Stop trying to out-calculate the machine and start feeding the machine better signals was the theme from Ginny Marvin, Google’s Ads Product Liaison, during a recent episode of the Ads Decoded podcast she hosts. To many, it sounded like a victory lap for automation and seemed to set the industry on fire. To others, it felt like a final surrender of the steering wheel.
We are currently navigating a mass handover of campaign control to automated systems, and the speed of this transition is frequently outpacing our understanding of what we are surrendering. The numbers confirm that this isn’t just a trend; it is the new baseline for performance marketing. More than 1 million advertisers have now adopted Google’s Performance Max globally. On Meta, Advantage+ campaigns now account for 35% of all U.S. retail ad spend. Even TikTok has seen its Smart+ automated solutions jump from a mere 9% to 42% of performance campaigns in a single year.
The platform narrative is seductive. Google recently rolled out new steering and reporting updates for Performance Max, including audience exclusions and budget reporting, to address the long-standing “black box” criticism. According to Meta’s own engineering data, advertisers who adopted Advantage+ creative features saw an average 22% increase in return on ad spend, although results vary significantly based on first-party data quality and campaign maturity. But there is a dangerous gap between these platform claims and real-world performance that every SEO and paid media specialist needs to acknowledge.
A new report from Adtaxi hits the nail on the head: AI does not replace strategy; it magnifies it. If you provide the algorithm with strong data inputs and a clear definition of business value, then you get powerful outcomes. If you provide weak inputs, then you simply produce “accelerated inefficiency.” The machine will spend your budget with incredible speed, but it cannot navigate the strategic complexity that exists outside its training data.
In the era of GEO and entity-based search, the discipline required to feed ad platforms accurate, high-quality signals is the same discipline that builds brand authority in organic and AI-driven search results. When we talk about “the machine,” we are really talking about an interconnected ecosystem of data. If your ad campaigns are optimizing for surface-level metrics rather than true business outcomes, then you are essentially training the platforms to misunderstand your most valuable customers. If your SEO campaigns don’t include the prompt topics that your target audience is using, then read this.
We see this in the attribution gap on platforms like TikTok, where traditional last-click models fail to capture up to 79% of the conversions that automated systems are actually driving. Without a human expert to validate and measure these systems against real-world goals, we are just watching the algorithm spend money in a vacuum.
I contacted Jennifer Flanagan, vice president of Marketing at Adtaxi by email, and she countered that the lack of transparency in these systems creates a genuine risk where systems optimize for platform-defined metrics rather than business health. She correctly identified human experts as the “steadying hand” of strategy that machine learning cannot replicate.
The Lesson For 2026
It’s a clear lesson that you cannot “set and forget” your way to market leadership. The most successful marketers follow a strict rule of resource allocation: Invest the vast majority of your energy into human talent and strategy, and let the remaining fraction go toward the tools themselves. AI is running more of your advertising than you probably realize. The only question that matters now is whether you are running the AI, or if you are simply watching it spend your budget.
Google quietly updated one of its Google Ads help pages with a clarification that could raise concerns for some advertisers.
The updated documentation suggests that search terms shown in reporting for AI-powered Search experiences may not always reflect a user’s exact query. Instead, some reported search terms may represent Google’s interpretation of user intent.
The change applies to experiences tied to AI Mode, AI Overviews, Google Lens, and autocomplete.
Search Terms Reports have long been used to understand query intent, identify negative keywords, review compliance concerns, and spot optimization opportunities. While the report has never provided full visibility, advertisers generally assumed that when a search term appeared in reporting, it reflected the actual query entered by the user.
For some newer AI-powered Search experiences, that may no longer be the case.
What Google Changed
The updated language appears within Google’s help documentation around ad group prioritization. The page explains how Google determines which ad group enters an auction when multiple keywords or targeting methods are eligible to match the same search.
It was first discovered by Anthony Higman who posted about his findings on LinkedIn.
Within that documentation, Google now explains that search terms associated with AI-powered experiences may reflect the inferred meaning or intent behind a search instead of the literal query itself. The clarification specifically references AI Mode, AI Overviews, Lens, and autocomplete.
In practice, that means advertisers could see search terms in reporting that were never directly typed by the user. Instead, Google may surface a normalized or interpreted version of the interaction.
Historically, many advertisers viewed the Search Terms Report as a fairly direct reflection of user behavior. A user searched for something, a keyword matched, and the advertiser could review that query inside reporting.
For some AI-powered Search experiences, Google is now signaling that the reporting process may involve more interpretation before those search terms appear in the interface.
Why Google Likely Made This Change
This update likely reflects the practical challenges of reporting on newer AI-powered Search experiences, especially with the recent announcements of more ads coming to AI experiences.
Traditional Search reporting was built around direct keyword queries. AI-powered experiences like AI Mode, AI Overviews, Lens, and autocomplete do not always work that way.
Users may refine searches across multiple prompts, search visually instead of typing, or rely on autocomplete suggestions before finishing a query. In some cases, there may not be a single clean keyword query for Google to surface inside a traditional Search Terms Report.
From Google’s perspective, intent approximations may help standardize reporting across those interactions. A conversational AI search, a Lens query, and an autocomplete-assisted search may all require some level of interpretation before they can appear in reporting.
There’s probably also a privacy component to this.
As Search becomes more conversational, users naturally provide more context in their interactions. Google may not want to expose every raw AI prompt, image-based search, or conversational refinement directly inside advertiser reports.
Many advertisers will likely understand that reasoning. The problem is that some may also see this as another reduction in transparency at a time when Google Ads already relies heavily on automation, modeling, and inferred signals.
Should Advertisers Be Concerned About This Change?
Many advertisers will likely view this as part of a broader trend inside Google Ads.
Over the past several years, advertisers have already adjusted to reduced search term visibility, heavier automation, broader matching behavior, and more modeled reporting. This update adds another layer to that shift by signaling that some visible search terms may not represent the exact user query.
For advertisers who rely heavily on search term analysis, that creates obvious concerns.
Highly regulated industries often review search terms closely for compliance and brand safety. B2B advertisers use query reports to identify customer pain points and emerging use cases. Ecommerce advertisers use Search Terms Reports to build negative keyword lists, refine product segmentation, and better understand shopping behavior.
If reported terms become interpreted summaries instead of direct queries, advertisers may start questioning how confidently they can optimize against that data.
There are also still several unanswered questions around how these approximations actually work.
Google has not publicly explained how much interpretation occurs, whether advertisers can distinguish modeled terms from literal queries, how negative keywords interact with interpreted intent, how closely approximated terms reflect the original user phrasing, or whether reporting consistency could change as AI models evolve.
That lack of detail will likely make some advertisers uneasy.
A marketer could review a search term report and assume they are looking at direct customer language when the term may actually represent Google’s interpretation of the interaction. That distinction matters when advertisers are making optimization decisions, reviewing compliance concerns, or reporting insights internally.
Some Advertisers May Be Comfortable With This Change
On the other hand, there’s probably lots of advertisers who won’t see this as a big deal.
Some advertisers already optimize more around intent themes, conversion quality, and broader performance patterns than exact query language. For accounts heavily using broad match and Smart Bidding, interpreted search terms may not feel dramatically different from how optimization already works today.
There is also a practical challenge Google is trying to solve.
AI-powered Search interactions do not always produce simple keyword queries that fit neatly into traditional reporting. In some cases, a normalized intent summary may actually be easier for advertisers to review than fragmented conversational prompts or image-based searches.
That does not remove the transparency concerns, but it does help explain why Google may view interpreted reporting as a necessary adjustment for AI-powered Search experiences.
What Does This Mean For Future Optimization?
This update may push advertisers to rely less on literal query analysis over time, especially as more Search activity moves into AI-powered experiences.
For years, Search optimization has centered heavily around search term analysis. Advertisers mined queries for negatives, refined match types, identified customer language, and built campaign structures around tightly grouped intent.
If Search Terms Reports increasingly include interpreted intent instead of direct queries, some of those workflows may become less precise.
Optimization may shift further toward broader signals like landing page alignment, first-party data, conversion quality, audience behavior, CRM integrations, and overall content relevance.
That doesn’t make search term reports useless, though.
Advertisers may need to treat them more as directional insight rather than exact representations of customer language.
This could also change how marketers communicate reporting internally.
Many teams still use Search Terms Reports to demonstrate customer intent to executives, clients, or other stakeholders. If some reported terms now reflect modeled interpretations instead of literal searches, marketers may need to be more careful about how those insights are presented and explained.
A reported term may still reflect the general intent behind a search. It just may not represent the exact words the customer used.
Looking Ahead
This documentation update may end up being more important than it initially appears.
Search Terms Reports have long been one of the few places advertisers could directly connect user queries to campaign behavior. Google is now signaling that some of those reported terms may involve interpretation before they appear in reporting.
That will likely become more noticeable as AI-powered Search experiences continue expanding across Google Search.
For advertisers, the bigger issue may simply come down to clarity. If interpreted search terms become more common, many advertisers will likely want more visibility into how those terms are generated and how closely they reflect actual user behavior.
If you’ve been running Google Ads for more than a few years, your job description has changed without your consent. Match types that once signaled precision now target “related intent”; a 2023 rebuild made Broad Match competitive again; and Smart Bidding shifted the focus from keywords to outcomes like return on ad spend (ROAS) and cost-per-action (CPA). Now, with AI Max, keywords are becoming optional in Search campaigns altogether.
I joined Google in 2002 as one of its first few hundred employees and spent a decade as the first AdWords Evangelist. Back then, the keyword was the undisputed foundation of paid search. After 24 years in the industry, my conclusion is simple: Keywords are dead.
This isn’t a slogan. It is a technical reality. The core system is being replaced, even if the legacy interface remains. As users shift from search queries to conversational prompts, the “synthetic keyword” – a distillation of complex intent – is replacing the legacy keyword. We are moving toward an auction that runs on pure intent, with no keyword abstraction required. We aren’t there yet, but if you still define PPC as “picking the right keywords,” the ground is shifting under you.
Here is what we are losing, and gaining, as this transition plays out.
The Original Deal
For most of Google Ads’ history, keywords worked like a contract.
You agreed to put in the work to research relevant keywords for your business so that Google could show useful ads to searchers. You structured your account around them. You wrote ads that spoke to their intent. In return, Google agreed to only show your ads when matching queries, based on the match type you chose, lit up in the auction.
Exact meant exact.
Phrase meant phrase.
Broad was the wildcard for advertisers willing to trade precision for reach.
That arrangement gave us something valuable: diagnosability. When a campaign underperformed, you opened the search terms report and saw, line by line, exactly what you were paying for. Bad queries got negatived. Good queries got promoted. Match type was the main lever we had, and we used it carefully.
That’s the world I helped build. It worked for a long time because the tech underpinning the search experience was limited and couldn’t realistically do anything useful with more precise keywords that exceeded the max of 10 words.
What Changed, One Product Decision At A Time
The deal didn’t break in a single moment. It came apart over a decade of decisions that often raised advertisers’ blood pressure and brought us to this moment.
Close variants came first. Exact started including misspellings, then plurals, then function-word variations. By the mid-2010s, “exact match” was already a misnomer. The match type hadn’t changed, but the definition of a match had.
Smart Bidding shifted the center of gravity. Once bids were being set against conversion probability, the question of which keyword triggered the auction mattered less than the question of whether this user would convert. Match type became a throttle for how aggressively the system could explore new queries.
The 2023 Broad Match overhaul changed the narrative. Google invested real engineering into making Broad the semantically intelligent match type – and publicly reported ~25% more conversions in Target CPA campaigns. Advertisers who’d spent 15 years feeling Broad was a money pit were now being told Broad was the future.
AI Max is where the synthetic keyword shows up. Give Google your URL, your assets, and your business data, and the system finds the intent. From the advertiser’s side, keywords become optional. But the auction itself still runs on a keyword substrate. What’s changed is who picks the keywords that continue to underpin the ads auction and how visible those are to advertisers. Instead of you declaring a keyword list, Google now generates intent matches on the fly from the user’s prompt and your business signals.
And it isn’t just Google.
At Optmyzr, we recently started placing ads on ChatGPT. On OpenAI’s ad surface, keywords are optional from day one. You feed the system signals about your business, and it matches your ad to the shape of the user’s question rather than a phrase you pre-declared.
When the company that defined keyword advertising and the company reinventing search both land on keyword-optional intent matching, that’s a pretty clear signal that intent itself has outgrown the keyword as the unit of targeting. The signals now live in your pages, products, prompts, and context, not in a list you typed into an ad group.
What We’re Losing
I’m not going to pretend this transition is costless. Three things are being taken away.
Granular diagnosability is the first casualty. When a keyword-less campaign underperforms, the old debugging playbook of reading the search terms report, finding the bad queries, adding negatives, and tightening match type only half works. Negative keywords still exist and still matter. But the intent-matching engine is harder to reason about. “Why did my ad show here?” has a fuzzier answer in 2026 than it did in 2016.
The craft of account structure is second. For two decades, one of the hallmarks of a good PPC manager was the ability to architect a campaign. Tight ad groups. Themed structures. Clean branded-versus-non-branded separation. A lot of that structure was a proxy for control. Once the system handles more of the targeting, the strategic value of elaborate structure drops. Some of it was always over-engineering. But the practice of thinking carefully about how intent maps to campaigns was real craft, and it’s at risk of atrophying.
Training is the third. Junior PPC analysts used to build their intuition inside the search terms report. You’d watch queries for a week and start to understand how users phrase problems, how language drifts, how seasonal variations leak into the data. That was a masterclass in consumer psychology. A system that abstracts the keyword away also abstracts away one of the best teaching tools this discipline has ever had. And it removes our ability as marketers to detect shifts in consumer behavior that would normally help us evolve our strategy.
What We’re Gaining
But for all we’re losing in this necessary shift, we’re also gaining a few things.
Coverage of queries no keyword list ever catches. Zero-click queries, brand-new phrases, generational vernacular, localized slang. These are exactly the places where intent-based matching outperforms manual keyword selection. Not because humans are inattentive, but because the space is too big and too dynamic to enumerate.
A lower maintenance tax. Negative keyword lists that stretch into the thousands, endless query audits, SKAG construction, quarterly match type experiments. A lot of that work was overhead imposed by the gap between advertiser language and user language. Closing that gap algorithmically frees up hours for strategy, creative, and measurement.
Access to signals no advertiser can match manually. Google’s LLM-driven query understanding sees more of the user’s journey than any keyword list ever will. If you’re unwilling to let those signals into your targeting, you’re choosing to compete in an auction where your opponents have information you don’t.
The Data Already Shows The Shift
We just ran Optmyzr’s 2026 Match Type Study across nearly 130,000 non-branded campaigns in more than 14,000 accounts, totaling roughly $99 million in spend. It’s the clearest quantitative sign I’ve seen that practitioners are already adapting to this new reality, whether they’ve articulated it or not.
A few highlights that matter here:
Exact Match’s share of non-branded spend has collapsed from 37.1% in 2022 to 27.6% today. Most of that drop happened in the last 24 months. Advertisers haven’t stopped using Exact, but they are shifting towards less control and letting the AI handle more of the targeting.
On branded terms, the story flips. Exact Match delivers 6.61x ROAS at a $0.90 CPC, nearly double the ROAS of either alternative. Brand intent is known intent, and Exact still owns it.
Phrase Match is now the workhorse. It drives 40% of non-branded conversions and posts a 15.7% conversion rate, well above Exact’s 10.5% and Broad’s 8.5%. Phrase has become what Exact used to be: the default tool for scalable, intent-respectful discovery.
Broad Match keeps climbing. It now represents 38.8% of non-branded spend, the single largest bucket. Its ROAS still trails the other two, but its volume contribution makes it no longer optional for most advertisers.
The industry has already been migrating toward looser, more intent-driven matching on non-branded queries while preserving tight control where intent is certain. AI Max just turns the dial further in the same direction.
Menachem Ani, who runs the agency JXT Group and joined me on a recent episode of PPC Town Hall, described the same playbook from the trenches. Start new lead gen campaigns on manual CPC and Phrase Match. Collect good-quality traffic for a few weeks. Promote the winners to Exact. Only then layer Broad and Smart Bidding on top.
Exact, he said, is “too specific for a new campaign.” Broad is “overly aggressive right at the beginning.” Phrase is “the sweet middle spot” – flexible enough to find intent the advertiser didn’t think of, tight enough to keep the data usable.
It’s a big shift when the agency practitioners you’d expect to defend keyword-level control now independently arrive at “Phrase first, Exact later, Broad on top.”
5 Shifts Worth Making In The Next 6 Months
1. Separate Branded And Non-Branded Campaigns
This was always best practice. In a world where intent-based matching blurs campaign boundaries, a sloppy brand separation is the difference between 6.61x ROAS and 3.0x. Build a dedicated branded campaign. Lock it to Exact. Stop letting non-branded creep in.
2. Invest In The Signals Google Actually Reads – Including Offline Conversions
Your landing pages, feed quality, asset library, and business data aren’t just conversion-rate inputs anymore. They’re the targeting inputs AI Max uses to decide when you appear. If you spent 10 years refining keyword lists and one year refining URL and asset hygiene, flip the ratio.
For lead gen specifically, the highest-leverage move is piping qualified-lead data back to Google. Menachem’s rule of thumb was practical: Salesforce, HubSpot, and Zoho have native integrations. For anything else, Make.com or Zapier will send the events back for you. You need roughly 100 leads a month to generate the 30+ qualified conversions Smart Bidding and AI Max want to see before they’ll optimize toward them.
The difference between optimizing to “cost per lead” and “cost per qualified lead” is usually the difference between a campaign that looks good on a dashboard and one that actually grows the business.
3. Treat Negative Keywords As Your Last Line Of Control
In the AI Max era, negatives still work. They’re the most powerful remaining tool for saying “not that, not ever” to the machine. Maintain them aggressively. Automate the additions. This is where brand safety, budget discipline, and irrelevance prevention now live.
4. Test AI Max Where You’d Already Run PMax – And Test It With A Hold-Out
Menachem put this more cleanly than I could: “Use AI Max when you would use PMax.” The underpinnings are the same. The algorithm is pulling search and shopping signals together and deciding where your ad fits. The prerequisites are the same too: enough conversion volume, clean tracking, and a business the algorithm has been taught to recognize.
The accounts getting the most out of AI Max aren’t the ones that flipped the switch and walked away. They’re the ones that ran it against a proper hold-out, measured incrementality, and kept their keyword-based campaigns running on the traffic where those still had an efficiency edge – usually branded terms and proven high-intent exact queries.
5. Upgrade Your Own Skill Stack
The future PPC manager isn’t a keyword picker. They’re an intent engineer; someone who can translate business goals into the signals Google’s system will learn from, debug a semi-black box with query reports and experiments, and explain to a client what’s actually happening inside their account, even when the dashboard only shows aggregate results.
That’s a harder job than picking keywords. It’s also a more defensible one.
The Bigger Picture
I helped build a system designed for control; what’s being built now is a system designed for leverage. Conflating the two is why so many practitioners feel frustrated by tools that are actually performing. Control was about dictating terms to Google. Leverage is about feeding the engine the right signals and letting the auction execute at a scale no human team can match.
Our 2026 data shows the industry is already halfway through this transition. For PPC teams, the question isn’t whether to adapt, but how fast. The keyword as an advertiser artifact is dead. We are moving toward an auction powered by intent alone. Your job is no longer to defend the old interface, but to master the inputs the new one requires.
More Google Ads updates are arriving on the heels of Google Marketing Live 2026.
Google just announced several bidding and budgeting updates across Search, Shopping, and Performance Max campaigns, including a new beta called Journey-aware Bidding.
The feature aims to help Google Ads optimize towards the full lead-to-sale journey instead of relying mostly on front-end conversion actions like form fills.
Google also expanded Smart Bidding Exploration into additional campaign types and introduced new demand-led budget pacing updates for Search and Shopping campaigns.
The announcements mark one of the larger bidding-focused updates Google Ads has introduced since last year.
Journey-Aware Bidding Targets A Longstanding Lead Gen Problem
Journey-aware Bidding may draw the most attention from lead generation advertisers.
According to Google, Search campaigns using Target CPA bidding can learn from both biddable and non-biddable conversion goals.
That requires advertisers to track the full path from lead to sale.
Google says the feature helps its systems better understand downstream business outcomes instead of relying primarily on front-end conversion actions like form fills.
The feature is still in beta and appears to be designed for advertisers with longer sales cycles and more complex qualification processes.
That could include B2B advertisers, healthcare organizations, higher education institutions, and financial services brands.
Smart Bidding Exploration Expands Into More Campaign Types
Google also announced that Smart Bidding Exploration will expand into Performance Max and Shopping campaigns through upcoming betas.
Google first introduced the feature for Search campaigns last year.
It allows advertisers to set a ROAS tolerance. Then, that gives Google more flexibility to pursue additional queries that may fall outside tighter efficiency targets.
According to Google, Search campaigns using Smart Bidding Exploration saw a 27% increase in unique converting users on average.
Google plans to launch the beta for Performance Max campaigns with product feeds and Shopping campaigns in the coming weeks.
Google Introduces Demand-Led Budget Pacing
Google also announced new demand-led budget pacing updates for Search and Shopping campaigns.
The feature automatically shifts spend toward periods where Google predicts stronger consumer demand while reducing spend during slower periods.
Google says campaigns will still remain within monthly budget limits and daily spending caps.
The update builds on campaign total budgets, which launched earlier this year across Search, Shopping, and Performance Max campaigns.
What This Means For Advertisers
Journey-aware Bidding could be particularly useful for advertisers that already import offline conversions and CRM data back into Google Ads.
That may help advertisers with longer sales cycles better connect campaign performance to qualified pipeline and downstream revenue instead of lead volume alone.
The Smart Bidding Exploration expansion may also create opportunities for advertisers looking to scale beyond existing query coverage.
At the same time, some advertisers may approach those updates cautiously.
Advertisers operating under strict efficiency targets, compliance requirements, or tightly controlled query strategies may hesitate to give bidding systems broader exploration flexibility.
The budget pacing updates may also raise concerns for advertisers already frustrated with Google’s recent pacing changes tied to ad scheduling.
Google has stated that ads will still only run during scheduled days and hours. However, the company is increasingly pushing campaigns to spend toward full monthly budget targets within those available windows.
The demand-led pacing updates may also create new challenges for advertisers using scripts or third-party budget management platforms.
Many pacing systems rely on more predictable daily spend patterns to control budgets, dayparting, or campaign allocation.
If Google begins shifting spend more aggressively toward projected demand spikes, advertisers may need to recalibrate some of those pacing thresholds and automation rules.
That could be particularly important for agencies and enterprise advertisers managing large account structures with strict budget controls.
Looking Ahead
These updates also arrive just weeks before Google Marketing Live 2026, where Google will likely announce additional AI Max, bidding, and automation updates across Search and Performance Max campaigns.
Google has already expanded AI Max into more Search workflows over the past year, including broader query expansion, creative automation, and Dynamic Search Ads migration plans.
Given that direction, it would not be surprising to see Google continue adding more automation around bidding, targeting, budget pacing, and campaign management during this year’s event.
This month’s Ask the PPC question about AI ad placements is near and dear to my heart. We’ve seen ads begin showing up on AI surfaces since 2024, and yet, they still have an air of mystery about them:
“Ads are starting to show up in AI chat experiences. How should advertisers think about these new placements – and are they worth the budget?”
As I work for Microsoft, I can’t weigh in on competitor brand value for money. What I can do is speak about AI ads in general terms:
How to access AI ad inventory.
How to think about metrics for AI placements.
Building in budget (time and money) for AI placements.
How To Access AI Ad Inventory
There are effectively two ways to purchase AI ad inventory: directly through an AI-first platform, or as part of your broader paid media buys on major ad networks. Neither is inherently better or worse than the other, but they do require different strategies.
If you’re buying directly, then you know the media buy is 100% allocated to that AI surface, which makes it easier to design creative and measurement with that specific experience in mind. These buys are often available as cost-per-mille (CPM) or cost-per-click (CPC), depending on the platform and market.
Conversely, when you access AI surfaces through existing campaign types on broader ad platforms (for example, Performance Max/AI-assisted campaign types, Shopping, and Search), your creative may be adapted to fit the AI experience and the user’s intent in the moment.
This is why it’s critical to remember that AI is a fluid and dynamic placement. Rigid creative asks (including pinning), make it hard for AI creative to fully meet the needs of the human engaging with the AI.
If your brand has constraints (specific language that must be used, terms that can’t be included, etc.), most ad platforms are testing ways for humans to add constraints to how creative adapts to AI surfaces. That said, if your brand truly must lock in exact creative that must always serve, you may not be able to take advantage of AI surfaces to the same degree as less restrictive brands.
It’s worth calling out that AI-assisted campaign types (like AI Max and Performance Max) often have the best chance to show on AI surfaces due to their creative flexibility, broader matching, and dynamic audience mapping. That said, standard Search and Shopping formats can also be eligible depending on the experience, market, and query intent. Some platforms may also include rich creative formats (such as multimedia-style units) when they meet relevancy and policy requirements.
When AI suggests something, it’s important that the human doesn’t feel the recommendation is driven purely by sponsorship. That’s why many experiences clearly separate citations and other non-ad modules from paid placements, and why ad eligibility is typically held to a high relevancy bar. Additionally, structured commerce information (for example: accurate pricing, availability, shipping, returns, and customer service details) helps AI systems surface more reliable options and provides trust signals that reassure users they’re engaging with a legitimate vendor.
How To Think About AI Placement Metrics
Many make the mistake of thinking of AI as purely discovery or purely “bottom of funnel” performance. In practice, AI can compress consideration cycles dramatically; sometimes taking a user from discovery to conversion in under 30 minutes. In internal Copilot analyses, these placements have shown up to 25% greater relevancy versus comparable SERP placements for similar intents.
Image from author, May 2026
At the same time, AI placements bring an even stricter ad relevancy bar than conventional SERPs. This can lead to questions on volume as well as whether AI placements represent a meaningful stand-alone investment opportunity.
AI placements are merging the line between brand and performance media buys. This is why it’s critical to build in awareness for these metrics and why conventional return on ad spend (ROAS)/cost-per-action (CPA) goals might not be as useful for AI surfaces.
Some AI experiences let advertisers build audiences based on engagement signals from those placements. Others are structured more like awareness buys (for example, CPM-based inventory), where the primary goal may be exposure and consideration rather than an immediate on-platform transaction. If you judge those placements only on last-click conversions, they’ll often look weaker than they really are.
Leaning into data driven attribution (which has been the standard on Google for a while), allows you to get a fuller picture of how different engagements empowered the user to say yes to you.
Yet this remains a “performance marketer” mindset. To fully capitalize on AI placements, you also need to build in brand sentiment, citation share, and other awareness metrics.
This is why it’s critical to leverage AI visibility and on-site behavior tools to understand how often AI systems are turning to you for answers, and what users do after they land. Session replay and UX analytics tools (for example, Microsoft Clarity, Hotjar, FullStory, etc.) can help you spot friction, intent mismatch, and content gaps that matter across AI-driven and traditional traffic sources.
When reporting is limited or aggregated, focus on directional measurement: compare conversion quality (not just quantity), watch assisted conversion paths in data-driven attribution, and run structured tests (geo splits, time-based holdouts, or budget-in/budget-out experiments) to estimate incremental lift. Pair that with brand-aware signals like direct traffic, branded search demand, and “share of citation” in AI answers to avoid under-valuing upper- and mid-funnel impact.
Building Budget For AI Placements
Going back to the original question at the heart of this post: Are AI placements worth it?
If you believe a 194% better conversion rate (based on Microsoft internal data) is worth the creative flexibility AI placements often require, then it’s worth planning how to build budget and operational time around them. If you know you can’t say yes to that flexibility, the value prop won’t matter, because rigid compliance requirements will limit where and how creative can adapt. This is why most major ad platforms continue to offer options that honor strict creative and policy constraints.
As was shared earlier, major ad platforms can provide access to AI surfaces through existing campaign types. In many accounts, pricing has appeared directionally similar to comparable non-AI inventory once you normalize for intent and competition, though actual cost will vary by market, query class, and available supply.
AI-first ad platforms can price inventory differently, often reflecting more limited supply and stricter user-experience constraints around how many ads can appear. Practically, that means you may need enough daily budget to exit the learning phase and generate signal (clicks, engagement, conversions) before you can judge performance. Instead of anchoring on a universal CPC, build a test budget based on your category’s typical costs, your conversion rate, and the minimum volume you need to make a decision.
The other part of budgeting is the time to build/manage creative, targeting, and outcomes. AI creative includes options to let people know more information about your product/service prior to clicking through to your site/allowing the agent to complete the transaction through the AI.
Final Takeaways
Here are the most important things to remember about AI ads:
They won’t always serve, and if they do serve, it’s because the platform believes with a high degree of confidence that your ad will be a net benefit to the human engaging with the AI.
Privacy considerations mean split out metrics for AI surfaces are more complicated than conventional reporting.
Different AI surfaces apply different inventory valuation on the placements. The budget that works for one, might be over/under for another.
AI placements have been a part of the marketing mix for years; they just have more visibility now. Whether you access them through existing campaign types or lean into AI-first buys, the core question isn’t simply whether “they’re worth it.” In many cases, the data supports testing them, especially when you evaluate beyond last-click and account for incrementality.
The bigger question is whether your brand can say yes to the creative flexibility the AI era demands. Creative locked into rigid formats, or forced into a single, static landing experience, tends to perform less effectively on AI surfaces than creative that can adapt from intent, to message, to solution as the user’s needs evolve.
More Resources:
Featured Image: Paulo Bobita/Search Engine Journal
One of the more interesting moments in Google’s latest Ads Decoded podcast centered around a growing advertiser concern about AI-generated creative.
As more brands gain access to the same AI tools, will advertising eventually start feeling repetitive?
Ginny Marvin, Ads Liaison at Google, raised that question directly during the discussion, asking whether the industry was heading toward a “sea of sameness.”
The response from Charles Boyd, Groupe Product Manager for Creative at Google, offered a clearer look on how Google is positioning AI creative tools inside Google Ads and where the company believes advertiser differentiation still comes from.
Google Says AI Creative Should Expand Creative Variation
Throughout the episode, Google repeatedly framed AI creative tools as systems designed to expand variation, accelerate testing, and adapt messaging across different audiences and placements.
Google repeatedly positioned these tools as dependent on advertiser strategy and direction.
Boyd described the value of generative tools as “the ability to quickly create different creative styles and iterations at scale.”
A large part of the industry conversation around AI advertising has focused on concerns about generic outputs and loss of differentiation.
Google appears to be taking the opposite position.
The company seems to believe advertisers with a strong understanding of their audience, messaging, and brand voice will be able to scale those strengths more efficiently through AI-assisted creative workflows.
Instead, Google appears to be positioning AI as infrastructure that helps advertisers produce more combinations, more testing opportunities, and more audience-specific variations.
That distinction gives more context to how Google is approaching AI creative tools.
Google Wants Advertisers Steering AI Creative
Another phrase Google returned to multiple times during the episode was “advertiser-in-the-loop.”
The broader point was that automation should still include advertiser guidance and oversight.
Google highlighted several tools designed to give advertisers more control over how AI-generated assets are created:
Text guidelines
Brand guidance
AI briefs
Asset Studio
Video enhancement previews
Text disclaimers
Final URL expansion controls
Boyd explained that advertisers can now provide specific text instructions directly inside campaigns.
For example, a brand could tell Google not to describe products using certain language or positioning:
Google literally will check every asset that gets created against each one of the guidelines that you provide.
According to Google, advertisers can specify up to 40 text guidelines within a campaign.
That is a noticeable shift from earlier automation features, which often felt far more rigid from a brand and messaging perspective.
The addition of text guidelines, AI briefs, and expanded creative controls suggests Google is trying to give advertisers more influence over how AI-generated assets are created and adapted across campaigns.
Google Is Increasingly Focused On Creative Breadth
Another notable takeaway from the episode was how often Google discussed creative diversity and variation.
The conversation repeatedly touched on:
Multiple responsive search ads
Different landing pages
Different aspect ratios
Audience-specific messaging
Diverse asset combinations
Creative tailored to different stages of the customer journey
At one point, Boyd encouraged advertisers to consider having multiple responsive search ads with different landing pages inside the same ad group.
That guidance would have sounded unusual to many PPC practitioners several years ago.
Google’s reasoning is that systems like AI Max can dynamically combine the following o better align messaging with different user journeys:
Headlines
Descriptions
Landing pages
Audience intent signals
Search context
Asset combinations
This feels connected to a larger shift happening across Google Ads.
Campaign optimization increasingly revolves around combinations of signals instead of isolated assets or keywords.
Sarah Hathiramani, Director of Product Management for YouTube Ads, reinforced this idea when discussing Demand Gen and YouTube creative:
There may be different audiences that you’re going after, and those audiences are going to resonate with very different creative messages.
That point becomes more important as Google’s systems increasingly personalize creative combinations dynamically.
Veo Signals Where Google Thinks Creative Production Is Going
The episode also offered another look at how Google sees AI changing creative production itself.
Hathiramani discussed Veo integrations inside Google Ads and Asset Studio.
According to Google, advertisers can upload up to three images and generate multiple short-form video variations automatically.
Google positioned this as a way to reduce production barriers for advertisers that may not have dedicated video resources:
Instead of asking every advertiser to become an in-house video production company, we’re able to use Veo to leverage automation while maintaining transparency and control.
That could be particularly meaningful for smaller advertisers or brands that historically relied heavily on static image creative.
It also reflects a larger trend happening across Google Ads.
The company increasingly wants advertisers participating across more inventory types, placements, formats, and surfaces.
AI-generated creative helps reduce some of the operational burden required to do that.
At the same time, Google repeatedly stressed that advertisers still need strong inputs.
Marvin specifically noted that brands with a clear voice and point of view are likely to benefit most from these tools.
What This Means For Advertisers
One of the more noticeable themes throughout the episode was how often Google emphasized creative breadth.
Multiple landing pages, multiple responsive search ads, audience-specific messaging, different aspect ratios, and structured asset testing all came up repeatedly across Search, Performance Max, Demand Gen, and YouTube.
That guidance reflects how Google’s systems increasingly optimize around combinations of assets, intent signals, placements, and audiences rather than isolated ads or keywords.
For advertisers, that may require a shift away from building a small set of highly controlled assets toward developing broader creative coverage across different audience stages and formats.
Looking Ahead
This episode offered a clearer look at how Google is talking about AI creative internally ahead of Google Marketing Live.
The discussion repeatedly centered around advertiser controls, creative testing, audience-specific messaging, and broader asset variation across campaigns.
That may be one of the more important signals for advertisers paying attention to where Google Ads is heading next.
Google appears to be encouraging advertisers to build more adaptable creative systems rather than relying on a small set of static assets.
OpenAI has officially launched the next phase of advertising inside ChatGPT, introducing a beta self-serve Ads Manager alongside new CPC bidding and expanded measurement tools.
The update moves ChatGPT advertising further beyond its original pilot phase. Advertisers can now create and manage campaigns directly through OpenAI instead of relying only on managed partnerships and agency relationships.
While marketers already expected self-serve buying to arrive, this launch adds several pieces advertisers have been waiting for. That includes direct campaign management, click-based bidding, and conversion measurement capabilities.
OpenAI says U.S. advertisers can now register for access, upload ads, manage budgets, control pacing, and monitor campaign performance through the new platform.
What’s New With ChatGPT Ads
OpenAI originally launched ChatGPT ads with a smaller group of advertisers to test demand, delivery, and performance.
Since then, the company has expanded partnerships with major agency groups including Dentsu, Omnicom Group, Publicis Groupe, and WPP.
The company also added technology partners including Adobe, Criteo, Kargo, Pacvue, and StackAdapt.
Now, OpenAI is opening direct access through its own Ads Manager platform.
The rollout is currently limited and still in beta. OpenAI says it plans to gradually expand access as testing continues.
For advertisers, the move makes ChatGPT feel much closer to a traditional media buying platform than an experimental ad environment.
During the early pilot phase, advertisers primarily purchased ChatGPT ads on a CPM basis. OpenAI says CPC bidding gives advertisers more flexibility to align spend with engagement and downstream actions.
Many ChatGPT sessions involve active research and decision-making behavior. Users are often comparing products, evaluating services, or asking for recommendations before taking action elsewhere.
That creates a very different environment from passive scrolling on social platforms.
For performance marketers, CPC buying also creates a more familiar testing framework. Advertisers can evaluate traffic quality and engagement without relying entirely on impression-based buying models.
In a LinkedIn post, David Dugan, Head of Global Solutions at OpenAI stated:
What’s stood out most in my first month is how thoughtfully this is being built. We’re creating a new ads model – one that supports businesses and broader access to AI while staying grounded in clear principles around answer independence, privacy, and user control.
OpenAI says both CPM and CPC bidding will remain available moving forward.
More Conversion Measurement Coming
OpenAI also announced expanded measurement capabilities through Conversions API support and pixel-based tracking.
Advertisers can now measure actions like purchases, sign-ups, or lead submissions after someone interacts with an ad.
At the same time, OpenAI continues to emphasize privacy protections around ChatGPT advertising.
The company says advertisers will receive aggregated reporting and campaign insights without access to private conversations or personal user data.
That distinction will likely remain important as advertising inside AI platforms continues to expand.
OpenAI also says stronger conversion signals will help improve ad relevance and optimization over time.
What Advertisers Should Watch Next
This launch gives advertisers more legitimate ways to test ChatGPT as a performance channel.
Self-serve buying lowers the barrier to entry for smaller businesses and in-house teams. CPC bidding also gives marketers more control over how budgets are evaluated during early testing.
Still, advertisers should keep expectations realistic in the near term.
This platform is still early. Benchmarks are limited. Measurement standards are still developing, and user behavior inside AI platforms continues to evolve quickly.
The more interesting shift may be how quickly ChatGPT is adopting the same infrastructure advertisers expect from larger ad platforms.
Self-serve buying, conversion tracking, bidding flexibility, and partner integrations are now becoming standard parts of the platform.
Now that the ads platform is out, will you be testing ChatGPT ads in 2026?
Even the best-performing bid strategies eventually plateau. To scale, ad managers must periodically test new strategies to ensure the algorithm aligns with shifting business objectives.
However, testing isn’t as simple as clicking “apply.” In this post, you will learn a framework for identifying when to test, why standard experiments often fail, and the step-by-step process for implementing a bid strategy test that protects the ad account performance.
Phase 1: Identifying The Need For A Change
Before testing a new bid strategy, the ads account needs a data-driven signal that a change is necessary. Do not test for the sake of testing. Look for these four indicators:
Performance Plateaus: If the account has been optimized with tight ad creative, deliberate keyword match types, and aligned landing pages, yet the cost-per-acquisition (CPA) or ROAS has completely stalled, and the account has not been able to scale. When manual optimizations stop producing meaningful gains, it’s a sign the account’s underlying bidding model needs to shift to a new bid strategy.
Disconnected Goals: There is often a disconnect between what the business cares about (lead quality and closed revenue) and what the platform is currently chasing (lead volume). If the pipeline is full of junk leads, the bid strategy is optimizing for the wrong signal.
Reaching Critical Mass: Smart Bidding thrives on data liquidity. Once a campaign crosses the conversion volume threshold, which is typically 30 to 50 conversions within a 30-day window, the campaign has enough historical data to successfully support advanced bid strategies like target CPA (tCPA) or target ROAS (tROAS).
Scaling Operations: When the ad budgets increase significantly, moving from Maximize Conversions to a specific tCPA helps control costs and maintain efficiency during the scale-up phase.
Phase 2: Choosing Your Testing Method
There are two primary ways to run a bid strategy test. The best method depends on the business model and data environment for the ads account.
1. The Native Google Ads Experiment
The Pros: Using the native Experiment tool in Google Ads is the most scientific approach to testing. By running the control and the experiment simultaneously, the advertiser effectively controls for external variables like seasonality, sudden competitor shifts, or macroeconomic changes that could skew the results of a sequential (before-and-after) test.
The Cons: Despite the benefits, the standard experiment framework in Google Ads has significant structural flaws for certain advertisers:
Data Dilution: Split-testing inherently shrinks the data pool for each arm of the test. By cutting the budget and conversion volume in half, experiments can starve the Smart Bidding algorithm of the data it needs to exit the learning phase efficiently.
Incompatibility: Certain advanced configurations, such as Portfolio bidding strategies or shared budgets, do not play well with the experiment interface, limiting strategic options.
The Rigid Tech Problem: The ads interface forces the evaluation of success based on default columns rather than custom or “by time” metrics. When the platform fails to surface the specific backend metrics needed, the data won’t align with business reality.
2. The Sequential/Manual Framework
The limitations of native experiments become problematic for complex B2B or high-ticket B2C accounts. This is known as the long lead-time trap. In industries where a sale occurs 30, 60, or 90 days after the initial click, the Google Ads interface is fundamentally biased toward immediate, top-of-funnel “wins.”
To use this method successfully, the distinction between Conversion Value and Conversion Value (by Time) must be understood:
Conversion Value (by Time): Attributes value to the day the conversion was recorded.
Standard Conversion Value: Attributes financial value to the day the click occurred.
For long-cycle businesses, that distinction is the difference between a profitable campaign and a failure. Because native experiments favor immediate conversions, a bid strategy optimizing for high-quality, long-term revenue often looks like it’s failing in real-time.
Example: Consider a SaaS client with a 60-day sales cycle. The bid strategy is switched from Maximize Conversions to tCPA to improve lead quality. Initially, CPA increases and volume drops; the Google Ads UI flags the experiment as a failure. However, 60 days later, backend CRM data reveals that the leads generated during that period closed at a 40% higher rate, generating significantly more pipeline revenue.
In this scenario, a manual testing framework is superior because it allows for the accounting of delayed “by time” metrics that the interface cannot optimize for out of the box.
Phase 3: The 4-Step Bid Strategy Testing Framework
Moving beyond the native experiment tool in Google Ads, follow these steps to ensure an accurate test:
Step 1: Define Your North Star Metric
Before changing a single setting, look outside the Google Ads UI. Determine what success actually looks like for the business. This requires integrating CRM data or back-end sales figures. The North Star metric might be marketing qualified leads (MQLs), sales qualified leads (SQLs), or actual closed-won revenue, rather than just standard in-platform conversions shown in Google Ads.
Step 2: The Pre-Test Audit
Validate that your conversion tracking is actually capturing the true value of the user action. If you are feeding the algorithm the wrong data, you will not see success from your test. A best practice would be to implement offline conversion tracking (OCT) or value-based bidding parameters to ensure the ad platform and underlying AI understand the difference between a $10 lead and a $1,000 lead.
Step 3: The “Wait And See” Period
When an ad account switches to a new bid strategy, the account enters an algorithmic learning phase that typically lasts 7 to 14 days. During this learning period, performance will fluctuate as the system tests, recalibrates, and stabilizes.
Even more important is the account’s natural conversion lag. The bidding algorithm may adapt quickly, but the business’s actual revenue signals often take longer to surface. That delay in data creates a volatility window where early performance data can look worse or better than it truly is.
This is why it’s best to avoid making reactionary changes during this testing period. Allow the bidding algorithm to gather enough signal data and allow the lag to play out before evaluating ad performance or making adjustments to the campaign.
Step 4: Manual Analysis
Google’s default columns attribute value to the day the click happened. To see if the test worked, the Report Editor should be used to pull “Conversion Value (By Time).” This attributes the revenue back to the day the conversion actually occurred. This is the primary way to see if the new strategy is driving more profitable cohorts of traffic.
To ensure paid search campaigns remain competitive, every bid strategy test should be verified with backend data before making permanent bid strategy changes. The algorithm should not dictate success based on the incomplete metrics highlighted in the UI. If it is time for an ad account to scale, this step-by-step framework ensures the advertiser isn’t just spending efficiently, but growing profitably.
Google Ads has gone through a steady wave of changes over the past year.
Advancements in outputs like Demand Gen and AI Max, and shifts in how users interact with search have all changed how performance shows up in the data.
A new Q1 2026 benchmark report from Optmyzr, based on more than 21,000 accounts, offers a clearer view of how those changes are playing out across real accounts.
At a high level, the metrics look stable. Engagement is up, costs are holding relatively steady, and return on ad spend hasn’t moved much.
That might sound like business as usual, but the underlying trends tell a more important story about where growth is coming from and what advertisers should expect as they scale.
The clearest place to start is with engagement, which has been rising consistently across the dataset.
Engagement Is Rising, But It’s Coming From Broader Reach
Across five quarters, engagement improved consistently, led by rising click-thru rates (CTR).
Click-thru rate increased from 1.83% to 2.22%, a 21.31% gain year-over-year.
But, that increased CTR didn’t necessarily correlate with improved conversion rates or CPA.
In fact, conversion rate declined slightly by 0.96%, while CPA increased 4.41%. Impressions also dropped by roughly 11% year-over-year.
Optmyzr summarizes that shift by clarifying: “More clicks, from a smaller impression pool are converting at a marginally lower rate.”
Fred Vallaeys, Co-Founder and CEO of Optmyzr, shared his thoughts about the correlation of impressions and how to scale:
As AI-driven changes reshape the SERP, fewer impressions may be available, but each one carries more weight. That changes how advertisers should think about scaling performance.
Andrew Lolk, Founder of Savvy Revenue, had this to say about Google Ads efficiency:
All of Google Ads is a road to efficiency erosion. Any efficiency gain in any account running Smart Bidding (which is all) leads to higher volume. Nobody gains efficiency, and increases their ROAS target. We just chase higher volume.
Taken together, the data points to stronger engagement driven by a broader mix of queries and user intent, rather than purely more efficient conversions.
In practice, that means advertisers are reaching users in more places and at more stages of the decision process, not just capturing the same high-intent clicks more efficiently.
Mid-Market Accounts Continue To Outperform On ROAS Efficiency
Budget size sems to play a role in how performance scales.
Mid-market advertisers, defined as those spending between $10K and $50K per month, delivered the strongest returns in the dataset.
According to the Optmyzr report, this mid-market group reached a 566% ROAS, roughly 50% higher than both SMB and enterprise segments.
On the opposite spectrum, enterprise accounts reflect a different dynamic.
They recorded the highest CPA in the dataset at $16.00 and were the only segment where acquisition costs increased across all five quarters, with ROAS declining year-over-year.
This doesn’t necessarily suggest that larger budgets perform worse.
It shows how performance changes as accounts expand into broader coverage across queries and audiences.
As spend increases, growth comes less from the most efficient conversions and more from capturing additional demand beyond that core set.
Demand Gen Growth Reflects A Shift In How Conversions Are Captured
At the campaign level, the most significant changes come from format.
Demand Gen campaign volume increased 53.2% year over year, making it the fastest-growing format in the report, while Video campaign volume declined by 31.6%.
However, that decline isn’t necessarily tied to performance, but rather a migration from Video Action campaigns to Demand Gen.
Joe Martinez, Co-Founder of Paid Media Pros, provided his take on video performance:
Video is still performing well for us, but the campaign type we hold valuable has changed. Our Conversion-focused campaigns in almost all accounts have shifted to Demand Gen because that’s where our performance is. For any awareness play focused on views, we still test YouTube for the very low CPVs with skippable ads. But even still, we see better long-term attribution for future conversions with Demand Gen.
The underlying user behavior did not change, but how that behavior is tracked across campaign types did.
This is where interpretation becomes important.
What appears to be declining performance in one format often reflects a redistribution of conversions across multiple campaign types, especially as advertisers reach users across YouTube, Discover, and Search at different points in their journey.
Performance Max and Search Show A Familiar Tradeoff
Performance Max continues to expand, with campaign volume up 15.7% year-over-year.
CTR from this campaign type improved from 1.29% to 1.68%, while CPA increased and ROAS declined slightly.
This reflects a familiar tradeoff as campaigns scale.
Performance Max is designed to extend reach across multiple surfaces, which naturally introduces a broader mix of queries, placements, and user intent. As more advertisers adopt the format, competition increases within that expanded inventory.
Search, by comparison, remains the most stable campaign type in the dataset, with CTR reaching 12.15%, the highest engagement rate across all formats, and performance holding relatively steady despite a slight decline in volume.
The relationship between these two campaign types is becoming more interconnected.
Performance Max often captures earlier or less-defined intent, while Search continues to convert users who return with clearer intent later in the process.
As a result, growth is less about improving performance within a single campaign type and more about how these formats work together to engage users across multiple touchpoints.
E-Commerce and Lead Gen Show Different Paths To Growth
The report also highlights how performance varies by business model.
Lead generation accounts saw modest efficiency gains, with ROAS increasing from 248% to 267% even as CPA rose slightly, alongside nearly 20% growth in CTR.
E-commerce accounts, on the other hand, show a different pattern.
CTR increased 23.87% while CPC remained flat, creating more traffic at the same cost. At the same time, conversion rate declined by nearly 5%, and ROAS dipped slightly.
Kirk Williams, Founder of ZATO Marketing, provides his analysis around the e-commerce performance:
Image: Taken from Optmyzr Q1 Report, page 23
Expanding into broader queries and placements brings in more traffic, but not all of it is ready to convert on the first interaction.
That doesn’t automatically mean the traffic is less valuable.
It means more of the buying process is happening across multiple touchpoints, where users return through different campaigns before converting.
What This Data Means For Advertisers
The data in Optmyzr’s report doesn’t point to a decline in effectiveness, but it does show a shift in where conversions are coming from.
In many accounts, the same user is now being reached multiple times across different campaigns. They might first see a product through Demand Gen, come back through a Shopping ad, and then convert later through Search.
When that happens, conversions don’t always increase at the same rate as clicks. They just get spread across more interactions.
That can make performance look flatter than expected, especially if you’re used to measuring success from a single campaign or touchpoint.
Underneath the data, it doesn’t mean that your account performance is getting worse.
It’s more of a reflection of how people are actually making decisions now. They take more time, do more research, and often need multiple interactions before they convert.
From an advertiser perspective, that’s where the tradeoff comes in.
Showing up in more places usually means paying for some clicks that won’t convert right away. But those interactions still play a role in getting the user to come back and convert later.
If you’re not showing up there, you’re leaving those earlier interactions to other advertisers.
Where Performance Is Heading Next
The Optmyzr data reflects a platform that is changing how growth shows up in accounts.
Engagement continues to increase, costs remain relatively stable, and returns haven’t shifted dramatically. What is changing is how advertisers capture those results.
More of that growth is coming from multiple campaign types working together, rather than a single campaign driving the conversion on its own.
For advertisers, that means performance should be evaluated less in isolation and more across how campaigns support each other.
If conversion rates look flat or CPAs start to rise slightly, it’s worth looking at how different campaigns are contributing to the same conversion path, not just which one gets credit for the final click.
Performance Max was created to be the set-it-and-forget-it automation play Google dreamed up. But, five years in, the only way PMax works is when you actively guide it, and it literally drains budget when you treat it like a self-managing campaign.
The hybrid strategy, running Performance Max alongside Standard Shopping rather than replacing it, is proving to be the path forward and producing the most consistent results for DTC and ecommerce brands right now.
If your current setup is a single PMax campaign covering everything with a return on ad spend target you set 90 days ago, this is worth reading carefully.
Where PMax Actually Stands Right Now
A 2024 study by Optmyzr across 24,702 Performance Max campaigns found that 82% of advertisers were running PMax alongside other campaign types. And PMax consistently underperformed those other campaigns when they competed for the same traffic.
That tells you a lot about how the campaign type actually behaves in a real account versus how it is positioned.
PMax offers unmatched reach across all of Google’s inventory of Search, Shopping, YouTube, Display, Gmail, Discover, and Maps, from a single campaign. But, that reach comes with real tradeoffs in visibility and control that have frustrated ecommerce advertisers since it launched.
Google has made meaningful progress on the control side by providing campaign-level negative keywords (rolled out late 2024/early 2025), channel performance reporting now shows which properties drive conversions, and search theme inputs doubled from 25 to 50 per asset group.
The case that PMax is a black box is harder to make in 2026 than it was in 2022. But, it still requires real strategy to perform and active guidance.
Why The Hybrid Approach Works
The core insight behind the hybrid strategy is straightforward, where Standard Shopping gives you control and data visibility while Performance Max gives you reach and automated discovery.
Standard Shopping handles your core, known-intent traffic, whereas PMax handles full-funnel discovery across Search, YouTube, Display, Gmail, Discover, and Maps.
This hybrid approach gives you the most optimal approach.
The account structure we use that produces the best results for ecommerce clients has been:
Standard Shopping campaigns covering your top-revenue SKUs and product categories with tROAS targets and manual bid management levers.
A Performance Max campaign focused on new customer acquisition, with audience signals built around lookalike and in-market segments.
Brand exclusions applied in PMax to prevent it from taking away branded search traffic that your branded Search campaign should handle.
Campaign-level negative keywords filtering out low-intent queries like “free,” “cheap,” and competitor brand names, where cannibalization is not worth the impression cost.
This structure keeps conversion volume high in each campaign, which matters more than most advertisers realize. Spreading budget and conversions too thin across too many campaigns prevents the algorithm from learning effectively. The goal is enough segmentation to be strategic, not so much segmentation that the machine learning starves.”
The Feed Is Still The Biggest Lever
Most advertisers optimizing Performance Max are focused on campaign settings, but the bigger opportunity is usually in the product feed.
PMax pulls heavily from Merchant Center to serve Shopping placements, and feed quality directly shapes what the algorithm has to work with. Weak product titles, generic descriptions, and missing attributes produce weak output regardless of how the campaign is structured.
Strong product titles reflect the actual search terms buyers use, not internal naming conventions. Product descriptions should be what the product actually does, not a marketing tagline or a sentence pulled from the packaging. Keep it simple, no marketing jargon.
Margin management matters here, too.
Google’s algorithm naturally gravitates toward driving conversion volume and has no inherent preference for your profitable products over ones that drive volume. That means actively excluding low-margin SKUs from PMax or using product-level asset group segmentation to control where budget gets allocated.
For DTC brands with large catalogs, this is ongoing management, not a one-time setup.
Asset Groups: Where Most Campaigns Leave Performance On The Table
Thin asset groups are one of the most common underperformance patterns we see in PMax campaigns.
The algorithm assembles ads by combining headlines, descriptions, images, and video. When those inputs are limited or generic, the output reflects it.
A few things that consistently move results:
Separate asset groups by product category or audience segment. One asset group per campaign is usually not enough segmentation.
Include at least one video asset. Google’s algorithm favors campaigns with video, and Google’s Asset Studio now generates video inside Google Ads using Imagen 4 and Veo 3, which removes the production barrier for most brands.
Lifestyle imagery that shows the product in real use consistently outperforms plain product photography in upper-funnel placements like YouTube and Discover.
Headlines should cover both functional benefits and emotional payoffs, not just product specifications.
Channel context matters inside PMax, and a single creative won’t work for all placements. What works on YouTube pre-roll isn’t what works in a Gmail ad or a Discover placement; use some common sense. Google’s PMax algorithm will handle distribution, but the quality of what you feed it determines the ceiling.
Audience Signals Are Guidance, Not Targeting
Audience signals in PMax are one of the most misunderstood parts of the campaign type. Most advertisers set up audience signals in PMax and move on without really understanding what they do.
Signals are guidance.
You are telling Google what a great customer looks like, so it can go find more of them. The algorithm isn’t limited to that audience; it is using it as a starting point.
So, the goal when building signals isn’t to constrain reach, rather it’s to give Google the best possible examples of your highest-value customers.
For ecommerce, that means prioritizing your customer match list of past purchasers first, then layering in website visitors with meaningful engagement, and filling out the rest with in-market audiences. In-market adds breadth, but it is less precise on its own, so it works better as a complement than a foundation.
Do not tighten your ROAS target too soon! Setting aggressive ROAS targets before the algorithm has enough data can reduce total conversion volume dramatically; we’ve seen this happen up to 50%.
Give the signals room to work before you start pulling the levers.
Reading The Reports
Performance Max reporting has improved significantly, but it still requires some interpretation. As mentioned before, gone are the black-box days of PMax reporting, but there is still room for improvement.
Search Terms Report: The search terms report now lives at the campaign level instead of the asset group level, which gives you access to a lot more data. The catch is that search and Shopping traffic are blended together, so a single search term might be reflecting performance from both formats at once.
Channel Performance Reporting: If the majority of your PMax spend is going toward Display with very little coming from Shopping, that is a signal that something is off with your feed or your asset groups, and it is worth digging into.
Asset Group Segmentation: This is where you figure out which creative combinations are actually driving conversion value. Once you know that, it is pretty straightforward to lean into what is working and update what is not.
Protip: If you have not run an Uplift experiment yet, it is worth putting on the calendar. Uplift experiments test the actual incremental contribution of your PMax campaigns against everything else running in the account. This is where you can get real answers about whether PMax is actually working.
When PMax Is The Wrong Answer
In my experience, Performance Max needs a minimum of 30 conversions in the last 30 days to optimize effectively.
Below that threshold, the algorithm doesn’t have enough signal, and the results are inconsistent. If your account is not at that volume yet, Standard Shopping with tROAS is the more predictable path. Build conversion history first and layer in PMax once the data density supports it.
Google’s own documentation recommends Maximize Conversion Value with a target ROAS if you’re tracking values and want to drive as much value as possible. This is especially true for ecommerce, and revenue-first bidding tends to produce better outcomes than pure conversion volume.
For brands with niche products where query-level visibility is critical, or where creative control is tightly managed, Standard Shopping still produces more reliable and interpretable data. The hybrid approach only works well when both campaigns are actively managed.
What To Do Now
The advertisers getting the most out of Performance Max in 2026 aren’t treating it as automation that runs itself. In fact, there isn’t a single advertising channel or campaign where we let the automation run itself. They are inefficient and frankly damaging to your campaign and overall efficiency.
PMax amplifies whatever you feed it. Good strategy in, strong results out. Weak inputs, no structure, and the budget will find its way to impressions that don’t convert.
