTR, ad relevance, or landing page experience. Optimize accordingly.
2. You Are Over-Invested (Diminishing Returns)
It is possible to capture too much of the market. In my experience, if you are reaching 60%+ impression share on non-brand search in a competitive industry, your CPCs are likely inflated because you are paying a premium to capture the very last, most expensive sliver of available traffic.
The Fix: Switch from a maximize strategy to a target strategy, so that Google Ads isn’t forcing your budget to be spent in full. Or, expand your keyword set through additional keywords and/or broader keywords to open up new pockets of opportunity.
3. The Math Doesn’t Work (The Rule Of 2)
High CPCs are a problem if they break your business economics. Even if the traffic is high quality, if the cost of the click exceeds the revenue you can expect to make from that visit, the ads will never be profitable.
The Fix: For a quick and crude test, compare your average CPC to your revenue per session (Conversion Rate x Average Order Value). If your CPC is $2 but you only make $1 per visit on average, you are losing money on every click. Work on your conversion rate so that you are better equipped to handle this high-quality traffic
4. Irrelevant Matching
Sometimes, high CPCs occur because you are bidding on keywords that match to irrelevant but expensive queries. For example, a branding agency bidding on “branding agency” might match to “marketing agencies” – a highly competitive term that probably doesn’t align with their specialty.
The Fix: Keep an eye on your search terms report, and either restrict your match types or add negatives as needed.
5. Seasonality And Auction Dynamics
CPCs can spike due to external factors like Q4 seasonality or a new competitor entering the auction. While this isn’t a “mistake,” it is a warning that your efficiency is about to drop – or has already dropped – through factors beyond your control.
The Fix: Keep an eye on your impression share and auction insights, so that you can quickly spot anomalies and plan accordingly. For seasonal businesses, analyze year-over-year data as well as month-over-month, so that seasonal swings don’t take you by surprise.
Case Study: The $29 Click That Saved The Account
It’s one thing to know that, in theory, higher CPCs are better. It’s another thing to believe it, trust it, and let it happen to your campaigns. Allow me to share a real-life example with you from a local lead generation business.
The Challenge
My Google Ads coaching client, a digital marketing agency that specializes in home services businesses, hired me after becoming dissatisfied with their white-label PPC freelancer. The Google Ads campaign for one of their electrician clients was performing poorly, and he was threatening to fire the agency.
When we looked in the account, here’s what we saw:
Search campaign with 2100 keywords on manual CPC.
Average CPC: $1.77.
Conversion rate: 1.5%.
Conversions (leads): 6 per month.
Search impression share <10%>
The Change
I recommended a structural overhaul: a Search campaign with just 23 exact match keywords, with overhauled ad text to fix spelling errors (yes, really) and add clear value propositions like “No Call Out Fee.” And maximize conversions rather than manual CPC.
The Immediate Result
Four days after launching the new strategy, my client emailed me in a panic. The average CPC had skyrocketed from $1.77 to $29. He assumed that we had “broken” the campaign and asked, “Why am I paying $29 for a click?”
The Immediate Outcome
Despite the CPC sticker shock, the Search campaign was actually performing significantly better after just four days. Although the CPC had skyrocketed under maximize conversions bidding from $1.77 to $29 per click, the conversion rate had also skyrocketed from 1.5% to 27%. That meant that even though we were only four days into the new structure, the cost per lead had already decreased from $121 to $107.
High CPCs were the price of admission for quality leads in a competitive big city.
The Unexpected Plot Twist
The story didn’t end there. A few days later, the account’s “Auto-Apply Recommendations” surreptitiously added broad match keywords. Any Google Ads practitioner knows that this can tank your performance, but because the campaign was on a smart bidding strategy with sufficient conversion data – this actually improved performance even further. (I promise Google didn’t pay me to say that!)
In the two weeks that broad match keywords were turned on, the campaign generated 34 leads at an average CPA of $48.
Compare this to the month prior, when the electrician only got six leads from Google Ads at $121 cost per lead. Now, he was getting 34 leads in just two weeks, for a fraction of the cost – and anecdotally, he told my client that most were high quality.
The Victim Of Success
The problem eventually became too much success; the electrician was a small business owner and simply couldn’t handle the volume of leads from Google Ads. My client had to pause most of his ad groups, bringing lead volume back down.
But this case perfectly illustrates the high CPC paradox: A low CPC ($1.77) delivered junk volume. A high CPC ($29.00) proved the concept and delivered quality. A blended approach (broad match + smart bidding) eventually settled the metrics in the middle, but we never would have gotten there if we had optimized for cheap clicks from day one.
In Google Ads, Prioritize CPA And ROAS
As Google’s algorithms get smarter and more pervasive, our role as Google Ads practitioners continues to shift. We are no longer day-traders trying to buy individual clicks for pennies. We are investors looking for a return.
Stop optimizing for CPC. Instead, focus on cost per acquisition (CPA) or return on ad spend (ROAS). If you are acquiring customers within your target efficiency, the cost of the individual click is irrelevant. As our electrician found out, a $29 click that converts is infinitely more valuable than a $1.77 click that doesn’t.
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?
Digiday reports that an early version of ChatGPT’s ads manager, available to a subset of pilot advertisers, now shows cost-per-click bids ranging from $3 to $5, based on screenshots reviewed and verified by the publication.
Until now, advertisers in the pilot have paid on a CPM basis, meaning a flat rate per 1,000 impressions served. CPC pricing lets buyers pay only when a user clicks. Digiday reported the option is available to marketers already testing advertising in the pilot, not as a broad rollout. OpenAI didn’t respond to Digiday’s request for comment.
CPMs have fallen from $60 at launch to as low as $25 in some cases, per Digiday’s earlier reporting. Digiday also reported the minimum spend commitment has fallen from $250,000 at launch to $50,000, alongside the quiet release of a self-serve ads manager that gives a subset of pilot advertisers the ability to monitor impressions and clicks in real time.
What CPC Pricing Means For Buyers
CPM and CPC pricing serve different advertiser bases. Brand advertisers tend to plan around CPM. Performance marketers, who account for the majority of online ad spend, prefer to pay for clicks rather than impressions.
Adding CPC bidding opens the channel to a buyer category that has largely sat out the pilot. Nicole Greene, VP analyst at Gartner, told Digiday that the pricing change lets advertisers directly compare their results on OpenAI with those on other major platforms.
What ChatGPT clicks are worth depends on where they land relative to existing channels. According to ad agency Adthena (cited by Digiday), Meta CPCs run three to five times cheaper than Google Search, not because Meta’s inventory is worse, but because the intent behind those clicks is different. Social platform users tend to browse without a specific goal, while search users typically have one in mind.
The pricing drops ChatGPT into the same intent-and-value debate advertisers already face when comparing social clicks with search clicks.
Why This Matters
CPC bidding moves ChatGPT advertising into a territory where performance marketers can plan campaigns and compare costs directly against Google and Meta. Combined with the lower minimum spend, the channel is accessible to a wider buyer base than the enterprise tier that defined its launch.
SEJ’s Brooke Osmundson covered the implications for paid media teams in her analysis of whether ChatGPT Ads warrant real budget yet.
A CPM-only enterprise pilot has, in roughly 10 weeks, become a self-serve channel with a $50,000 minimum, lower CPMs, and now CPC pricing visible to a subset of advertisers. Each step down has opened the channel to a different category of buyer.
Looking Ahead
Paid media teams running search and social campaigns should compare ChatGPT’s clicks for intent quality and conversions. Measurement tools are limited and inconsistent, so teams must plan proxy measurement until OpenAI’s reporting improves.
OpenAI is hiring its first advertising marketing science leader, per Digiday. Until that role is filled, advertisers will be evaluating ChatGPT clicks largely on faith.
Google Ads has enabled call recording by default for eligible call flows associated with AI-qualified call leads, with exceptions for prior opt-outs and certain sensitive verticals.
A new Google support page describes the feature, which uses AI to evaluate phone conversations instead of relying on call duration alone to count conversions.
What Changed
Google Ads previously classified a phone call as a conversion primarily based on its duration. Google’s documentation says the new system analyzes call recordings to identify signals of intent, such as a caller asking about specific services, scheduling a consultation, or indicating readiness to purchase.
Google describes the classification as tiered.
Primary signal, call recording. If recording is on, AI evaluates the conversation and only qualified calls count as conversions.
Secondary signal, call duration. If a call can’t be recorded, duration determines whether it counts.
Tertiary signal, ad interaction. If no Google forwarding number is available, ad interaction data is used.
Call Details reports now include an AI-generated summary of each call and hashtags such as “#HighIntent” or “#ConsultationScheduled.”
Call Recording Defaults And Exceptions
Google’s settings page says call recording will remain off for advertisers who have already turned it off and for accounts Google has identified as operating in healthcare or financial services.
Advertisers in those categories can manually enable recording at any time, according to Google.
To turn recording off, advertisers can go to Admin > Account settings > Call ads > Call recording and select Off.
Where It Works
Call recording and AI-qualified conversions are currently limited to calls in which both the calling and receiving phone numbers are in the United States or Canada. Calls must route through a Google Forwarding Number, which requires call reporting to be enabled at the account level.
Only calls to call ads, call assets, and calls from website visits are eligible. Calls from location assets are not supported at this time.
Privacy And Compliance
Google’s settings page says callers will hear an automated message at the start of the call notifying them the conversation is being recorded for quality purposes. Advertisers agree to the Call Ads Supplemental Terms when using the feature and acknowledge they have given notice to employees or other parties who may participate in calls.
Google also says that recordings are used to evaluate lead quality, monitor spam and fraud, and improve the accuracy of conversion reporting.
Advertisers using call recording should review whether Google’s automated notification complies with their own legal obligations regarding recorded calls.
Why This Matters
Advertisers that don’t plan to use AI-qualified call leads are still producing recordings Google analyzes for lead quality, spam, and fraud, unless they turn recording off.
Smart Bidding now optimizes against AI-classified qualified calls when recording is on, and falls back to call duration when it isn’t.
Looking Ahead
Advertisers who prefer call duration as the primary signal can turn recording off in account settings. The duration threshold itself can be adjusted under Goals > Summary > Phone call leads > AI-qualified call leads.
You’ve got a whole library of winning ads from Meta to run on Google, but you don’t want to spend a ton of time setting up campaigns or becoming a Google guru. So, you take your existing creatives and pop them into Performance Max, spin up some ad copy, and let Google do its thing.
One campaign, one budget, and your entire product line targeting a broad audience – just like Meta taught you. When we audit ecommerce brands expanding to Google, this is the thinking we often see reflected in a highly consolidated account setup.
The logic makes sense if you think in Meta terms. Consolidate spend, let the algorithm find buyers, and scale what converts. It works on Meta because the platform is built on interest-based targeting. You define a pool, feed it plenty of creatives, and the system shows it to the right people.
Except … Google doesn’t work that way. Targeting is driven by active search intent, so a consolidated, broad structure doesn’t give the algorithm better signal – just noise. So, your account ends up burning through your $20,000/month budget without the architecture needed to distinguish between demand that was on its way to being captured and truly net new revenue.
If you live in the world of direct-to-consumer (DTC) and ecommerce brands and operate this way, you aren’t being careless. You’ve mastered one of the most competitive paid channels available and are simply applying that expertise to a platform that operates on entirely different principles.
Let me fix it.
Why Account Structure Is Vital To Success
Every search query in Google is a person telling you something – not a demographic or an interest category inferred from content they’ve engaged with. Explicit, real-time signal that someone is looking for what you offer right now.
That signal is the foundation of everything Google Ads is. Smart Bidding reads it, query matching acts on it, the auction gives it weight, and your campaign structure puts you in a position to capitalize on it.
This is why structure in Google Ads carries more consequence than it does on many other paid channels. Campaigns without clear segmentation and defined boundaries prevent the algorithm from learning efficiently. This spreads budget across queries that don’t reflect the same intent and makes you compete against yourself, leading to outcomes that don’t map to your actual business goals.
The other dimension is economics. Different products carry different margins, average order values, and conversion rates. A structure that treats all of them the same can’t divert spend toward products where it actually makes sense. You end up with an account that converts but doesn’t necessarily generate optimal returns.
And here’s a secret: Sometimes, I never run PMax at all. And if I do, I set it up in a way where it’s not going to just recycle Meta traffic but focus on as much net new as possible (even blocking brand, retargeting, and existing customers can’t get you to 100% net new). But if you have a very heavy Meta presence and PMax looks like it will over-index on recycling traffic, I’d move towards Shopping so we can move the needle.
3 Mistakes That Erode Efficiency For Google Ecommerce
1. Launching Every Campaign Type At Once
The instinct to go broad from day one is understandable. You have products to sell with multiple campaign types available to you and a budget ready to deploy. So you build out brand Search, Shopping, Performance Max, and YouTube, and wait for the data to come in.
The problem is that each of those campaigns needs impressions, clicks, and conversions to learn. When you split a less-than-astronomical budget across five campaign types, none of them gets enough volume to learn efficiently. Visibility is low across the board, and data is slow to compound, and Google’s machine learning systems are starved of the information they need to do better for your account.
Your account is running, but it isn’t moving. At the end of the quarter, you’ll still have no meaningful insights and won’t be able to optimize with confidence.
A smarter approach could be to start with just a couple of campaigns, like Search plus Shopping. This lets you get wider product visibility without being constrained by budget. Once those campaigns have data behind them and are generating returns, you layer in PMax, YouTube, and other formats one by one.
This way, each new move has a foundation to build on rather than competing for scraps.
2. Putting The Same Products In Multiple Campaigns
When your flagship product lives across multiple campaigns, they compete against each other in the same auction. That means a split budget, divided impressions, and not enough conversion momentum for any campaign to become meaningfully better.
Reporting is just as damaging. Sales come through, but you can’t tell which campaign was responsible. Attribution, which is already murky when two platforms are involved, gets harder. And optimization decisions get made with incomplete data.
Clean product segmentation across your account solves all three problems. Each product has a home, which makes performance readable. And when something isn’t working, you know exactly where to look.
3. Segmenting Performance Max Asset Groups By Audience Signal
Performance Max gives you audience signals as an input – customer lists, past purchasers, site visitors. The temptation is to use those signals as the basis for how you divide your asset groups. One group for past buyers, one for prospecting, one for lapsed customers.
The problem is that audience membership has nothing to do with the economics of what you’re selling. A past buyer and a new visitor can both be in the market for your highest-margin product. Structuring asset groups around who they are rather than what you’re selling means your budget isn’t organized around the products that actually matter most to your business.
A more effective approach is to build asset groups around shared product themes – bestsellers, new releases, bundles, seasonal offers. This way, the creative, the budget, and the optimization signal are all pointed at a coherent set of products with similar business value. Performance Max can still find the right audience. Your job is to give it the right product context to work with.
3 Proven Examples Of Google Ads Account Structure For Ecommerce
Example 1: Single-Product DTC Brand
A brand selling one hero product with a few variants (sizes, colors, or bundles) doesn’t need a complex account structure, just a disciplined one.
Start with two campaigns:
Branded search captures anyone searching for you by name (high intent), protects your brand equity, and tends to convert at a lower cost – so remember not to use automated bidding.
Either Performance Max or Shopping to drive product discovery.
If you choose PMax, divide asset groups by variant type rather than audience: one for the core product, one for bundles, one for any subscription or multi-unit offers. This keeps creative and budget in line with how the product is actually sold rather than who you think is buying it.
Adding both retail campaigns or YouTube before the first two layers capture enough conversion data only splinters your budget and stops the algorithm from learning anything meaningful to optimize against.
Example 2: Multi-Product DTC Brand With Bestsellers
Brands with larger catalogs make a common structural mistake: treating all SKUs equally. A single PMax campaign with one asset group covering 40 items gives Google no basis for prioritization and will spend where it finds the path of least resistance, which isn’t always where your margins are.
The better approach is to build asset groups around product tiers.
Bestsellers – products with the strongest sales velocity and healthiest margins – get their own asset group with dedicated creative and the largest share of budget.
New releases get a separate asset group because they need impression volume to gather data and shouldn’t compete directly with proven performers.
Include lower-margin, specialty, or slow-moving SKUs but cap their spend, or exclude from PMax entirely and handle them through a Shopping campaign where you have more direct control.
This structure makes performance readable by economic impact level. When a bestseller starts to slip, you see it immediately. And when a new release gains traction, you can promote it without disrupting the rest of the account.
Example 3: Seasonal DTC Brands
For brands with strong seasonal demand, like gifting or back to school, the structural challenge is running seasonal campaigns without damaging the learning of evergreen ones. The approach here is to treat seasonal pushes as additions to the account, not replacements.
Evergreen PMax stays live and funded at a baseline level throughout the year.
When a seasonal moment approaches, a separate PMax campaign is layered on with its own budget, asset groups built around the seasonal offer, and a defined run window.
Seasonal spend is then contained so that when it ends, the evergreen campaign’s learning history is unaffected.
When the seasonal campaign winds down, asset groups are paused rather than deleted. Conversion data accumulated during each period is preserved and available when the next seasonal cycle begins, which shortens the relearning period significantly compared to building a new campaign from scratch each time.
Make This Read Worthwhile: Product Segmentation Exercise
Meta finds customers by matching your offer to people’s interests. Google finds customers who are actively looking. What both platforms share is that the systems are increasingly in charge of the operational side: Smart Bidding, Advantage+, Performance Max. These tools make decisions about who sees your ads, when, and at what cost. The advertiser’s job has shifted from button pusher to signal architect.
On Google, that starts with how your campaigns and product/asset groups are organized.
Your Next Step To Value
Before you change any settings or adjust any budgets, try this product segmentation exercise.
Pull your catalog and group SKUs by shared characteristics: bestsellers, new releases, bundles, seasonal offers, margin tiers. The goal is to understand which products belong together and which need their own dedicated focus.
Once you have that, look at whether retargeting is siloed or folded into your broader activity. It should be a standalone campaign as blending it with prospecting dilutes performance data and makes it harder to read what’s actually driving new customer acquisition.
These two steps alone will give you a clearer foundation than many DTC brands have as they start layering in Google Ads as a channel.
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.
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Featured Image: Paulo Bobita/Search Engine Journal
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.
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.
