Tag: Campaign Optimization

  • Why Frontloading Ad Spend Backfires—and How I Scale

    Why Frontloading Ad Spend Backfires—and How I Scale

    Why frontloading your ad spend usually backfires

    I don’t recommend launching most paid media campaigns with the biggest budget available.

    When I see advertisers spend aggressively before validating performance, the outcome is often predictable: acquisition costs rise, optimization slows, and stakeholder confidence weakens when the promised results fail to appear.

    I prefer a phased rollout because it gives a campaign time to generate meaningful data, improve bidding efficiency, and reveal which audiences, keywords, and creative ideas deserve more investment.

    Here, I’ll explain why frontloading ad spend usually backfires, when a more aggressive launch may be justified, and how I scale budgets without sacrificing long-term performance.

    Fire bullets before cannonballs

    For those of us who make a living driving growth through paid media, there’s one problem almost as frustrating as a tiny advertising budget: an advertiser determined to spend too much, too soon.

    I believe every paid media launch should follow a deliberate plan. As Jim Collins wrote in Great by Choice, successful companies fire “bullets” first, learn from the results, and then fire “calibrated cannonballs” with greater confidence.

    In my experience, most campaigns aren’t ready for a cannonball on day one. The algorithms are still learning, Quality Scores haven’t matured, and I don’t yet know which audiences, keywords, or creative assets will perform best. That is usually when acquisition costs and inefficiencies are highest.

    I recognize that exceptions exist. Years of relevant historical data or unusually strong evidence may occasionally justify a more aggressive launch, but I consider those situations rare.

    More often, I see frontloaded spending create expensive lessons instead of faster growth. The following scenarios illustrate why companies choose this approach and why I usually recommend a measured rollout instead.

    Your budget isn’t a KPI

    I never confuse the amount spent on advertising with actual performance, regardless of how an ad platform labels its reporting columns.

    The Modify Columns workflow in Google Ads. Its Performance bucket is… not actual performance.
    The Modify Columns workflow in Google Ads. Its Performance bucket is… not actual performance.

    From what I’ve observed, street-smart owner-operators tend to begin with careful ad budgets. Deep-pocketed decision-makers are more likely to focus on how much they are capable of spending.

    By deep-pocketed decision-makers, I mean anyone from a high-ranking Fortune-something executive or venture capitalist to a serial entrepreneur who has suddenly received an unusually generous investment from a single backer.

    When Nassim Taleb praises people with “skin in the game,” I take the point to be that risk looks different when I must personally bear its consequences. Risk asymmetry allows a splashy failure to hurt the company far more than it hurts the person who encouraged the gamble.

    Google Trends chart comparing U.S. searches for “bruno mars concert” and “concert near me” over the past year, with a sharp Bruno Mars spike in early 2026.
    Bruno Mars briefly steals the spotlight: Google Trends shows his concert query surging to peak popularity in early 2026, while “concert near me” maintains steadier interest across the year.

    Directly or indirectly, I’ve analyzed close to 1,000 ad accounts over the years. The pattern has been clear: advertisers that overspend early in pursuit of hypergrowth often flame out and lose stakeholder support.

    Dig deeper: PPC budgeting in 2026: When to adjust, scale, and optimize with data

    Four frontloading arguments—and why I question them

    1. “It’s a land grab. We need to spend aggressively to gain market share quickly.”

    I rarely consider this a prudent strategy, but I understand the motivation behind it.

    The goal is to capture market share and secure a first-mover advantage before new entrants catch up. I can think of plenty of fast-moving customer acquisition environments, particularly among technology startups, where that prospect feels irresistible.

    I once joined a project to help a startup with a greatly diminished, modest, incremental Google Ads campaign. What shocked me was how little the company had learned—and how little money remained after it had raised more than $250 million. Nearly all of that funding had been burned, including large sums spent on advertising, and there wasn’t going to be more where it came from.

    My team helped the company measure KPIs such as “new accounts that actually led to revenue” and “lifetime revenue from those accounts.” Despite three years of relentless nine-figure spending, no one had made those outcomes a serious measurement priority.

    I’ve also seen bootstrapped startups become carried away after celebrating their first $1 million to $2 million in “real” venture funding. They may have less money to burn, but the faulty logic is the same—and the risk can be even greater.

    Over the years, I’ve helped niche SaaS startups such as Clio in legal practice management and SuccessFactors in HR management achieve prominence without pretending they were already operating at their future scale.

    I don’t see small beginnings or cautious ad budgets as barriers to unicorn status. I can match customer acquisition spending to a company’s current growth stage without sentencing it to permanent smallness.

    For initial paid growth, I recommend defining the addressable market relatively tightly. I save the “huge addressable market” story for longer-horizon conversations with investors instead of using it to justify immediate overspending.

    To keep early-stage growth in perspective, I remind myself how a behemoth like Uber began. Its seed round was $1.25 million, and the company was valued at a modest $4 million.

    I’m happy to think big, but I don’t try to act bigger than the company really is when the money and product-market fit aren’t there yet. If the business establishes a meaningful lead, network effects and access to more capital can accelerate growth later.

    Futuristic rocket launches from an industrial ad campaign control center with gauges for budget, bids, conversions, optimization, and quality score.
    A paid media campaign blasts toward growth, but the glowing budget controls offer a warning: validate performance, optimize carefully, and earn the right to scale.

    I often wonder why founders race through essential growth stages by setting their newly raised—but finite—cash on fire. Sometimes investors encourage it. In other cases, the growth team treats fresh funding like permission to spend without restraint.

    I know what happens when the hangover arrives. Investors see high churn, stratospheric customer acquisition costs, or few tangible signs of customer acquisition at all. They react as though they have been mortally wounded, even when they helped create the strategy.

    I always return to unit economics because they still matter. Other founders may appear to have repealed the laws of economics, but I remember the familiar parental warning: “If Billy jumped off a cliff, would you do it too?”

    2. “We’ll learn faster.”

    I agree that predictive bidding algorithms perform poorly when conversion and value signals are sparse. More data can help them recognize the patterns associated with higher-value sessions.

    My team also needs to move through feedback loops to understand what works, what fails, and how the campaign should evolve.

    One genuine benefit of higher volume is that I can discover necessary negative keywords more quickly. With low query volume, many bad searches may remain hidden in “Other Search Terms” for a long time.

    Even so, I find that spending becomes counterproductive beyond a certain point. More money does not automatically turn incomplete data into reliable insight.

    • I consider the length and variability of the sales cycle. If two or three months normally pass between the first ad view and a sale, forcing too much budget into month one leaves me running ads almost blind, with little opportunity to learn and iterate before the money is gone.
    • I watch for self-inflicted CPC inflation. If I charge into an auction that has reached a workable equilibrium and bid too aggressively, I may raise my own costs and prompt competitors to bid higher as well.
    • I expect early metrics to be relatively weak because the campaign hasn’t established mature Quality Scores. In one account, CPCs eventually fell by 80% as Quality Scores developed and our optimizations took effect. I was relieved that the initial pilot had used a modest budget.

    I see little logic in pouring a flood of money into what may be the worst ROI environment the campaign will ever face. Even four to six weeks later, ROI is often substantially better as Quality Scores mature and statistical confidence improves.

    Dig deeper: Stop looking for the perfect PPC budget split

    3. “We’re pre-revenue, and our investor wants a quick market-size estimate.”

    Whenever I hear this argument paired with a hefty new check, I have to ask: What could possibly go wrong?

    To me, this takes the land-grab strategy even further into the intellectual ether. Customers—or almost any other concrete business outcome—may not be the immediate goal.

    From where I sit, the investor’s instruction often amounts to this: “We don’t care if we spend a huge amount of cash in the first month. Just get us a pile of data.”

    Glowing blue streams of people converge on a search bar and digital portal, symbolizing SEO traffic, AI visibility, and customer acquisition.
    As AI reshapes search, every glowing path to discovery carries commercial value—turning SEO investment into a conversation about pipeline, risk, and customer acquisition costs.

    When a powerful backer’s name comes up, it’s tempting for everyone to shrug and think, “Billionaire knows best.” I’ve watched teams dutifully throw money at a performance channel without asking it to perform, only to learn 35 days later that the investor won’t contribute another penny. The founder is then left without a credible Plan B.

    I can imagine the next investor arriving with a few basic questions.

    • “Q: What is the company, exactly? I mean, what product or service do you provide?”
    • “A: We’re still figuring that out, but we know there must be a gold mine in there somewhere, given how many music fans are searching for [music examples redacted to protect the innocent].”

    I don’t consider that a true launch because the project was never clearly defined in the first place.

    To be fair, I do believe fail-fast market research can be valuable. My team once spent about $10,000 over a short period for a client exploring a telecommunications business model. The test gave him a definitive answer about demand patterns, and he decided not to enter that vertical.

    I regard Google Ads as an invaluable market research tool when I use it with discipline. I define a meaningful business outcome and require potential customers to clear a credible threshold of intent. If I don’t need that level of evidence, I can explore the question with Google Trends, Google Analytics on a purpose-built content site, Semrush, or a dedicated market research company.

    Free Google Trends market research shows “bruno mars concert” giving “concert near me” a solid run for its money.
    Free Google Trends market research shows “bruno mars concert” giving “concert near me” a solid run for its money.

    In an unusual research scenario like this, my goal is to control waste. I may not be able to eliminate it entirely, but I can keep it proportional to the value of the answer I’m seeking.

    4. “A vendor won’t work with us unless we spend more immediately.”

    I know that some ad platforms, third-party software tools, and managed services impose steep minimums. I also understand why advertisers feel pressured by FOMO to overspend for entry into an exclusive club.

    I think the early OpenAI ad pilot offered a timely example. Steep minimums and uncomfortably high CPMs appeared to exclude the typical advertiser.

    I won’t twist myself into a pretzel to rationalize wildly overpaying for every ad interaction. Eventually, the market may become more accessible. I only have to compare how easy it is to begin with StackAdapt in programmatic advertising against the higher barriers associated with Google DV360 and The Trade Desk.

    When I advise a smaller company, I encourage it to grow first and enter a more demanding platform only when its size and budget justify the move. I see this as a version of The Millionaire Next Door logic: buying a house I can’t afford or driving a luxury car doesn’t make me wealthy. It might prevent me from ever getting there.

    Dig deeper: How to diagnose and fix the biggest blocker to PPC growth

    Earn the right to scale

    My core conclusion is that frontloaded ad spending often destroys the support a campaign needs to succeed. I don’t want to taint an entire channel—or the company’s broader growth function—by accelerating so hard that the campaign skids into a ditch. I can travel much farther after building solid traction.

    For an owner-operated business with real skin in the game, this is about more than stakeholder confidence. Severe waste isn’t merely bad optics; it can threaten the company’s future.

    So, when an overconfident investor or ad platform sales representative urges me to go from “zero to sixty in 3.5,” I’m inclined to tap the brakes. I would rather earn the right to scale than discover too late whether the airbags work.


    Inspired by this post on Search Engine Land.


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  • ChatGPT Ads Updates: New Drafts, Audiences and Formats

    ChatGPT Ads Updates: New Drafts, Audiences and Formats

    I’m seeing OpenAI continue to build out ChatGPT Ads with a new round of updates for advertisers. In an email, ChatGPT Ads announced changes across ChatGPT Ads Manager and the broader ad experience, including custom audiences, a new overview tab, suggested ad drafts, a refreshed static ad card format, and expanded availability in Japan and South Korea.

    Here is what stands out to me from the latest update.

    Custom audiences: I can now upload audience lists with 25,000 or more users to include or suppress audiences from campaigns. OpenAI is also allowing bid multipliers for audiences at the ad group level, which gives advertisers more control over how aggressively they want to reach specific segments.

    Overview tab: The new overview tab gives me a more centralized place to monitor account health, review recommended tasks that may improve campaign performance, and analyze key performance metrics in a larger, more flexible trend chart.

    Side-by-side comparison of current and new ChatGPT ad card formats for Heirloom Groceries, showing a grocery image, ad label, and refreshed layout.
    A before-and-after look at ChatGPT's refreshed static ad card, turning a small sponsored grocery prompt into a cleaner, more readable format with larger visuals and a clear Ad badge.

    Suggested ad drafts: If a campaign needs broader content coverage to improve delivery, I may see an option to select “Add new ad” from the campaign view. This feature uses existing website metadata to prefill an ad draft with an image, title, and description, which I can then review, edit, and assign to a campaign and ad group. Importantly, OpenAI says this does not generate new copy or imagery with AI.

    Japan and South Korea expansion: ChatGPT Ads are now live in Japan and South Korea. That means campaigns can target users in both markets, giving advertisers more reach if they do business there.

    Refreshed static ad card format: OpenAI is also rolling out a refreshed static ad card across web and mobile. I see this as a cleaner, more compact format designed to be easier to read while giving visuals more prominence. This format had already started appearing in late June.

    Large Google logo over colorful stacks of digital pages and folders, symbolizing search advertising, web content, and online marketing updates.
    A bold Google logo sits atop layered, colorful digital documents, evoking the fast-moving world of search marketing, ad formats, campaign assets, and platform updates.

    Why I care: ChatGPT Ads are still new, and OpenAI is clearly moving quickly. New targeting tools, reporting views, draft workflows, market expansion, and format tests all point to a platform that is still taking shape.

    My takeaway is simple: I need to keep watching these changes closely, test them as they become available, and continue refining ad creative, audience strategy, and campaign structure as ChatGPT Ads matures.


    Inspired by this post on Search Engine Land.


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  • I Let groas Run Google Ads: What Really Changed Fast

    I Let groas Run Google Ads: What Really Changed Fast

    I have watched paid search change into something far faster and less forgiving than the old reporting rhythm was built to handle. Auction dynamics shift by the hour, competitor bids move in real time, and search behavior changes across devices, times of day, and audience segments before a monthly report can even catch up.

    For me, the real cost has always lived in the gap between a performance signal and the moment a person can respond. groas is built to close that gap every hour of every day, and the data shows what can happen when that response loop gets dramatically shorter.

    When I sign up with groas, the process starts with a human account manager auditing the existing Google Ads account in detail. This is not a quick surface check. Campaign structure, keyword strategy, bidding logic, budget allocation, conversion tracking, quality scores, search term reports, and auction insights all get reviewed.

    I see that audit as the foundation for everything that follows. groas optimizes toward the goals and account structure defined in the roadmap, so a clean conversion hierarchy, accurate tracking, and a well-organized account give the system stronger signals to work with. That early human judgment matters because it shapes the machine’s operating environment.

    From there, I like that the rollout is paced across the first 60 days. The system does not start moving aggressively before it understands the account it is working in.

    Weeks 1 to 2, observation: groas ingests historical performance data, establishes baselines, and maps patterns across search terms, device performance, time-of-day variance, and audience behavior. During this stage, no changes are made while the system learns the account.

    Weeks 3 to 4, calibration: The system starts making targeted optimizations, including bid adjustments, negative keyword additions, match type refinements, and budget reallocations between campaigns. These are deliberate campaign-by-campaign changes, so each move can build on the last.

    Weeks 5 to 6, traction: I begin to see early changes show up in the data. Performance shifts become visible across ROAS, conversion value, and wasted spend as the optimizations compound.

    Weeks 7 to 8, scaling: Around the 60-day mark, the account has usually stabilized enough for groas to scale. More budget moves into the campaigns and keywords with the strongest conversion history, expanding from a proven base instead of guessing.

    Google Ads performance dashboard showing conversions, ROAS, conversion value and cost metrics with multicolor trend lines for April 2026.
    A Google Ads performance snapshot tracks April 2026 shifts in conversions, ROAS, conversion value and cost, highlighting the volatility behind paid search optimization.

    Once groas is running, I see it work across the full account the way a skilled team would, except it does not stop. It writes and tests ad copy, deploys dynamic landing pages that adjust around each search, turns ad groups on and off when performance calls for it, moves budget where it earns the most, and adjusts bidding strategies in response to live signals.

    Anything a person can do inside Google Ads, groas can do too, around the clock.

    Capability matters, but results matter more.

    The clearest way I can explain the value of continuous, full-surface management is through a real account groas took over. It was a high-spend search account in a tough paid search category: a U.S.-based online mobile recharge platform that lets people instantly top up prepaid mobile phones across major U.S. carriers without creating an account or paying added transaction fees.

    This business operates in prepaid wireless, serving many pay-as-you-go and underbanked customers who recharge monthly or even more often, usually right when their balance runs out. That model puts Google Ads at the center of growth.

    Demand is intensely intent-driven. When someone’s credit runs out, they search for a way to recharge and often buy within minutes. Capturing that moment is the whole game. But it is also a punishing channel to manage profitably because transactions are low-value and high-volume, margins are thin, and the auction is crowded with carrier brand terms and generic “recharge” and “top up” searches.

    In an account like this, a few cents of wasted CPC multiplied across hundreds of daily conversions can decide whether the account is profitable or quietly leaking money.

    In this account, a conversion meant a completed recharge. So the numbers are not abstract to me. Every point of ROAS and every additional daily conversion means more recharges processed and more revenue generated on the same budget base.

    Google Ads performance dashboard showing conversions, ROAS, conversion value and cost with multi-line PPC trend chart from May 5 to June 5, 2026.
    A Google Ads reporting view tracks PPC performance after optimization, with conversions, ROAS, conversion value and spend moving across a month of campaign activity.

    The comparison looked at two account reporting periods: before groas assumed optimization and after.

    Spend: up 18% to $164,000.

    ROAS: up 30%.

    Average CPC: down 15%.

    Conversions per day: up 29%.

    Conversion value: up 44%.

    Cost per conversion: down 14%.

    The clearest improvement was return on ad spend. ROAS rose from 1.02x to 1.32x, which is roughly a 30% improvement in value returned for each dollar spent.

    Google Ads performance dashboard showing conversions, cost, ROAS and conversion value trends after connecting to groas.
    A Google Ads trend chart marks the moment groas was connected, with conversion, cost, ROAS and value lines tracking performance shifts through spring 2026.

    At the same time, average cost per click fell from $2.34 to $2. But the more important point is what the account did with the clicks it paid for. Conversions and conversion value both grew faster than spend, which means each dollar worked harder than it had under the previous setup.

    Daily conversions rose from 571 to 739, about 29%. Daily conversion value rose even faster, from $4,702 to $6,772, or roughly 44%.

    What stands out to me is that these gains came through consolidation, not expansion. groas focused spend into 10 active search campaigns, down from 17.

    Budget that had been spread thinly across underperforming campaigns was redirected into the keywords and campaigns with the strongest conversion history. Fewer campaigns, lower click costs, and more value returned created a cleaner, more focused account.

    That is what an account looks like when waste is removed and budget is concentrated where it can compound.

    The mechanism behind results like these is speed plus breadth of attention. Under traditional management tied to weekly or monthly reporting cycles, an underperforming search term might run for 7 to 14 days before anyone acts. A target CPA can drift far from its goal between reviews. An autonomous system narrows the time between signal and response to hours while watching every campaign at once.

    As groas gathers more data on audience behavior, search patterns, and conversion value, its decisions become more precise. Budget can then concentrate further into the campaigns that return the most value.

    That is the structural difference I see between autonomous management and periodic manual review. Each optimization creates new data, and that data informs the next decision. A system running continuous observe-and-optimize cycles can draw more signal from the same account over time.

    Futuristic data archive with glowing server-like filing cabinets, stacked documents, and network lights symbolizing AI marketing data infrastructure.
    Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.

    Business context still belongs with the people who understand the business. When a client launches a new product line, changes pricing, or redefines which conversions matter most, that direction has to come from a person. groas optimizes toward the goal it is given, and setting that goal is strategic work.

    Creative is where I see the human and machine layers working together most clearly. groas writes and tests ad copy and landing page variations at a pace no human team could match, while the people on the account define brand voice, positioning, and creative direction. The strategist shapes the message, and groas finds the specific wording and layout combinations that convert.

    For businesses ready to see better results

    If I am looking at a current setup that runs on monthly reports and weekly changes, I expect to find a steady gap between what the data says and what actually happens in the account. That gap is where budget gets wasted and opportunities close. In the account above, it showed up as more than 15 active search campaigns, many spending inefficiently, with budget spread too thin to compound.

    groas’s onboarding is structured to keep the transition low-risk. The first two weeks are analysis only, measured changes follow, and meaningful performance shifts usually appear within the first month or two, with scaling beginning around day 60. Live campaigns keep running throughout calibration, and the initial audit grounds changes in context from the start.

    For businesses that have stayed with the same agency for a long time without material improvement, I would expect the audit alone to surface issues that have gone unaddressed.

    Get started here.

    For agencies running groas white-label

    I do not think execution-layer account management scales well on its own.

    Continuous optimization, bid management, negative keyword maintenance, and budget pacing take a lot of time at volume. As an agency adds clients, it usually has to add headcount or accept that some accounts get less attention than others. Most agencies know exactly which accounts are underserved.

    With groas handling execution autonomously across a client portfolio, I can see the team shifting toward strategy, client relationships, and new business.

    The work that differentiates an agency is also the hardest to automate. Clients see stronger results, and team capacity moves toward the work that creates the most value.

    Get started here.


    Inspired by this post on Search Engine Land.


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  • How I Make My Marketing Stack Work Harder With AI

    How I Make My Marketing Stack Work Harder With AI

    I see performance marketing under more pressure than it has faced in a decade. Budgets are flat or shrinking, expectations keep rising, and AI is quickly raising the standard for what “good” performance actually looks like.

    For years, I watched performance marketing rely on a familiar playbook. When performance plateaued, teams added another vendor. When targeting weakened, they bought another dataset. When activation became difficult, they layered on more technology. But as budgets tighten and the demand for immediate ROI grows, constantly expanding the stack is no longer sustainable.

    The challenge I see for enterprise marketers is not that they lack data. It is that they struggle to operationalize the data they already have.

    At the same time, AI is revealing a hard truth about modern marketing architecture. Most AI failures are not really model failures. They are data failures. Even the most advanced agent, model, or automation workflow cannot make up for fragmented customer profiles, disconnected activation systems, or stale audience definitions. Yet much of the customer data platform conversation still centers on launching more AI agents.

    I think that misses the point.

    The real question is not whether a platform has an AI agent. It is whether my data foundation can support the leap from automating tasks to partnering on strategic outcomes.

    For too long, the industry treated self-service as the north star. The goal was to help marketers avoid engineering tickets and data science queues. That made sense for the last decade, but it also turned marketers into manual operators of complex systems. The new bar is not just self-service. It is self-directed performance at scale.

    I see a fundamental shift in the marketer’s job-to-be-done. We are moving away from the operational burden of building and managing audiences and toward the strategic work of setting outcomes. Instead of spending the day wrangling segments, I can define the goal, whether that is maximizing customer lifetime value or reducing churn, and let the system suggest the best audience definitions and activation paths. When intelligent agents are connected to a clean data foundation, I move from managing technology to orchestrating outcomes. That is the new blueprint for performance.

    At mParticle, we describe this approach as a performance engine: a model where the data foundation and activation layer work as one connected system. The goal is not simply to collect customer data. It is to make that data immediately useful for performance outcomes.

    Futuristic data archive with glowing server-like filing cabinets, stacked documents, and network lights symbolizing AI marketing data infrastructure.
    Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.

    Audience Agent is one example of that idea in action. I can describe what I want in plain language, such as high-value customers who have not repurchased in 60 days, and the agent proposes the underlying logic for me to review and approve.

    For me, the shift is not about handing everything over to automation. It is about working in a marketer-led workflow with an expert collaborator beside me. The longer I work with it, the better it understands the business, the data, the customers, and the patterns that actually move performance. That understanding is only as strong as the data foundation behind it, and ours was built for this long before AI made the need obvious. The marketer leads. The agent elevates and expands the work. Together, they push what is possible.

    That same philosophy shows up in capabilities such as Audience Expansion and Household Reach. Audience Expansion helps me identify additional high-potential users directly from first-party datasets, without depending on third-party lookalike audiences or outside data sources. It gives teams more precise control over the balance between scale and quality.

    Household Reach addresses one of digital marketing’s most persistent blind spots: buying decisions rarely happen in isolation. By using first-party customer data and enriching it with trusted third-party signals, Household Reach helps marketers engage the full decision-making unit, not only the individual who converted first.

    The key distinction is simple. I only need to bring my first-party data. The householding solution handles the rest, helping me reach more of the household without spending extra resources building additional audiences or manually configuring campaigns.

    What connects these approaches is a different mindset. Better performance should not require more vendors, more engineering resources, or more external data. It should come from extracting more value from the customer relationships brands already understand.

    In this era of intense performance pressure, I believe the advantage will go to marketers who stop looking for more vendors to solve every problem. Success will not come from adding more tools to the stack. It will come from using a stronger data foundation to meet rising expectations and activate more of the data we already own.


    Inspired by this post on Search Engine Land.


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  • ChatGPT Ads Audience Lists: What Marketers Need to Know

    ChatGPT Ads Audience Lists: What Marketers Need to Know

    ChatGPT ads

    I am seeing OpenAI roll out the ability to upload audience lists inside ChatGPT Ads. The new option appears under the “Tools” section and is labeled “Audiences.”

    My read is that this gives advertisers a way to target campaigns based on the audience lists they upload to the platform, which should make ChatGPT Ads more useful for more precise ad targeting.

    ChatGPT Ads Manager Audiences screen showing an empty audience list and a button to create the first audience.
    A new Audiences area appears in ChatGPT Ads Manager, inviting advertisers to upload customer lists for campaign targeting and audience filtering.

    More details. I can upload raw or hashed emails and phone numbers and use them as audience filters for campaigns running on ChatGPT Ads.

    Create audience dialog in ChatGPT Ads for uploading email or phone customer lists as CSV or TXT files for ad targeting.
    A ChatGPT Ads audience upload form shows how advertisers can add customer lists, choose identifier type, and submit CSV or TXT files for campaign targeting.

    What it looks like. I spotted screenshots of the feature from Craig Graham and Joss Froggatt on LinkedIn. Here is what the Audiences option looks like in the platform:

    Neon Google search bar with microphone icon over a futuristic digital data background, representing search technology and SEO updates.
    A glowing Google search bar cuts through streams of digital data, capturing the fast-moving world of search, shopping visibility, and SEO innovation.

    Why I care. I see this as another sign that OpenAI is continuing to build more customization and targeting controls into its new ChatGPT Ads platform.

    For advertisers and marketers, audience uploads could make the platform more practical and more performance-focused. If the targeting works well, it may help improve conversions, strengthen ROI, and make ChatGPT Ads a more serious option in paid media plans.


    Inspired by this post on Search Engine Land.


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  • Get More From Microsoft Advertising With AI Signals

    Get More From Microsoft Advertising With AI Signals

    How to get more from Microsoft Advertising than a campaign import

    When I see Microsoft Advertising campaigns struggle to scale, the issue is often not the platform itself. It is usually that the account is being treated as a copy of a strategy built somewhere else.

    Importing campaigns can get me live quickly, but it is only the beginning. Real performance comes when I add human judgment, Microsoft-specific structure, clean measurement, business-specific controls, and enough creative assets to help AI understand what I am actually selling.

    The strongest accounts I see have a shared pattern: import is the starting point, visual creative opens more demand, and AI works best when I give it the right structure, signals, measurement, and guardrails.

    Here is how I approach Microsoft Advertising when I want more than a simple campaign import.

    Note: I’m a Microsoft employee, and I have written this as objectively as possible. I have also included community-sourced hidden gems where they help highlight useful features.

    1. I start with import, but I do not stop there

    Import is useful because it removes friction. It can bring over campaign structure, assets, and settings from Google, Meta, or Pinterest so I can launch faster. The mistake is assuming that a successful import means the Microsoft Advertising strategy is finished.

    Imported campaigns often preserve yesterday’s assumptions. I still need to make Microsoft-specific decisions about budget, bidding, audiences, creative, measurement, reporting, and AI-powered opportunities.

    Decide whether sync helps or holds the account back

    One of the first choices I review is whether future changes from the source platform should keep syncing into Microsoft Advertising. If I only want to mirror another platform, automatic sync can reduce maintenance. If I want to build a Microsoft-specific strategy, automatic sync can quietly overwrite the optimizations I make after launch.

    To see the full list of import settings, I go to Manual import > Advanced settings. From there, I review which settings should stay, which should change, and which Microsoft-specific opportunities were never part of the original structure.

    Review budgets, bids, currency, and Microsoft-only options

    Imported budgets may not match the opportunity or efficiency available in Microsoft Advertising, especially when I can consolidate campaigns and use ad-group-level controls instead.

    Imported bids can also carry assumptions from another platform. I want Microsoft Advertising to have room to optimize for its own auction dynamics, audiences, and conversion data.

    Screenshot of a LinkedIn post by Hana Kobzová praising LinkedIn Profile Targeting and Job Seniority for B2B Microsoft Advertising precision.
    A PPC expert highlights LinkedIn Profile Targeting as a Microsoft Advertising hidden gem, especially for B2B campaigns that need to reach senior decision influencers.

    Review Microsoft-specific settings after import

    Import cannot choose Microsoft-specific opportunities for me. After launch, I review the settings that can materially change performance.

    • LinkedIn profile targeting: I can bid up or down, observe performance, and use LinkedIn profile data as a Performance Max audience signal across Company, Industry, Job Function, and Seniority.
    • Ad-group-level scheduling and location targeting: I can override campaign-level schedules and location targets at the ad group level, including whether ads serve in the user’s time zone or the account’s time zone.
    • Impression-based remarketing: I can target, exclude, or adjust bids based on someone seeing my ad. It does not require an existing email list or pixel, and members can remain on the list for up to 30 days after a single impression.
    • Multimedia ads: These visual-heavy ads have their own auction, can appear on the same SERP as my text ad, and may also serve in Copilot.
    • Cross-account portfolio bidding: If I need to launch a new account for the same brand, I can let it benefit from conversion data in an existing account.
    • Microsoft Clarity: I can use this free behavioral analytics tool to understand how people and AI engage with my site, where landing pages create friction, and which grounding queries may connect AI systems to my content.
    • Creative and editorial considerations: Microsoft has stricter advertising policies than many platforms, but it also allows useful capabilities such as exclamation points in headlines and disclaimers of up to 500 characters that do not take up ad space. If I enable disclaimers, my ads will only serve when the disclaimers can appear alongside them.

    2. I build the signal foundation before optimizing

    Account-level settings can look overly technical, but I treat them as the foundation for AI performance. They determine whether automation learns from clean data or from messy, misleading signals. Settings such as business attributes also help me communicate why customers should choose the business.

    Verify conversion tracking and attribution before changing bids

    Even the best bidding strategy cannot make up for incomplete conversion data. Before I blame bids, keywords, audiences, or creative, I verify that conversion and attribution data are flowing correctly.

    • Microsoft Click ID (MSCLID): This helps connect ad clicks to conversion activity.
    • View-through conversions: These help me understand the role visual creative plays before a conversion happens.
    • Simplified conversion setup: This enables intelligent conversion action creation.

    Without verified tracking, it is easy to diagnose the wrong problem. What looks like a bidding issue may actually be incomplete or inconsistent conversion data.

    If the organization relies heavily on UTM parameters, I also validate how auto-tagging and manual tagging interact. My goal is clean reporting, not duplicated parameters or attribution confusion caused by mislabeling.

    Treat creative inputs as signals

    When enabled, Microsoft Advertising can use images from landing pages to create more relevant ad experiences. If the site has strong, brand-safe, well-maintained imagery, this can improve creative coverage without forcing me to manually build every variation for every campaign type.

    AI-optimized creative works best when the site already gives it good material. If the pages include images I would not want in ads, or if the imagery is sparse, text-heavy, or poorly matched to the offer, I upload the assets I want the system to use. Auto-retrieved images reduce friction, but they do not replace creative strategy.

    Use account-level negatives carefully

    Account-level negatives can eliminate unwanted traffic patterns across the account. Microsoft supports phrase and exact match negatives. If I need to remove a root problem, phrase match is often the better option. If I need to block a specific search term, exact match may work better. Neither negative match type accounts for close variants.

    I only use account-level negatives for terms I am confident should not serve anywhere in the account. More nuanced exclusions belong at the campaign or ad group level.

    3. I use structure and controls to help AI perform

    Microsoft Advertising gives me useful controls, but my goal is not to micromanage every lever. I want to give AI cleaner inputs, stronger guardrails, and fewer structural problems to solve.

    Purple Microsoft Advertising graphic stating Search Partner Network low-quality impressions delivered to advertisers fell 20% over the past year.
    Microsoft reports a 20% reduction in low-quality Search Partner Network impressions, crediting earlier invalid activity detection, stronger quality signals, and tougher enforcement.

    Concentrate signals instead of fragmenting them

    Ad-group-level location and ad schedule settings can reduce the need to create duplicate campaigns or split budgets across multiple accounts.

    I have seen advertisers create separate campaigns only to support different geographies or schedules. In many cases, I can manage those settings at the ad group level, simplify the structure, and concentrate conversion volume.

    That matters because automated bidding usually performs better with stronger, more consistent signals. When possible, I aim for at least 30 conversions in 30 days. That level of signal gives automated bidding a better chance to make stable decisions than a fragmented structure with thin conversion volume.

    Use scheduling, location, and disclaimers as guardrails

    I always review location targeting. Microsoft Advertising supports geographic targets, radius targeting, and exclusions, but city-, county-, metro-, or DMA-level strategies may be more practical than forcing ZIP codes.

    If Microsoft does not support a specific location target, it defaults to the next-highest level, such as ZIP code to city or city to DMA. If I need narrow targeting, I look closely at exclusions.

    Avoid unnecessary learning volatility

    Large bid or budget changes can create volatility while the system adjusts. As a general rule, I try to keep bid or budget changes below 15% over a 14-day period when I want to avoid unnecessary learning disruption. Larger changes may still be necessary, but I make them intentionally.

    Seasonality adjustments help when I expect a temporary conversion rate change because of a sale, event, promotion, or other short-term spike. Data exclusions help when conversion tracking breaks or reports misleading data that I do not want automated bidding to learn from. These tools are not bidding hacks. They protect automation from learning the wrong lesson.

    Use conversion value rules whenever possible

    The cleanest way I can communicate value to the bidding algorithm is through conversion value rules grounded in accurate conversion tracking. These rules let me create if/then logic for devices, audiences, and locations, then add a monetary amount or multiply conversion value.

    Microsoft supports bid adjustments across audiences, devices, demographics, locations, and time. Multiple adjustments can compound. If a user qualifies for several categories at once, the bid may become more aggressive than I intended.

    Before I add another layer, I ask whether I truly want to spend more to reach that audience, in that location, on that device, at that time. If I want the algorithm to understand value, meaningful conversion values and conversion value rules are usually stronger signals. If values are not reliable, CPA-oriented bidding with carefully chosen adjustments can still work.

    Microsoft Advertising graphic showing 45% higher indexed conversion rate and 1.5% lower indexed cost per conversion at network level.
    Microsoft Advertising reports network-level gains, with indexed conversion rates up 45% and indexed cost per conversion down 1.5%, tied to cleaner traffic quality.

    4. I use audiences, inventory, and creative to shape demand

    Microsoft’s differentiated audiences, inventory, and creative formats can help me generate and shape new demand instead of only capturing demand that already exists.

    Use LinkedIn profile targeting intentionally

    LinkedIn profile targeting is still one of the most distinctive audience capabilities in Microsoft Advertising. I can apply bid adjustments based on company, industry, job function, and seniority.

    Multiple targets within the same LinkedIn profile category act as “or” statements, while targeting across categories narrows the signal. A company target plus a seniority target is more restrictive than two company targets. That can be powerful when intentional and expensive when accidental because bid adjustments compound.

    For B2B advertisers, this can be especially useful, but it is not limited to enterprise brands. Any business selling to specific professional audiences can use these signals to prioritize valuable traffic.

    For example, if I am trying to reach someone traveling for work with local experiences or travel gear, I might bid up on a “Business development” job function in an industry with a conference happening in the next two to three weeks.

    Build audiences from exposure, not just site visits

    Traditional remarketing depends on someone visiting my website. Impression-based remarketing gives me another option: building audiences from people who have been exposed to my advertising.

    A prospect may not click the first time they see the brand, especially in formats such as Audience ads, Premium Streaming, or Multimedia ads. Impression-based remarketing lets me continue the conversation later instead of treating the first exposure as a failed interaction. An impression can become the starting point for an audience strategy.

    Reevaluate search partners and exclusions

    Many advertisers disable search partners because they assume the inventory behaves like display network expansion on other platforms. I do not start with that assumption. Search partner inventory is still search inventory, and Microsoft provides publisher visibility, so I can evaluate it directly.

    Recent Microsoft studies have shown a 45% improvement in conversion rates and a 20% reduction in low-quality impressions tied specifically to Search Partner inventory, independent of advertiser optimization.

    If specific publishers are not performing, I use the available controls. I can manage unlimited exclusion lists at the MCC account level, and each list can exclude up to 2,500 URLs. If I need to protect a campaign’s ability to target a placement, such as when Performance Max and Audience ads run together, I exclude domains surgically instead of cutting off useful inventory.

    LinkedIn comment from Dii Pooler about separating multimedia ads from branded search campaigns to gain more SERP real estate.
    A PPC strategist highlights a practical Microsoft Advertising tactic: run multimedia ads separately from branded search to expand visibility without self-competition.

    Use Multimedia ads to expand SERP presence

    Multimedia ads participate in their own auction and can appear in prominent visual placements on the search results page. A traditional search ad and a Multimedia ad can both appear for the same brand, increasing my presence on the SERP.

    I can enable Multimedia ads at the campaign level and then use ad-group-level decisions to direct budget toward or away from the format.

    They matter because they can amplify visual presence, serve as ads in Copilot, and qualify for impression-based remarketing. Their value is not limited to direct-click performance. They can connect search visibility, visual storytelling, and remarketing strategy.

    Use Audience ads to expand reach

    I use Audience ads, including display, native, and video, as a controlled way to expand reach, support full-funnel strategy, and build remarketing inputs that inform other parts of the account.

    Audience ads support audience strategies, placement preferences, content category controls, and creative preview before launch. For organizations that require legal, brand, product, or executive approval, preview capability can make review much easier.

    Use creative and editorial details to reduce friction

    Microsoft Advertising has editorial policies I need to understand instead of assuming every platform evaluates ads the same way. Claims such as “best,” “number one,” or other superiority language need clear landing page support.

    Microsoft Advertising also allows some emphasis I might not expect, such as one exclamation point in headlines, but that flexibility does not remove the need for substantiated claims and clean final URLs.

    Editorial issues are often misdiagnosed as platform friction. In many cases, the issue is one specific asset rather than the entire ad. Final URL problems are more fundamental and can prevent an ad from serving at all.

    Extensions and visual assets can help brands communicate more value before users reach the landing page, especially in competitive categories where plain text may not provide enough differentiation.

    5. I treat PMax, AI Max, and Copilot as AI opportunities with guardrails

    I find Microsoft’s approach to AI most useful when I view it as augmentation rather than replacement. Human-centered AI should help me scale thoughtfully while preserving consent, transparency, and trust.

    Screenshot of a LinkedIn post by Ben Luong praising Microsoft Clarity for summarizing mobile usability pain points and odd click behavior.
    A marketer highlights how Microsoft Clarity surfaces real user friction, from mobile testing gaps to visitors tapping images they mistake for links, offering useful context for ad and landing page optimization.

    Know what Performance Max is designed to enable

    Performance Max can be powerful, but it requires a different mindset from traditional campaign structures. Asset groups are not ad groups. There is no asset-group-level equivalent to ad-group negatives, and I cannot force one asset group to take priority over another.

    Performance Max is built for AI-driven allocation. If strict control is the priority, traditional Search, Shopping, and Audience campaigns may provide clearer governance. When I want to influence Performance Max, I focus on the inputs that matter most.

    • Strong audience signals: I include impression-based remarketing and LinkedIn profile targeting, which are unique to Microsoft.
    • Relevant creative: Copilot can pull creative from the landing page and adapt existing creative with tonal shifts, rewrites, or formatting improvements.
    • Thoughtful search themes: I avoid duplicating exact match keywords as search themes because exact match keywords take priority in the auction.
    • Meaningful conversion tracking: I make sure conversion tracking and conversion values are accurate because Performance Max needs conversions to perform effectively.
    • Clear landing pages: The landing page must communicate the offer clearly. If it does not, the algorithm may struggle to match the right queries, and people may struggle to do business with me.

    If I run the same search theme as an exact match keyword, there is a strong chance the exact match keyword will serve instead of the Performance Max campaign. I prefer to use search themes as testing grounds rather than duplicates.

    Performance Max website URL reporting gives me URL-level visibility into spend, clicks, impressions, and conversions. That gives me more to work with than impression-only reporting and can make automated campaign testing easier to justify.

    Separate campaigns when budget separation matters

    If budget separation matters, I create distinct campaigns instead of forcing multiple business objectives into one Performance Max campaign. Microsoft’s capacity of 300 Performance Max campaigns, compared with Google’s 100, can be useful when budget priorities genuinely need separation.

    For example, if I have two equally important products with drastically different tROAS goals, I would not want them to share budgets because I cannot specify which asset group or product should take priority. Separate campaigns with distinct budgets and tROAS goals are usually a cleaner fit.

    My rule is simple: if related assets and audiences can share a budget, I consolidate Performance Max campaigns to strengthen conversion volume. If budget separation matters, I build that control at the campaign level instead of trying to force it through asset groups.

    Evaluate AI Max and Copilot for new opportunities

    AI Max now addresses many of the use cases that once made Dynamic Search ads valuable. If my goal is to let Microsoft AI better match queries, creative, and landing pages, AI Max may be the better place to test.

    That does not mean I abandon existing high-performing campaigns. It means I stay intentional about whether I am investing in legacy dynamic functionality or AI-powered capabilities built on Microsoft’s latest technology.

    Ads can appear in relevant Copilot experiences when Microsoft determines there is clear commercial intent and the ad may help the user. Ads have served in Copilot since 2024. The goal is not to force ads into AI answers. It is to preserve a useful experience for the user.

    Neon Google search bar with microphone icon over a futuristic digital data background, representing search technology and SEO updates.
    A glowing Google search bar cuts through streams of digital data, capturing the fast-moving world of search, shopping visibility, and SEO innovation.

    Copilot is not a separate campaign type I manually opt into. Performance Max, AI Max, exact, phrase, and broad match search campaigns, Multimedia ads, and Shopping ads are all eligible to serve in Copilot. Performance Max and AI Max have the easiest time serving there because they can adapt to AI-driven experiences.

    Use generative AI as a creative workflow and diagnostic tool

    Copilot can help me brainstorm, rewrite, refine, and adapt creative across Performance Max, responsive search ads, Multimedia ads, Audience ads, and other campaign types. It does not replace the marketer. It reduces friction between strategy and iteration.

    Ad Studio can generate new creative assets and make adjustments such as background modifications, seasonal refinements, location-specific tailoring, and additional aspect ratios. I see its best use as accelerating iteration once the creative strategy is already clear.

    AI-generated assets can also help me diagnose how clearly the site communicates. If the outputs accurately represent the business, the site is probably sending clearer signals. If they repeatedly miss the mark, the landing pages, messaging, or content structure may be confusing both AI systems and people. The Performance Max campaign generator can be a useful diagnostic shortcut for the same reason.

    6. I use reporting and Clarity before blaming the auction

    No amount of AI, bidding nuance, or audience strategy can compensate for poor measurement. Microsoft Advertising provides strong reporting visibility, and I use it before making media-only decisions.

    Use transparent reporting to make better decisions

    Microsoft provides visibility into every search term that generates a click as part of its transparency approach. I use that visibility to understand what is really happening behind performance changes.

    • Genuinely wasteful: There may be no business case for targeting that search.
    • An AI-driven match: The query may look questionable until I examine the customer journey with behavioral analytics.
    • A landing page issue disguised as a traffic problem: Before I add a negative keyword, I evaluate post-click behavior to see whether the landing page or conversion tracking is the real issue.

    Use Microsoft Clarity before making campaign changes

    Microsoft Clarity answers one of the most important questions in campaign diagnostics: what happens after the click? It can show whether users engage with the page, get confused, abandon forms, run into technical issues, or complete actions that are not being tracked correctly.

    I want Clarity in the diagnostic process before I make major campaign changes.

    • If people arrive and get stuck, the issue may be the landing page experience.
    • If they complete the desired action but conversions do not appear in Microsoft Advertising, the issue may be tracking.
    • If they arrive and immediately disengage, the issue may be creative alignment, traffic quality, or the offer itself.

    Clarity can also help me understand how AI systems interact with my content, including the grounding queries that led AI systems to cite the domain and recommendations for improving citation opportunities.

    If AI systems cite the domain as relevant, that can validate the content strategy. If they do not, or if the queries reveal mismatches, that may point to gaps in how the content communicates value.

    I apply Microsoft-specific optimizations deliberately

    I can import existing campaign structures and assets while still taking advantage of Microsoft-specific capabilities. AI can play a central role, act as an occasional assist, or be used selectively, but scaling becomes harder without some level of AI adoption.

    Testing Microsoft Advertising does not require a massive investment. It does require getting the fundamentals right: conversion tracking, bid-to-budget ratios, and creative that reflects the channel’s visual nature.

    When I get those fundamentals right, Microsoft Advertising gives me search term transparency, GDPR-compliant impression-based audiences, and opportunities to reach people across the surfaces where they work, live, and play.


    Inspired by this post on Search Engine Land.


    crushpress.ai community screenshot
  • How I Cut Google Ads Invalid Clicks by 50% With Audiences

    How I Cut Google Ads Invalid Clicks by 50% With Audiences

    A Google Ads targeting tactic that cut invalid clicks by 50%

    Advertisers are projected to lose $172 billion a year to ad fraud by 2028. I have seen how quickly that problem can move from an abstract industry statistic to a very real performance issue inside a Google Ads account.

    The risk is especially high in competitive industries where CPCs are expensive and every wasted click hurts. One client I worked with was in exactly that situation, and invalid click activity was dragging campaign performance below any profitable level.

    After testing the usual defenses, I adjusted Google Ads audience targeting in a way that reduced invalid-click activity by 50% and brought the campaigns back to profitable performance.

    Case study: How I cut invalid clicks by 50%

    The client sold book editing and ghostwriting services. The search terms triggering the ads were relevant and high intent, but the traffic was not converting anywhere close to the level needed for profitability.

    The warning signs appeared quickly. Google was reporting a 60% to 80% invalid click rate. Microsoft Clarity recordings showed bot-like behavior from Google Ads traffic. Many search terms had click-through rates above 80%, and some were even above 100%. GA4 and other analytics tools also showed far fewer sessions than the number of clicks reported in Google Ads.

    I tested third-party click fraud tools first, but they did not produce any measurable improvement in performance.

    Next, I filed an investigation with Google. Google agreed that suspicious activity existed, but said it had already caught all of it and had not charged the account for those clicks.

    Google Ads invalid click investigation response

    I was still confident that Google was not filtering out all of the invalid activity, so I decided to use the targeting controls inside the account more aggressively.

    I added 540 Google-defined audiences to the Google Search campaigns and set them to Targeting.

    The result was immediate. The invalid click rate dropped by 50%, and the conversion rate rose back to profitable levels.

    Image

    Here is why I tested this approach, why I believe it worked, and what advertisers should understand before trying it in their own accounts.

    What click fraud and invalid clicks actually are

    Google defines invalid clicks as clicks on ads that do not come from genuine user interest. That includes intentionally fraudulent activity, accidental clicks, and duplicate clicks.

    In practice, this can include actual fraud, such as competitors clicking ads, as well as less malicious behavior like accidental double-taps.

    Google does not charge advertisers for clicks it determines are invalid. If Google initially charges for a click and later classifies it as invalid, it credits the advertiser back for that activity.

    Why the usual defenses can fall short

    Google catches a lot of invalid click activity, but this account showed me that the system is not perfect.

    That gap is why so many third-party click fraud tools exist. Most of them try to identify suspicious IP addresses and block them before they can keep costing advertisers money.

    The challenge is that fraudsters understand how these tools work. They can cycle through IP addresses with VPNs and avoid being stopped by a system that only blocks previously identified addresses.

    If a tool blocks an IP address after suspicious activity occurs, that may help only if the same IP address is used again. When the source keeps changing, old IP exclusions lose much of their value.

    There is also a platform limit to consider: Google allows a maximum of 500 IP address exclusions per campaign.

    Image

    The tactic: Add audiences set to Targeting

    I started thinking about what might separate fraudulent traffic from legitimate traffic. Google’s predefined audiences stood out because Google builds hundreds of audience segments from demographics, search behavior, and browsing behavior.

    For example, someone researching private jet companies and Rolex watches might be classified by Google as a luxury shopper and added to that audience.

    My hypothesis was simple: fraudsters who constantly rotate IP addresses may not also be building normal-looking online behavior profiles that fit neatly into Google’s predefined audiences.

    So I added most of the available audiences to the Search campaigns. I was not trying to target only audiences that matched the ideal customer. I was using the audiences as a filter for users who carried enough Google audience signals to look more like real people.

    The key detail is that I chose Targeting, not Observation.

    When I use Targeting, Google limits ads to people who trigger the keywords and also belong to the selected audiences.

    When I use Observation, Google simply reports how people in those audiences engage with the ads compared with everyone else. The ads can still show to anyone who triggers the keywords.

    I would only test this tactic in accounts with unusually high invalid click rates. It can create real downside, including the risk of blocking legitimate searchers who do not fit inside Google’s predefined audience segments.

    How to test this in your own account

    In a Search campaign, go to Audiences > Edit audience segments > Targeting > Browse. Then select the audiences you want to add and click Save.

    Image
    Google Ads audience targeting setup

    Common questions about fighting click fraud

    Will Google refund clicks it identifies as invalid?

    If Google identifies a click as invalid when it happens, I am not charged for that click. If Google identifies the click as invalid later, the account receives a credit toward future advertising.

    How do I see how many invalid clicks I am getting?

    The Invalid activity credit report in Report Editor inside the Google Ads UI provides the most detailed reporting.

    I look at two key metrics there: Invalid clicks, which are clicks I was not charged for, and Credited clicks, which are clicks I was originally charged for but later credited back.

    Google Ads invalid activity credit report

    I can also add the Invalid clicks and Invalid click rate columns at the campaign level, though not at the ad group or keyword level.

    What is a normal invalid click rate?

    A February study found an 11.4% invalid click rate across 43,700 accounts.

    Industry makes a major difference. While the average invalid click rate for StubGroup clients is very close to that study’s finding, I have seen advertisers in competitive industries with invalid click rates above 40%.

    Should I file an investigation with Google?

    If I have reason to believe Google is charging for invalid clicks, I consider filing an investigation here.

    Why this approach worked best

    Using Google’s predefined audiences as a filter cut this account’s reported invalid click rate in half. More importantly, it blocked activity that Google had said it was already catching, which turned failing campaigns into profitable ones.


    Inspired by this post on Search Engine Land.


    crushpress.ai community screenshot
  • Why Google Ads Structure Can Make or Break Performance

    Why Google Ads Structure Can Make or Break Performance

    How campaign structure shapes Google Ads performance

    When I audit Google Ads accounts, it is easy to focus first on the obvious issues: keywords, bids, ad copy, and Quality Scores. But one of the biggest performance barriers I see is not buried inside a single campaign setting. It is the way the account was structured from the start.

    Campaign structure shapes how Google’s machine learning reads the account, how budget moves across goals, and whether useful data is collected in one place or scattered across too many campaigns. When the structure is wrong, I am not just leaving performance on the table. I am making the algorithms work harder with weaker signals.

    That is why I look closely at structure across standard Search campaigns, Performance Max, and Smart Bidding. The account architecture often determines whether optimization efforts can actually work.

    How campaign structure shapes Google’s learning

    I used to see advertisers treat campaign structure mainly as a cleanup exercise: tidy ad groups, logical naming, and campaigns separated by product line or geography. To Google’s systems, though, structure means something much more important.

    Every campaign is a data container. The way I segment campaigns determines which signals Google can pool together for bidding and targeting decisions. When the structure is scattered, the learning is scattered too, and optimization becomes slower and less accurate.

    Smart Bidding and automation usually perform better when more data is concentrated in fewer campaigns. Google’s algorithm needs meaningful volume, often around 30 to 50 conversions per campaign per month, to move beyond the learning phase and make reliable predictions. If I spread conversions across too many campaigns, each campaign can end up starved of the data it needs.

    A common example is an ecommerce account with 12 separate Search campaigns, one for each product category. Each campaign averages 8 to 12 conversions per month. Smart Bidding is active, but no campaign consistently exits the learning phase.

    In that situation, the fix is usually consolidation.

    Over-segmentation breaks Smart Bidding

    Smart Bidding strategies such as Target CPA, Target ROAS, Maximize Conversions, and Maximize Conversion Value depend on real-time signals like device, location, time of day, audience, search query, and more. Google weighs those signals together to predict which auctions are worth entering and how much to bid.

    When I see campaigns that are over-segmented, I usually see the same problems appear. First, conversion volume is too low, so each campaign operates below the level Google needs for confident bidding decisions. That often leads to unstable CPAs and CPCs.

    Second, learning phases last too long. Every budget change, bid strategy switch, or structural edit can trigger a new learning period. Over-segmented accounts can feel permanently stuck there, never reaching their full potential.

    Third, signal consolidation is missed. Bidding signals do not freely transfer across campaigns. A branded campaign cannot teach the algorithm inside a non-branded campaign, even when both campaigns share the same conversion goal.

    Finally, bid cannibalization becomes a real risk. When multiple campaigns compete in the same or overlapping auctions, I can end up driving up my own costs and creating avoidable inefficiency.

    The result is an account that looks optimized on the surface, with Smart Bidding enabled, audiences attached, and conversion tracking active, but still underperforms because the structure underneath is working against every optimization layered on top of it.

    The impact of Performance Max

    Performance Max adds another layer to campaign structure. Unlike Search campaigns, PMax runs across Google inventory, including Search, Display, YouTube, Gmail, Discover, and Maps. It uses asset groups and audience signals to guide automation, which makes setup more important and more complicated.

    Asset group segmentation

    I think of asset groups inside PMax as mini-campaigns. Google uses them to understand context, match creative to searches, and optimize delivery. When asset groups are too broad, mixing unrelated products, audiences, or themes, the algorithm has a harder time matching the right creative to the right situation.

    I prefer to segment asset groups by product category or service line, audience intent level such as prospecting versus retargeting, and creative theme or offer type.

    This gives Google clearer signals about what each group is meant to accomplish, which can improve both creative matching and bidding efficiency.

    PMax and Search campaign overlap

    One of the most damaging mistakes I see in accounts running both Search and Performance Max is failing to set clear boundaries between them. PMax can serve across all placements, including branded and non-branded searches, so it can compete with Search campaigns if I do not define where each campaign type should operate.

    Without proper segmentation, PMax can cannibalize high-intent branded search traffic and inflate costs on terms I might have won more cheaply through Search. Search campaigns can lose impression share they otherwise would have captured, and attribution becomes harder to interpret because it is less clear which campaign is truly driving performance.

    My preferred solution is to use campaign-level negative keywords, brand exclusions, and clear audience segmentation. PMax should complement Search campaigns, not compete with them.

    Budget allocation and automation conflict

    PMax runs as a single campaign with a single budget, but because it delivers across multiple channels, budget allocation happens dynamically. When PMax and Search campaigns are not organized around clear goals, Google may spend on the easiest placements rather than the best ones.

    Structural choices, such as whether I run one PMax campaign or split campaigns by product line, directly affect how budget is distributed and how well automation can support business goals.

    Match type strategy and its structural implications

    Match types are often treated as a keyword-level decision, but I see them as a structural decision too. Running broad match, phrase match, and exact match across separate campaigns, or even separate ad groups, without a coherent strategy can create overlap and wasted budget.

    Google Ads looks very different than it did a few years ago. Broad match now casts a much wider net, and Google increasingly pushes advertisers to pair it with Smart Bidding. That combination can work, but only when the campaign structure gives the algorithm enough support.

    Broad match with Smart Bidding works best when there is enough conversion data, a clear goal, and enough traffic for Google to learn from. In a fragmented account, broad match can make the problem worse. It brings in more searches, but the algorithm does not have enough clean data to make good use of them.

    The safer approach is to keep match types within fewer campaigns, use negative keywords to prevent campaigns from bidding against each other, and review search term reports regularly so I can tighten boundaries where needed.

    Keyword and ad group architecture: When granularity becomes an obstacle

    Single Keyword Ad Groups, or SKAGs, are mostly a thing of the past, but many accounts still carry their legacy: hundreds of tiny ad groups with one or two keywords and nearly identical ads. That level of detail made sense when advertisers managed bids manually. Today, it often works against Smart Bidding.

    Too many ad groups create the same data problem at a smaller scale. Responsive search ads perform better when they have more to learn from, including which headlines get clicked, which asset combinations work, and how auctions behave. That learning happens faster when ad groups are consolidated around broader themes.

    I usually aim for three to five tightly themed ad groups per campaign instead of dozens of micro-segmented groups. Each ad group should include enough keyword variation to generate useful data while staying focused enough to preserve message relevance.

    The goal is maximum signal quality. If structural granularity does not improve data consolidation, it is usually unnecessary complexity.

    Conversion goals and campaign alignment

    Structure also determines which conversion actions each campaign optimizes toward, and I consider goal misalignment one of the quietest performance killers in Google Ads.

    If multiple campaigns share a poorly defined conversion goal, or if different campaigns optimize toward different actions without a clear hierarchy, Smart Bidding receives conflicting instructions. It may optimize toward micro-conversions like page views or add-to-carts when the real objective is form fills or phone calls. It may also treat goals as equal when one is clearly more valuable than another.

    A structurally sound account connects campaign goals to business objectives, not just platform metrics. It separates primary conversions from secondary tracking actions, and it uses accurate conversion values when campaigns rely on value-based bidding.

    Performance Max is especially sensitive to conversion goal quality. Because PMax controls its own bidding and placement decisions, it will optimize aggressively toward whatever I tell it matters most. If that signal is wrong, the campaign may optimize efficiently toward the wrong outcome.

    Signs your structure is hurting performance

    Structural problems rarely announce themselves clearly. I usually see them show up as issues that are easy to blame on ads, bids, or audiences.

    Persistent learning phase warnings are one sign. Campaigns may be frequently flagged as limited by learning even when budgets are consistent. Unstable CPAs or ROAS are another warning, especially when performance swings do not settle over time.

    I also watch for high impression share lost to budget when total budgets seem adequate, disproportionate spend flowing into a small number of campaigns, limited visibility into PMax search terms, and declining Quality Scores as the account grows across too many ad groups.

    When two or more of these symptoms appear at the same time, I treat structure as a likely root cause. Bid adjustments and creative testing will not fix the problem until the foundation is corrected.

    A framework for structural audits and consolidation

    Restructuring an active account carries risk. Any major structural change can trigger learning phases and temporary performance disruption, so I consolidate carefully and use data as the guide.

    Step 1: Assess conversion volume by campaign

    I start by identifying which campaigns consistently generate 30 or more conversions per month and which fall below that threshold. Campaigns with low volume are usually candidates for consolidation.

    Step 2: Map audience and intent overlap

    Next, I look for campaigns that compete against each other for similar searches or audiences. Overlap creates waste, and structural waste is one of the most expensive forms of inefficiency.

    Step 3: Evaluate PMax and Search boundaries

    Then I audit how PMax and Search interact. I want to know whether brand terms are being captured by the right campaign type and whether negative keywords are in place to prevent cannibalization.

    Step 4: Simplify ad group architecture

    From there, I move away from SKAG-style granularity and toward theme-based groupings. Ad groups that serve overlapping intent should usually be consolidated into broader, cleaner themes.

    Step 5: Align conversion goals

    Finally, I audit conversion actions across all campaigns. Primary goals should match real business outcomes, and value-based bidding inputs should reflect actual revenue data whenever possible.

    Important: I would not restructure everything at once. I would start with the highest-spend campaigns, monitor performance through the learning phase, and validate results before moving to the next round of consolidation.

    Campaign structure comes first

    I see campaign structure as the foundation of Google Ads performance. When it is right, Smart Bidding, Performance Max, and audience targeting can work with stronger signals, clearer goals, and more efficient budget allocation.

    When it is wrong, no optimization layered above it can fully solve the problem. Bids cannot fix fragmented data. Creative cannot correct misaligned conversion goals. Performance Max cannot prioritize efficiently when its boundaries with Search are unclear.

    The biggest performance improvements in Google Ads often do not come from a new bid strategy or a sharper headline. They come from stepping back, auditing the account architecture, and rebuilding the foundation everything else depends on.

    Structure first. Optimization second.


    Inspired by this post on Search Engine Land.


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  • Microsoft PMax Experiments: Smarter Testing Arrives

    Microsoft PMax Experiments: Smarter Testing Arrives

    I’m seeing Microsoft bring experimentation into Performance Max campaigns, giving advertisers a more practical way to test campaign changes and measure incremental impact without disrupting live performance.

    What’s new: Microsoft is adding two Performance Max experiment types designed to help advertisers understand whether their campaigns are truly driving better results.

    Uplift experiments help me measure the incremental impact of Performance Max campaigns by comparing results against a control group.

    Upgrade experiments give me a way to compare an existing campaign with an upgraded Performance Max version before I fully roll out the change.

    For eligible accounts, both experiment types are available under Campaigns > Experiments.

    Why I care: Until now, Microsoft Ads experiments were limited to Search campaigns. Bringing testing into Performance Max gives advertisers a safer path to validate changes, improve performance, and make more data-driven decisions before committing budget.

    Image

    Between the lines: As Microsoft expands experimentation, it has also renamed its existing experiment offering to Search optimization experiments. That distinction helps separate traditional Search testing from the new Performance Max testing capabilities.

    I see this as part of Microsoft’s broader push to give advertisers more advanced optimization tools across automated campaign formats.

    The bottom line: Microsoft is closing an important gap in its Performance Max offering. With dedicated uplift and upgrade experiments, advertisers can test with more confidence and get a clearer view of the real impact of automated campaigns.

    First spotted: The help docs were spotted by PPC News Feed founder Hana Kobzová.

    Dig deeper: Microsoft’s help docs include details on the Uplift experiment for Performance Max and the Upgrade experiment for Performance Max.


    Inspired by this post on Search Engine Land.


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  • Why I Treat Creative as the Best Broad Targeting Filter

    Why I Treat Creative as the Best Broad Targeting Filter

    Across Google Ads, Meta, and TikTok, I’m seeing platforms push advertisers toward broader, AI-driven targeting. Performance Max, Advantage+ campaigns, and TikTok’s automated audience expansion give algorithms more room to find converters, but they also reduce how much control I have over exactly who sees each ad.

    That shift is changing how I think about campaign qualification.

    As targeting becomes broader, creative has become one of the most important signals for both people and algorithms. I no longer see audience qualification as something that happens only inside targeting settings. More and more, it happens inside the message itself.

    In other words, broad targeting is making creative my best qualifier.

    The shift from audience qualification to creative qualification

    For years, I treated targeting as the primary lever for improving lead quality. If I needed prospective graduate students, I could layer education interests, demographics, and remarketing audiences. If I needed patients looking for specialized care, I could build audiences around health-related behaviors and intent signals. If I needed insurance shoppers, I could narrow targeting by age, life stage, and consumer interests.

    Those approaches are not disappearing, but I can see their influence shrinking. Platforms increasingly ask me to provide broad audience inputs, strong conversion signals, and compelling creative, then let machine learning determine who is most likely to convert.

    Meta’s Advantage+ ecosystem, Google’s Performance Max campaigns, and TikTok’s recommendation engine all operate on this principle.

    The challenge is that algorithms still need signals.

    Conversion data remains the strongest signal, but I believe creative is becoming more important in helping platforms understand who should engage with an ad. Every headline, image, video, and call to action gives the system more context about the intended audience and the desired action.

    Creative is no longer just a persuasion tool. I now treat it as a targeting signal.

    Why broad targeting requires more intentional creative

    I still see many advertisers create ads as if targeting will do all the audience qualification for them.

    The messaging stays broad because the assumption is that audience settings will narrow who sees the ad. But when platforms expand delivery beyond tightly defined segments, vague creative can attract engagement from people who are unlikely to become qualified leads.

    The consequences are familiar: lower lead quality, higher cost per qualified lead, less efficient optimization, and noisier conversion data.

    That is why I need creative that clearly communicates who the offer is for, and just as importantly, who it is not for.

    The goal is not simply more clicks or more video views. The goal is engagement from the right people.

    When my creative clearly identifies the audience, users can self-select. Qualified prospects lean in. Unqualified prospects move on. Both outcomes improve campaign performance and give machine learning systems cleaner signals.

    Higher education: When creative becomes the targeting layer

    Higher education is one area where I see this shift clearly.

    Historically, campaigns relied heavily on demographic filters, education interests, degree status, and segmented audience lists to reach prospective students.

    Today, many strong-performing campaigns use broad lookalike audiences, Advantage+ audiences, or broad prospecting structures designed to maximize audience size and algorithmic learning.

    But broader audiences create a real challenge.

    If I am promoting an online Master of Science in Data Analytics program, I do not need just any prospective student. I need prospects who meet specific admission and career criteria. They may already hold a bachelor’s degree. They may have professional experience. They may want to move into leadership or pivot into a more technical career path.

    Rather than relying only on targeting settings to communicate those distinctions, I would build them directly into the creative.

    Consider the difference between a generic headline like “Advance your career with a Data Analytics degree” and a qualifying headline like “Built for bachelor’s degree holders ready to advance into leadership – earn your online M.S. in Data Analytics.”

    The second example immediately signals who the program is for. Undergraduate prospects are less likely to engage, while qualified graduate prospects are more likely to click, convert, and reinforce positive optimization signals.

    In that case, the creative itself becomes the qualification mechanism.

    Google Performance Max: Creative guides the algorithm

    Google Performance Max may be the clearest example of this industry-wide shift.

    Despite the name, audience signals are not strict targeting controls. I treat them as starting points that help Google’s systems learn. Ultimately, Google decides where and to whom ads are shown across Search, YouTube, Display, Discover, Gmail, and Maps.

    Because I have less direct control over audience selection, creative assets become increasingly important in helping Google’s systems understand who should respond.

    Imagine I am helping a healthcare provider promote orthopedic services. A generic headline might say, “Expert Care for Your Health Needs.” While that may be technically accurate, it gives very little context about the intended audience.

    A stronger alternative would be, “Persistent Knee Pain? Meet with Our Orthopedic Specialists.”

    That second headline identifies a specific need, a specific audience, and a specific solution. Users immediately know whether the message applies to them, and Google’s systems receive stronger engagement signals from people actively experiencing that problem.

    The same principle applies across insurance, legal services, financial services, and education.

    When my Performance Max creative clearly identifies the audience and their need state, I help Google’s machine learning systems learn faster and optimize toward more qualified outcomes.

    TikTok: The first three seconds matter more than ever

    TikTok has always relied heavily on content signals to determine who sees a video.

    As the platform continues investing in automation and audience expansion, creative becomes even more critical.

    I pay close attention to the opening seconds of a video because they often determine not only whether a user keeps watching, but also how TikTok categorizes and distributes the content.

    For lead generation campaigns, I want qualification to begin immediately.

    A graduate program might open with, “Already have a bachelor’s degree and looking for your next career move?”

    An insurance provider might start with, “Shopping for Medicare coverage this year?”

    A law firm specializing in workplace injury cases could lead with, “Were you injured on the job within the last 12 months?”

    These openings accomplish two things at once.

    First, they quickly tell viewers whether the content is relevant to them. Second, they give TikTok’s algorithm stronger behavioral signals about who engages with the video.

    Qualified prospects are more likely to continue watching and take action. Unqualified viewers are more likely to scroll past. Over time, that self-selection process improves audience learning.

    Creative is now a performance lever

    One of the biggest mistakes I can make today is treating creative as something that happens after strategy and targeting are finalized.

    In increasingly automated advertising environments, creative is strategy.

    The message, visuals, hooks, and calls to action no longer serve only a branding or conversion role. They help platforms determine who should see the ad in the first place.

    That means I need creative and media teams working together more closely than ever.

    When I build campaigns, I ask whether the creative clearly identifies who the offer is for, whether it communicates relevant qualifications or prerequisites, whether an unqualified prospect would immediately recognize that the message is not intended for them, and whether I am helping both users and algorithms understand the ideal audience.

    If the answer is no, the campaign may be relying too heavily on targeting to solve a problem that creative is now better positioned to address.

    The future of qualification is creative

    As Google, Meta, and TikTok continue expanding AI-driven targeting, I expect advertisers to have even less control over audience selection than they do today.

    Qualification does not disappear. It shifts into the creative itself.

    What once happened primarily through audience settings is increasingly happening through messaging, visuals, and creative strategy.

    To thrive in this environment, I need to write headlines that identify the intended audience, create videos that establish audience fit in the first few seconds, and build qualifications, prerequisites, and intent signals directly into the message.

    Every ad speaks to two audiences at once: the user and the algorithm.

    Platforms are handling more targeting than ever, but they still need direction.

    Increasingly, that direction comes from creative. In a world of broad targeting, creative is not just the message. It is the qualifier.


    Inspired by this post on Search Engine Land.


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