Category: PPC

  • 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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  • Google’s AI Ad Disclosures Bring Needed Transparency

    Google’s AI Ad Disclosures Bring Needed Transparency

    I’m watching Google add a new layer of AI transparency to ads across Search, YouTube, and Discover. The company said its new How this ad was made section will appear inside My Ad Center, giving people a clearer view of whether AI played a role in the ad creative they see.

    The panel will show whether an ad was created or modified with AI. I see this as a meaningful expansion of Google’s ad transparency tools, especially as more advertisers rely on generative AI to produce images, copy, and other campaign assets at scale.

    What it looks like. I’ll be able to access the disclosure from the three-dot menu or the info icon on an ad. In the screenshot Google shared with Search Engine Land, the My Ad Center panel includes a dedicated section explaining how the ad was made.

    Google will handle some disclosures. When advertisers use Google’s own generative AI ad tools, Google will automatically add the disclosure inside My Ad Center.

    Google My Ad Center screen showing a How this ad was made AI disclosure for an ad created or edited with AI.
    Google’s My Ad Center adds a clear AI disclosure, helping users see when ad creative may have been created or edited with generative AI.

    For advertisers using third-party AI tools, Google said they will have control over whether to disclose AI use. Depending on local requirements, an AI label may also appear directly on the ad, either automatically or after the advertiser uses that control.

    Why I care. AI-generated ads are getting easier and faster to create, so disclosure matters more than ever. I want to know when creative was made or changed with AI because requirements can vary by market, platform, and ad format.

    Existing ad rules still apply. Google said its ad policies still prohibit misleading or deceptive advertising, whether AI was involved or not. This update adds more visibility into how an ad was made, but it does not change the requirement that advertisers clearly identify who they are and what they are promoting.

    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.

    Earlier AI safeguards. Google already embeds imperceptible signals, including SynthID, into content created with its generative AI tools. Election advertisers are also required to disclose synthetic or digitally altered content in political ads, under a policy Google introduced in 2023.

    The announcement. Google shared more details in Expanding AI transparency in ads.


    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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  • Why MMM Still Demands Clean Data and Human Judgment

    Why MMM Still Demands Clean Data and Human Judgment

    I see marketing mix modeling (MMM) becoming easier to access, but I do not think it has become easy to get right.

    After several conversations about MMM adoption, I keep hearing the same concern: “We believe in MMM, but we do not know how to get started.”

    My answer is that open-source platforms have lowered the barrier to entry in a meaningful way. What they have not lowered is the level of expertise required to produce results that are trustworthy, explainable, and useful for decision-making.

    Open-source MMM has changed the starting point

    I am seeing MMM adoption accelerate because marketers need more durable measurement methods. Almost half of U.S. marketers expect to invest more in MMM over the next year, and many now rank it as one of the most reliable measurement approaches available.

    The open-source shift is real. Three production-grade libraries now give teams a practical way to approach MMM across a wide methodological spectrum.

    • Robyn (Meta, R): I see this as the most approachable starting point because it includes automated hyperparameter search through Nevergrad, Pareto frontier model selection, decomposition, and response curve plots. It is also the one I use most often because it is highly customizable.
    • Meridian (Google, Python/TensorFlow): I view Meridian as a more rigorous option, especially because it uses Bayesian inference, geo-level priors, and principled uncertainty quantification. The tradeoff is a steeper learning curve.
    • PyMC-Marketing (PyMC Labs, Python): I consider this the most flexible path. It offers a full probabilistic model that comes closest to academic-grade Bayesian MMM, but it also demands the most statistical fluency.

    This generation of tools has removed the old $150,000 to $500,000 consulting gate that used to be the primary path into MMM. A team with R or Python expertise and reasonably clean historical data can now run a model in-house.

    Chart showing marketing mix modeling costs dropping from a $150k-$500k consulting gate to near-zero open-source tools while expertise needs stay high.
    Open-source R and Python tools have lowered the cost of starting with marketing mix modeling, but the expertise needed to produce trustworthy, actionable MMM remains the real ceiling.

    The caveat I always make explicit is this: “free tool” does not mean “free model.” The software may be free, but the domain expertise needed to configure it correctly is not. That expertise is a major part of the value.

    The vendor landscape is crowded and complicated

    I also see a fast-growing SaaS layer built on top of open-source MMM. To evaluate it clearly, I find it helpful to separate vendors into a few practical groups.

    Data-layer-first vendors

    Platforms like Rockerbox and Northbeam started with attribution and data collection, then added MMM. Their advantage is usually pipeline speed and data access, not deep modeling flexibility or customization.

    Measurement-first vendors

    Platforms such as Measured, Analytic Partners, Ekimetrics, and Nielsen Gracenote tend to offer more rigorous modeling and enterprise-grade capabilities, usually at a higher price point.

    Google Meridian and GA360

    I think Google’s decision to open-source Meridian is both a generous contribution to the field and a strategic move. When a walled garden funds and packages a measurement methodology that can be used to evaluate its own channels, I believe it is worth maintaining healthy skepticism about priors, defaults, and assumptions, even when the code is transparent.

    Chart comparing open-source marketing mix modeling libraries Robyn, Meridian, and PyMC-Marketing along a spectrum from approachable to statistically rigorous.
    Open-source MMM tools now span a clear trade-off: Robyn offers the most approachable starting point, Meridian adds Bayesian rigor, and PyMC-Marketing pushes deepest into statistical flexibility.

    The practical vendor question I keep coming back to is simple: who owns the data layer, and does that ownership create conflicts in the modeling layer?

    Challenge 1: Data access can quietly break MMM

    I think data access is the most underappreciated MMM implementation blocker. A well-specified model needs more than a quick export from a dashboard.

    • I usually want two to three years of weekly data as a baseline, so the model can capture at least two full seasonality cycles and enough spend variation to learn from.
    • I need consistent channel-level spend granularity, not just a broad “digital” bucket. Search, social, display, video, and other channels need to be separated.
    • I need offline channels such as TV, OOH, radio, events, and direct mail, even though they often live in different systems, belong to different teams, and use incompatible time periods.
    • I need external covariates, including macro indicators, competitor activity, pricing data, and product launch calendars.
    • For B2B, I often need even more history because longer sales cycles and lower conversion volumes make the data requirements more demanding.

    In practice, I often find that the real blocker is the six-week data archaeology project that happens before modeling begins. Finance owns revenue. The brand team owns TV. The agency owns digital spend. A spreadsheet from 2021 may be the only record of trade promotions.

    The model is only as good as the data archaeology behind it, and that is rarely the part anyone highlights in a vendor demo.

    Challenge 2: I still have to roll up my sleeves

    AI assistants have lowered the syntax barrier. They can scaffold a Robyn run, generate a Meridian configuration, or help debug a PyMC model. What they cannot reliably do yet is make the judgment calls that determine whether an MMM is credible.

    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.
    • I still have to decide where to land on a Pareto frontier across hundreds of model solutions, balancing NRMSE against DECOMP.RSSD tradeoffs.
    • I still have to know whether Nevergrad’s optimizer has meaningfully converged or simply landed in a local minimum.
    • I still have to configure adstock transformation parameters, including Weibull shape and scale or geometric decay, so they reflect realistic channel behavior.
    • I still have to diagnose why a model gives a channel an implausible contribution and decide whether the fix is a prior, a data correction, or a variable exclusion.

    In other words, if I try to vibe code my way into MMM, I may end up with a model that appears to work but is wrong in ways I will not catch. The scripting is not the hardest part. The real work is validating the output, including using channel-specific incrementality experiments to calibrate the model.

    Challenge 3: Human expertise is not optional

    Even if the tools mature enough for AI to run a competent default MMM, I still see human expertise as essential. The irreplaceable work is encoding business context that no model can infer from the data alone.

    • Adstock and carryover context: I need to know whether a TV buy carries over for four weeks, paid search carries over for three days, or a brand awareness campaign decays over months. That knowledge usually lives with channel experts, not inside the dataset.
    • Saturation curve shape: I need to recognize when a channel is probably approaching diminishing returns before the model says so, and I need to question the model when it suggests something implausible.
    • Guardrails and anomaly handling: I need to explicitly model or flag COVID troughs, product launches, pricing shifts, and macro disruptions as structural breaks. AI does not automatically know a client had a pricing crisis in Q3 2022.
    • Interpretation sanity checks: If a model assigns 40% of contribution to TV for a brand spending $2 million on TV, I need the experience to say, “That feels wrong,” and investigate. That intuition is earned, not computed.
    • Organizational translation: A technically correct model has little value if I cannot explain why it recommends moving 15% of search budget to CTV in language a CMO and CFO will act on.

    I start with the groundwork before the model

    The best place to begin is not the model itself. I start by understanding what data is needed, who owns it, and who can help interpret it in the context of real marketing decisions.

    None of that is quick or easy, but it is essential if I want meaningful insight from MMM, whether I choose an open-source library or a subscription-based platform.

    As a practical first step, I would download Robyn’s demo script and experiment with sample data before applying MMM to my own business data. That kind of hands-on testing makes the strengths, limits, and judgment calls much clearer.


    Inspired by this post on Search Engine Land.


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  • Google Merchant Center Drops “Next” in Simple Rebrand

    Google Merchant Center Drops “Next” in Simple Rebrand

    I’m adjusting how I refer to Google’s shopping platform now that Google has dropped “Next” from Merchant Center Next. Going forward, the product is simply called Google Merchant Center.

    Google made the change official in a Merchant Center announcement, saying, “The platform you use today will simply be referred to as Google Merchant Center.” For anyone managing product feeds, shopping campaigns, or merchant accounts, this is mainly a naming update rather than a product change.

    I remember when Google Merchant Center Next was introduced in 2023 as the newer version of the old Google Merchant Center. Over the past few years, more merchants, site owners, and advertisers moved into that updated experience.

    At this point, it appears that Merchant Center Next has effectively become the standard experience. So Google is removing the “Next” branding and returning to the simpler name: Google Merchant Center.

    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.

    Google said users will start seeing the “Next” branding removed from Help Center articles, email communications, and the Merchant Center interface.

    Google also clarified that no action is required and that the name change does not affect existing accounts. In other words, I do not need to update settings, migrate anything, or make account-level changes because of this rebrand.

    Why does this matter? When I talk about Google’s merchant tools now, I can leave off “Next” and just call the platform Google Merchant Center. Honestly, that is what many of us were already calling it anyway.


    Inspired by this post on Search Engine Land.


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  • 5 Critical Questions I Ask Before Buying Any AI Tool

    5 Critical Questions I Ask Before Buying Any AI Tool

    AI now shows up in nearly every corner of marketing, and for every useful initiative I see, it feels like 10 vendors appear with a tool that claims to solve it.

    When this wave first started, I took more vendor calls and answered more outreach than I do now. Over time, I noticed I was asking the same core questions again and again to decide whether an AI tool was actually worth deploying.

    If I feel overwhelmed by AI vendor pitches, these are the five questions I use to separate useful solutions from noise. They help me understand what the tool does, whether it solves a real business problem, and whether the vendor is the kind of partner I would trust with my budget, data, and team’s time.

    1. What problem does your tool solve?

    I start here because I want to understand the purpose of the tool and, more importantly, whether the value it creates connects to real business outcomes.

    If a vendor cannot clearly explain the challenges or use cases the tool addresses, I assume it was not purpose-built for a real problem my team faces. That applies whether I am evaluating it from an in-house perspective or on behalf of an agency. I am cautious when vendors lead with feature-heavy language but cannot explain the business benefits those features are supposed to deliver.

    If a vendor can identify at least one existing team problem and explain how the tool improves business outcomes, I keep the conversation going. My next question is usually for a case study that shows how the tool was used and what results it delivered for an organization similar to mine in size, market, or vertical.

    I look for benefits such as increasing output or identifying tracking gaps that speed up troubleshooting. I do not rush to buy a tool simply because it promises to save time, even if that promise is true. I need to know how I will use that extra time before I can decide whether the savings are meaningful.

    2. What expertise do you have in the space where this tool solves a problem?

    This answer tells me whether the vendor built the tool for advertisers or merely at advertisers.

    Technical skill matters, but so does understanding how a media buyer actually spends the day. If the vendor does not have direct experience in media buying, I want to hear how the team researched the market and how those insights shaped the product.

    A shallow understanding of the problem is a red flag for me. I do not expect every sales rep to have deep domain expertise, but someone on the team should. If I am seriously considering the tool, I want access to that person early in the process.

    When a vendor has a credible story about identifying a problem I recognize firsthand and building a solution around it, I find that compelling. A founding mission tied to my actual challenges gives me more confidence that the tool can make a real difference in performance.

    3. What case studies, real use cases, and results can you share?

    In a fast-moving AI market, I treat case studies as essential. I want to know whether the vendor has a strong track record with customers like me or whether I would be one of the first teams testing the product in my space.

    If I would be an early adopter, I weigh the tradeoffs carefully. I might gain an advantage by finding a growth accelerator before competitors do. I might also spend time working through bugs, giving detailed feedback, or discovering that the tool does not deliver what was promised.

    If I cannot trust the tool, or if I will need to provide a lot of feedback just to make it useful, I have to decide whether the potential payoff is big enough to justify the time and money. In most cases, that bar should be high.

    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.

    If I am clearly going to be an early adopter and the vendor will not offer flexible contract terms that reduce my risk, I consider that a nonstarter. Established tools may be less flexible on pricing because they can already prove consistent value. Newer tools that take a hard line on price and contract terms are much less likely to become strong long-term partners.

    For established vendors, I want specific and relevant case studies with real numbers from advertisers in a similar space, at a similar size, or with a similar use case.

    For early-stage companies, the best answer is honesty. If a vendor says, “You’d be one of our first clients in this vertical. Here’s what we’ve seen elsewhere, and here’s what that partnership would look like,” I see that transparency as a positive sign.

    4. Who owns my data, and how is it being used to train models?

    I am still surprised by how quickly people share data with AI tools in the rush to find a competitive edge. Before I sign anything, I take data ownership and model training terms seriously.

    I watch for any answer suggesting that my data could be used to train shared or third-party models without my explicit consent. I also treat vague answers, deflections, or terms of service that conflict with the salesperson’s verbal explanation as major warning signs.

    I own my data, full stop.

    The vendor should be able to clearly explain where my data is stored, how long it is retained, whether it is used for model training, and what happens to it if I stop using the tool. If model training is involved, I want that training limited to refining my own instance. Most importantly, I want those commitments in the contract, not just in a conversation. If the language is missing, I insist that it be added before I sign.

    5. What does implementation actually look like, and what does success require from our team?

    Before I commit budget, I need to understand the real cost of adopting the tool. That cost is not just the subscription price. It includes the time, internal lift, integration work, training, QA, and possible disruption to the existing martech stack.

    If the tool requires resources my team does not have, or if I cannot realistically dedicate the time needed to use it well, I do not consider it a smart investment yet. A lot of wasted martech spend could be avoided by asking this question and taking the answer seriously.

    I do not expect every tool to fit every organization, but I do expect implementation to be clear and the product to be intuitive enough for the team to adopt. If people cannot understand it, trust it, or fit it into their workflow, it will not create the value the vendor promised.

    I do not let AI hype rush my decision

    I know firsthand that many AI tools sound too good to be true, and often they are. I still want to stay curious and ambitious, but I balance that with caution.

    I also remind myself that AI adoption is still early. If a tool feels too expensive, too difficult to onboard, or too rigid in its contract terms compared with its track record, I am willing to wait. A better option may appear in the next few months.

    When I am unsure, I ask for a free trial. If integrating the tool will not create too much work for the team, a trial can be the best way to decide whether I have found a real competitive advantage or just another AI pitch dressed up as one.


    Inspired by this post on Search Engine Land.


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  • Why I Turn Off Google Search Partners in Google Ads

    Why I Turn Off Google Search Partners in Google Ads

    When I’m running Google Ads in 2026, one setting I always check carefully is “Search Partners.” It often appears in campaign settings as a simple way to extend reach beyond Google Search, and on the surface, that sounds useful.

    But more reach does not automatically mean better reach. In my experience, Search Partners can bring traffic, but the quality of that traffic is usually the problem.

    For most advertisers, I would not leave Search Partners enabled by default. I’d rather start with the main Google Search results page, prove the campaign can convert, and only then consider whether extra volume is worth testing.

    What are Google Search Partners?

    Google Search Partners are third-party sites that use Google-powered search results. When someone searches on those sites, your ad may be eligible to show there. This network can include YouTube, directories, other search experiences, and even parked domains.

    That sounds like a broader opportunity, but I usually see a familiar pattern: lots of impressions, plenty of clicks, and cheaper CPCs than Google Search. The issue is that cheaper clicks are not always useful clicks. Real conversions and meaningful business value from these placements are often limited.

    If I’m using conversion-focused Smart Bidding, I often see Search Partner spend fall naturally over time. The bidding system eventually learns that those placements are not producing the conversions it wants, so it stops pushing budget there.

    How Search Partners differ from the Google Display Network

    I see advertisers confuse Search Partners with the Google Display Network all the time. Some websites can be involved in both, but the intent and placement logic are different.

    The Google Display Network is made up of websites and apps that use AdSense, where ads can appear while people browse content. It can show up as a placement option in Demand Gen, Video campaigns where it is called “Video Partners,” and Performance Max campaigns.

    Search Partners are tied to search-based activity. That is why they apply to Search, Shopping, and Performance Max campaigns rather than standard Display placements.

    How I audit Search Partner performance

    I do not recommend taking anyone’s word for it, including mine. The better move is to check what Search Partners are actually doing inside your own Google Ads account.

    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.

    For Search or Shopping campaigns

    In Google Ads, I go to the campaign view, select Segment, and choose Network (with search partners). This splits performance into separate rows for Google Search and Search Partners, which makes the difference much easier to see.

    What I usually find is a lot of Search Partner impressions and clicks, often at lower CPCs than Google Search. But when I look for true conversions, the results are usually weak unless the account is tracking something shallow or easy to manipulate, such as a page view or a low-friction form fill.

    For Performance Max campaigns

    Performance Max works differently. Search Partners are required for this campaign type, so I cannot simply opt out. What I can do is monitor the activity through the Channel Performance report.

    If I see heavy Search Partner spend in a Performance Max campaign, I treat it as a signal to review conversion tracking, bid strategy settings, and the quality of the conversion actions being used for optimization.

    Check the Content Suitability report

    For more transparency, I also check the Content Suitability report under Insights and reports. This report can show the actual websites or YouTube channels where ads appeared on the Search Partner network.

    That list is often enough to make the decision clear. Once I see where the ads have been running, I usually find many placements that look low quality, irrelevant, or simply not worth the spend.

    In Google Ads, many decisions really do depend on the account, the market, and the goal. This is one of the few areas where my starting recommendation is straightforward.

    If I’m building a new Search or Shopping campaign, I leave Search Partners unchecked. After the campaign is performing well and has strong conversion data, I may test Search Partners for added volume. Until then, I keep the budget focused on the main Google SERP.

    This article is part of the ongoing Search Engine Land series, Everything you need to know about Google Ads in less than 3 minutes. In each edition, Jyll highlights a different Google Ads feature and explains what advertisers need to know to get better results in a quick 3-minute read.


    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.

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    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.


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