This post was sponsored by Trendos. The opinions expressed in this article are the sponsor’s own.
Are my competitors running ChatGPT ads?
Is there an ad library for ChatGPT sponsored results?
How do I track who’s advertising in AI answers?
Your highest-intent buyers are asking ChatGPT about your category right now.
A sponsored placement appears below the answer, and if a competitor bought it, they’re intercepting clicks at the exact moment buyers are ready to decide.
Unless you run every relevant prompt yourself, competitors are undermining your AI visibility in the moments that matter most, and you can’t see any of it.
What This Walkthrough Covers
This is a walkthrough of the manual process to find out who’s bidding against your category, and where you can see exactly who’s buying ads in your customers’ ChatGPT answers without doing it yourself.
OpenAI launched ChatGPT ads for US Free and Go users on February 9, 2026.
By spring, 600+ advertisers had placements running against high-intent prompts:
Software comparisons.
Weekend trip planning.
“What’s the best crm tool?”
These queries used to live on Google; now they showcase inside of ChatGPT as ads.
ChatGPT ads appear inside the answer experience as a sponsored card below the response.
After ChatGPT answers a prompt, a sponsored card renders below the response, visually separated and clearly labeled “Sponsored.” The card includes the advertiser name, favicon, a short headline, a tight body description (~19 words on average), and a link to a destination page.
Screenshot of [Which CRM is the best?] on ChatGPT, May 2026
OpenAI does not currently publish an ad library equivalent to Meta’s or Google’s, and no central searchable database of every active ChatGPT ad exists. To see who’s running ads, you have to run prompts in eligible US sessions and capture what appears.
For monitoring purposes, four data points define what a competitor is doing in a given ad:
Ad title: the headline copy a competitor is running
Ad description: the body sentence(s) under the headline
Final URL: the destination they’re sending traffic to
Impression share: how often a competitor’s ad shows on a given prompt across many runs
You need all four to read the competitive picture.
Title and description tell you how they’re positioning.
Final URL tells you whether they’re sending to a generic homepage, a category page, or a comparison.
Impression share, the percentage of total ad impressions on a given prompt that went to a specific advertiser, turns “I saw them once” into “they own this prompt.”
Step 1: Map The Queries Your Buyers Are Already Asking
Build a prompt list that represents how your buyers actually talk to ChatGPT. You’re not optimizing for impressions on broad terms. You’re surfacing competitor activity on the conversations that lead to your category.
Start with the questions you already know convert in paid search and high-intent organic.
Then translate them into how someone would phrase the same need to ChatGPT. People don’t search ChatGPT the way they search Google. They write full sentences with context, constraints, and intent.
A working prompt list for a paid search manager in any commercial category should hit 30 to 50 prompts and cover:
Direct comparisons (“best [category tool] for [use case]”, “[Brand A] vs [Brand B]”).
Recommendation prompts (“I need a [tool] for [job to be done], what should I look at?”).
Switching prompts (“alternatives to [Brand]”).
Use-case fit prompts (“which [tool] is best for [small team / enterprise / specific industry]”).
Pricing prompts (“affordable [tool] for [audience]”).
Pull from your branded and category SQL data, top organic keywords, and any customer-facing inputs you have (support tickets, sales calls, on-site search logs, review mentions), so the list represents real buyer language, not what you assume they say.
If your competitors are bidding on prompts you haven’t mapped, you’ll never see them; your ad library starts and ends with your own prompt list.
Pro Tip: Use Ad Radar to pull in your prompt list and keep it running continuously.
Step 2: Run Each Prompt In A ChatGPT Session
Once you have the prompt list, run it, and pay attention to the session setup, where the data either becomes useful or becomes noise.
Run each prompt and screenshot the response, including any sponsored card that appears below the answer.
Do not run each prompt once.
ChatGPT’s ad auction doesn’t show the same ad to every user on the same prompt; different sessions surface different advertisers depending on bid, relevance signals, and rotation.
A single run captures one auction outcome, not the competitive set.
To get a usable read on any given prompt, plan for at least 20 to 30 runs across multiple days.
Vary the session: clear cookies between batches, and pace runs across mornings, afternoons, and evenings. Run all 30 in 10 minutes from the same session and you’re sampling one slice of the auction.
Step 3: Capture The Four Data Points That Define A Competitor’s Ad
For every sponsored placement that shows up, record the same four fields, in the same place, every time. Otherwise you can’t compare across runs.
The four data points to capture per impression:
Ad title: the exact headline copy in the sponsored card. Copy character for character. Headlines change.
Ad description: the body sentence(s) under the headline. Roughly 19 words on average right now, but range varies. Capture the full text.
Final URL: the destination URL the card links to. Strip UTMs to identify the canonical landing page, but keep the full URL in a secondary column so you can analyze tracking patterns later.
Impression share: calculated, not observed directly.
For each prompt, count how many times each advertiser appeared out of total runs. If you ran a prompt 25 times and Competitor A showed in 12 of them, that’s a 48% impression share on that prompt for the run window.
Screenshot of Google Sheets, May 2026
Tag each row with the prompt that triggered the ad, the date and time of the run, and the session details (Free or Go, Location). Set up your spreadsheet so you can pivot impression share by prompt, by competitor, and by week.
Ad copy iterates fast. The same advertiser may run three or four different titles against the same prompt within a single week as their team tests creative. Final URLs change too; a competitor might rotate between a homepage, a comparison page, and a category landing page to test conversion. Capture only the title and you miss the iteration patterns and the URL strategy, which is most of what tells you what your competitor is doing.
Step 4: Repeat Often Enough To See Share Of Voice Over Time
A one-shot read on competitor ad activity will mislead you. You’ll catch whoever happened to win the auction the day you ran prompts and miss the rotation that happens every other day. Decide on budget from a single-day snapshot and you’re deciding on noise.
To see the share of voice, meaning who actually owns this category in ChatGPT, you need a recurring cadence. The minimum that gives you signal:
Daily runs on your top 5 to 10 highest-value prompts (the queries closest to purchase intent)
Weekly runs on the full 30–50 prompt list
Monthly trend pulls to see how competitors gain or lose share over rolling 30-day windows
Paid search managers have auction insights, ad libraries, and dozens of third-party monitoring tools for Google. For ChatGPT ads, they have none of that yet. ChatGPT ads are a new auction running against the same buyer intent, and right now most teams don’t have visibility into who’s bidding against them. If competitors are already in your customers’ ChatGPT answers, you’ll find out from your own monitoring or from a pipeline gap you notice too late to act on.
Ad Radar runs the prompt monitoring continuously and surfaces every advertiser, every prompt, every creative iteration. See continuous visibility into competitor ChatGPT ad activity in your category.
Cost-per-click (CPC) remains one of the most closely scrutinized metrics in digital advertising for both business owners and expert practitioners. This is understandable; it’s a tangible, easy-to-track metric that offers immediate gratification when it drops and immediate anxiety when it rises. After all, if your average CPC increases from $2 to $5, it’s natural to assume your campaign is performing worse.
However, it’s strategically wrong to evaluate your CPC in isolation. In modern Google Ads account structures, particularly those using Smart Bidding, I’ve noticed that a higher CPC is frequently a sign of account health, while a rock-bottom CPC can be a huge red flag.
We’ll explore why this paradox exists, delineate the scenarios where high CPCs signal success versus inefficiency, and use a real-life case study to illustrate the problem with focusing on CPCs – and what high-value metrics you should prioritize instead.
Why High CPCs Often Signal High Quality
If you transition from manual bidding to smart bidding strategies like maximize conversions or target ROAS, you will likely notice an immediate increase in your average CPC. It can be jarring, but this is a fundamental feature of how the algorithm operates.
Remember, cheap clicks are cheap for a reason: Your competitors didn’t want them! If you focus solely on driving down CPCs, you risk optimizing your account for the low-quality “leftover” traffic. However, when you use smart bidding, while you still pay per click, you are not optimizing for clicks; you are optimizing for the probability of a conversion, and potentially even the probable value of a conversion. This is how you align your business goals with your Google Ads campaigns’ goals, and the unintended (but necessary) side effect may be higher CPCs.
If this occurs, recognize that you are now bidding on conversion probabilities, not keywords. In the old world of manual CPC, you bid a flat rate for a keyword. In the new world, Google’s smart bidding algorithms analyze millions of data points in real-time – including device, location, time of day, operating system, browsing history, audience membership, and even the unique query itself – to assess user intent.
The algorithm is designed to bid aggressively for users who signal a high likelihood of converting. For example, if a user is searching for your specific solution, has a history of converting on similar offers, and is searching during business hours, the system will bid higher to win that auction. You are paying a premium to ensure your ad appears before the most valuable users.
Conversely, the algorithm bids down (or not at all) on users who are unlikely to convert. These might be users who frequently click ads but never buy, or users searching with low-intent informational queries. By avoiding these low-value clicks, your overall traffic volume may decrease, and/or your average cost per click may rise, because you have removed the “cheap” denominator from your equation.
The result should be expensive traffic, but traffic that actually turns into revenue.
In some industries like insurance, law, or emergency services, CPCs can reach an eye-watering $100 or $150 per click. This is simply the cost of doing business in a competitive market where a single client is worth thousands of dollars. If your Average Order Value is high, a high CPC is not a bug; it is a feature of a healthy, competitive auction, and the potential of those clicks for your business.
If High CPCs Often Indicate Quality, What Do Low CPCs Indicate?
If you are seeing CPCs under $1.00 for non-brand search campaigns, you should investigate immediately. Extremely low costs may mean you are purchasing inventory that your competitors have rejected.
Junk Inventory: Low CPCs often indicate you are inadvertently opted into the Google Display Network or Search Partners. These networks frequently drive lower-intent traffic compared to the primary Search Engine Results Page (SERP).
Broad Match or AI Max mis-matches: Cheap clicks can result from loose keyword matching, where your ads appear for irrelevant, low-competition queries. The root cause of this issue is usually a poor conversion tracking setup and/or the wrong bid strategy; you’ll want to fix the root cause of both issues
However, it is also possible that you’re lucky! I’ve seen non-brand CPCs in the $0.10 to $0.90 range, in 2026, for niches like alcohol and hair salons. Low competition and high-quality ads can mean you get to enjoy low CPCs with zero consequences. Sadly, this is usually the exception, not the rule.
Context Matters: The Non-Search Exception
It is critical to note that the logic of “High CPC = High Quality” changes significantly when you move away from Search. In non-search campaigns, you are interrupting users rather than capturing active intent, so the metrics behave differently.
Display & Demand Gen: On the GDN, “good” metrics are often misleading. A high CTR (usually over 1%) is usually a sign of accidental clicks or bot activity. While CPCs here are generally low, extremely low costs (pennies) typically signal placement on low-quality sites. This is why prioritizing the higher quality inventory on Demand Gen, like Discover and Gmail, is often worth it, even with slightly higher CPCs than Display.
Video (YouTube): High CPCs on Video are meaningless because the primary goal is views, not clicks. You should be optimizing for cost per view (CPV) or cost per reach (CPM), not CPC.
Performance Max: Since PMax blends all of these networks, CPC serves as even less of a diagnostic tool. A very low average CPC ($0.10-$0.50) can suggest the campaign is leaning heavily on Display/Video inventory. A higher CPC can indicate it is successfully winning auctions in Search and Shopping. Your Channel Performance Report will be a more useful optimization tool than looking at blended CPC.
The Counter-Argument: When High CPCs Are A Red Flag
While high CPCs can indicate quality, they are not a free pass to ignore your costs altogether. There are specific scenarios where a high CPC is still a warning sign of inefficiency. This is where your judgment as a skilled practitioner needs to come in:
1. Your Quality Score Is Low
If your Quality Score is low (specifically 5 or below), then you are overpaying for your clicks to compensate.
The Fix: Check your keyword report, add the Quality Score columns, and see which component is the most “Below Average”: Expected CTR, ad relevance, or landing page experience. Optimize accordingly.
2. You Are Over-Invested (Diminishing Returns)
It is possible to capture too much of the market. In my experience, if you are reaching 60%+ impression share on non-brand search in a competitive industry, your CPCs are likely inflated because you are paying a premium to capture the very last, most expensive sliver of available traffic.
The Fix: Switch from a maximize strategy to a target strategy, so that Google Ads isn’t forcing your budget to be spent in full. Or, expand your keyword set through additional keywords and/or broader keywords to open up new pockets of opportunity.
3. The Math Doesn’t Work (The Rule Of 2)
High CPCs are a problem if they break your business economics. Even if the traffic is high quality, if the cost of the click exceeds the revenue you can expect to make from that visit, the ads will never be profitable.
The Fix: For a quick and crude test, compare your average CPC to your revenue per session (Conversion Rate x Average Order Value). If your CPC is $2 but you only make $1 per visit on average, you are losing money on every click. Work on your conversion rate so that you are better equipped to handle this high-quality traffic
4. Irrelevant Matching
Sometimes, high CPCs occur because you are bidding on keywords that match to irrelevant but expensive queries. For example, a branding agency bidding on “branding agency” might match to “marketing agencies” – a highly competitive term that probably doesn’t align with their specialty.
The Fix: Keep an eye on your search terms report, and either restrict your match types or add negatives as needed.
5. Seasonality And Auction Dynamics
CPCs can spike due to external factors like Q4 seasonality or a new competitor entering the auction. While this isn’t a “mistake,” it is a warning that your efficiency is about to drop – or has already dropped – through factors beyond your control.
The Fix: Keep an eye on your impression share and auction insights, so that you can quickly spot anomalies and plan accordingly. For seasonal businesses, analyze year-over-year data as well as month-over-month, so that seasonal swings don’t take you by surprise.
Case Study: The $29 Click That Saved The Account
It’s one thing to know that, in theory, higher CPCs are better. It’s another thing to believe it, trust it, and let it happen to your campaigns. Allow me to share a real-life example with you from a local lead generation business.
The Challenge
My Google Ads coaching client, a digital marketing agency that specializes in home services businesses, hired me after becoming dissatisfied with their white-label PPC freelancer. The Google Ads campaign for one of their electrician clients was performing poorly, and he was threatening to fire the agency.
When we looked in the account, here’s what we saw:
Search campaign with 2100 keywords on manual CPC.
Average CPC: $1.77.
Conversion rate: 1.5%.
Conversions (leads): 6 per month.
Search impression share <10%>
The Change
I recommended a structural overhaul: a Search campaign with just 23 exact match keywords, with overhauled ad text to fix spelling errors (yes, really) and add clear value propositions like “No Call Out Fee.” And maximize conversions rather than manual CPC.
The Immediate Result
Four days after launching the new strategy, my client emailed me in a panic. The average CPC had skyrocketed from $1.77 to $29. He assumed that we had “broken” the campaign and asked, “Why am I paying $29 for a click?”
The Immediate Outcome
Despite the CPC sticker shock, the Search campaign was actually performing significantly better after just four days. Although the CPC had skyrocketed under maximize conversions bidding from $1.77 to $29 per click, the conversion rate had also skyrocketed from 1.5% to 27%. That meant that even though we were only four days into the new structure, the cost per lead had already decreased from $121 to $107.
High CPCs were the price of admission for quality leads in a competitive big city.
The Unexpected Plot Twist
The story didn’t end there. A few days later, the account’s “Auto-Apply Recommendations” surreptitiously added broad match keywords. Any Google Ads practitioner knows that this can tank your performance, but because the campaign was on a smart bidding strategy with sufficient conversion data – this actually improved performance even further. (I promise Google didn’t pay me to say that!)
In the two weeks that broad match keywords were turned on, the campaign generated 34 leads at an average CPA of $48.
Compare this to the month prior, when the electrician only got six leads from Google Ads at $121 cost per lead. Now, he was getting 34 leads in just two weeks, for a fraction of the cost – and anecdotally, he told my client that most were high quality.
The Victim Of Success
The problem eventually became too much success; the electrician was a small business owner and simply couldn’t handle the volume of leads from Google Ads. My client had to pause most of his ad groups, bringing lead volume back down.
But this case perfectly illustrates the high CPC paradox: A low CPC ($1.77) delivered junk volume. A high CPC ($29.00) proved the concept and delivered quality. A blended approach (broad match + smart bidding) eventually settled the metrics in the middle, but we never would have gotten there if we had optimized for cheap clicks from day one.
In Google Ads, Prioritize CPA And ROAS
As Google’s algorithms get smarter and more pervasive, our role as Google Ads practitioners continues to shift. We are no longer day-traders trying to buy individual clicks for pennies. We are investors looking for a return.
Stop optimizing for CPC. Instead, focus on cost per acquisition (CPA) or return on ad spend (ROAS). If you are acquiring customers within your target efficiency, the cost of the individual click is irrelevant. As our electrician found out, a $29 click that converts is infinitely more valuable than a $1.77 click that doesn’t.
For most of the history of paid search, performance measurement followed a clear cause-and-effect relationship.
Advertisers controlled the inputs inside their campaigns like bid strategies, keyword and campaign structure, ad copy, and landing pages. All these factors contributed to conversion performance in some shape or form.
When performance changed, the explanation was usually traceable. For example, a new keyword theme improved conversion rates. Or, a bidding strategy increased efficiency.
That simple cause-and-effect framework is breaking down in real time, and has been for a while.
Over the past several months, Google has accelerated its transition toward AI-driven campaign types like Performance Max, Demand Gen, or assets inside those like AI Max or AI-driven ad creative components.
Not only do these change how campaigns are set up and managed, but they also change how performance must be measured.
Advertisers increasingly receive conversions from queries they did not explicitly target, from creative assets that are automatically assembled, and from placements distributed across multiple channels. In this environment, measuring performance by analyzing individual campaign inputs becomes less useful.
The real challenge is understanding how automated systems generate outcomes.
This article provides a measurement framework for that reality. It explains what has changed in advertising platforms, how PPC teams can evaluate performance when automation controls more of the auction, and how practitioners can communicate results clearly to leadership.
The Current Measurement Crisis In PPC
Right now, most discussions about AI in PPC tend to focus on automation features like campaign types, targeting capabilities, ad creative development, and bid strategy expansion.
But, there’s a deeper shift happening in measurement but not talked about as much.
Automation introduces a larger set of variables influencing each auction. When the platforms make targeting, bidding, placement decisions (and more) dynamically, isolating the impact of individual campaign inputs becomes difficult.
Recent platform updates have not only changed how campaigns are managed, but also how performance should be interpreted. The connection between action and outcome is less direct, and in many cases, partially obscured.
Several platform developments illustrate why traditional measurement methods are becoming less reliable.
AI Max Expands Queries Beyond Keyword Lists
In my opinion, AI Max represents Google’s most aggressive step toward intent-driven matching.
Instead of relying solely on advertiser-defined keywords, AI systems evaluate contextual signals, user behavior patterns, and historical performance data to match ads with queries that may not exist in the account.
Not only that, but AI Max goes beyond search terms. It also has the ability to change your ad assets for more tailored messaging when Google deems appropriate.
For PPC managers, this introduces a structural shift in how to measure performance. Conversions may originate from queries that were never explicitly targeted.
And we knew that something like this was coming. Back in 2023, Google first publicly used the word “keywordless” in communications when talking about Search and Performance Max.
Source: Mike Ryan, X.com, March 2026
For example, a retailer who bids on “trail running shoes” may now appear for search terms like:
“best shoes for rocky terrain running”
“ultra marathon footwear”
“durable hiking running hybrids”
These queries reflect the same intent, but they don’t map cleanly back to the original keyword strategy.
Instead of trying to force these queries into keyword-level reporting, try analyzing performance by grouping into intent clusters. By evaluating conversion rate and revenue at the category level, teams can maintain strategic clarity even as query matching expands.
Google Ads already does a decent job of this in the Insights tab within the platform. They have a “Search terms insights” report that groups queries into “Search category,” where you can see conversions and search volume.
Screenshot by author, March 2026
Performance Max Distributes Spend Across Multiple Channels
Performance Max can further complicate measurement by distributing budget across Search, YouTube, Display, Discover, Gmail, and Maps.
Up until last year, there was little-to-no transparency in how spend was allocated across those channels. Back in April 2025, Google launched the long-awaited feature of channel reporting to the PMax campaign type. It now shows channel-level reporting, better search terms data, and expanded asset performance metrics.
For example, say you have a $40,000 monthly PMax campaign budget and see this channel breakdown:
Channel
Spend
Conversions
Search
$18,500
310
YouTube
$10,200
82
Display
$7,100
45
Discover
$4,200
28
If Search drives the majority of conversions, but YouTube consumes a large portion of spend, PPC marketers could try the following:
Test separating out branded search outside of PMax.
Refine asset groups to improve search alignment.
Run controlled experiments comparing PMax vs. Search.
Measurement becomes an exercise in interpreting how the system allocates spend rather than controlling each placement.
Ads Are Beginning To Appear Inside AI Conversations
Conversational search introduces an entirely new layer of complexity into PPC measurement.
Google is now testing shopping results embedded directly within AI Mode, allowing users to compare products without leaving the interface.
Google isn’t the only one doing this. ChatGPT announced on Jan. 16, 2026, that it would begin testing ads for its Free and Go users in the United States.
No matter which platform is running or testing ads in AI conversations, it’s clear that the measurement gap hasn’t been solved, and leaves many PPC managers with unanswered questions.
In my own recent search, I came across ads at the end of an AI Mode thread when I searched “noise cancelling headphones”:
So, if I were to click on one of those sponsored ads but convert at a later time, that attribution is unclear right now. Will my conversion be measured from the AI recommendation, the product listing click, or a later branded search?
These journeys challenge traditional attribution models, which were built around linear click paths rather than multi-step AI interactions.
Why Traditional PPC Metrics Are No Longer Enough
Many PPC reporting dashboards still rely on communicating metrics like impressions, clicks, conversion rate, and return on ad spend.
While some of those metrics remain useful, they no longer tell the full user story when bringing in automated and AI-driven environments.
These three shifts explain why.
1. Attribution Windows Are Expanding
AI-assisted search increases both the length and complexity of user journeys.
Research from Google and Boston Consulting Group show that “4S behaviors” (streaming, scrolling, searching, and shopping) have completely reshaped how users discover and engage with brands.
When AI introduces product recommendations earlier in a user’s journey, the time between initial interaction and conversion often grows. This could be because that user is still at the beginning of their research phase. Just because you’re introducing a product earlier, does not mean that they’ll be ready to purchase it any earlier.
So, what can marketers do about that gap now? Here are a few helpful tips to better understand how users are engaging with your business:
Review conversion lag reports in Google Ads.
Analyze time-to-conversion in GA4. Are there any differences or shifts in the last three, six, or nine months?
Extend attribution windows to 60-90 days where appropriate.
This ensures automated systems receive more accurate feedback on what (and when they) drive conversions.
Organic Search Is Losing Click Share
Search results now include everything from AI Overviews, scrollable shopping modules at the top, and expanded ad placements across all devices.
This reduces organic traffic even more and shifts more demand capture towards paid media.
From a measurement standpoint, PPC should be evaluated alongside organic performance when possible.
Tracking blended search revenue provides a more accurate view of total search performance, rather than isolating paid channels.
AI Systems Optimize For Outcomes Rather Than Inputs
Traditional PPC management focused on inputs like keywords, bids, and ad copy to influence performance directly.
AI systems work differently. Instead of optimizing individual levers, they evaluate large sets of signals in real-time to determine which combinations are most likely to drive conversions.
This changes what measurement needs to do. Instead of asking which specific keyword or bid strategy adjustment improved performance, marketers need to evaluate whether the platform is producing the right business outcomes.
As platforms take over more of the execution, measurement has to focus less on the mechanics and more on whether automation is driving profitable, meaningful results.
The New Measurement Stack For AI-Driven PPC
If AI is now controlling more of the auction, then PPC teams need a different way to evaluate performance.
The old measurement stack was built around visibility into campaign inputs. You could look at keyword performance, search terms, ad copy, device segmentation, and bid adjustments to understand what was working. That model starts to fall apart when automation is making many of those decisions on your behalf.
The replacement becomes a new measurement stack that advertisers should look at in these four layers:
Profitability.
Incrementality.
Blended acquisition efficiency.
First-party conversion quality.
Together, these give marketers a more accurate picture of whether automation is actually helping the business grow.
Start With Profit, Not Just ROAS
ROAS still has value, but it should no longer be treated as the primary success metric in highly automated campaigns.
The problem is that AI-driven systems are often very good at capturing demand that already exists. That can make campaign efficiency look strong on paper, even if the business is not gaining much incremental value.
A campaign with a 700% ROAS may still be underperforming if it is primarily driving low-margin products, repeat purchasers, or orders that would have happened anyway.
That is why profitability should sit at the top of the measurement stack.
Instead of asking, “Did this campaign generate enough revenue?” marketers should be asking, “Did this campaign generate profitable revenue?”
For ecommerce brands, this could mean incorporating:
Contribution margin.
Product margin by category.
Average order profitability.
New customer revenue vs. returning customer revenue.
A simple starting point is to compare campaign revenue against both ad spend and cost of goods sold.
For lead gen advertisers, the same principle applies, just different incorporations:
Qualified lead rate.
Sales acceptance rate.
Close rate by campaign.
Revenue per opportunity.
If AI is optimizing toward cheap conversions that never turn into revenue, the system is learning the wrong lesson.
Add Incrementality To Separate Demand Capture From Demand Creation
The second layer of the stack is incrementality. This is where many PPC measurement frameworks still fall short.
Automation can be highly effective at finding conversions, but that does not automatically mean it is generating new business. In many cases, AI systems are simply getting better at intercepting users who were already on their way to converting.
If your campaign is mostly capturing existing demand, performance may look strong inside the ad platform while actual business lift remains modest.
This is why incrementality testing has become much more important in the AI era.
For PPC teams, this means at least part of measurement should be designed to answer: “Would this conversion have happened without the ad?”
You don’t need an enterprise-level media mix modeling to get started. A few practical approaches include:
Geo holdout tests. Pause or reduce spend in a small set of markets while maintaining normal activity elsewhere.
Use Google incrementality testing. Google reduced the minimum of testing incrementality in its platform to just $5,000, making it more affordable for many advertisers.
Branded search suppression tests. In select markets or windows, test the impact of reducing branded spend where brand demand is already strong.
Answering this question does not mean automation is bad. It means PPC teams need a better way to distinguish between platform efficiency and true business lift.
Use Blended CAC To Measure Search More Realistically
The third layer of the new measurement stack is blended acquisition efficiency.
As AI Overviews, AI Mode, and other search changes continue to reduce traditional organic click opportunities, PPC should not be measured in a vacuum.
That is especially true for brands where paid and organic search are increasingly working together to capture the same demand.
A campaign may appear less efficient in-platform while still playing a critical role in maintaining total search visibility and revenue.
That is where blended customer acquisition cost (CAC) becomes useful.
Blended CAC looks at total acquisition spend across relevant channels and divides it by the total number of new customers acquired.
The formula for this is simple:
Total acquisition spend ÷ total new customers = blended CAC
This gives leadership a much more realistic picture of what it actually costs to grow the business.
It also helps PPC managers explain why paid search may need to carry more weight when organic search visibility declines due to AI-driven search features.
In other words, this metric helps move the conversation away from “Did Google Ads hit target ROAS?” and toward “What is it costing us to acquire a customer across modern search systems?”
Make First-Party Conversion Quality The Foundation
The final layer of the stack is first-party data quality. This is the part many advertisers still underestimate.
As platforms automate more of the targeting, bidding, and matching logic, the quality of the signals you send back becomes even more important. If the platform is deciding who to show ads to and which conversions to optimize toward, your job is to make sure it is learning from the right outcomes.
That means not all conversions should be treated equally.
If a lead form completion, low-value purchase, repeat customer order, and high-margin new customer sale are all fed back into the system the same way, automation will optimize toward volume, not value.
For PPC teams, that means the measurement stack should include a serious review of conversion quality inputs, including:
Offline conversion imports.
CRM-based revenue mapping.
New vs. returning customer segmentation.
Lead quality or opportunity-stage imports.
Customer lifetime value indicators where available.
This is where measurement and optimization start to overlap.
If the wrong conversions are being measured, the wrong outcomes will be optimized.
That is why first-party data is not just a reporting issue. It is the foundation of the entire AI-era measurement stack.
What To Show Your CMO Or Clients
One of the most difficult aspects of managing automated campaigns is explaining performance to leadership teams.
Executives often expect reporting frameworks built around the mechanics of traditional campaign management. In automated environments, those indicators tell only a small part of the story.
A more effective reporting structure focuses on three layers that connect advertising performance to business outcomes.
The first layer should always focus on the metrics that leadership teams care about most. Revenue growth, contribution margin, and customer acquisition cost provide a direct connection between marketing activity and company performance. These indicators allow executives to evaluate marketing investments in the same framework they use to evaluate other business decisions.
Instead of presenting keyword-level reports, PPC leaders should begin with a clear summary of how paid media contributed to revenue and profit during the reporting period. If revenue increased by 18% quarter over quarter while customer acquisition costs remained stable, that outcome provides a far more meaningful signal than any individual campaign metric.
The second layer of reporting should explain how paid media contributes to the broader acquisition ecosystem. As AI-driven search experiences reshape the visibility of organic results, paid media often carries a larger share of the responsibility for capturing demand.
Blended customer acquisition cost provides an effective way to communicate this relationship. By combining marketing spend across channels and dividing it by the total number of new customers acquired, organizations gain a clearer understanding of the overall efficiency of their acquisition strategy.
This approach also helps executives understand how paid search interacts with organic search, social advertising, and other marketing channels. Rather than evaluating PPC in isolation, leadership can see how the entire acquisition system performs.
The final layer of reporting should focus on experimentation and strategic insights. Automated systems constantly evolve, and the best way to evaluate them is through structured experimentation.
Reports should include summaries of campaign experiments, including:
The hypotheses tested.
The metrics evaluated.
The outcomes observed.
For example, if enabling AI-driven query expansion increased conversion volume while maintaining acceptable acquisition costs, that result provides valuable guidance for future campaign structure decisions.
Equally important is identifying metrics that are becoming less relevant.
Keyword-level performance reports, average ad position, and manual bid adjustments were once central components of PPC reporting. In automated campaign environments, those metrics often provide little strategic value. Continuing to emphasize them can distract leadership from the outcomes that truly matter.
Effective reporting in the AI era should emphasize growth, profitability, and strategic learning rather than operational mechanics.
Measurement Gaps That Still Exist
Despite improvements in automation and reporting transparency, several emerging advertising experiences remain difficult to measure.
One example is the growing presence of personalized offers within AI-driven shopping experiences. Google’s Direct Offers feature allows retailers to surface dynamic discounts during AI-generated shopping recommendations. While the feature may influence purchase decisions, advertisers currently have limited visibility into how frequently those offers appear or how strongly they influence conversion behavior.
Without that visibility, marketers cannot easily determine whether the discounts are generating incremental revenue or simply reducing margins on purchases that would have occurred anyway.
Another emerging measurement challenge involves conversational commerce. Google has begun exploring “agentic commerce” systems where AI assistants help users research and purchase products across multiple retailers.
In these environments, the user journey may involve several conversational prompts before a purchase occurs. The traditional concept of an ad impression or click may become less meaningful when AI systems guide the user through a multi-step research process.
As these experiences evolve, marketers will need new attribution models capable of evaluating influence across conversational journeys rather than isolated interactions.
These developments highlight the importance of ongoing experimentation and advocacy from advertisers. Measurement frameworks will need to evolve alongside the platforms themselves.
The Future Of PPC Measurement
Automation has changed the mechanics of paid advertising, but it has not eliminated the need for strategic oversight.
If anything, the role of human expertise has become more important.
AI systems are extremely effective at executing campaigns across large datasets and complex auctions. What they cannot do on their own is define the business outcomes that matter most or interpret performance within the broader context of organizational growth.
The most effective PPC teams are adapting to this reality. Instead of focusing exclusively on the mechanics of campaign management, they are investing more effort in defining profitability metrics, designing incrementality tests, and building reporting frameworks that connect advertising performance to business outcomes.
Measurement in the AI era will look different from the measurement frameworks that defined the early years of paid search. The focus will shift away from controlling individual campaign inputs and toward understanding how automated systems generate value for the business.
For PPC practitioners and marketing leaders alike, that shift represents the next stage in the evolution of paid media strategy.
1. Identify Which AI Platforms Are Driving Your Visitors
Each LLM and answer engine has different logic, leading to different outputs for the same prompts. It’s important to understand which AI chatbots are aligned with your brand before making decisions that inform a larger AI search or SEO strategy.
Different LLMs Are Driving Leads In Different Industries
Not all AI platforms send leads the same way.
ChatGPT = Speed. ChatGPT dominates overall lead volume at 90.1% of AI-referred leads, with especially strong numbers in healthcare and automotive industries, where people want instant options.
Perplexity = Research. Perplexity accounts for 6.3%, but it punches well above its weight in high-consideration sectors. In Travel & Hospitality and Manufacturing, nearly one in ten AI leads comes from Perplexity, roughly ten times the rate seen in other industries.
Google’s Gemini holds 2.4% of AI-referred leads and is gaining traction in Business Service and Manufacturing, likely because users lean on its Google Workspace integration.
Claude, with 1.2% of lead generation, is carving out a niche in both Real Estate verticals and also with Marketing Agencies. Especially in areas where consumers tend to do more specific and detailed research before reaching out.
How To Accurately Track AI Prompt Visibility
AI search isn’t one channel. It’s a set of distinct platforms, each with different behaviors and industry strengths. So, repeat this AI prompt research phase for each LLM.
Identify the LLMs that matter most for your vertical. Use the data above as a starting point. If you’re in healthcare or automotive, prioritize ChatGPT visibility. High-consideration service? Pay attention to Perplexity. B2B or manufacturing? Gemini should be on your radar.
Test how each platform describes your business. Go to ChatGPT, Perplexity, Gemini, and Claude and ask them questions your customers would ask. “Who’s the best [your service] in [your market]?” See if you’re being recommended. If not, note who is and what content those competitors have that you don’t.
Create content that answers the questions AI platforms are fielding. LLMs favor well-structured, authoritative, fact-rich content. Publish service pages, FAQs, comparison guides, and local content that directly answer the kinds of questions consumers ask these platforms.
2. Connect AI Traffic To Actual Conversions
Connecting AI-driven leads to actual revenue in your reporting is key to understanding how to prioritize your marketing activities. Without visibility into AI lead attribution, you’re making decisions in the dark, which is an expensive place to be.
However, if you can identify AI as the source of your best leads, you instantly know how to pivot your SEO strategy.
How To Track AI Traffic & Attribute Conversions Across ChatGPT, Gemini, and Perplexity
As more money flows through AI search, the ability to attribute leads from specific LLMs isn’t a nice-to-have. It’s the difference between knowing what’s working and throwing budget at a black box.
What you need is the ability to trace a lead from the AI platform where it originated, through the call, form, or chat where it converted, all the way to the revenue it generated. That full-funnel visibility is what separates data-driven teams from everyone else.
ImplementLLM-specific attribution. Use a platform that can identify which AI model referred each lead. CallRail’s AI search engine attribution, for example, automatically tags whether an inbound call came from ChatGPT, Perplexity, Gemini, or Claude, not just “AI.” That level of granularity is what makes it possible to actually optimize by channel.
Create custom GA4 channel groups for AI traffic. In Google Analytics, go to Admin > Data Display > Channel Groups and create a custom channel group that isolates AI referral traffic by source. This lets you compare AI-driven sessions and conversions against your other channels.
Add “How did you hear about us?” to your intake process. Self-reported attribution (SRA) is a simple but powerful complement to digital tracking. Add it to your intake forms and train front-desk or sales staff to ask on calls. CallRail’s SRA feature lets you capture this data at the conversation level, so you can compare what callers say against what your analytics show. The gaps will reveal exactly where your tracking is falling short.
Connect AI Traffic to Calls, Forms & Sales Pipelines
Call tracking lives in one platform. Form submissions in another. Text conversations somewhere else entirely. Sound familiar?
When your lead data is fragmented like that, it’s surprisingly hard to answer basic questions. Which campaigns drive your best leads? Is AI search actually improving results? Where are leads falling off between first contact and conversion?
Make sure you are monitoring every lead interaction for complete funnel visibility. Teams need clear insight into every conversation-whether it comes through calls, forms, texts, or chats. And by channel- Paid Search, Video, SEO, Paid Social, and Content, for example.
Unifying those touchpoints isn’t just a reporting upgrade. It’s the foundation for any AI-ready lead strategy. Without it, every optimization decision you make is based on an incomplete picture. And in a landscape moving this fast, incomplete data leads to costly missteps.
How To Attribute Calls & Form Fills To AI Search
Take a good look at what is happening with your Voice Assistants. Are forms going to a shared inbox and being missed? Are calls not being answered while another line is in use or after business hours? How long is it taking to follow up with leads? Are those leads going to the competition after you miss the first call?
Consolidate your lead tracking into one platform. If calls, forms, texts, and chats are living in separate tools, you’re creating blind spots. CallRail’s unified lead intelligence platform captures every touchpoint in a single dashboard, so you can see the full customer journey from first AI search to closed deal, and finally answer the question: which channels are actually driving revenue?
Map every conversion point to a marketing source. For each way a lead can reach you -phone call, web form, text, live chat- make sure you can trace it back to the campaign, channel, or keyword that drove it. Use dynamic number insertion for calls and hidden fields on forms to capture source data automatically.
Build a weekly reporting cadence around lead quality, not just volume. Don’t just count leads, score them. Review which sources produce leads that actually convert to appointments and revenue. This is the reporting your clients care about, and it’s how you prove the value of your work
28% of business calls go unanswered. Many of those leads never call back.
Take a good look at your Voice Assistants here. Are your forms going to a shared inbox where they sit unread? Are calls going unanswered because another line is busy or it’s after hours? How long does it take your team to follow up with a new lead? And if you miss that first call from an AI-referred prospect who already has high intent and is ready to buy. Are they going straight to your competitor?
Right now, AI search can understand your customers in real time and answer any question they need, making them perfectly ready to convert into a lead.
Think about how the traditional funnel used to work. Someone searches, browses a few sites, reads some reviews, maybe sleeps on it, then reaches out. There were days, sometimes weeks, of consideration built into the process.
AI has collapsed that timeline dramatically, and AI-directed callers skip the browsing phase entirely.
They’ve already done their research inside the LLM. By the time they call, they’re ready to make a decision. And they expect you to be ready, too. When a prospect has been pre-qualified by an AI recommendation, every minute of delay costs you revenue.
And the stakes go beyond individual calls.
On platforms like Google, answer speed directly impacts your ad rankings. Faster response times earn better placements on Local Service Ads and PPC -meaning slow follow-up doesn’t just lose you a lead, it quietly erodes your visibility and drives up your cost per lead over time. The agencies winning in an AI-search world aren’t just the ones showing up in LLM recommendations. They’re the ones ready to convert the moment the phone rings -day or night.
Apply AI Where Your Team Is Stretched Thinnest: Use AI to Capture & Qualify Leads Automatically
You can’t automate everything. But knowing where to apply AI, specifically, where your agency or internal team is most stretched, is the difference between using it effectively and adding technology for its own sake.
For most agencies and SMBs, the highest-impact bottleneck is follow-up.
If your clients are missing calls, responding slowly, or losing leads somewhere between the first touch and a booked appointment, that’s exactly where AI can deliver immediate, measurable value.
The key to success here is utilizing AI-powered platforms that can answer inbound calls around the clock, qualify leads in real time, capture intake details, and even book appointments automatically. Early adopters have seen answered calls increase by 44%. That’s not a marginal improvement. It’s the kind of shift that directly impacts revenue and client retention.
How To Set Up AI-Assisted Lead Handling
When you can connect your AI-assisted lead handling back to attribution data and revenue outcomes, you’re no longer just reporting on activities. You’re proving ROI. And that’s what earns long-term client trust- and moves agencies from being seen as just a lead source to being a true growth partner.
Deploy an AI voice agent for after-hours and overflow calls. Start with the windows where your team is least available -evenings, weekends, and lunch hours. CallRail’s Voice Assist answers, qualifies, and captures lead details automatically, so no high-intent caller falls through the cracks. Early adopters have seen answered calls increase by 44%.
Automate follow-up texts immediately after missed calls. If a call does go unanswered, trigger an automatic text within seconds: “Hi, we just missed your call -how can we help?” This simple automation recovers a meaningful percentage of leads that would otherwise be lost.
Connect your AI lead handling back to attribution. Make sure the leads captured by AI tools feed into the same reporting dashboard as your other channels. If your AI agent books an appointment at 9 pm on a Saturday, you should be able to trace that back to the Google Ad or AI search referral that started the journey.
The shift isn’t on the horizon. It’s already here.
It’s time to build AI-aware attribution so you can see what’s actually driving leads, unify your data so you can act on it, and respond fast enough to capture the high-intent leads AI search is already sending your way.
AI and automation in ad platforms are well established. Google Ads and Microsoft Advertising are heavily invested in automated features, and the technical barrier to entry has never been lower. However, that accessibility comes with a tradeoff.
Two common challenges surface when bringing a PPC team in-house:
Campaigns are easier to launch than they are to explain and analyze.
Machine-driven decisions risk going unquestioned without an outside perspective.
Those challenges point to something CMOs probably already know: Automation doesn’t eliminate the need for human judgment. It raises the requirements for it. Even with strong AI tools in place, experienced PPC practitioners are still writing strategy, creating ad copy, and manually updating targeting.
This article covers two structural paths for managing that reality.
All in-house means your internal team manages PPC end-to-end, with no agency or external consultant involved.
Hybrid means your internal team handles day-to-day execution and internal oversight while an external specialist or consultant provides strategy, auditing, and a second set of eyes.
Both models can work. The goal is to match machine automation with human accountability and independent performance checks. Without that structure, an in-house team can end up in a bubble where the ad platform’s suggestions dictate all of the optimization decisions.
Is Your Organization Ready? What To Assess Before You Hire
Before you post a job description, determine whether your company is ready to manage the technical work that comes with modern PPC search ads. Hiring an internal team is a long-term commitment.
The Shift In Daily Tasks
The role of the search marketer is shifting from manual campaign creation to evaluating and guiding automated systems. The human role is increasingly about checking what the AI creates and stepping in to do the work the ad platform can’t do well on its own.
That last part matters so much more than most job descriptions reflect. In my experience, AI-generated ad copy is often not platform-ready, and strategy still requires a human who understands the brand, the profit model, and the customer. If your candidates are only talking about managing manual bids and features, they may not be ready for the current landscape. You need people who can navigate automated systems and know when to override them.
Input And Data Quality
Because AI success depends on signal strength, an in-house PPC team’s value is directly tied to their ability to connect and maintain clean data. Ad platforms rely on:
Conversion tracking.
CRM integration.
Audience modeling.
Bidding inputs.
Tools such as Google Ads Data Manager (connecting external products inside Google Ads) and offline conversion uploads mean managing data should be a core responsibility of in-house PPC specialists.
Poorly configured conversion tracking or incomplete data signals can lead automated bidding to optimize toward low-value actions, if the data isn’t managed effectively in-house. You can’t expect a machine to give you good results if you’re feeding it bad information.
If You Are Hiring, Look For These Skills
If you’ve decided to build fully in-house, hiring criteria should shift toward business data management and the ability to work alongside AI without taking every single suggestion.
1. Understanding Business Margins
Most PPC managers haven’t had to think in depth about COGS (Cost of Goods Sold) or return rates, but that’s changing.
The bar is rising for in-house hires. A team that can connect ad spend to net profit, not just revenue, is far better positioned to make smart decisions as automation takes over the mechanical work.
2. Owning The Post-Click Experience
The PPC team must care about what happens after the user lands on the site. Creative quality and landing page performance are directly tied to conversions and what the algorithm learns over time.
AI-driven traffic efficiency can be thrown off by a poor landing page experience. Your internal hires should have a working knowledge of landing page testing and website user experience.
3. Ad Copy And Strategic Judgment
AI can generate ad copy, but it can create variations that are missing marketing strategy or brand-ready messaging. Your team needs to evaluate, rewrite, and at times reject what the ad platform produces.
The same applies to strategy. Automated systems optimize toward the goals you set, but setting the right goals and interpreting performance still require a skilled human. Hire for that judgment, not just ad platform knowledge.
4. Technical Data Strategy
Your team needs to know how to build and maintain first-party data connections, such as CRM data and customer match uploads.
Your team’s job is to ensure the right signals are flowing to the right campaigns at the right time. Technical data competency should be a core requirement for the job.
Why A Hybrid Model May Work Better
Even when hiring and data processes are going well, blind spots can happen inside fully internal teams. Three issues can show up:
Brand blindness from working primarily inside a single account.
Lack of independent auditing on spend and profit.
Difficulty pushing back on ad platform pressure.
An external perspective adds accountability that internal teams can have trouble providing for themselves. In an environment where so many features are automated, that accountability matters more because teams don’t tend to deep dive into the automations.
1. The Problem With Brand Blindness
Internal teams are focused on one brand. That focus builds deep expertise, but it can limit perspective. For example, when performance changes, it’s difficult to determine whether the change reflects a platform-wide trend, an industry shift, or a campaign-specific issue.
Working across many industries gives specialist consultants a reference point that internal teams may not have. They can tell you if a performance drop is happening to everyone in the industry or just to you.
2. The Need For Independent Auditing
An external partner acts as an independent auditor for your search spend. They can help confirm that internal goals line up with actual business profit rather than ad platform metrics.
It’s easy for internal teams to grow comfortable and focus on vanity metrics like ROAS (Return on Ad Spend). An objective third party can help show you exactly how much actual profit your search spend is generating.
3. Managing Ad Platform Pressure
Internal teams are the primary target for PPC ad platform representatives. These reps frequently push recommendations such that are auto-applied and display network serving that eat up budgets and prioritize the platform’s revenue over your business.
Independent experts are less likely to follow these suggestions without questioning them. They provide the pushback needed to ensure spend is justified by performance, not the platform’s optimization score.
Structuring The Partnership For Success
Consider a division of labor that draws on internal brand knowledge and external expertise. This hybrid approach offers the most protection for your ad spend.
What The In-House Team Should Own
Data Ownership: Managing the privacy and quality of your customer signals.
Creative Guidance: Ensuring brand voice stays consistent across AI-generated ads.
Ad Copy and Strategy: Writing, evaluating, and refining what the ad platform produces.
Sales Coordination: Connecting PPC spend with internal inventory levels and sales cycles.
What The External Specialist Should Own
Strategic Roadmap: Providing a long-term view of where the search industry is heading.
Advanced Analysis: Proving the true value of your spend through profit-based measurement.
Objective Auditing: Serving as an independent check against ad platform recommendations.
Successful PPC teams in an AI-first search environment won’t be worried about who automated the fastest. They’ll be more thoughtful and strategic about defining what the machine does and what a human approves.
Matching Structure To Accountability
The decision to go fully in-house or hybrid isn’t permanent. What matters is that your structure matches the level of accountability your ad spend requires.
If your team has clean data, strong hiring, and the ability to question what the ad platform suggests, a fully in-house model can work. But if no one is challenging the machine’s recommendations, you have a gap that’s hard to fix from the inside.
A hybrid model doesn’t mean your internal team isn’t capable. It means you’re building in a check that protects your budget from blind spots.
Whatever you choose, the people managing your PPC need to understand your business at the profit level, not just the platform level. Automation handles the mechanics. Your team handles the judgment.
For businesses just beginning to test the waters in paid media, identifying the right channels to start with is foundational to success. Splitting a budget prematurely among too many platforms is not likely to yield positive results, and launching on a platform that’s not a good fit for a business can cause difficult conversations around the value of paid media as a whole.
In this article, we’ll review a series of questions to ask when determining the PPC channels you should use.
What Are Your Business Goals?
Of course, the ultimate answer to this question for most businesses is to drive return on investment (ROI). But think through what you are seeking to achieve in the near and short term, and how you expect paid media to contribute.
Some potential answers include the following:
Selling products online.
Driving foot traffic to physical stores.
Generating leads via contact forms and/or phone calls.
Driving signups for online accounts.
An ecommerce business should consider platforms and campaign types that allow for syncing a shopping feed, such as traditional Shopping campaigns or Performance Max in Google or Microsoft.
Is your company a startup that is unfamiliar in a market with established players? If so, then branding-focused campaigns on social channels may be worthwhile for an initial focus.
You can build awareness first, and then use retargeting audiences to reach individuals who have engaged with ads, as well as launch paid search to capture intent from those who first see your brand ads. YouTube can also be an effective channel for showcasing your brand as well as building viewer-based audiences.
If your product is a straightforward product or service that people need (i.e., tax preparation services), you may not need to establish brand familiarity first, and can likely lead with search ads to meet people while they are looking for what you provide.
What Is Your Product/Market Fit?
What individuals are you seeking to sell to? Think about how you can match up available options for targeting on an ad platform to your desired audience.
If your product or service is easily identifiable with search terms (for instance, furnace repair), search can be a good place to start, as you’ll reach people who are in immediate need. Keywords are easy to define, and you know individuals will be making use of search engines to find your service.
If you’re promoting a product that has a very precise usage and little margin for error in relevance, campaign types likely to go broader with targeting are not ideal for a starting point. For instance, if you’re selling wheel bearings for industrial trucks, you’re better off launching with a traditional search campaign than a Performance Max campaign that may struggle to narrow in on the relevant audience.
If you’re promoting a product with potential wide appeal and opportunity for visual representation (such as colorful phone cases), running a Meta campaign with broad targeting may be a good route to both showcase the product and reach people likely to engage with it.
Have paid media campaigns ever been run before, or are you starting from scratch? If there is historical data from past campaigns, review that to see what channels may or may not have performed.
Of course, be sure to take into context how campaigns were set up, and don’t completely write off a channel because it may not have worked in the past. Shoddy campaign builds, mediocre offers, and poor landing page experiences may have all contributed to poor results.
What Data Can You Send Back To Ad Platforms?
In an ideal world, you should send conversion data for the most valuable actions, such as marketing/sales qualified leads and completed sales. In reality, this setup can sometimes take time and complexity to get in place, and not every business has the infrastructure in place from the beginning to track to this level.
If you don’t have conversion data for “down funnel” conversions, such as reaching a qualified lead status, focus on campaign types that allow for more control over targeting to start, such as search or LinkedIn. Avoid campaign types like Performance Max, Display, or Demand Gen that may generate questionable leads if you are just optimizing for a form fill.
Additionally, data integrations can tie into audience creation, such as syncing lists of individuals who have submitted an initial contact form to be nurtured with retargeting ads. Analyzing match rates for your lists across various platforms may provide clues as to which channels your audience is most likely engaging with.
What Is Your Budget?
A starting budget is a crucial piece in both determining what ad platforms are realistic to run on and whether to launch on one or more platforms to start.
While the ideal budget amount for launching on a new platform can be subjective, generally, you should avoid splitting a low budget between multiple platforms. Using a more limited budget in one channel, such as paid search, Google Demand Gen, or Meta, is often the best option to get started.
Additionally, some platforms require higher budgets in order to realistically get off the ground. For instance, LinkedIn tends to have high CPCs and needs enough data to be able to optimize toward those likely to convert. In my experience, monthly spends lower than $10,000 are not likely to give you the volume you need to succeed on that channel.
Do you have a stockpile of creative or access to design resources? If image creative is a hurdle, starting out by launching in search can be an easier lift as you only need to plan for text-based assets.
Thankfully, AI-based image creation tools, such as Google’s integration of Nano Banana Pro into Ads, can help to make generating creative less of a challenge, depending on your industry. Of course, if you need specific product photography or are in a heavily regulated industry with compliance restrictions, the use of AI tools may not be an option. AI-generated images should always be reviewed for brand accuracy and quality, and outputs may not always meet professional standards.
If you have video production capabilities or can develop an AI-generated video that works for your brand, video-centric channels like YouTube may be an option. However, you need to think about ensuring that the videos you have are tailored for the channels they’re on. Repackaging a TV ad is not likely to work on TikTok, where videos should have a more personal and informal feel.
Start Planning And Start Testing
Once you’ve asked these questions about your brand and laid out initial goals, brand familiarity, data, budget, and assets, you can begin building out campaigns. After launching, you can then start gathering data and working towards expansion into additional channels.
If you’ve made it this far, driving leads is no longer a challenge for you.
The real issue is what happens after your leads come in.
Are you seeing more missed calls than usual?
Worried about not being able to follow up in time and losing the sale?
Poor handoffs of hot leads to your sales team cause leads to go cold, meaning your marketing budget spend is going to waste.
As speed-to-lead becomes a critical factor in conversion, agencies are being asked to prove ROI when clients struggle to respond fast enough. This disconnect is forcing teams to rethink how lead handling fits into campaign performance and long-term client trust.
In this session, Anthony Milia, President of Milia Marketing, and Bailey Beckham Constantino, Senior Partner Marketing Manager at CallRail, share how agencies are using AI to improve:
Closing & conversion rates.
Client communication speed.
What You’ll Learn
Why Attend?
This webinar provides practical guidance for agencies looking to protect performance and demonstrate real results. You will gain clear examples and frameworks to improve conversions and client confidence heading into 2026.
Register now to see how AI-driven lead handling is shaping agency success in 2026.
🛑 Can’t make it live? Register anyway, and we’ll send you the on demand recording.
In this webinar, the marketing leadership team at DigiCom, a 2025 Inc. 5000-listed ecommerce growth agency, breaks down how they are running Google Ads at scale in 2026.
With hands-on experience managing PPC programs totaling $200M+ in ad spend across multiple accounts, they will share how high-growth brands are structuring paid search, Performance Max, and YouTube campaigns to meet shoppers where they are and drive consistent returns.
You will gain practical PPC strategy frameworks you can apply immediately, along with the chance for select attendees to receive a live Google Ads audit during the webinar. If you are responsible for scaling paid media performance in 2026, these strategies are worth studying.
Register now to get a clear, founder-led Google Ads playbook for scaling profitably in 2026.
🛑 Can’t make it live? Register anyway, and we’ll send you the on demand recording after the event.
This post was sponsored by Channable. The opinions expressed in this article are the sponsor’s own.
If you’ve ever watched your best-selling product devour your entire ad budget while dozens of promising SKUs sit in the dark, you’re not alone.
Google’s Performance Max (PMax) campaigns have transformed ecommerce advertising since launching in 2021.
For many advertisers, PMax introduced a significant challenge: a lack of transparency in budget allocation. Without clear insights into which placements, audiences, or assets are driving performance, it’s easy to feel like you’re flying blind.
The good news? You don’t have to stay there.
This guide walks you through a practical framework for reclaiming control over your Performance Max campaigns, allowing you to segment products by actual performance and make data-driven decisions rather than hope AI figures it out for you.
The Budget Black Hole: Where Your Performance Max Ad Spend Actually Goes
Most ecommerce brands start by organizing PMax campaigns around categories. Shoes in one campaign. Accessories in another. That seems logical and clean but can completely ignore how products actually perform.
Here’s what typically happens:
Top sellers monopolize budget. Google’s algorithm prioritizes products with strong historical performance, which means your star items keep getting the spotlight while everything else struggles for visibility.
New arrivals never get traction. Without performance history, fresh products can’t compete, so they never build the data they need to succeed.
“Zombie” products stay invisible. Some items might perform well if given the chance, but static segmentation never gives them that opportunity.
Manual adjustments eat your time. Every tweak requires you to dig through data, make changes, and hope for the best.
The result? Wasted potential, uneven budget distribution, and marketing teams stuck reacting instead of strategizing. You’re already doing the hard work; this framework helps that effort go further and helps you set and manage your PPC budget efficiently and effectively.
How To Fix It: Segment Campaigns By What’s Actually Working
This approach creates dynamic groupings that automatically shift as performance data changes with no manual reshuffling.
Step 1: Classify Your Products into Three Groups
Start by categorizing your catalogue based on real performance metrics: ROAS, clicks, conversions, and visibility.
Image created by Channable, January 2026
Star Products
These are your proven winners, with high ROAS, strong click-through rates, and consistent conversions. Your goal with stars is to maximize their potential while protecting margins.
Set higher ROAS targets (3x–5x or above based on your margins).
Allocate budget confidently.
Monitor to ensure profitability stays intact.
Zombie Products
These are the “invisible” items that haven’t had enough exposure to prove themselves. They might be underperformers, or they might be hidden gems waiting for their moment.
Set lower ROAS targets (0.5x–2x) to prioritize visibility.
Give them a dedicated budget to gather performance data.
Review regularly and promote graduates to the star category.
New Arrivals
Fresh products need their own ramp-up period before being judged against established items. Without historical data, they can’t compete fairly in a mixed campaign.
Create a separate campaign specifically for new launches.
Use dynamic date fields to automatically include recently added items.
Set goals focused on awareness and data collection rather than immediate ROAS.
Step 2: Define Your Performance Thresholds
Decide what metrics determine which bucket a product falls into. For example:
Zombies: ROAS below 2x or insufficient data, low click volume, goal is testing and learning.
New Arrivals: Date-based (for example, added within last 30 days), goal is building visibility.
Your thresholds will depend on your margins, industry, and historical benchmarks. The key is defining clear criteria so products can move between segments automatically as their performance changes.
Step 3: Shorten Your Analysis Window
Many advertisers’ default to 30-day lookback windows for performance analysis. For fast-moving catalogues, that’s too slow.
Cross-channel consistency compounds your optimization efforts. A product that’s a “zombie” on Google might be a star on TikTok, or vice versa. Unified segmentation helps you connect products to the right audiences on the right channels and distribute budget accordingly.
Step 5: Build Rules That Move Products Automatically
Here’s where the real efficiency gains come in. Instead of manually reviewing every SKU, create rules that automatically shift products between campaigns based on performance.
For example:
If ROAS exceeds 3x–5x over your analysis window – Move to Stars campaign
If ROAS falls below 2x or clicks drop below your average (for example, 20 clicks in 14 days) – Move to Zombies campaign
If product was added within a set time limit (for example, the last 30 days) -Include in New Arrivals campaign
This dynamic automation ensures your campaigns stay optimized without requiring constant manual intervention.
Get Smart: Let Intelligent Automation Do the Heavy Lifting
Image created by Channable, January 2026
The steps above work—but implementing them manually across thousands of SKUs and multiple channels is time-consuming. Product-level performance data lives in different dashboards. Calculating ROAS at the SKU level requires combining data from multiple sources. And building automation rules from scratch takes technical resources most teams don’t have.
This is where the right use of feed management and the right use of PPC automation really helps. For example, it can merge product-level performance data into a single view and let you build rules that automatically segment products based on criteria you define.
To see what this looks like in practice, Canadian fashion retailer La Maison Simons offers a useful reference point. They faced the same challenges-category-based campaigns where top sellers consumed the budget while newer items never gained traction.
After shifting to performance-based segmentation, they saw measurable improvements without increasing ad spend:
ROAS nearly doubled over a three-year period
Cost-per-click decreased while click-through rates improved
Average order value increased by 14%
Their dedicated new arrivals campaigns consistently outperformed expectations
Perhaps most notably, their previously “invisible” products became some of their strongest performers once they received dedicated visibility
The takeaway isn’t about any single tool, it’s that performance-driven segmentation works. When you stop letting one popular item take all the budget and start giving every product a fair shot based on data, the results tend to follow.
Segment by performance, not category: Budget flows to what works, not what’s familiar
Use 14-day windows for fast-moving catalogues: Capture fresher signals, reduce wasted spend
Give new products their own campaign: Build data before judging against established items
Automate product movement between segments: Save time and stay responsive without manual work
Apply logic across all paid channels: Compounding optimization across Google, Meta, TikTok, and more
Your Next Step
Performance Max doesn’t have to feel like handing Google your wallet and hoping for the best. With the right segmentation strategy, you can restore control, surface overlooked opportunities and make smarter decisions about where your budget goes.
Curious whether your product data is ready for this kind of optimization? A free feed and segmentation audit can help you find gaps and opportunities, no commitment, just clarity.
Because better data leads to better decisions. And better decisions lead to results you can actually control.
Remarketing lists continue to be one of the more dependable tools inside PPC accounts, especially for search campaigns. They give advertisers clearer control over who sees ads, how bids are adjusted, and how messaging aligns with prior brand interaction.
As tracking becomes more constrained and audience signals less granular, first-party data carries more weight in day-to-day performance.
Remarketing allows you to act on what users have already done, rather than relying entirely on inferred intent or broad audience definitions.
Where many accounts fall short is in how those lists are actually applied. Lists get created, added at observation, and then largely ignored.
Without a clear purpose tied to bidding, exclusions, or messaging, remarketing ends up being underutilized.
The strategies below focus on remarketing lists that directly influence PPC decisions. Each example is designed to support how users move through the funnel and how accounts are realistically managed, not how they look in theory.
Top-Of-Funnel & Awareness Remarketing Strategies
These three remarketing strategies cover the basics of top-of-funnel marketing and utilize different campaign types to help leverage your RLSAs.
1. Target Users Who Have Engaged With A Video Campaign And Encourage Them To Take Action
If you’ve tried YouTube Ads in any form and have struggled to determine or quantify success, then this strategy might be for you.
YouTube ads are a great way to gain awareness of a product, service, or brand – but how do you get a new user to take action from that first touchpoint?
Enter in remarketing lists.
Google Ads allows you to create different types of remarketing lists based on your YouTube videos. There are two key requirements for using this list type:
These lists can only be used in other YouTube or Search campaigns – not Display.
Your YouTube channel must be linked to your Google Ads account.
To set up YouTube remarketing lists, navigate to Tools > Shared Library > Audience Manager.
In Audience Manager, hit the “+” button to start segmenting your YouTube remarketing lists.
Screenshot by author, January 2026
From there, Google gives a multitude of options to start leveraging your YouTube video engagement for remarketing. These options include engagement from:
Views to videos.
Subscribes to the channel.
Visits to the channel.
Likes on videos.
Add videos to playlist.
Shares of videos.
Further, you’re able to segment further to make your remarketing lists as specific as possible:
Screenshot by author, January 2026
To leverage these newly created YouTube remarketing lists, try adding them to your existing Search campaigns as “Observation Only” at first to understand if these users are more likely to interact with your campaigns versus someone who hasn’t seen your YouTube videos.
Taking it a step further, you can create new Search campaigns that specifically target these users.
The benefit is that you can provide different messaging to these users who have already interacted with your brand.
2. Exclude Low Quality Or Irrelevant Website Traffic From Search Campaigns
If you’ve run any type of awareness campaign, you’ve likely seen a boost in traffic overall, including irrelevant webpages or low-quality visitors.
What do we constitute as low-quality or irrelevant webpages?
Any page that wouldn’t result in a purchase, such as:
Careers page.
Investors page.
Advertise with us page.
Customer Service page.
Users who stayed on the website for less than one second.
Excluding these types of website visitors from the get-go can help make your remarketing efforts more cost-efficient in the long run.
3. Create Lookalike Audiences From Your Own First-Party Data
Using Google’s affinity audiences or attributes that consider someone at the top of funnel for your product or service can be daunting, especially if you’re a small business or have a limited budget.
It may feel that you don’t have a lot of options to reach new users without paying dearly for it.
But, have you ever thought about using your most valuable assets to build awareness?
Leveraging your own first-party data to create Lookalike audiences gives you more leverage than third-party data, such as Google’s affinity audiences, to reach like-minded people of users who already love your brand.
To create an audience like this, there are a few options to consider:
Create a remarketing list of past purchasers using Google Ads or Google Analytics.
Upload a list of past purchasers to Google Ads.
Depending on the size of these lists, you’ll have the option to create a Lookalike audience and use it for either YouTube, Display, or Search.
The example below shows what a remarketing list based on a completed purchase URL looks like when created in Google Ads:
Screenshot by author, January 2026
I personally like to use Google Analytics when creating remarketing lists because you have many more segmentation or filtering options to be as specific as you need to be.
As a reminder, your site must be tagged and linked with either your Google Analytics property or Google Ads tag.
Consideration Stage Remarketing Strategies
These four remarketing strategies help move the user from the consideration to the purchase phase quicker using different bidding strategies and offers.
4. Increase Bids For Qualified Visitors Of Your Site Who Haven’t Made A Purchase
An easy way to leverage qualified users in your existing Search campaigns is to increase the bid on those users simply.
You don’t need to create separate campaigns for these users if you don’t want to. Segmenting these users and manipulating the bids on them keeps your account management under control.
To use this strategy, you’ll first need to create a remarketing list of users who haven’t made a purchase yet. You can use qualifications only to include people who:
Have made it to the cart checkout.
Visited a certain number of pages.
Spent a certain amount of time on site.
Visited certain categories/high-value product pages.
Once you have created those, it’s time to add them to an existing Search campaign and increase the bid.
What this means is that you’re willing to pay more for their click because they’ve already interacted with your brand in some way.
In your Search campaign, navigate to “Audiences” on the left-hand side.
In this example, I’m setting the audience at the campaign level, but you can set it at the ad group level as well.
Make sure to choose “Observation,” so you’re still able to capture other new users who are researching your brand.
Screenshot by author, January 2026
Once you’ve added your qualified remarketing list, it’s time to increase your bid adjustment.
Still, in the Audiences tab, you’ll see your remarketing list added.
In the columns, you’ll see “Bid Adjustment.” Choose the “pencil” icon to change the bid as you see fit. In this example, I’m going to increase the bid by 15%.
Screenshot by author, January 2026
Once you’ve implemented this change, be sure to continuously check back on the audience performance and determine if bids need to be changed based on performance.
5. Increase Bids For Users Who Have Completed A Micro-Conversion
This strategy is similar to the example above, except for the type of user you want to target.
If a user has completed a micro-conversion of any sort, they’re likely a high-qualified user to make a purchase.
What are examples of a micro-conversion? Depending on your product or service, these could include:
Signing up for emails or newsletters.
Downloading an ebook.
Signing up for a webinar.
Requesting a free sample.
These types of conversions show a user is active in research mode and seriously considering your brand.
By increasing the bid in your search campaigns for these users, you’re saying you’re willing to pay more for their clicks because they’re that much more likely to convert.
The process of setting this strategy up is the same as above, with the exception of creating a remarketing list based on the success of these micro-conversions.
6. Test Maximize Conversion Value With Cart Abandoners
This remarketing strategy would require you to create a separate campaign targeting only cart abandoners.
You may be asking, “Why not just use Maximize Conversion Value for everyone?”
If you’ve ever tested out the Maximize Conversion Value bidding strategy in Google Ads, you’ll know exactly why.
The reasons I don’t recommend using this for all campaigns include:
You can’t set any maximum ceiling values.
Not all users are ready to purchase.
By segmenting a search campaign specifically for cart abandoners, you can test this bidding strategy at a lower threshold – and with the most qualified users who are most likely to make a purchase.
Similar to the above examples, this strategy tells Google that you’re willing to be more flexible in how much you pay for someone to make a purchase.
And what better way to test this than with users who were almost ready to make that purchase?
To set this strategy into motion, you first need to create a remarketing list of “Cart Abandoners.”
This will look different for everyone, but it will likely be URL-based and able to be created in either Google Analytics or Google Ads.
After that list has been created, it’s time to set up your new search campaign.
This campaign can be a duplicate of any other search campaign. Just make sure to exclude your Cart Abandoner list from that existing campaign. We don’t want any crossover here!
When creating the new campaign, this is where you’ll set the bid strategy to “Maximize Conversion Value” in the settings.
Screenshot by author, January 2026
Google Ads does give you the option to set a target return on ad spend, giving you somewhat control over campaign performance.
Depending on how much flexibility you have in your marketing budget, you can either leave that blank or set a target.
If you do set a target ROAS, make sure not to set it too high right away. Otherwise, the campaign won’t be able to effectively learn.
7. Create Offers Based On The User’s Interaction Timeline
Did you know you can create the same remarketing list of users, but segment them by the number of days?
Say you had a cart abandoner and wanted to move them toward purchase ASAP. You may be willing to give them a higher discount since the purchase was still new in their mind.
If they still haven’t purchased within three days, you may choose to still give them a discount, but not as high as the first offer.
After seven days, you still want them to keep your product top-of-mind, but that discount or offer may change again because they’ve waited so long.
So, how do you go about setting up this strategy?
First, you’ll want to create three different remarketing lists (for this example only).
Create cart abandoner audiences separated out by one day, three days, and seven days.
In Google Ads, you simply change the “membership duration” for each list. An example of where to change that during list creation is below:
Screenshot by author, January 2026
Once these lists are created, I recommend setting up different ad groups for each list. You’ll want different ad groups because the offer will be different for each list.
The last crucial piece of targeting cart abandoners is to exclude purchasers from your campaign. You will do this in the “Audiences” tab of your campaign and add your “Purchasers” remarketing list as an exclusion.
Post-Purchase Journey Remarketing Strategies
Once a user has made a purchase, that’s not necessarily the end of their journey!
These remarketing strategies enable past purchasers to become your most valuable asset and opportunities for repeat purchasers to become brand advocates.
8. Cross-Promote Other Products Based On A User’s Purchase Behavior
One of the best ways to create a repeat purchaser is to recommend complementing products based on a user’s purchase.
For example, say you’re a makeup brand, and a user just purchased their first tube of lipstick and mascara from you.
An effective remarketing strategy would include creating lists of past purchasers segmented by product category. This enables you to cross-promote other products and exclude product types they’ve just purchased.
In this example, you may create a remarketing list of users who have bought lipstick or mascara. You can then use that list to remarket products like foundation or eye shadow to encourage a repeat purchase.
These lists and strategies would work well in Dynamic Remarketing Ads or Google Shopping Ads. Because these products are much more visible, you’d want to use those campaign types to your advantage.
9. Exclude Past Purchasers To Maximize Spend Efficiency
As mentioned in strategy No. 7, you’ll want to exclude past purchasers from current acquisition campaigns to maximize spending efficiency.
An example of lazy remarketing is for a user to see an ad for a product they have already purchased.
Not only does that create a bad taste for the user, but that means you’re wasting valuable marketing money on people who have already purchased.
Now, there are certainly times when you’d not want to exclude past purchasers, especially if your product is a repeat purchase.
But, in these examples, your search campaigns are likely going after new users.
To exclude past purchasers, go to Audiences on the left-hand side of your campaign, then find the “Exclusions” table.
Screenshot by author, January 2026
10. Create Brand Advocates From Your Existing High-Value Customers
It’s true when they say that your customers are your best advocates. They have put their trust in you to deliver a high-value product or service that they have come to know and trust.
So, how do you turn them into advocates?
This remarketing strategy still includes utilizing that same past purchaser list. A few different options you could potentially offer past purchasers:
Create a referral program and give discounts to each person who purchases.
Offer discounts based on providing a positive public review.
Just because someone has purchased from you once does not mean they become a loyal customer. Sometimes it takes additional motivation to want to purchase again.
Loyalty or referral discounts are a great way to keep your existing customers coming back to you, as well as utilizing their own referral vehicles to generate new customers.
Creating referral programs is a low-cost and efficient multi-channel awareness strategy that is mutually beneficial for you – the brand and the customer.
Using Remarketing Lists With Intent, Not Just Coverage
Remarketing lists are most effective when they are built to support specific decisions inside your account. That includes how aggressively you bid, who you exclude, and where you shift budget based on user behavior.
Rather than treating remarketing as a single tactic, it works better as a system layered throughout the funnel. Lists tied to meaningful actions, like product views, cart activity, or prior purchases, tend to deliver far more value than broad, catch-all audiences.
As broader targeting becomes less reliable, remarketing offers a level of control that is increasingly hard to replace. When lists are thoughtfully segmented and actively used, they help PPC managers spend more efficiently and act with more confidence.
The real impact of remarketing does not come from how many lists you create. It comes from how intentionally those lists shape your bidding, targeting, and messaging decisions.
