Tag: Smart Bidding

Why Frontloading Ad Spend Backfires—and How I Scale
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.

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.

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.

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.”

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.

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.
July 10, 2026
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.

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.

July 8, 2026
Why Google Ads Structure Can Make or Break 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.

July 3, 2026
Google Shopping Bidding Update Gives Me More Control

I’m seeing an important shift for Standard Shopping campaigns: Google is bringing Maximize Conversion Value bidding to these campaigns without requiring a Target ROAS. That gives advertisers more room to pursue value-based optimization without immediately being locked into a specific return target.

What’s happening. Google is rolling out Maximize Conversion Value bidding for Standard Shopping campaigns, and advertisers no longer have to set a Target ROAS to use it.

Before this update, if I wanted to optimize around conversion value in Standard Shopping, I generally had to use a Target ROAS bidding strategy. Now, this new option lets campaigns focus on maximizing conversion value while giving Google’s bidding system more flexibility to find the highest-value opportunities.

Why I care. This matters because I can now use Google’s value-based bidding in Standard Shopping without being constrained by a Target ROAS goal. That gives me more flexibility while preserving the control and transparency that many advertisers still prefer in Standard Shopping campaigns.

It may also reduce the need to run feed-only Performance Max campaigns just to access Maximize Conversion Value bidding. For advertisers who prefer tighter campaign control, that is a meaningful change.

Between the lines. I know many advertisers have continued to favour Standard Shopping because it offers more visibility and control than Performance Max. But when they wanted flexible value-based bidding, they often created feed-only Performance Max campaigns as a workaround.

With this update, that workaround may no longer be necessary for some accounts.

Why advertisers should care. I can now combine the structure and transparency of Standard Shopping with a more flexible automated bidding strategy. In practical terms, this could simplify campaign setups, reduce unnecessary Performance Max usage, and make account management cleaner.

The bottom line. Google is narrowing one of the biggest feature gaps between Standard Shopping and Performance Max. For me, this gives advertisers another reason to keep using Standard Shopping while still benefiting from automated value-based bidding.

First spotted. Performance marketer Yash Mandlesha spotted the update and shared the option on LinkedIn.

Inspired by this post on Search Engine Land.

July 3, 2026
Bad Conversion Data Is Quietly Wrecking Google Ads

I used to think bad data mainly meant bad reporting. Now, in Google Ads, I see it as something much more expensive: bad delivery. When conversion data is wrong, it does not just make a dashboard confusing. It can train campaigns to spend budget chasing the wrong people.

As automation takes over more of the ad-buying process, from creative generation to bidding, data has become one of the few inputs I can still control. It may also be the most important one, because automation can only optimize toward the signals I give it.

I keep coming back to one question: what is worse, a brilliant ad shown to the wrong audience or an average ad shown to the right one? The first burns budget on people I do not want. The second may not win every click, but when someone does engage, at least they are closer to the customer I actually need.

That is why I have to ask myself a harder question before launching any automated campaign: did I spend more time verifying the data than writing the ad copy?

The cost of bad data has changed

A few years ago, bad tracking was mostly a reporting problem.

If a tag fired twice, a conversion was mishandled, a value came through incorrectly, or offline conversions stopped working for a few weeks, the main result was a dashboard that did not add up. It was frustrating, but the damage was usually limited. Someone would eventually question the numbers in a monthly review, I would trace the issue, fix it, and the next report would look cleaner.

That same data now feeds the algorithm buying paid media. Smart Bidding does not wait for me to interpret a report or sit through a monthly review. It reads conversion data and acts on it before I may even notice that something is broken.

The same wrong number now creates a very different outcome. A bad number in a report requires an explanation in a meeting. A bad number in a conversion action used for bidding costs money immediately, because the algorithm does not know the signal is wrong.

It simply optimizes toward that signal the moment it sees it, and it does so efficiently.

Google does not understand my funnel or my business

Google may let me label conversion actions as “lead,” “opportunity,” or something similar, but those labels are mainly for organization. The platform does not truly understand where each conversion event sits in my funnel.

What it sees is a conversion event with a numeric value attached to it, usually a currency value. It does not inherently know that a newsletter signup might be worth $2 in eventual value, a lead might be worth $60, and an opportunity might be worth $400. To Google, those are conversion events. Without better signals, it has no real context that one may be worth 200 times another.

The algorithm is not optimizing for my business outcome by default. It is optimizing for the data I provide. If that data is wrong, the optimization will be wrong too.

For example, if every form submission fires the same conversion with the same default value, I give the system no clean way to separate low-intent inquiries from high-value prospects. The algorithm treats them the same. And because low-quality leads are often cheaper to acquire, it can quickly flood the account with them.

The cost per lead may drop from $40 to $25, and the dashboard may make performance look more than 35% better. But behind that cleaner metric, the pipeline can dry up as genuinely qualified inquiries quietly fall by half.

Dig deeper: Why better signals drive paid search performance

3 ways bad data quietly wrecks delivery

Bad data can show up in different ways, but I see three issues that are especially likely to derail campaign delivery.

1. Wrong event

If I optimize for a top-of-funnel action like a page view while the real conversion events happen further down the funnel, the algorithm learns to buy more of those cheap events. The problem is that the lower-funnel activity may never follow.

2. Wrong value

If I count every conversion equally, or assign every conversion the same placeholder value, I hide the real differences in business value. When actual value can vary by 10 times or more, the algorithm will often chase the easier, lower-value conversions because they are cheaper to acquire.

3. No data

This problem does not get discussed enough. A complete break in conversion data can damage a campaign faster than almost anything else.

On Day 1, the algorithm starts wondering where the conversions went. By Day 2, it begins assuming they may not be coming back. By Day 3, it can start making serious bidding changes. Within a week, many campaigns can throttle themselves down to almost nothing.

How I pick the right signal for Google

So how do I fix this? I start by choosing the signal that best represents business value, not just the easiest action to count.

Take a typical lead generation business. Some leads will never convert, while others may be worth 10 times as much as the rest.

If the form asks the right qualifying questions, I may already know which leads are which. But if I optimize for every submitted lead using a target CPA, I am telling Google that all leads are equally valuable.

Imagine an account spending $20,000 a month at a $40 target CPA and generating about 500 leads. Only 150 qualify, and maybe just 50 are genuinely high value. A basic lead may be worth $60, a qualified lead may be worth $200, and a high-value lead may be worth $600. That is a 10 times spread in value.

In that situation, I have several ways to improve the optimization signal.

Optimize for a qualified lead: I can create a new conversion action, such as “qualified lead,” and fire it only when a lead has real value. Then I can move the target CPA strategy to that conversion action, knowing the campaign will ignore leads with no value. The advantage is that I train the campaign on a more meaningful signal. The downside is that every qualified lead is still treated equally.

Assign conversion values and use target ROAS: I can add a currency value to the qualified lead based on the potential revenue it could generate if it becomes a sale. Then I can switch the campaign to target ROAS, allowing Google to optimize for return instead of simply counting leads. The tradeoff is that it may still buy larger numbers of lower-value leads if it can acquire them at the right price.

Optimize for a high-value lead: I can create a “high-value lead” conversion event that fires only for top-tier leads, with or without a conversion value. Then I can optimize with either target CPA or target ROAS, depending on whether I care more about acquisition cost or return. The advantage is stronger lead quality. The downside is that, depending on spend and volume, the data may be too limited to support this approach until the account scales.

These are only a few possible optimization signals, and they do not even go deeper into the funnel. I can apply the same thinking to lower-funnel milestones by creating separate conversion actions for events such as contacted lead, qualified contact, or high-value contact.

Targeting and measurement can be different

This sounds simple, but the conversion event I optimize for and the one I report on are not always the same. In many cases, they should not be the same. One trains the algorithm. The other tells me how that training is performing.

In the example above, a client or internal stakeholder may still want to see cost per lead. That is a valid metric. But the campaign may be optimizing for the Qualified Lead conversion, not the original lead submission.

I can keep the original lead conversion running purely as a reporting metric, so stakeholders still get their cost-per-lead view while the campaign bids on the qualified lead signal that actually reflects business value.

Same campaign. Two conversions. Two very different jobs.

That brings me back to the question I started with: did I spend more time verifying the data than writing the ad? In an automated account, data is no longer just measurement. Data is strategy.

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July 3, 2026
Google Ads API Ending Smart Campaign Creation: My Take

I see Google’s latest Google Ads API change as another clear move away from legacy automation and toward newer AI-driven campaign types, especially Performance Max.

Beginning August 3, 2026, Google says developers will no longer be able to create new Smart Campaigns through the Google Ads API. For me, the key detail is that this change is about new campaign creation only.

Existing Smart Campaigns are not being shut down. They can keep serving ads, and advertisers and developers will still be able to update and manage those campaigns through the API.

What changes is the ability to create brand-new Smart Campaigns through API workflows. If I depend on automated campaign setup, that is the part I would review now.

I care about this because it signals where Google wants advertisers to go next. Smart Campaigns may continue running, but the path for new API-based campaign creation is moving toward newer products such as Performance Max, Search campaigns, and Demand Gen campaigns.

Google is specifically pointing advertisers toward Performance Max as the primary alternative. Since Performance Max runs across Google’s advertising inventory and uses AI to automate more of the campaign process, it fits the broader direction Google has been taking for years.

I also see this as part of a wider consolidation around automated campaign formats. Google has increasingly emphasized systems that handle bidding, targeting, and creative optimization across channels, and limiting new Smart Campaign creation reinforces that shift.

For developers, the practical next step is to audit any application that creates Smart Campaigns before the August 3, 2026 deadline. The affected requests are campaign creation operations where advertising_channel_type is set to SMART and advertising_channel_sub_type is set to SMART_CAMPAIGN.

After August 3, attempts to create new Smart Campaigns through the API will fail. In version 24 of the Google Ads API, developers will receive a SmartCampaignError.CREATION_FAILED error.

In version 23 and earlier, the same type of request will return an OperationAccessDeniedError.CREATE_OPERATION_NOT_PERMITTED error.

My main takeaway is that advertisers, agencies, and software providers should not treat this as a last-minute technical cleanup. If campaign creation is built into an internal tool, onboarding flow, or platform integration, I would start mapping the replacement path now.

Google is not ending existing Smart Campaigns, but it is removing a key creation path for new ones. To me, that is a strong signal that future campaign planning should center on Performance Max and other AI-driven Google Ads campaign types.

Dig deeper: Changes to Support for Smart Campaigns in the Google Ads API

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July 3, 2026
Exciting Update: Google Ads Revives Target CPA and ROAS Names
I’ve got some news to share about Google Ads that’s made my analytical heart skip a beat. Google’s latest update is set to simplify how advertisers differentiate between target-based and volume-based bidding strategies.

This month, Google is reinstating the names we’ve all been familiar with: “Maximize conversions with a Target CPA” will revert to the plain and simple Target CPA, and “Maximize conversion value with a Target ROAS” will go back to just being Target ROAS. It’s like welcoming back an old friend!

Why this matters to us. This change brings clarity to whether our campaigns are focused on maximizing volume or achieving specific performance targets. It’s a small shift with potentially big implications for how I manage my ad strategy.

The fine print.

Maximize Conversions is still there for us advertisers who aim to drive the maximum number of conversions without overspending on our budget.

Maximize Conversion Value remains for those of us focused on squeezing out the absolute highest conversion value possible, balancing along the fiscal tightrope.

What’s staying the same. Rest assured, this update is purely cosmetic. The mechanics beneath the surface remain untouched.

Google assures us that there will be:
- No shifts in bidding behavior
- No change in campaign performance
- No action required from advertisers like you and me
Our campaigns will continue to roll forward just as they always have.

For the API enthusiasts out there. Google is aligning the user interface more closely with the API, which is great if, like me, you’re juggling integrations, reporting tools, and campaign workflows. The goal is to accurately recognize standalone TARGET_CPA and TARGET_ROAS strategies.

For those utilizing the API, Google encourages us to stay alert to upcoming adjustments related to:
- The BiddingStrategyType enum
- Standalone TargetCpa and TargetRoas messages
- Optional target settings within MaximizeConversions and MaximizeConversionValue
The takeaway. Although Smart Bidding itself remains stable, the simplification in naming might make setting up and reporting on campaigns that little bit easier to navigate, thanks to Google’s fresh clarity on strategy names.

Inspired by this post on Search Engine Land.

The fine print.

Maximize Conversions is still there for us advertisers who aim to drive the maximum number of conversions without overspending on our budget.

Maximize Conversion Value remains for those of us focused on squeezing out the absolute highest conversion value possible, balancing along the fiscal tightrope.

What’s staying the same. Rest assured, this update is purely cosmetic. The mechanics beneath the surface remain untouched.

Google assures us that there will be:
- No shifts in bidding behavior
- No change in campaign performance
- No action required from advertisers like you and me
Our campaigns will continue to roll forward just as they always have.

For the API enthusiasts out there. Google is aligning the user interface more closely with the API, which is great if, like me, you’re juggling integrations, reporting tools, and campaign workflows. The goal is to accurately recognize standalone TARGET_CPA and TARGET_ROAS strategies.

For those utilizing the API, Google encourages us to stay alert to upcoming adjustments related to:
- The BiddingStrategyType enum
- Standalone TargetCpa and TargetRoas messages
- Optional target settings within MaximizeConversions and MaximizeConversionValue
The takeaway. Although Smart Bidding itself remains stable, the simplification in naming might make setting up and reporting on campaigns that little bit easier to navigate, thanks to Google’s fresh clarity on strategy names.

Inspired by this post on Search Engine Land.
June 19, 2026
PPC Budget Mastery for 2026: Smart Adjustments and Data Optimization

In 2026, PPC budgeting goes beyond simply setting spending levels. It’s about understanding when to adjust budgets, scaling campaigns effectively, and how data informs Google’s automation in these decisions.

Over the years, Google’s automation has been driven by the signals supplied to it. In 2026, these signals are processed faster and more precisely, making clean signal architecture more crucial than ever.

While the fundamentals of budget management remain constant, the speed at which a poorly structured account can drain your budget has increased significantly.

Two Budget Mechanics You Must Grasp Now

Before tweaking targets, audiences, or bid strategies, it’s essential to comprehend how these two budget controls operate.

The Ad Scheduling Pacing Change

Google now paces campaigns with ad scheduling towards the full 30.4x monthly billing cap, regardless of how many days your ads run. Previously, a $100 daily budget targeted around $2,200 across 22 weekdays. Now, it targets $3,040 in the same period, and the billing ceiling remains unchanged.

If your campaigns utilize ad scheduling, you need to recalibrate your daily budget based on your total monthly spend rather than active days, setting it by dividing your monthly target by 30.4. For example, a $2,200 monthly target becomes a $72 per day budget if calculated this way. However, 24/7 campaigns remain unaffected.

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Campaign Total Budgets

Available for Demand Gen, Search, Standard Shopping, Performance Max, and YouTube campaigns, campaign total budgets let me set a fixed spending ceiling over a defined period instead of managing a daily limit. This window is from three to 90 days for some campaigns, while others can extend up to a year.

While there is no daily spend cap, allowing flexibility, it’s crucial to monitor these closely, especially when running alongside ongoing campaigns. Additionally, the budget type cannot be altered post-campaign creation, making committed decisions at setup vital.

What Actually Governs Google Ads Budget Spending

Efficiency Targets Usually Constrain Spend Before Budgets

In Smart Bidding strategies, efficiency targets often restrict spending before budget caps do. With a set tCPA of $50, if leads cost $80, the system reduces bids to avoid surpassing your target. It appears as if there’s a budget problem, but it’s actually a target problem.

I must initially set targets closer to the market conversion rates and then fine-tune them to align with my true goals. When close, the 10%-20% margin aids in navigating those final conversion opportunities effectively.

Performance Max Decides Where Your Budget Goes

Performance Max automatically allocates budget across various channels like Search, Shopping, and YouTube, with Google determining the split, not me. Excluding my brand can prevent paying for redundant conversions from Search campaigns.

Checking my negative keyword lists ensures clarity in branding and budget allocation. This helps avoid misallocation and focuses resources effectively.

AI Max Expands Ad Appearances

AI Max, available since April, expands query matching beyond my keyword list, generates ad copy from existing assets, and dynamically targets landing pages. Monitoring the initial spend distribution closely helps maintain alignment with intended strategies.

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The Signal Problem Impacting Budget Allocation

An insurance broker using Smart Bidding faced a disconnect: a 416% rise in conversion volume didn’t reflect in revenue due to form starts mistaken for completions. The system optimized for interactions, but the alignment with Cyrillic-language spam was costly without benefiting the pipeline.

This reflects a broader issue in lead generation: equal weight is assigned to all form fills, leaving Smart Bidding unable to distinguish high-value leads from irrelevant submissions.

Primary conversions must be meaningful actions that properly guide Smart Bidding. Secondary engagements belong in reports to avoid skewing bidding data.

For accounts outside the current beta, extending conversion windows to 90 days and assessing performance over these periods can help counteract issues arising from longer sales cycles.

Using First-Party Data for Budget Guidance

Customer Match, with a 540-day max membership duration, remains crucial in guiding automation toward valuable traffic. For effective budget allocation, I focus on exclusion before expansion, targeting acquisition budgets toward new prospects.

Retention strategies should be run separately to maintain consistency in conversion goals. It’s vital that exclusions, available from the start, streamline acquisition efforts effectively.

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Strategic Scaling in 2026

For ongoing daily budget campaigns, weekly increases of 10-20% are still relevant. For scheduled campaigns, I focus on monthly targets divided by 30.4 instead of daily adjustments.

Using Smart Bidding Exploration in open beta for Performance Max can increase unique conversions by exploring new queries. I evaluate results over 60-day windows to make informed decisions.

Demand-led pacing, complementing daily management, tracks predicted high demand periods to optimize spend within budgetary limits. For B2B accounts, longer evaluation periods safeguard against undervaluing long cycle campaigns.

Inspired by this post on Search Engine Land.

June 18, 2026
Discover Google’s Latest Smart Bidding Innovations

I’m excited to share that Google has introduced new methods for advertisers to expand their campaigns while keeping a close grasp on efficiency targets. This expansion in Smart Bidding Exploration is sure to be a game-changer.

Google is unveiling a new series of updates designed to help advertisers discover fresh demand, take advantage of seasonal opportunities, and achieve more consistent campaign performance. I’ve always valued predictable outcomes in advertising, and these updates seem to focus exactly on that.

What’s new. The enhancements include a larger scope for Smart Bidding Exploration, the introduction of a new Promotion Mode beta, and updates to bidding target optimization specifically for campaigns with limited budgets.

Driving discovery. This enhancement allows me, as an advertiser, to set a return on ad spend (ROAS) tolerance, so my campaigns can capture additional conversion opportunities from search queries that currently might be overlooked.

From what I’ve seen, campaigns utilizing this feature experience about an 18% boost in unique converting search query categories and a 19% increase in overall conversions.

This capability is now extended to Performance Max campaigns without product feeds and is being tested in beta for Shopping ads within both Performance Max and Standard Shopping campaigns.

Peak period bidding. The new Promotion Mode empowers advertisers to adjust ROAS targets temporarily and increase the daily budget during peak periods like seasonal events, new product launches, and flash sales. I think this is a fantastic tool for maximizing high-demand opportunities.

What else is changing. Starting August 17, Google will update bidding target optimization for budget-constrained campaigns with the aim of delivering more consistent performance. This aligns better with our CPA and ROAS targets, which is reassuring for me as a campaign manager.

Notifications will begin rolling out in Google Ads on July 6, alerting advertisers about recommended campaign adjustments. I appreciate such timely updates that help me stay ahead in planning.

Why we care. These advancements allow Google’s AI bidding systems to explore incremental conversions beyond our current keyword and audience settings. This potential unlock of new demand could be pivotal in redefining campaign success for me.

The Promotion Mode stands out for retailers and seasonal advertisers by enabling temporary adjustments to ROAS targets and budgets during peak periods without needing a complete campaign overhaul. Additionally, the changes in bidding optimization aim at making performances more predictable in campaigns limited by budget.

The bottom line. Google’s recent bidding updates are designed to help advertisers, like me, find new conversion opportunities, react more assertively during peak demand times, and maintain consistent performance as campaigns scale.

Inspired by this post on Search Engine Land.

June 15, 2026
Evolving PPC Skills: Transforming from Keyword Manager to System Optimizer

I’ve noticed how AI-driven Google Ads has revolutionized the PPC landscape. My role has evolved from merely executing campaigns to designing signals and guiding the conversion system.

In the past, PPC was all about having control – managing keywords, match types, bids, crafting ad copy, and structuring campaigns to make the algorithm follow my lead.

Back then, proficiency in Excel and pivot tables distinguished the best ad managers. Agencies and PPC experts thrived on their execution skills. Greater control over variables meant better job execution, a strategy that worked well for PPC’s first decade.

However, Google Marketing Live (GML) 2026 heralded a significant shift for PPC. The focus moved from tactical control to system optimization, from managing keywords to signal design, and from setting up campaigns to aligning with machine strategy.

With AI-driven Google Ads, it’s evident that execution alone is no longer a competitive advantage. As Selin Song from Google Customer Solutions emphasized, execution has become a commodity.

Here’s what the new skill set involves.

I’ve learned to design inputs – the new keyword research. Knowing what inputs to provide the system helps it find the right audience on my behalf.

With AI Max for Search, I’m using a mix of broad match, keywordless targeting, text customization, and URL expansion. This strategy surfaces queries my keyword list wouldn’t catch, leading to an average of 7% more conversions or conversion value at a similar CPA/ROAS.

Feeding the system accurate conversion data is crucial. If conversion actions are irrelevant, the system solves the wrong problems, and that responsibility falls on me.

In terms of product and feed data, optimizing feeds with Conversational Attributes helps display products effectively in AI-generated responses. Ensuring audience signals are precise also shapes system operation, particularly with new prospects.

The days of relying solely on keyword lists are long gone; today’s system demands a strategic approach with the right inputs to automation.

Value signal architecture has replaced traditional bid management. My focus is now on providing robust signals like first-party data and accurate conversion values to Smart Bidding.

The advent of demand-led budget pacing means I set parameters rather than control pacing. Understanding product margins, inventory, lifetime value, and cash flow guides me in providing the right signals instead of merely setting bids.

Journey-aware bidding allows me to optimize the full conversion journey, not just the endpoint, requiring a well-instrumented conversion path connected back to the ad platform for effectiveness.

System prompting is today’s copywriting. AI Brief powered by Gemini helps me guide AI Max using brand-specific briefs to ensure it represents the brand accurately without over-constraining creativity.

I’ve learned to write briefs that effectively convey brand strategy, assisting AI in maintaining brand integrity in every campaign impression.

Budget architecture has taken precedence over daily budget adjustments. Campaign total budgets automate the process, and interpreting auction behavior in predictive systems has become my focus.

I rely on missed opportunity reporting to make informed decisions about budget constraints and optimize growth opportunities within the architecture I construct.

Measurement literacy has surpassed mere Quality Score management. Feeding the system quality signals helps it make informed decisions and optimizes bidding behavior through robust data integration.

It’s crucial now to ask business-relevant questions that the system can optimize toward meaningful outcomes. Communicating system behavior in business language is becoming a survival skill, alongside maintaining human oversight to ensure strategic alignment.

GML 2026 confirmed we’re already in this new phase. Thriving today means understanding the system’s needs and strategically providing those inputs to achieve business objectives.

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June 3, 2026