Tag: Campaign Strategy

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 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
LinkedIn Ads CPC Benchmarks: What I Budget vs Google
I know LinkedIn Ads has a reputation for being expensive, and at first glance, the data backs that up. Across the client accounts I analyzed, LinkedIn’s average CPC was $11.12, compared with $5.45 on Google Ads.

But that simple comparison misses the more useful story. When I compare the cost of reaching new, high-intent B2B buyers, the gap gets much smaller. Non-branded Google Search campaigns averaged a $12.48 CPC, while comparable LinkedIn prospecting campaigns averaged $13.94.

To understand how LinkedIn CPCs really compare with Google Ads across campaign types and industries, I reviewed more than $700,000 in LinkedIn ad spend and compared it with CPC data from the same accounts on Google Ads.

What I included in this analysis

I focused on CPC and performance data from clients that had active campaigns on both LinkedIn Ads and Google Ads over the past year.

The main questions I wanted to answer were straightforward: What CPCs are we actually seeing? Do CPCs change by ad objective and industry? And how do those costs compare with Google Ads?

For LinkedIn Ads, I analyzed more than $700,000 in spend across 63,000+ clicks and 8.1 million impressions.

The clients fell into two main business categories: B2B SaaS, which represented approximately 97% of spend, and professional services.

I looked at LinkedIn CPCs by ad set objective and business category. For Google Ads, I pulled CPC data from the same client accounts across branded search, non-branded search, Demand Gen, and display campaigns.

Client names are withheld. The date range for this analysis was May 2025 through May 2026.

LinkedIn looks more expensive, but the comparison needs context

LinkedIn’s blended average CPC across all objectives was $11.12. Google’s blended average CPC across all campaign types was $5.45. On the surface, LinkedIn costs about twice as much per click.

There is an important caveat. In Google Ads, a large share of those lower-cost clicks came from display campaigns, which averaged $0.89 per click, and branded search, which averaged $1.71 per click. Both are naturally less expensive because display generally reaches lower-intent audiences, while branded search captures people already looking for your company.

When I narrow the comparison to the cost of reaching new, high-intent audiences, the difference becomes much less dramatic.
- Google Ads non-branded search averaged a $12.48 CPC across the clients in this study.
- LinkedIn prospecting campaigns, excluding retargeting and using lead generation, website conversion, or website visit objectives, averaged a $13.94 CPC.
I used those LinkedIn objectives because they most closely represent high-intent direct-response campaigns, which makes the comparison with non-branded search more useful.

When I compare the cost of reaching a new audience, LinkedIn is still more expensive, but it is not twice as expensive. In practical terms, I am looking at roughly $12 CPCs on Google and $14 CPCs on LinkedIn.

LinkedIn CPCs change a lot by objective

One of the clearest findings in this data set is how widely LinkedIn CPCs vary by campaign objective.
- Website visits: $6.75
- Brand awareness: $8.34
- Website conversions: $4.84
- Engagement: $4.45
- Lead generation: $31.29
- Video views: $71.43
Lead generation campaigns, where LinkedIn lead gen forms capture contact information directly inside the platform, cost nearly five times more per click than website visit campaigns.

That higher CPC can still make sense because these campaigns often convert at much higher rates than ads that send people to a website or landing page.

Here is the full breakdown of CPCs by campaign objective:

The number that jumps out most is video views. CPCs for those campaigns look extremely high, but cost per view is the more relevant metric there, so CPC alone can be misleading.

If I were planning a LinkedIn campaign focused on click volume or site traffic, I would budget for CPCs in the $6-$8 range. For lead gen ads, which in my experience often produce stronger conversion rates and better lead quality, I would plan for $30+ CPCs.

LinkedIn CPCs also change by industry

The two business categories in this analysis showed noticeably different CPC profiles on LinkedIn.
- B2B SaaS: $11.02 average CPC on $681,000 in spend
- Professional services: $15.25 average CPC on $23,000 in spend
I would be careful not to overstate that comparison because the spend levels were very different. B2B SaaS had a much broader mix of campaign types, which likely affected the average CPC. The professional services campaigns also used very specific targeting, which may have pushed CPCs higher.

B2B SaaS CPCs by campaign objective:

Professional services CPCs by campaign objective:

One interesting twist is that lead gen CPCs in professional services were lower than website visit CPCs. Lead gen CPCs were also much lower for professional services than they were for B2B SaaS.

If I were budgeting for a professional services firm on LinkedIn, I would factor in $15-$20 CPCs. For B2B SaaS, I would plan for a wider range, roughly $7-$35, depending on the campaign objective.

How this compares with Google Ads

The pattern is fairly consistent across channels. Professional services had higher CPCs than B2B SaaS in this data set. Even when I compare only non-branded search between the two industries, the CPCs are closer, but professional services still comes out higher.

Here is the breakdown of Google CPCs by campaign type:

What I would budget for LinkedIn Ads

Your targeting will have a major impact on CPCs and budget needs, but I use this data as a practical planning framework.

Minimum viable budget: $3,000-$5,000 per month

Below this level, I would not expect enough traffic to drive meaningful lead volume or conversions. You may still be able to get started, but trend-spotting will be slow, and you will probably be limited to one or two campaigns.

Testing and learning: $5,000-$10,000 per month

At this level, I would expect enough budget to run two or three objectives, launch more campaigns, test creative and audiences, and generate more meaningful lead volume.

Scaling: $10,000+ per month

With this budget, I can run always-on brand awareness and thought leadership campaigns alongside lead gen and website visit campaigns. I can also support event registrations, test more advanced list-targeted campaigns, and use retargeting without starving direct-response efforts.

For B2B SaaS or professional services companies with an ACV above $20,000, I would rarely recommend starting LinkedIn with less than $5,000 per month. A single closed deal worth $30,000-$50,000 in ACV can justify meaningful investment, even at a $500+ CPL, as long as the pipeline quality is there.

The B2B channel mix I recommend

For most B2B clients, I do not see LinkedIn and Google as either-or channels. I use them for different jobs.

Use Google Ads and Microsoft Ads for intent capture

Non-branded search reaches buyers who are actively researching. Branded search and remarketing are lower-cost and essential. If someone is searching for your category keywords, I want your brand to be visible.

I also use Demand Gen and Performance Max where they make sense to fill gaps and support brand awareness.

Use LinkedIn Ads for audience-led demand generation

If the ideal customer profile is highly specific, such as VP-level decision-makers at mid-market SaaS companies, LinkedIn’s targeting is hard to replace. No other platform gives me the same ability to reach that kind of professional audience at scale.

Run both channels in parallel

The strongest setup is to run both channels together. Google captures existing demand. LinkedIn helps create new demand and keeps the brand visible to the exact buyers I want in the pipeline.

Why I still think LinkedIn is worth the higher CPCs

LinkedIn is more expensive than Google on a raw CPC basis. But when I compare the platforms more fairly, with both reaching cold, qualified B2B buyers, the gap narrows significantly.

Higher CPCs can still be worth paying if they put the brand in front of the right customers earlier in the decision-making process. Over time, that can be more valuable than relying only on high-intent keywords after buyers have already narrowed their list of options.

The best scenario is for the brand to become an active part of the buyer’s decision, shaping the narrative before competitors do it instead.

My take is simple: I use LinkedIn Ads to build intent and tell the story, and I use Google Ads and Microsoft Ads to capture intent. The right budget depends on targeting, but I want enough spend to generate at least 100 clicks per month. Anything less usually means spending money without giving the system enough data to learn from.

Inspired by this post on Search Engine Land.
July 3, 2026
Enhance Your B2B Ads: Microsoft Adds LinkedIn Job Seniority Targeting
I recently discovered some exciting news from Microsoft Ads that could be a game-changer for advertisers like myself. They’ve expanded their LinkedIn targeting capabilities to include job seniority filters. This allows me to target audiences with more precision in both Search and Audience campaigns.

This new feature means that I can now target users based on their job seniority, a wonderful addition for those of us focusing on B2B marketing. Thanks to LinkedIn data, I can reach audiences at various levels of seniority.

What’s the scoop? According to Navah Hopkins, Microsoft Advertising has added job seniority targeting to its LinkedIn Profile targeting, allowing me to utilize it within Search and Audience campaigns.

This update provides me the ability to choose from 10 different seniority levels, ranging from CXO to Volunteer. This flexibility is available at both the campaign and ad group levels, making it easier to segment my audiences effectively.

Why is this vital for us? In the world of B2B marketing, it’s often challenging to separate decision-makers from operational staff in search campaigns. With this new job seniority targeting, I can better align my messaging and bidding strategies with the right audience segments, ultimately improving my campaign performance.

Understanding who is interacting with my ads is crucial, especially in organizations with long sales cycles or multiple stakeholders. It’s not just about conversions; it’s about knowing who is behind them.

A closer look: Unlike other platforms, Microsoft’s integration with LinkedIn provides a unique perspective of professional identity that allows me to better understand user interactions.

Not only can I apply these filters directly within my campaign settings, but I can also utilize them in observation mode to gather insights without limiting my reach.

How do I benefit?

Customize messaging by seniority: I can create targeted ad groups for different audience levels, like executives or individual contributors, tailoring my messaging to each group’s expectations.

An executive-focused strategy might highlight business growth, while campaigns targeting practitioners could focus on efficiency gains.

Analyze conversions by seniority: Observation mode helps me assess conversion performance across different seniority levels, answering questions crucial to my strategy:

Where are my conversions coming from? Are they decision-makers or influencers? Is my budget effectively spent? Which seniority levels bring in high-quality leads?

Enhance audience testing: This feature offers an extra layer of reporting, helping me make informed optimization and expansion decisions. If I’m importing from other platforms, this insight is invaluable for discovering performance patterns unique to Microsoft Ads.

Availability: This powerful tool is now accessible in select markets across the Americas, EMEA, and APAC regions, including countries like the United States, Canada, Brazil, and more.
- Americas: Argentina, Brazil, Canada, Chile, Colombia, and others.
- EMEA: Egypt, Nigeria, Saudi Arabia, and South Africa.
- APAC: Australia, India, Japan, among others.
The takeaway: Microsoft Ads continues to leverage its LinkedIn integration as a standout feature in B2B advertising. By aligning search intent with professional profiles, I gain deeper insights into not just what my audiences search for, but who the searchers are.

Inspired by this post on Search Engine Land.
June 16, 2026
Unlocking AI Success with Google Display Exclusions

When I manage digital marketing campaigns, accidental clicks, bot traffic, and low-quality placements can really muddy the data. That’s why I rely on strategic exclusions to keep my optimization efforts on track.

Let me unpack how Google Display Network (GDN) placement exclusions have evolved from basic account hygiene to vital components in AI-driven optimization strategies.

Traditionally, blocking undesirable placements meant compiling extensive lists of unwanted URLs and mobile app categories. This helped safeguard brand integrity and ensured I wasn’t wasting my budget on traffic that wouldn’t convert.

In the past, ensuring our ads dodged clickbait blogs and mobile games was crucial. GDN exclusions have now taken on a more strategic role, influencing Google’s optimization signals for automated campaigns.

This shift means I can use placement exclusions not just for blocking but as a strategic tool to sidestep low-quality traffic and unreliable conversion signals. Here’s how it works.

In traditional PPC, placement exclusions served dual purposes: they protected brand safety and conserved my advertising budget.

No one wants their brand next to inappropriate or clickbait content. The GDN offers vast inventory, but much of it can be high-click and low-conversion, making exclusions essential.

Even high-profile sites could become budget drains without contributing to conversions. Thus, large exclusion lists and regular audits became routine practices to manage ad placements efficiently.

However, AI has changed how I approach this. With Smart Bidding algorithms like Target CPA and Target ROAS, optimization is more nuanced. Google’s AI actively seeks out the right audiences, and the data-quality matters significantly here.

Without strategic exclusions, AI might gravitate towards cheap, high-volume placements. I’ve seen how accidental clicks and low-quality sites appear promising due to high CTRs but ultimately fail to convert.

Strategic placement exclusions provide guidance, ensuring AI avoids these pitfalls by directing it towards more beneficial data signals.

By refining where the AI can operate, I reintroduce human intent into automated systems, steering campaigns with a strategic hand on the wheel.

For brand awareness, I allow ads on premium sites while excluding lesser-known directories. This ensures visibility on reputable platforms.

Conversely, for direct response campaigns, I block costly broad-reach sites, pushing AI towards niche sites where conversion intent is high.

Blocking unwanted placements early in a campaign prevents unnecessary spending during the AI’s learning phase, allowing for more effective targeting from the get-go.

By excluding malicious bot-heavy sites, I prevent ‘signal poisoning,’ ensuring the AI optimizes based on genuine user interactions.

Advanced tactics involve running automated scripts to routinely exclude budget-draining placements and blocking mobile apps unless explicitly targeted. These strategies keep the AI focused on valuable data, minimizing waste.

Adopting these strategic exclusions enhances campaign performance significantly, transforming basic blocklists into a powerful performance edge.

Inspired by this post on Search Engine Land.

June 3, 2026
Boost Your Google Ads Visibility in AI Overviews with These Strategies

I’ve discovered that AI Overviews are changing the way Google Search displays paid ads. Nowadays, it seems like there’s more pressure to get my ads to appear in AI-generated responses, as direct search results provide fewer opportunities for clicks.

Google suggests that Shopping, Performance Max, and AI Max for Search campaigns are best suited for this evolution. However, just choosing the right campaign isn’t enough. I need to ensure the quality of my feeds, optimize my landing pages, and use effective audience signals and creative content strategies to boost my ads’ chances.

Enable Google-Recommended Campaigns for AI Overviews

I’ve found that Google is quite clear about which campaign types are most likely to appear in AI Overviews. Interestingly, these opportunities are often overlooked by experienced marketers due lack of full control.

Despite this, I’ve come to understand that combining control with data and an understanding of search intent will benefit both me, as an advertiser, and the searcher. This involves strategizing beyond picking the right campaign types, focusing instead on fully optimized feed data and content alignment.

To boost my visibility in AI Overviews, I’ve enabled Google’s recommended campaigns to sync with the feature, particularly Shopping, Performance Max, and AI Max for Search, utilizing broad match keywords and smart bidding with final URL expansion.

Shopping Campaigns

Learning that the original keywordless campaign relies heavily on my data feed quality, I’ve focused on creating a well-built and optimized product data feed, using high-quality images, and ensuring my titles and descriptions are thorough.

I’ve realized how crucial the product data feed is in determining ad visibility for specific queries. When high-intent questions are asked, the AI Overview can feature a product carousel, enhancing the prominence of shopping results.

Performance Max Campaigns

In Performance Max, I’ve seen how keywordless campaigns utilize page content, data feeds, and audience insights to decide ad display. These inputs are key in determining ad visibility for queries.

Enabling Final URL expansion has allowed my ads to appear in more searches by leveraging page content for user query relevance.

AI Max for Search Campaigns

By using existing keywords as a starting point, AI Max for Search expands beyond to determine ad delivery strategies. This means keywords signal intent rather than dictate ad display.

I’ve noticed that AI Max uses search term matching and asset optimization to target queries unaddressed by traditional keyword targeting.

6 Best Practices for Ad Campaigns

To improve my chances of being featured in an AI Overview, I’ve optimized my campaigns by focusing on creative, copy, schema, and link-building techniques to reinforce brand authority.

1. Diversify Your Assets

With campaigns like AI Max and Performance Max, I’ve realized the importance of using varied creative assets. Incorporating informative headlines, descriptions, and visuals in multiple formats allows for diverse ad placements.

2. Use a Conversational Tone

Understanding Google’s approach, I’ve shifted from generic sales pitches to a conversational tone in my Responsive Search Ads, using language that assists the user rather than typical sales jargon.

3. Be Clear and Informative

By answering key questions succinctly, my ads now have a better chance of being highlighted in AI Overviews. A focus on information-rich landing pages has proven essential.

4. Check Schema Markup and Links

I ensure my schema markup is thorough and aligned with my content. Linking to reputable sources builds authority, and collaborating with my SEO team has enhanced these practices.

5. Guide Automation with Audience Signals

I recognize the lack of control in these campaigns, so I’ve guided automation using strong audience signals, exclusions, and negative keywords to refine my targeting strategies.

6. Regularly Monitor Campaigns

Regular monitoring is crucial for brand safety and profitability. Reviewing search terms, landing pages, and ad assets ensures my message remains consistent and aligned.

Adapt Your Approach for AI Overviews

Adapting to conversational AI Overviews requires me to focus on maximizing visibility on the SERP. Emphasizing data feed quality, content alignment, and creative diversity turns this shift into an opportunity for growth.

Inspired by this post on Search Engine Land.

May 27, 2026
Mastering Negative Keywords: Your 2026 Strategy Guide

I’ve always believed that negative keywords are more than just a checklist. In 2026, they represent strategic decisions that shape how the algorithm interprets your ad account.

If you’re still viewing negative keywords as a mere maintenance task, you’re missing out. Each exclusion signals who you intend to target, what you’re willing to pay for, and how you expect your campaigns to perform.

Let me share six key decisions that define today’s negative keyword strategy, and explain their growing significance.

Negative keywords help shape our campaigns so the right ad appears in front of the right audience. Achieving alignment between the user’s search query, your ad, and the landing page is crucial for creating an exceptional user experience.

When this alignment is absent, budget is wasted, click-through rates (CTR) decline, Quality Scores suffer, and cost-per-click (CPC) rises. These challenges can make the algorithm seem like it’s working against you.

However, many of us weren’t taught how negative keywords fit into an overall account strategy, only how to add them. Let me delve into these six critical strategic choices.

Determining how aggressive to be with negative keywords is the first decision every account manager needs to make, yet it’s often overlooked.

Are you relentlessly removing every low-performing search term? Are you deliberately allowing space for keyword opportunities? Or do you find yourself somewhere in between?

There isn’t a universal right answer, but it is essential to choose your level of aggression. A growth-focused account may need a less aggressive approach, whereas an efficiency-focused account might require more aggression. This choice should align with the account’s goals and performance metrics.

Using the right match types for negative keywords is crucial. Most advertisers default to one type without understanding why.

Here’s my breakdown:

Use negative exact match for strictly removing specific long-tail variations, negative phrase match for groups of related queries, and negative broad match for eliminating words that indicate a misaligned audience.

A well-thought-out negative keyword strategy utilizes all three match types, each serving a distinct purpose.

When should you add negative keywords? This is a consideration I’ve seen approached in various ways by different account managers.

Some add negatives weekly regardless of data, while others only when conversions drop, or during quarterly reviews. The right approach depends on your goals and data-driven insights.

For growth-focused accounts, trigger addition when a query exceeds three times your target CPA over 90 days without conversion. For efficiency-focused accounts, use a stricter budget-focused trigger.

The timeframe for reviewing data when deciding on negative keywords is another crucial factor.

A 30-day window might be too aggressive unless dealing with short-term promotions. A 90-day window is balanced and often recommended, while a 365-day window may be conservative, excellent for long buying cycles.

Choosing the correct timeframe informs smarter strategic decisions.

The role of AI in campaign sculpting through negative keywords is increasingly pivotal.

Decide how much control you want versus how much you rely on the machine. Some eliminate competitor keywords, yet others let them through for conversions.

While AI holds more information than us, sculpting is necessary for communicating your intent.

In 2026, we have more options than ever for managing negative keywords effectively.

You can conduct a manual review, use AI tools for suggestions, or let AI handle it fully. The key is balancing efficiency with oversight according to the comfort level and stakes of the account.

In every era, a few principles remain true. Keep your search terms report in check, make sure to update negatives as your campaign evolves, and always remain flexible to changes in user intent.

Ultimately, efficient advertising starts with strategic exclusion. What we choose not to target often holds equal importance to what we do target.

Inspired by this post on Search Engine Land.

May 6, 2026
Boost PPC Performance by Measuring Paid Social Impact
I sometimes find it challenging to measure the true impact of my paid social campaigns on PPC performance. Despite not always seeing conversions directly within the social platform, these ads can significantly influence my overall marketing efforts.

To truly understand how paid social affects my other marketing channels, including PPC, I’ve found a few strategies that help me set up and measure effective tests.

Step 1: Determine Your Hypothesis

I always start by clarifying what I want to learn from my tests. Defining a realistic hypothesis that I can evaluate with available data is crucial.

For example, I often use the following hypothesis to measure the influence of social traffic on PPC:
- Search lift hypothesis: Increasing social media spend will boost brand search volume and PPC CTRs.
- Logic:
  - Social ads build brand awareness, prompting more people to search for my brand during research and purchase stages.
  - As more people become familiar with my brand, they tend to click on PPC ads more, regardless of search terms, enhancing both brand and non-brand CTRs.
  - Exposure to my brand boosts trust, potentially increasing conversion rates.
- Measurement:
  - Track impression and click volume for branded terms.
  - Monitor CTR changes for brand and non-brand terms.
  - Observe conversion rate changes for these terms.
My hypothesis varies, sometimes focusing on the lift from social spend or a surge in direct traffic.

Step 2: The Test

Setting up test parameters is my next step. It’s essential to avoid simply comparing results before and after changes due to possible seasonal effects. A geographic split test is typically my go-to method.

In this test, I increase social spend in specific geographies and analyze PPC data from these areas versus others. While selecting geographies, I control for various factors, such as regional televised sports events or confined TV commercials, to ensure my test results are valid.

It’s crucial to compare control and experimental groups by similar factors like income levels and region types. I also ensure my budget can accommodate anticipated increases in social spent, preventing budget limitations from skewing results.

Evaluating the impression share before and after allows me to ensure budget constraints don’t impact my outcomes.

Step 3: The Measurement

When starting measurement, I keep it simple, comparing platform data to see changes prompted by stopping social spend across all channels like TikTok, LinkedIn, Facebook, etc.

Upon halting social spending, I’ve observed mixed conversion rate results, with some regions showing increases and others decreases, though an overall drop in conversions was common.

Depending on my analytics setup, I delve into more complex analyses, looking at conversion touchpoint differences, visitor overlap rates between social and paid search, or different attribution models.

Before initiating any tests, I ensure that my measurement capabilities are robust enough to understand and interpret results accurately.

Step 4: Evaluation Beyond Test Criteria

While running tests, I measure results against my hypothesis but also look at additional variables that may provide further insight.

In one case, a brand I tested on believed they could cut down on brand advertising without affecting their search volume. However, a drop in common brand terms contradicted this. An evaluation across various factors showed unpredictable results that required expanded analysis.

I rely heavily on my experience to sniff out anomalies and conduct further internal evaluations.

When results seem unexpectedly drastic, I question whether it’s a quirk or if other factors, like recent AI-driven changes, are silently influencing outcomes.

What to Do With Your Social Impact Tests

The test setup is straightforward:
- Define your hypothesis.
- Choose how to test, preferably using a geographic split.
- Ensure you can measure the outcomes appropriately.
- Run the tests and evaluate the hypothesis-related metrics.
- Assess additional metrics for further insights or testing ideas.
For some, social channels like Facebook are top converters, while others see poor outcomes in isolation, necessitating tests to guide budget allocation strategies.

In these scenarios, companies with substantial social media spending reduce to test impact, while others might increase spending to assess performance changes.

Results vary widely across companies, with some seeing significant performance lifts and others noticing minimal changes, underscoring the need for personalized testing.

Conducting geographic split tests can offer incredible insights into how social media campaigns bolster or detract from other marketing channels.

Inspired by this post on Search Engine Land.
April 28, 2026
Boost Ad Campaigns with AI: Emotional Triggers & ROI Tips
I’ve discovered the power of turning AI into a strategic ad partner using prompts that dive deep into buyer emotions, target high-intent audiences, and tackle objections.

Many of us are already tapping into various generative AI tools to breathe life into our marketing ideas and boost the effectiveness of ad campaigns.

Using prompts isn’t just a solo brainstorming alternative; it’s a productivity booster that opens up a world of possibilities.

In this guide, I’ll share some of my favorite marketing prompts for ad campaigns, designed to spark creativity in crafting your own prompts.

Why Use Prompts for Online Ads?

Prompts are your fast track to brainstorming ad elements like triggers, emotions, actions, and your target audience.

The beauty of prompts is they’re versatile. You can tweak outputs across different channels and initiatives like ads, emails, and social media.

Getting closer to optimal campaigns from the outset means saving time, a real boon for low-budget efforts that are hungry for feedback.

The prompts themselves make all the difference. Craft strong questions to extract valuable insights from large language models (LLMs).

Feeling stuck? Ask AI tools for prompt recommendations or use mine. Here’s a selection I often use for online ads.

Emotional Trigger Prompt

Purchases are fueled by emotions, so it’s essential to tap into what makes your audience feel.

Try this prompt: “What are the top emotional triggers that would make X audience buy Y product?”

As an example, I explored what emotional triggers would prompt parents to purchase math learning software for their kids. The LLM highlighted key triggers alongside scarcity and urgency hooks:
- Fear of falling behind: Anxiety and a protective instinct. Example: “Ensure your child never falls behind in math.”
- Desire to give kids a competitive advantage: Ambition and pride. Example: “Equip your child with math skills that top students develop years ahead.”
- Relief from homework stress at home: Relief and peace of mind. Example: “Say goodbye to math homework battles at home.”
Purchase Intent Prompt

Explore these questions to identify who’s ready to buy your product or service now:
- Who is most likely to buy immediately?
- Who needs convincing?
- Who will never buy?
To prevent wasting ad spend, focus on audiences poised for purchase and steer clear of those unlikely to buy.

Keep probing which audiences are most likely to convert. Use the LLM’s feedback to get more specific with your ads.

In the math software scenario, the LLM advised that parents of struggling kids in math were the best converters due to high urgency and low friction.

The second-best group? Homeschooling parents, motivated by the need to manage the entire curriculum. This insight allowed us to craft ads and test conversions.

Overcoming Objections Prompt

Addressing objections is crucial for sealing the deal. Ask for three to five potential objections buyers might have about your product.

In our math software example, the LLM identified these objections:
- My child already has too much screen time.
- Will this actually improve my child’s math skills?
- It’s too expensive.
Next, craft a persuasive counter-argument for each using logic, emotion, and evidence. For “it’s too expensive,” consider:
- Logic: “Less than the cost of a tutor.” Establishes a higher anchor, making the price seem reasonable without calling it cheap.
- Emotion: “Don’t let your kids fall behind in math.”
- Proof: “80% of students improve by one letter grade in two months.”
Psychological Profile Prompt

Request a comprehensive psychological profile of your ideal customer from an LLM. Use questions like:
- What are your ideal customer’s fears?
- What are their frustrations?
- What do they envy?
- What do they pretend doesn’t bother them?
- What keeps them up at night?
In the math software scenario, I asked, “What or who do my ideal customers envy?”

The response indicated parents envy children in enrichment or advanced classes, seeking future educational opportunities.

Here’s a message for them: “Help your child stay ahead instead of playing catchup.”

The Lifetime Value Prompt

Sustain long-term success by focusing on customer lifetime value (LTV) instead of one-time sales.

Consider these questions:
- Why might your customers stick around?
- Why might they buy more?
- What retention strategies are effective?
For a luxury furniture brand, we turned these into a brief playbook to boost LTV. The LLM suggested shifting from a transactional relationship to a long-term design partnership.

For instance, segment your customer base and use direct mail for your highest-value group by sending a lookbook. Though it seems old-school, it can result in a higher LTV than general mailings.

Your clients deserve strategic thinking and clear priorities. AI tools help us achieve that, supporting both strategy and execution.

Fix Lagging Average Order Value Prompt

When performance dwindles, it’s tempting to ask sweeping questions about metrics like return on ad spend (ROAS).

But that’s a path well-trodden, often leading to generic, uninspired checklists.

We grapple with B2C and B2B search query overlaps. Focusing on B2B users is challenging but crucial for securing high-value, long-term customers.

We noticed a likely cause of a B2B client’s lagging ROAS: average order value (AOV) as reflected in Google Ads’ Value/Conv. Smart Bidding had shifted to high-converting but lower-quality sessions, impacting performance.

We enlisted an LLM to ascertain and address the issue.

With Ads Advisor (Gemini) in Google Ads, the initial response focused on trivial consumer scenarios, like holiday themes.

Upon refining the prompt, we received more targeted, actionable suggestions, saving valuable time.

We doubled down on audience targeting, emphasizing specific Google audience segments and first-party audiences with value rules.

AOV increased. While it didn’t promise higher order values, it honed focus on B2B intent and reduced low-priority consumer purchases.

Key performance metrics improved, guiding the path to growth and profitability.

Better Prompts Lead to Better Campaigns

Begin simply — incorporate one or two of these prompts into your next campaign, tweak the outcomes, and expand from there. Over time, you’ll establish a repeatable system where AI becomes integral to your marketing workflow.

Inspired by this post on Search Engine Land.
April 9, 2026
Avoid These Costly Google Ads Mistakes for Ecommerce Success
Expanding beyond paid social? Discover how I learned to structure campaigns, control spend, and unlock demand without depending solely on the Meta playbook.

My paid social campaigns were thriving. I understood my audience intimately, had a tight creative process, and watched results improve each year. Naturally, when leadership proposed expanding into Google Ads, I was thrilled—envisioning it as a new revenue channel.

But sticking to our existing strategy only led to difficult conversations. Google demands different tactics—intent signals and campaign structures vary, and common budget-draining mistakes aren’t always obvious. Many brands mirroring their Meta strategy end up with flashy dashboards but disappointing balance sheets.

From my experiences, six frequent mistakes can cause substantial damage before they’re even noticed. They’re what I’ve seen most often with ecommerce brands transitioning to Google Ads—and each error is reversible.

Mistake 1: Treating Google like a retention channel

Utilizing Google Ads for retention and brand defense is possible, but relying solely on it as a strategy is problematic. I often notice brands new to the platform diving straight into Performance Max. Initially, the ROAS shines bright, making everyone happy. However, when the right question surfaces—”Are we truly growing or just capturing purchases?”—issues arise.

For example, a client approached me with branded search and retargeting doing most of the work in PMax—a mere tax on demand already created elsewhere, leading to stagnant revenue. Although ad spend was soaring, growth wasn’t.

Acquiring new customers requires a different setup, like:
- Shopping campaigns to highlight products to new audiences.
- Search campaigns centered on non-branded, high-intent keywords.
- Layered PMax configurations to bypass defaulting to easy conversions.
When Google grants vast access to new audiences, focusing solely on closing disregards most of this opportunity.

Dig deeper: Ecommerce PPC: 4 takeaways that shape how campaigns perform

Mistake 2: Not knowing how to leverage Google’s core levers

Although paid social expertise is somewhat transferable to Google, I’ve observed four major gaps. Let me share them with you in more detail.

Search intent: Social media ads interrupt, but search ads meet users actively seeking your offerings, transforming campaign structure, ad copy, and keyword targeting entirely.

Data feed optimization: An optimized product feed enhances visibility and targeting in Shopping or Performance Max campaigns.

Keyword research: Understanding match types and search intent is critical for reach and cost efficiency.

Landing pages: Engaging landing pages outperform product pages for high-intent but unfamiliar visitors.

Dig deeper: 7 Google Ads search term filters to cut wasted spend

Mistake 3: Allowing operational issues to interrupt campaign momentum

Consistent data is key for Google’s algorithms. Every unintended campaign pause can reset learning, causing weeks of degraded performance and wasted spend.

Common disruptions include:
- Payments: Bill lapses, leading to campaign pauses, overshadow the actual cost when factoring in downtime recovery.
- Tracking and feed integrity: Broken pixels and feed errors silently degrade performance.
Setting up automated alerts and regular audits can prevent these costly errors.

Mistake 4: Overly granular campaign structures

Detail-oriented advertisers may over-segment campaigns, believing it provides control. However, widespread budget allocation hinders Google’s automation from optimizing effectively.

Instead, tight, well-funded campaigns optimize better and are more manageable.

Dig deeper: How to find and fix the root cause of low conversions

Mistake 5: Leaving campaigns on Max Conversion Value without ROAS targets

Max Conversion Value aims for conversion volume, neglecting cost efficiency. A realistic ROAS goal encourages the algorithm to maximize efficiency. Setting this correctly is crucial.

Dig deeper: How each Google Ads bid strategy influences campaign success

Mistake 6: Underfunding campaigns, keeping them in learning mode

Underfunding during the learning phase results in indefinite stalled progress. Adequately funding new campaigns from the outset fosters quicker, more accurate results.

Expanding beyond Meta to include Google is a strategic move, accessing actively expressed demand. These pitfalls aren’t deterrents but guideposts for smoother transitions and optimized strategies.

For early adopters, start with my guide on expanding from Meta to Google Ads. If seeking further optimization, learn how to sidestep Google’s automation traps.

Inspired by this post on Search Engine Land.
April 2, 2026