Tag: Campaign Optimization

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.

Inspired by this post on Search Engine Land.

July 3, 2026

Semantic PPC and SEO Tactics That Still Win With AI

Why advanced semantic techniques still matter in PPC and SEO

Now that I can use AI to generate keywords and launch a paid search campaign in minutes, it is tempting to think the hardest part of PPC and SEO work has already been handled.

But I still need more than fast keyword output if I want structured, scalable performance. I need to understand how search actually works, how people phrase intent, and how noisy search term data can distort a campaign if I do not organize it properly.

That is where semantic techniques such as n-grams, Levenshtein distance, and Jaccard similarity continue to matter. I use them to interpret messy data, apply real client context, and build frameworks that AI alone cannot reliably produce.

What I learn from n-grams in PPC and SEO analysis

I think of n-grams as the “n” words that make up a keyword. In the search term “private caregiver nearby,” I can break the phrase into smaller pieces that are easier to analyze.

3 unigrams (one word): “private,” “caregiver,” and “nearby”
2 bigrams (two consecutive words): “private caregiver” and “caregiver nearby”
1 trigram (three consecutive words): “private caregiver nearby”

I use n-grams because they simplify large keyword lists without stripping away the patterns that matter.

For example, I recently restructured several campaigns that had more than 100,000 search terms. By using n-grams, I reduced those lists into much more workable sets.

~6,000 unigrams.
~23,000 bigrams.
~27,000 trigrams.

Once I have those smaller sets, I can spot patterns quickly. If every keyword containing the “free” unigram performs poorly, I can exclude “free” as a broad match negative.

On the other hand, if I see that “nearby” performs especially well, I may test more local variations, build location-specific landing pages, or adjust campaign structure around that intent.

I still have to respect the limits of this method.

I need a large volume of search terms, so this approach usually works best for accounts with bigger budgets.
As “n” gets larger, the output becomes less useful because the data expands again. At that point, I usually need more advanced methods such as Levenshtein distance or Jaccard similarity.

How I cluster keywords with n-grams

When I analyze SEO and PPC data, I often deal with huge volumes of long-tail search terms. Many appear only once and carry very little standalone data.

N-grams help me turn that chaotic long-tail data into clearer, more manageable intelligence.

That intelligence helps me reduce wasted spend, find new opportunities, and build a structure that can scale.

I start by exporting search term data. In PPC, that includes cost, impressions, clicks, conversions, and conversion value by search term.
For each n-gram, I sum cost, impressions, clicks, conversions, and conversion value.
Then I calculate CPA, ROAS, CTR, CVR, and any other metrics that matter for the account.

With a shorter and more digestible dataset, I can rank the top-spending n-grams that do not convert, which often become negatives, and the ones that do convert, which become positives.

From there, I build ad groups around recurring n-grams that consistently drive performance.

For example, I may find that emergency-related n-grams such as “24/7,” “same day,” or “urgent” deliver higher conversion rates. I would segment those terms so I can control budget, bidding, and messaging more precisely.

Bottom line: I use n-grams to isolate themes that deserve special attention.

Once I have identified those themes, it becomes much easier to build advanced paid search structures around high-impact n-grams and improve ROI.

Dig deeper: How to uncover hidden gems in your paid search accounts

How I use Levenshtein distance to improve keyword quality

Levenshtein distance measures the minimum number of single-symbol edits, including insertions, deletions, or substitutions, needed to turn one string into another.

That may sound complicated, but the idea is simple once I put it into practice.

The Levenshtein distance between “cat” and “cats” is 1 because I only need to add the “s.” Between “cat” and “dog,” the distance is 3.

One common PPC use case is finding brand and competitor misspellings inside search term reports.

For example, “uber” and “uver” have a Levenshtein distance of 1, so I would feel confident excluding the misspelled version from non-brand campaigns.

I can apply the same logic to keyword relevance.

If the distance between a keyword and the search terms it matches is too high, such as 10 or more, those terms probably have very little in common with the keyword and deserve review.

A low distance usually tells me those queries are close enough to be safe and do not need the same level of manual inspection.

How I consolidate PPC keywords with Levenshtein distance

After I use n-grams to create initial keyword clusters, I may still have thousands of search terms to organize into a practical campaign structure.

Manually sorting through 6,000 unigrams is not realistic. This is where Levenshtein distance becomes especially useful.

Venn diagram showing sets A and B with their overlapping intersection labeled A&B, illustrating Jaccard similarity for SEO and PPC keywords. — A simple Venn diagram visualizes how Jaccard similarity measures the shared overlap between keyword sets A and B in semantic PPC and SEO analysis.

My goal is to merge ad groups that target nearly identical keywords so I do not end up with an overly granular, SKAG-like structure.

Too much granularity makes reporting and account management harder. It can also create inefficient bidding and wasted spend.

Using the same dataset, I calculate the Levenshtein distance between queries across different ad groups.

Then I identify the closest keyword and ad group using a predefined threshold. A threshold of 3, for example, gives me a high degree of accuracy.

This helps me consolidate keywords and ad groups with confidence. If I use a looser threshold, such as 6, I can also group or name ad groups by broader similarity or intent.

Here is a simple example showing why these three keywords can be grouped together:

Levenshtein distance	24/7 plumber	24 7 plumber	247 plumber
24/7 plumber	0	1	1
24 7 plumber	1	0	1
247 plumber	1	1	0

Dig deeper: How to use negative keywords in PPC to maximize targeting and optimize ad spend

How I go further with Jaccard similarity

In PPC, I use Jaccard similarity as a practical proxy for understanding the overlap between two sets of n-grams.

The calculation is straightforward: I divide the number of shared unigrams between two sets by the total number of unique unigrams across both sets.

It sounds technical, but I visualize it simply:

Jaccard similarity = Red / Green

Here are a couple of concrete examples I use to explain the concept:

“new york plumber” and “plumber new york” = 1 because all three unigrams appear in both sets, just in a different order.
“new york plumber” and “NYC plumber” = 0.25 because only “plumber” is shared, and there are four unigrams in total.

Jaccard similarity is a helpful first step for deduplicating similar keywords. I see it as a bridge between old phrase match logic and broad match modified logic.

But it has an important limitation: it does not understand meaning.

In the example above, “new york” and “NYC” should be treated as equivalent, but the Jaccard calculation sees them as different.

To handle that kind of nuance, I need more advanced techniques, which I would treat as the next layer of analysis.

How I combine Jaccard similarity and Levenshtein distance

Consider a cybersecurity course campaign with the following top 10 keywords:

Keyword	Semrush average monthly searches in the U.S.
cybersecurity courses	5,400
cybersecurity online course	1,900
free cybersecurity courses	1,300
online cybersecurity courses	1,300
cybersecurity course	1,000
cybersecurity courses online	880
google cybersecurity course	880
cybersecurity courses free	720
cybersecurity free courses	590
cybersecurity online courses	480

By combining singular and plural versions, along with reordered versions of the same idea, I can reduce that top 10 into a more actionable top four.

“Cybersecurity courses.”
“Cybersecurity courses online.”
“Free cybersecurity courses.”
“Google cybersecurity course.”

I could use n-grams to do this, but scaling n-gram analysis across thousands of keywords can quickly become overwhelming.

A more efficient approach is to use both similarity metrics in sequence.

First, I apply Levenshtein distance to consolidate very similar queries.
Then I use Jaccard similarity to deduplicate reordered variants.
At each step, I sum the usual KPIs, including cost, conversions, and other performance metrics, so the analysis stays actionable.

The result is a clear, compressed structure that can hold up even as search term volume grows.

How I restructure paid search campaigns with semantic techniques

With the right semantic techniques, I can restructure massive keyword sets quickly and still produce consistent, high-quality results.

AI can absolutely help me create an initial summary, but I do not rely on it entirely.

Otherwise, I run into the classic problem of “garbage in, garbage out.”

Broad match can be powerful, but it also introduces more noise. These techniques help me verify that the queries I am matching stay aligned with campaign goals.

I use n-grams, Levenshtein distance, and Jaccard similarity to apply client context to raw search data and build a stable structure around real intent.

If the process feels overwhelming at first, I use this summary to decide which technique fits the job:

Scenario	Best technique	Why
Identify high-intent patterns in huge search-term exports	n-grams	Surfaces themes fast; reduces dimensionality
Clean duplicate / near-duplicate keywords at scale	Levenshtein distance	Captures spelling + structural similarity
Deduplicate reordered or slightly varied keyword strings	Jaccard similarity	Order-insensitive token-based comparison
Create scalable clusters for campaign rebuilds	Combo: Levenshtein → Jaccard → n-gram	Sequence gives accuracy + compression

For me, the main lesson is simple: AI can accelerate PPC and SEO work, but semantic analysis gives that work structure, signal quality, and strategic control.

Inspired by this post on Search Engine Land.

July 3, 2026

Empower Your Marketing with Shopify’s AI Campaign Autopilot
Shopify has just launched Campaign Autopilot, an innovative tool powered by AI designed to streamline marketing efforts. By taking the reins of campaign creation, management, and optimization across various channels, it’s set to significantly ease my workload as a merchant.

Imagine having the power of Campaign Autopilot directly within the Shopify admin. This feature is in its early access stage but is already offering tremendous support in marketing automation.

What’s happening? With AI technology, Campaign Autopilot orchestrates marketing campaigns on my behalf across channels like Meta, Shop Campaigns, and email, enhancing my marketing strategy effortlessly.

Additional support is in the pipeline for platforms such as ChatGPT Ads, Microsoft Advertising, and Snapchat—making it a versatile tool for future needs.

What makes this system stand out is its ability to autonomously handle campaign setup, financial planning, and constant adjustments based on real-time performance, leaving me time to focus on other aspects of my business.

Why I care. By simplifying the complex world of multi-channel marketing, Campaign Autopilot provides me with a user-friendly platform that traditionally relied on the expertise of agencies or specialized teams. Now, I can set my budget and objectives while Shopify’s AI takes care of the intricate details.

How it works. I decide on a monthly budget, select channels to collaborate with, and set guidelines. From there, Campaign Autopilot executes:
- Creating and launching campaigns.
- Distributing my budget across channels.
- Adjusting expenditures based on feedback.
- Suggesting automated email initiatives.
- Evaluating and refining campaign effectiveness on an ongoing basis.
I have full control—approving or tweaking campaigns, modifying budgets, or halting actions whenever necessary.

How it stands out. Campaign Autopilot redefines contemporary campaign management by sidestepping traditional, more labor-intensive methods.

Its unique approach taps into performance insights gleaned from millions of Shopify stores, offering data-driven enhancements and budget allocations.

Moreover, it functions separately from existing Meta or Shop ads, ensuring previously planned campaigns remain unaffected.

The bigger picture. Shopify is not just about ecommerce anymore. It’s now moving into the realm of growth and customer acquisition by embedding AI deeper within its merchants’ operations.

Industry trends show a shift towards autonomous marketing systems, which can run campaigns with minimal human intervention, constantly optimizing performance along the way.

What to keep an eye on. Shopify will be expanding its channel support further, potentially integrating with platforms like ChatGPT Ads, Microsoft Advertising, and Snapchat.

There’s also the AI assistant, Sidekick, which I can use for reviewing recommendations, triggering actions, and keeping a close watch on campaign outcomes.

Dig deeper. Interested in more details? Check out Introducing Campaign Autopilot: AI-powered Marketing Built into Shopify.

First spotted. This update came to my attention courtesy of Digital Marketing Consultant Susan Richards-Benson via a LinkedIn post, where she recommended it as a game-changer for smaller eCommerce brands.

Inspired by this post on Search Engine Land.
June 23, 2026
Mastering PMax to Capture Net New Customers Effectively

As I explore the potential of Performance Max for acquiring new customers, I realize that without proper setup, it’s easy to see inflated dashboard metrics that obscure the reality of your profitability.

One major pitfall is recycling traffic from Meta. Paid search and social traffic often overlap, leading to the dreaded scenario where platforms each claim credit for conversions they didn’t fully drive.

Many direct-to-consumer (DTC) brands I talk to boast about their growing numbers. But upon deeper inspection, it’s clear that those ‘new’ customers frequently originate from existing brand efforts, shared between different ad platforms.

These overlapping sales, while still revenue, can be deceiving. Their true cost is higher than often reported, eroding actual profit without proper intervention.

Rather than limiting yourself to one ad channel, utilizing an effective system to measure genuine customer acquisition is key.

Using brand and audience exclusions along with Customer Match data, I have developed a four-step framework to target genuine new customers through Performance Max, minimizing overlap across platforms.

Steps like excluding specific audiences and leveraging first-party data can help Performance Max focus on new customers instead of warm leads.

By refining these strategies, we’re optimizing how our ad spend contributes to true customer acquisition and enhancing overall profitability.

Inspired by this post on Search Engine Land.

June 23, 2026
Discover YouTube’s New AI Tools for Enhanced Insights

Google has just unveiled some exciting AI-powered tools on YouTube. These tools are designed to reveal creator trends, enhance understanding of audience behaviors, and optimize marketing campaigns.

YouTube’s expansion of its toolset for creator marketing and campaign intelligence now includes features powered by Gemini. With these updates, I’m able to delve deep into identifying trends, understanding the creator audiences, and boosting the performance of my campaigns.

What’s happening: Google has introduced several insights and optimization tools across YouTube and Google Ads. As a marketer, these tools give me crucial visibility into trends, creator performance, and audience behavior.

The opportunity to make smarter creative and media planning decisions is more important than ever, especially in an AI-driven marketing world. That’s exactly what these new tools are designed to support.

Why I care: With deeper insights into YouTube trends, I can see which creators are resonating most with audiences and assess how my brand is performing in terms of both paid and organic content. This empowers me to make smarter choices about creator partnerships and campaign strategies.

What’s new:

More detailed trend insights: Google Ads’ Insights Finder now provides even more detailed trends in the U.S., giving advertisers like me a better view of what’s capturing attention on YouTube.

Brand Pulse data in Insights Finder: With the integration of select Brand Pulse metrics, I can now evaluate both my paid and organic efforts from a single location.

New creator insights API: The fresh Content & Creator Insights API offers agencies and partners more detailed information about YouTube creators and their audiences, enhancing my media planning and creator selection process.

Gemini-powered creative recommendations: Soon, Gemini will offer creative optimization suggestions for Demand Gen campaigns, including tips on visuals and creative elements that could boost performance.

The bigger picture: As content created by influencers plays a growing role in purchasing decisions and brand discovery, advertisers like me are keen to spot trends early and gauge creator impact effectively.

Google is banking on AI to help marketers like myself uncover insights quickly and plan more efficient campaigns.

Bottom line: YouTube is providing brands and agencies more data on trends, creators, and campaign performance. Using Gemini, these insights can be transformed into more robust creative and media decisions.

Inspired by this post on Search Engine Land.

June 23, 2026
Master Google Ads: New Bid Strategy Updates Revealed

I’ve come across important news about Google Ads that could significantly impact how we manage our campaigns. Google is on the verge of altering its target-based bidding strategies, particularly for campaigns running on limited budgets.

Mark your calendar for August 17th when these changes will take full effect. But don’t worry, a Bid Target Adjustment Tool will be available as of July 6 to help us prepare and adjust our goals accordingly.

What’s going on? Google’s update aims to closely align target-based bidding strategies such as Target CPA with our set goals, even when budget constraints come into play.

They’re introducing a new tool that allows us to tweak our targets before the updates hit, which is crucial for maintaining our campaign performance.

Why should we care? If your campaigns are currently exceeding their target CPA or ROAS goals, they might not continue to do so post-update without adjustment. This update is meant to ensure budget-constrained campaigns stay true to their targets.

For example, if my campaign is achieving a $5 CPA against a $10 target, the performance might shift towards $10 unless I make some changes.

Thankfully, the new tool is there to help us proactively update our bidding goals before the changes roll out. If we don’t take advantage of this, we might end up paying more per conversion or see our performance realign with Google’s targets instead of our historical results.

Why is Google doing this? Google wants to reduce fluctuations and provide more predictable results when we tweak or adjust our budgets.

The tool is designed to help us synchronize our bidding targets more closely with actual business outcomes before the automatic implementation begins.

What should we do? It’s a good time for us to reevaluate campaigns using target-based strategies and verify if our current targets still align with desired results.

Notifications will be sent through Google Ads accounts before the update, and the Bid Target Adjustment Tool can highlight which campaigns might be affected.

Key takeaway: For those of us with campaigns that consistently outperform their targets, maintaining current performance might require tweaking target settings instead of leaving them unchanged.

Bottom line: Google is tightening the link between target-based goals and campaign performance. It’s now more essential than ever for us as advertisers to keep bidding targets updated consistent with our business objectives.

Inspired by this post on Search Engine Land.

June 22, 2026
Understanding Google’s New Rules for Demand Gen Audience Targeting
Recently, I discovered that Google has updated its personalized advertising policy documents. This change clarifies how restrictions on sensitive audience targeting are applied to Demand Gen and Discovery campaigns, especially when promoting sensitive products or services.

The big picture. The update is part of Google’s “Restricted targeting in Personalized Advertising” policy documentation. It focuses on providing a clearer understanding of potential ad serving limitations rather than implementing new policy restrictions.

What’s changing. In June, Google updated its help documents to offer more insights on how Demand Gen and Discovery campaigns intersect with personalized advertising restrictions.

These changes particularly address campaigns targeting products and services associated with sensitive interest categories.

The fine print. It’s important to note that this update serves as a clarification of existing policy guidance and is not a new policy announcement.

Google states that the revised documentation now includes more information regarding the serving implications when advertisers use audience targeting for products or services falling into restricted categories.

Sensitive interest categories can include areas such as:
- Health conditions
- Financial hardship
- Personal difficulties
- Other topics that Google considers sensitive under its personalized advertising policies
Between the lines. In using Demand Gen campaigns, I heavily rely on audience signals and personalized targeting to reach users on platforms like YouTube, Discover, and Gmail.

As the usage of Demand Gen grows, the need for clarity on how Google’s sensitive interest policies affect audience eligibility, reach, and campaign delivery has become more critical.

Google’s documentation update indicates a response to these inquiries by providing us with clearer guidance on when targeting restrictions might limit campaign performance.

Why now. This clarification arrives as Demand Gen becomes a major component of Google’s advertising ecosystem and more advertisers are reallocating budgets from Discovery campaigns to Google’s AI-powered audience products.

Why we care. For those of us running campaigns in regulated or sensitive industries, understanding these restrictions has become pivotal in our campaign planning and audience strategies.

What to watch. If you’re handling Demand Gen campaigns in sectors like healthcare or financial services, it’s vital to review the updated guidance to see if targeting choices might affect your reach or ad delivery.

Inspired by this post on Search Engine Land.
June 5, 2026
Unlocking New Controls in Google AI Max for Branded Searches
I recently came across a fascinating development in Google Ads that’s really worth discussing. Google seems to be testing new branded search controls within AI Max campaigns, which might just give advertisers a better way to separate branded from non-branded traffic.

If you’re like me, you’ve probably faced challenges with AI Max campaigns capturing branded searches, especially since their launch. It seems Google might finally be addressing this common concern by offering more control over how these campaigns interact with branded queries.

What’s happening. Some advertisers have reported a fresh ‘Branded Searches’ control option within AI Max campaigns. This feature potentially allows us to dictate how the campaigns handle brand-associated searches.

The option includes three settings:
- Show ads on all relevant searches (default strategy)
- Manage branded searches via inclusions and exclusions
- Restrict ads to only appear on unbranded searches
Why we care. For those of us managing campaigns, one major critique of AI Max has been its tendency to capture branded traffic. This traffic is often already covered by dedicated brand campaigns, leading to complications.

Campaigns that pull in branded traffic can pose several issues:
- Increased costs for likely conversions
- Complexities in attribution across different types
- Diminished clarity on incremental gains
- Worries of AI Max overshadowing branded efforts
The ability to focus on purely unbranded searches, newly introduced, could help direct AI Max towards fresh demands and new prospects.

Between the lines. Up until this point, preventing AI Max from engaging in branded queries required exclusion lists. A native setting would simplify this and potentially offer more insight into brand intent handling.

The big picture. Google seems committed to adding more oversight to automated campaigns, reacting to our calls for greater transparency and control over AI.

If these controls are deployed widely, it could indicate Google’s acknowledgment of our traffic management concerns, as they forge ahead with AI automation.

What to watch. Whether this is a full release, a selective test, or just an experiment is still unclear. Keep an eye on your AI Max settings and stay alert for updates from Google regarding branded search controls.

Bottom line. This new control in AI Max might soon empower advertisers to distinctly separate branded and non-branded traffic—something many of us have long requested. But for now, it’s an observation rather than a confirmed rollout.

First spotted. This development was originally highlighted by Paid Search specialist Thomas Eccel, who shared his discovery on LinkedIn.

Inspired by this post on Search Engine Land.
May 29, 2026
Don’t Be Fooled: The Truth About B2B PPC Metrics

More conversions and higher ROAS are not always indicative of increased pipeline or revenue. I’ve discovered how to measure incremental value more accurately, and I’m excited to share it with you.

As a B2B PPC advertiser, I now have more options than ever before to gauge success. Previously, all I had was form-fill data. Now, with offline conversion data, I can feed invaluable insights into Google Ads and Microsoft Ads.

I’ve realized that while it’s tempting to measure every possible metric, optimizing them all is impractical. If you chase everything, you might end up achieving nothing substantial.

Determining if I’ve driven true incremental value and identifying the right success metrics for B2B PPC campaigns was crucial. Often, the metrics that truly matter aren’t the ones I initially focused on.

I’ve witnessed advertisers integrate offline conversions and get thrilled with a spike in total conversions, only to be hit with disappointment when there’s no boost in the bottom line.

After incorporating numerous conversion actions and setting them all to primary, advertisers, including myself, saw conversion counts rise, but not their actual impact. We were essentially counting the same leads multiple times.

This led to inflated platform-reported ROAS. Attaching conversion values to each action, which is advisable, also resulted in false increases. Both scenarios result from faulty calculations.

Solely focusing on average CPA proved misleading. It can mask the marginal CPA, the cost of acquiring an additional conversion as marketing expenditure increases, potentially leading to overspending as the account scales.

Setting up conversion values is crucial for understanding offline conversions. But it’s easy to get stuck if the conversion value isn’t known until it processes further down the pipeline.

Even if using actual conversion values is impossible, assigning relative values is still beneficial. I learned this through a simplistic example scenario.

Here, whenever I employed arbitrary values, I made sure to validate them against real data to ensure bidding algorithms responded accurately. This adjustment improved the relative perceived value of MQLs and SQLs for better alignment with true business goals.

By doing this, within just a couple of weeks, we managed to significantly boost MQL and SQL volumes while keeping leads flat, ultimately delivering higher-quality leads at the same cost.

Experimenting with campaign-specific goals allowed Smart Bidding to focus strictly on down-funnel actions, which fine-tuned our optimization efforts.

However, if lower funnel actions yielded low volumes, I noted automation might struggle due to insufficient signals. Adjusting strategy with this understanding ensured clearer outcomes.

To measure success effectively, beyond traditional CPA and ROAS, I focused on incremental conversions, evaluating them against baselines to understand the financial sensibility of further investments.

The most reliable measure of incremental value was mapping CRM data back to actual paid search campaigns. This helped in identifying assets and campaigns that, while generating fewer leads, drove significant pipeline growth.

Understanding this dynamic was critical in recognizing diminishing returns and preventing unfounded overspending on non-cost-effective channels.

Inspired by this post on Search Engine Land.

May 29, 2026
Uncovering the Hidden Flaw in a ‘Perfect’ PPC Campaign

I recently sat down with Veronika Höller for an enlightening discussion on PPC campaigns in an episode of PPC Live The Podcast. We delved into a scenario where a seemingly flawless campaign was secretly underperforming, uncovering the real issue beneath the surface.

From “perfect” campaigns to zero revenue

Initially, Veronika encountered an impeccably organized account. It had all the right elements: a clean structure, compelling creatives, and well-allocated budgets with conversions rolling in. But there was one glaring omission—it wasn’t generating any revenue.

This discrepancy prompted us to investigate further, revealing that while surface metrics such as impressions, clicks, and conversions appeared promising, the true business impact was lacking. The unraveling began here.

The real issue: nothing stood out

The breakthrough came not from within the account but by stepping outside it. During competitor research, Veronika noticed that the brand’s messaging was indistinguishable from its competitors. There was no compelling reason for users to choose their products over others.

From a user’s perspective, the ads weren’t incorrect; they were simply forgettable. In a saturated market, being simply “good” wasn’t enough. The revelation was not about performance but positioning.

Starting again — from scratch

Veronika boldly decided to reconstruct everything from the ground up. This involved crafting new messaging, developing fresh creatives, and establishing a comprehensive strategic blueprint. A pivotal change was identifying not only the ideal customer but also defining who they were not targeting, utilizing anti-ICPs to refine the messaging.

This reset also incorporated enhanced localization, creating tailored landing pages for different markets, and formulating platform-specific strategies instead of simply recycling campaigns across channels. It was much more than optimization—it was a complete overhaul, and it succeeded.

The mistake that nearly broke everything

Looking back at earlier times in her career, Veronika recalled a major misstep that will resonate with many PPC professionals. She had implemented a recommended target CPA but failed to adjust the budget accordingly.

This oversight led to a halt in campaign delivery and a significant drop in performance, all of which went unnoticed over the weekend. By Monday, the damage was done, and the client was understandably upset.

Owning the mistake — and fixing it fast

Veronika didn’t shy away from the situation. She promptly admitted her mistake, provided an explanation, and took full responsibility. This transparency shifted the client’s initial frustration into collaboration, as there was no defensiveness, only a structured plan for resolution.

The takeaway was invaluable: one must never apply recommendations blindly and should always consider the entire context before implementing changes.

Why failure is part of getting good

For Veronika, mistakes aren’t something to avoid—they’re a stepping stone to mastery. “You can only be good if you fail,” she asserted.

This philosophy now influences her work approach and mentorship style. Mistakes signal progress, experimentation, and improvement.

Furthermore, sharing these experiences helps others steer clear of similar pitfalls.

The biggest issue she still sees today

Despite evolving PPC landscapes, tracking remains a persistent issue. Many setups suffer from flawed implementations, reliance on micro conversions, and misconfigurations in tools like Google Tag Manager.

In a world dominated by smart bidding and automation, inaccurate data not only constrains performance but leads it astray. Even the most stellar campaigns can falter without precise tracking.

AI won’t fix average marketing

Veronika emphasized that AI isn’t a magic bullet for improving outcomes. Feeding it mediocre data yields mediocre results.

Many marketers erroneously rely on AI tools for account analysis without a proper understanding of the necessary enhancements. AI can’t create uniqueness; it can only optimize existing inputs. Distinctive strategies still demand human ingenuity.

The mindset that matters now

The most significant takeaway isn’t about tactics; it’s about mentality.

Perfection isn’t the goal. Avoid following recommendations blindly, and don’t assume tools will think for you. Instead, rely on your instincts, experiment, and accept that mistakes are a valuable part of the journey.

In performance marketing, the real hazard isn’t failure but becoming invisible by playing it safe.

Inspired by this post on Search Engine Land.

May 8, 2026