Google’s product-level reporting now captures a wider share of the activity associated with Performance Max and several other campaign types. That added visibility can help advertisers understand product results across more of Google’s inventory, but it also creates an abrupt break in reporting continuity.
The practical challenge is interpretation: a chart may rise because more activity is being counted, not because ads suddenly became more effective. Advertisers therefore need to distinguish a measurement expansion from a true performance change.
What changed in product-level reporting
Search Engine Land reports that, as of June 15, Google expanded Performance Max product reporting beyond Search network activity. Previously, reported metrics such as cost and conversions covered products served through Search networks and Standard Shopping campaigns.
The expanded scope includes product performance data from the following eligible campaign inventory:
All Performance Max networks
Video campaigns
App campaigns
Demand Gen campaigns where product data is available through Google Merchant Center
This is primarily a reporting change. It gives advertisers a broader view of where product interactions occur, but the source does not indicate that the campaigns themselves were altered by the update.
Why performance charts may show a sudden jump
When a report begins counting activity from additional networks, its totals can increase even when underlying campaign behavior remains stable. Search Engine Land says advertisers may see higher impressions, clicks and other metrics as a one-time consequence of the wider reporting scope.
That distinction matters because a larger reported total is not automatically evidence of improved targeting, stronger creative or better bidding. Performance should be judged only after determining whether the apparent change came from campaign results, measurement coverage or a combination of both.
Organic visibility, paid media and artificial intelligence merge into one connected search ecosystem, where vivid data streams link a creative website with a powerful analytics dashboard.
Key takeaways for advertisers
Product reports now include more eligible Google Ads inventory than they did before the June 15 change.
Sudden increases in reported activity may reflect newly included networks rather than genuine growth.
Results from before and after the reporting expansion are not directly comparable without qualification.
Network-level filtering and clear report annotations can reduce the risk of misreading the change.
How to handle historical comparisons
The reporting boundary creates a discontinuity in time-series analysis. A month-over-month comparison that crosses June 15 may combine two different measurement scopes, so the percentage change alone cannot explain what happened.
Advertisers can make those reports more useful by marking the date of the methodology change and explaining it in client or stakeholder summaries. Where possible, periods measured under the same scope should be compared with one another. If a report must cross the boundary, any observed lift should be presented as potentially influenced by expanded coverage.
This caveat also applies to internal benchmarks, forecasts and automated dashboards that rely on historical trends. The underlying data may still be valuable, but the change in scope needs to remain visible to anyone using it for decisions.
A practical review workflow for affected accounts
A disciplined review can prevent a measurement change from being mistaken for a campaign win or loss:
Identify reports and dashboards that use Performance Max product-level data.
Check whether the analysis period spans the June 15 reporting change.
Use the Network (with search partners) filter to examine where the newly reported activity originated.
Review impressions, clicks, cost and conversions in context instead of treating any single increase as proof of improvement.
Add a concise methodology note to recurring reports and explain the change to stakeholders.
Google Ads specialist Bia Camargo highlighted the notice, according to the source, and cautioned that clients should be prepared for apparent gains caused by expanded measurement. That communication step is important because broader reporting is useful only when decision-makers understand what changed.
As future reporting periods accumulate under the new scope, comparisons should become easier. Until then, advertisers should treat June 15 as a measurement boundary and require network-level evidence before crediting a spike to campaign optimization.
AI assistants may be software, but the people using them still follow schedules. That creates patterns in when AI tools attract attention, answer questions, and influence decisions.
Try Profound Blog offers one central observation: every AI assistant has a daily and weekly rhythm, but that rhythm varies by platform, region, and user. The source does not provide supporting measurements, so the useful takeaway is a framework for investigation rather than a universal timetable.
Blue work and green non-work lines show hourly patterns for ChatGPT, Claude, and Gemini, split into weekday and weekend rows, with most curves highest around late morning to afternoon.
The rhythm belongs to usage, not the assistant
An AI system does not begin a workday in the human sense. Any apparent schedule is more likely to reflect when people open a platform, what they use it for, and how it fits into their routines.
Blue work and green non-work lines trace hour-of-day patterns for North America, Europe, Latin America and Asia, split into weekday and weekend rows.
A tool associated with professional tasks may see a different pattern from one used for personal questions. The distinction matters because a broad label such as “AI traffic” can hide meaningful differences among audiences and use cases.
Four heatmaps plot share by hour and day of week for ages 18-29, 30-49, 50-64 and 65+, with the darkest weekday bands around late morning.
Why one schedule cannot describe every audience
The source specifically cautions that timing is not consistent across platforms, regions, or users. Each dimension can change how an observed pattern should be interpreted:
The five blue heatmaps show share percentages by hour and day of week for income groups, with many darker cells appearing from late morning through afternoon.
Platform: Different products can serve different purposes and attract different usage habits.
Region: Local time, working patterns, and audience location can shift periods of activity.
User: Individual needs determine whether an assistant is used for work, study, research, planning, or another task.
These variables make a single global “best time” an unreliable assumption. A pattern found in one segment should not automatically be applied to another.
Side-by-side weekday charts show writing highest for ChatGPT, programming/tech highest for Claude, and multimedia highest for Gemini, with weekend shifts.
Key takeaways
AI assistant activity can form recurring daily and weekly patterns.
Those patterns may differ across platforms, regions, and individual users.
Timing should be evaluated within a defined audience and use case.
The source states the principle but does not supply data for specific hours or days.
How teams can evaluate timing responsibly
For marketers, publishers, and product teams, the practical response is to examine their own evidence. Analysis should begin with a clear question: which platform, audience, region, and outcome are being measured?
Side-by-side charts titled "Granular topic rank by DOW" trace colored topic rankings from Monday through Sunday for ChatGPT, Claude, and Gemini.
Teams can then compare consistent time periods, use the relevant local time zone, and separate audience segments where possible. They should also distinguish between activity and impact. A busy period does not necessarily produce the most valuable visits, recommendations, conversions, or customer outcomes.
Any apparent rhythm should be treated as a working pattern rather than a permanent rule. User behavior, product design, and the mix of use cases can change, so conclusions need periodic review.
What the source does not establish
Try Profound Blog does not identify peak hours, preferred weekdays, regional differences, or platform-specific results in the supplied material. It also does not describe a study or methodology. Claims about exact schedules would therefore go beyond the available evidence.
The defensible conclusion is narrower: AI usage has timing patterns, and context determines what those patterns mean. Organizations that want actionable answers will need to measure the audiences and outcomes that matter to them.
My eight-year-old daughter desperately wanted a Nintendo Switch. Her “evil” parents—my spouse and I—refused to buy one for her.
She was too young to get a job, so she did what any resourceful child would do: she opened a lemonade stand in front of our house.
She did more than set out a table and a pitcher, though. She designed what amounted to a high-stakes A/B test.
Her hypothesis was simple: if she could persuade more people to stop, she could sell more lemonade and reach her Nintendo Switch goal faster.
Variant A was her two-year-old sister, Julie, stationed out front to attract attention.
Variant B was our dog, Ginger.
I know what I would have guessed.
The dog. Obviously, the dog.
But Julie won—and it was not even close.
The only metric that mattered
The funny part is that my daughter did not really care about the A/B test result. She was not interested in how many people stopped at the stand or which variant produced the best response.
She cared about one outcome and one outcome only:
At this lemonade stand, the cute-dog advantage loses: Variant A, featuring the seller’s young sister, wins the visibility A/B test over Variant B’s golden retriever.
Did she make enough money to buy the Nintendo Switch?
I believe marketers are facing a similar problem right now.
Generative engine optimization (GEO) is the practice of increasing a brand’s visibility in AI-generated answers across platforms such as ChatGPT, Gemini, Perplexity, and AI Overviews.
I can track AI visibility, citation share, impressions, rankings, and nearly every other signal available. Meanwhile, leadership is asking a much simpler question:
Is any of this helping the business grow?
I answer that question with a simple test I call the Dollar Rule: if I cannot put a dollar sign in front of a metric, I treat it as a channel metric rather than a business metric.
That distinction captures the central measurement challenge in GEO.
Most of the numbers we track are valuable operational signals. They show us what is happening within the channel, but leadership wants to understand the resulting business impact.
GEO emerged at precisely the moment attribution was becoming less reliable.
Traditional SEO measurement relied on a straightforward journey: someone searched, clicked, visited a website, and converted. We could trace that path and connect it to an outcome.
The Dollar Rule turns imperfect GEO attribution into a business case: align metrics with outcomes, verify directional signals, then translate performance into financial language leaders value.
AI search disrupted that model.
I now see buyers forming opinions and making decisions before they ever reach a company’s website. That makes AI’s influence much harder to capture with conventional attribution.
AI search broke attribution
I see buyers discovering brands through AI-generated answers, citations, publishers, forums, reviews, videos, and many other sources. Those touchpoints can shape a decision long before a click occurs, and much of that influence never appears cleanly in analytics.
That is why I see so many teams struggle to justify GEO investments. The visibility is real, and the influence is real, but the attribution is frequently incomplete.
I do not believe waiting for perfect attribution is a sound strategy. Increasingly, it is simply a convenient reason to avoid acting.
When I want leadership to support GEO, I need to connect its influence to business outcomes—even when I cannot connect every interaction to a conversion.
How I make the financial case for GEO
The biggest mistake I see marketers make is trying to prove attribution before proving value.
Before I worry about attribution, I ask whether I am measuring something the business actually considers important. That is where the Dollar Rule becomes useful.
I have found that justifying a GEO investment usually comes down to three actions:
I align my metrics with business outcomes.
I verify that those metrics reliably point me in the right direction.
I translate the evidence into language a CFO understands.
My Dollar Rule is deliberately simple:
Precision can form a tight cluster in the wrong place; accuracy keeps evidence centered on the outcome that matters. For GEO measurement, a useful estimate can beat an exact but irrelevant metric.
If a number does not translate into dollars, I treat it as a channel metric, not a business metric.
I focus on revenue opportunity, revenue at risk, payback period, and customer acquisition cost. Those metrics live on a P&L, and they are the numbers leadership teams use to evaluate investments.
In my experience, CFOs do not allocate budget because an attribution model looks impressive. They allocate budget based on credible expectations of financial return, risk, and growth.
That principle changes how I measure and present GEO.
I measure influence, not just attribution
AI search did more than change discovery. It changed what I can realistically measure.
Traditional organic attribution assumes a clean sequence: search, click, visit, convert.
AI platforms increasingly answer questions before a click, influence buyers across multiple touchpoints, and withhold the referral data marketers once relied on.
That leaves me in an unusual position: a GEO campaign may be influencing pipeline even while the analytics platform struggles to prove it.
One estimate illustrates the gap. Loamly estimates that roughly 70% of AI-influenced traffic appears as Direct traffic in GA4, making a substantial share of AI’s contribution difficult to trace through traditional attribution models.
I do not take that measurement gap to mean measurement is impossible. I take it as a reason to broaden the evidence I examine.
When attribution is incomplete, business value tips the scale: a credible estimate of revenue impact can guide GEO investment better than a perfectly precise tally of clicks.
Instead of asking only, “How many clicks did we receive from AI search?” I ask:
Is our branded search growing?
Are prospects arriving already familiar with our positioning?
Are we being cited in AI answers for questions that drive revenue?
I would not treat any one of these signals as definitive. When I combine them, however, they can create enough confidence to support a responsible investment decision.
That is the essential difference between GEO measurement and traditional SEO measurement. I am not simply measuring a click path; I am measuring market influence.
I believe the marketers who adapt fastest will stop treating attribution as a traffic-sorting exercise. We will combine quantitative signals with qualitative evidence because the goal is not absolute certainty. The goal is confidence that our GEO investment is moving the business in the right direction.
Why I may be measuring the wrong thing
I do not think SEO or GEO metrics are inherently wrong. The problem is that they can be highly precise without being relevant to the business outcome I am trying to influence. They tell me exactly what happened inside a channel, but not whether the business is moving in the right direction.
SEO tools are packed with precise numbers. The challenge is that many of those numbers have only a weak connection to business outcomes.
Precise = exact
Accurate = connected to business outcomes
I have found that leadership would rather receive a roughly correct estimate of revenue impact than a perfectly precise count of clicks.
I studied engineering in school, where we spent a great deal of time discussing precision: how exact and repeatable a measurement is, right down to the decimal point.
The fuzzy math equation turns a qualitative sales signal into a figure leaders understand: a 10% competitor-content mention rate translates to $12 million in annualized pipeline at risk.
In marketing, I see that kind of precision in organic clicks, rankings, impressions, and click-through rates. Tools such as Google Search Console can give me extremely exact figures for those channel activities.
The problem is that a precise channel number is not necessarily accurate in the business sense. I consider a measurement accurate when it tells me whether I am getting closer to an outcome that matters.
Even when those measurements are not perfectly precise, I find them more useful if they point toward the bullseye: the business outcomes leadership cares about.
Knowing that a page received 40 organic clicks is precise. It tells me almost nothing about whether we are winning or losing in the market—just as a visitor count did not tell my daughter whether she was close to buying her Nintendo Switch.
That is how I apply the Dollar Rule in practice. When attribution is incomplete, I translate the evidence I do have into a directional estimate of business impact.
Why I put revenue ahead of attribution
For me, a rough number tied to revenue beats an exact number tied only to channel activity.
When reliable attribution is unavailable, I build the case from signals I can actually access and then work through the math.
I do not use fuzzy math to replace SEO metrics or attribution. I use it alongside them when traffic-based attribution cannot capture the influence taking place.
One of our healthcare clients gave us a useful example.
Prospects were arriving at sales calls already convinced of claims that were not true.
Climb from channel data to executive value: translate SEO impressions and citations into pipeline and lower CAC, then show leadership what matters—$122K in revenue and a three-month payback.
We traced the source to a competitor’s comparison page. That page was shaping buyer perceptions long before our client had an opportunity to present its side of the story.
We recommended publishing content that would counter the narrative, but the leadership team did not believe there was enough evidence to justify a response. We needed to make a stronger business case.
SEO tools estimated that the competitor’s page received roughly 40 organic visits per month. Whether that estimate was right or wrong was beside the point: it did not measure the page’s influence on active buyers.
So we looked for evidence that was closer to the business outcome.
We spoke with our client’s salespeople. They told us that roughly 10% of qualified B2B discovery calls included unprompted mentions of specific claims from the competitor’s page.
That was not a clean number suitable for an exact attribution model, but we could not dismiss it. The influence was real, and it was showing up during live sales conversations.
We used that evidence to build a directional calculation:
10% mention rate on discovery calls
× 1,200 qualified B2B sales calls per year
× $500,000 average contract value
A competitor’s comparison page earns 64% of citations on decision-stage AI questions and surfaces in 10% of discovery calls—putting an estimated $12 million in pipeline under its narrative.
× 20% average win rate
= $12 million in annualized revenue being influenced by the competitor’s narrative
I did not present this as a forecast or a formal attribution model. It was a directional estimate of how much revenue the competitor’s messaging could influence.
That reframing changed the conversation. We stopped debating 40 clicks per month and started discussing $12 million in influenced revenue.
That is the number we brought to leadership—not impressions or citation share, but $12 million in revenue being influenced by a page our client had declined to counter. That is a number a CFO immediately understands.
I lead with value metrics
If we enter a GEO campaign review and lead with rising citation share or growing impressions, our CMO may lose interest and our CFO may wonder what those numbers mean financially. In the worst case, we can lose budget because leadership cannot see the return.
Here is how we framed the situation for our client’s leadership team:
I have learned that leadership funds marketing campaigns based on business impact. Translating a problem into dollars changes the nature of the discussion.
The decision-makers did not need certainty. They needed a credible financial story supported by leading indicators, observable momentum, and enough evidence to inspire confidence.
I focus on what the business values
That is what my eight-year-old intuitively understood at her lemonade stand. Her goal was never to count visitors. Her goal was to buy the Nintendo Switch.
A neon-lit smartphone imagines advertising inside ChatGPT, highlighting how AI platforms are reshaping brand discovery, GEO strategy, and the measurement of marketing influence.
GEO has created anxiety because it disrupted attribution models we relied on for years. But I remind myself that attribution was never the ultimate objective.
The real objective is business growth.
If I can connect GEO activity to revenue opportunity, revenue at risk, pipeline influence, or customer acquisition, I do not need perfect certainty to justify the investment.
I need credible evidence that our GEO campaigns are moving the business in the right direction.
Precise metrics tell me what happened. Relevant metrics tell me whether we are winning.
Before I deliver my next GEO report, I can examine every metric on the page and ask one question:
If this metric doubled tomorrow, would the business care?
Then I ask the follow-up:
Can I translate this metric into revenue opportunity, revenue at risk, pipeline influence, or customer acquisition cost?
If I cannot, I am probably reporting channel impact rather than business impact—and that is unlikely to justify the next GEO investment.
I’m seeing Google roll out a new set of Demand Gen updates designed to help advertisers improve creative performance, reach more potential customers across YouTube, and measure campaign results with more clarity.
For me, the bigger story is that Demand Gen is becoming less about manually adapting assets and more about using AI-assisted tools to make creative work harder across Google’s most visual surfaces.
Demand Gen campaigns are built to drive discovery and conversions across Google’s visual placements. With these latest updates, I see Google trying to reduce creative friction while giving advertisers better visibility into what is actually moving performance.
Google says the enhancements arrive as YouTube continues to show value for customer acquisition. The company cited research from Measured showing that 72% of incremental conversions on YouTube come from new customers.
What’s new. I’m watching Demand Gen add expanded video resizing capabilities, giving advertisers the ability to automatically transform creative into more aspect ratios, including vertical-to-square, vertical-to-landscape, and square-to-landscape formats.
That matters because it should make it easier to adapt existing creative for different YouTube placements without having to produce every version manually from scratch.
Why I care. Expanded video resizing can help existing assets fit more YouTube inventory, Gemini can provide AI-powered recommendations before launch, and new web-to-app measurement can give marketers a clearer view of how Demand Gen campaigns influence app installs and return on ad spend.
Gemini joins the creative workflow. Google is also bringing Gemini-powered recommendations directly into the Demand Gen campaign creation process, which makes AI guidance part of the asset selection workflow instead of a separate optimization step.
When advertisers choose image and video assets, Gemini will offer automated suggestions for optimizing creative for YouTube. I see this as a way for marketers to improve asset choices before campaigns go live, rather than waiting for performance data after launch.
Better app measurement. Demand Gen now includes Web to App Acquisition Measurement, allowing advertisers to measure when web campaigns lead users to install an app.
The new reporting gives me a more complete way to evaluate campaign performance because it attributes app installs generated through Demand Gen campaigns. That should help advertisers better understand the full impact of their media spend.
The bottom line. I see Google’s latest Demand Gen updates as a practical combination of AI-powered creative guidance, more flexible video optimization, and broader measurement tools that can help advertisers improve performance while gaining clearer insight into customer acquisition.
I’ve discovered that measurement is truly the cornerstone for all we achieve in performance marketing. Without precise measurement, everything I recommend, implement, and optimize becomes mere speculation. Today, maintaining accurate measurement is more challenging than ever—and it’s only getting more difficult.
With regulatory crackdowns and growing privacy concerns, paired with elongated multi-touch journeys, we face a measurement crisis. Brands that still rely on outdated tactics are missing the mark when it comes to modern measurement challenges.
If your brand falls into this category, it’s time I help you rebuild your measurement foundation—from integrating first-party data (crawl), to creating cross-channel reporting for actionable insights (walk), to advanced media mix modeling (MMM) and incrementality testing for true media lift (run).
The crawl: Building a first-party data foundation
By integrating first-party data into our performance marketing channels, I can move beyond reliance on third-party signals. While those metrics offer surface-level insights, they don’t reveal how channels impact our business goals.
Audience integration
The first step involves integrating CRM data into our paid media platforms. This includes:
Remarketing to abandoners.
Creating exclusion lists for current subscribers or recent purchasers.
Compiling priority contact lists.
I might be uploading lists today, but integration enhances targeting by connecting to up-to-date audience lists for media platform targeting.
Offline-conversion tracking
For lead-gen businesses like ours, setting up offline conversion tracking (OCT) is crucial. It reveals the bottom-line impact of our media on sales, passing sales data back to platforms for campaign attribution.
Once OCT is in place, we can optimize for lower-funnel, higher-quality conversion steps in the sales cycle or even begin optimizing toward revenue to enhance our return on ad spend.
Server-side tracking and consent mode
To progress from crawl to walk, I need to move from client-side to server-side tracking.
By adopting server-side tracking, we bypass browser-based tracking and instead rely on our first-party data. This approach ensures data accuracy and resilience as privacy restrictions increase and cookies become obsolete.
Partner integration uses pre-built connectors for setup through platforms like Shopify or Google Tag Manager.
Direct API requires a development team to handle complex data or custom backends.
The walk: Cross-channel reporting integration
With a robust measurement foundation, my next step is breaking down platform silos to understand the full ecosystem.
Going beyond last click
After implementing server-side tracking, I created a clean data pipeline. Yet, traditional attribution models neglect the full-funnel customer journey.
To address this, I recommend using data warehousing solutions like BigQuery to centralize your data and apply custom logic, thereby gaining insights across the ecosystem.
Unified reporting dashboards
Integrating evolved attribution with unified reporting dashboards, like Looker Studio, allows me to visualize data across the funnel and obtain actionable insights into what platforms are truly driving volume and conversions.
The run: Media mix modeling and incrementality testing
With a comprehensive, everyday view of performance, significant questions persist about growth potential and offline performance measurement.
By employing media mix modeling and incrementality testing, I can discern the full impact of media investments at a macro level to make informed decisions.
The holistic view through MMM
I view MMM as my compass, providing a holistic, quantitative guide for paid media investments, helping me analyze the relationship between inputs and business outcomes.
Pulse checks with incrementality testing
Incrementality testing offers validation for MMM and helps evaluate if specific tactics or channels are driving true incremental lift by comparing test and control groups.
The sprint: Clean, integrated, and validated first-party data
With first-party data integrated through server-side tracking and cross-channel reporting, I’ve built a robust measurement foundation. Guided by MMM and validated by incrementality testing, I’m now ready to sprint towards a more informed and successful marketing strategy.
