7 Google Ads Shortcuts Every PPC Manager Should Be Using via @sejournal, @brookeosmundson

Managing PPC accounts is already time-consuming, especially when attention gets pulled toward tasks that don’t meaningfully impact performance.

Over time, accounts accumulate extra keywords, inconsistent negatives, and small inefficiencies that make everyday management harder than it needs to be.

Fortunately, Google Ads includes several built-in tools that help streamline these tasks.

These seven shortcuts can help you manage accounts more efficiently while also surfacing insights faster, so you can spend more time improving performance instead of maintaining clutter.

1. Remove Duplicate Keywords

As accounts mature or change management over time, it can be easy to lose track of what keywords are being bid on.

This is especially true when one account manager structures campaigns and ad groups a certain way, and then another manager takes over and starts implementing their own structure.

It would be time-consuming to comb through all the account keywords to find duplicates.

Luckily, the Google Ads Editor has a very handy feature that will do this for you!

You can access it from the top menu under Tools.

Duplicate keywords tool in Google Ads Editor.
Screenshot by author, March 2026

The duplicate keywords tool gives you many options so you can be intentional in how it defines duplicate keywords.

For example, you can choose a strict word order or any word order.

You may want to choose a strict word order if you’re mostly concerned with Exact Match keywords.

But any word order can be a great way to clear out broad match searches or phrases that are just the same words in a different order.

You’re able to scope the keyword duplicates tool from:

  • Search, Shopping, and Performance Max campaigns.
  • Display, Video, and Demand Gen campaigns.
Duplicate keyword tool in Google Ads Editor.
Screenshot by author, March 2026

Another helpful option to be mindful of is the one for Location of duplicates.

An example of why you might want it only looking at certain groups would be if you have campaigns that are duplicates but set to show to different devices or different geographies.

They’re intentionally duplicated in those instances, so you’d only want to check for duplicates within each individual campaign.

2. Use Negative Keyword Lists

Since we’re on the topic of keywords, let’s switch to a feature that will help you organize negative keywords in an account.

Negative keyword lists are a great way to exclude specific categories of keywords across multiple campaigns or the entire account.

As with trying to find duplicate keywords, it can be time-consuming to go through all the negative keywords that have been added to a campaign or ad group over time.

Negative keyword lists allow you to group certain keywords together into a list and can then be attached to different campaigns.

You can find this in the Google Ads online interface by going to Tools >> Shared Library >> Exclusion lists. From there, you’ll find a tab for “Negative keyword lists” or “Placement exclusion lists.”

Where to find negative keyword lists in Google Ads interface.
Screenshot by author, March 2026

For example, you may already have a huge list of irrelevant keywords that you wouldn’t want to show up for any campaign.

Create an “Irrelevant Keywords” (or whatever you choose to name it) list, and apply that keyword list to all campaigns in the account.

Another example of how to use negative keyword lists is to separate branded terms from non-branded terms.

Simply create a negative keyword list of all brand terms, searches, or phrases, and attach that list to all non-brand campaigns.

This ensures that there’s no crossover between brand and non-brand performance.

3. Use Labels To Manage Ad Creatives

The Label function in Google Ads is a powerhouse for account organization and time-saving.

In my opinion, it’s one of the most under-appreciated features in Google Ads.

While labels can be added to a campaign, ad group, and keyword level, using them for time-sensitive copy or routine testing to turn things off/on is where it shines!

It is also a huge help if you want to compare higher-level messaging or before/after efforts with copy tests.

You can add a label to any ad by checking the box next to the ad versions you want to label and then choosing Label in the blue toolbar that appears:

Google Ads label function.
Screenshot by author, March 2026

You can then check the labels you want to apply to those ads or create a new label.

In this example, they want to easily test a new message related to a specific promotion happening on their website. There isn’t an easy way to see a comparison without filtering for each ad type.

Labeling each ad quickly makes it easier.

Another handy way to use labels and ads is for scheduling.

After you label the ads as outlined above, select the ones that you want to turn on for a certain date and time. Check the box next to the ads, and then go to the blue toolbar and click on Edit.

Screenshot by author, March 2026

From here, you can create rules for all the ads you selected with all kinds of timing and condition parameters.

You’d repeat this step each time you want something to turn off and then also to turn on.

4. Quickly Test Campaign Elements With Experiments

Speaking of streamlining ad creation and testing, another handy way to do this is by using the Experiments feature.

This is located under the Campaigns section on the left-hand menu.

Screenshot by author, March 2026

Click on the “All experiments” section, and then click the blue “plus” (+) button to start creating your own custom experiment.

Screenshot by author, March 2026

From there, you’ll be able to choose from multiple options:

  • Performance Max experiment.
  • Demand Gen experiment.
  • Video experiment.
  • App uplift experiment.
  • Custom experiment.
  • Optimize text ads.

One of the things I love about this option is you have the ability to set up the percentage split of your audience.

It can help you force a 50/50 split, whereas in regular ad testing, Google auto-optimizes.

Another thing I love about experiments is that it’s easy to indicate if there’s a clear winner.

Screenshot by author, March 2026
Screenshot by author, March 2026
Screenshot by author, March 2026
Screenshot by author, March 2026

In the example above, one of the experiments run showed a statistically significant change in clicks. This made it an easy decision to apply the experiment to the original campaign for better performance.

5. Use Notations For Important Account Changes

Keeping a log of an account history can be tough in Google Ads. There are so many moving parts, outside things that influence results, and then multiple people managing an account over its lifespan.

This can create issues when trying to analyze performance.

For example, you’re looking at year-over-year data and notice the numbers were so much better the previous year. Why?

It could be due to certain holidays that fall on different dates each year.

Or, maybe the brand got a huge PR bump that caused a lot of attention and searching.

Using notes can help you log that external history and save tons of time trying to dig and piece together this kind of analysis.

How do you add notes?

First, simply click on the performance graph.

When you hover on the graph line, the date and performance metrics appear, along with a blue Add Note option. You can type your note in that.

Screenshot by author, March 2026

Once you have notes in the account, they will appear as a little square along the dateline of the graph.

Cost and CTR graph
Screenshot by author, March 2026

Clicking on it will show you the notes left and the date they were made.

6. Use Filters To Quickly Identify Optimization Opportunities

When managing a busy account, it’s easy to spend too much time scrolling through campaigns, ad groups, and keywords trying to find what needs attention.

Instead of manually digging through every view, Google Ads allows you to create filters that instantly surface areas worth reviewing.

Filters can be applied to almost any table in Google Ads, including campaigns, ad groups, keywords, and search terms. Once created, they allow you to quickly isolate specific performance conditions.

For example, you might create filters to identify:

  • Keywords with high spend but zero conversions.
  • Ads with a low click-through rate.
  • Search terms generating high impressions but few clicks.
  • Campaigns pacing ahead or behind budget.

Creating a filter is simple. In most table views, click the Filter icon at the top of the table and define the conditions you want to see.

Once saved, filters can be reused anytime you review that view.

Over time, this becomes one of the fastest ways to spot inefficiencies or optimization opportunities without manually reviewing every row of data.

Instead of searching for problems, filters bring the most important ones directly to you.

7. Review Insights & Recommendations

Last but not least, the Insights and Recommendations tabs in Google Ads.

I’ve found these tabs to be a huge time-saver to help me identify key changes in performance week-over-week or month-over-month.

We’re all busy. It’s easy to miss high-level insights when we’re so “in the weeds” with our accounts every single day.

The Insights and Reports tab within the “Campaigns” left-hand menu provides insights into an account as a whole or down to the campaign level.

Screenshot by author, March 2026

It also drills down to other elements of a campaign, like search term insights or audience insights.

Knowing where to focus my time and effort from these insights saves a lot of time, so I can focus on analyzing the problem and coming up with solutions.

The Recommendations tab is also found on the left-hand menu and provides a wide assortment of recommendations for your account.

This is also where an account’s “Optimization Score” lives, and applying or dismissing recommendations directly impacts that score.

I don’t recommend applying every recommendation that Google suggests just to increase the Optimization Score.

For example, one of the recommendations that would have provided a 9.9% boost in Optimization Score would be to link a Merchant Center account. But this account is not in the ecommerce vertical, so the recommendation makes no sense and wouldn’t be valid.

This tab is useful for account managers to look at the context of an account and easily apply recommendations that make sense.

Screenshot by author, March 2026

These are usually broken down into categories:

  • Bidding and budgets.
  • Keywords and targeting.
  • Ads & assets.
  • AI Essentials.
  • Automated campaigns.

For example, this recommendation suggests removing redundant keywords to more easily manage the account. Especially with match types loosening, applying this recommendation makes sense, and Google automatically does it for me.

Remove redundant keywords recommendation.
Screenshot by author, March 2026

That means I can spend more time strategizing and analyzing an account instead of doing the normal “busy work” of having to manually go in and review each keyword to decide what to pause.

Making Google Ads Management Easier

Google Ads has become more complex over the years, and that complexity can make everyday account management slower than it needs to be.

Many of the features above exist specifically to simplify that work. Tools like labels, experiments, shared negative lists, and audience observation help keep accounts organized and easier to analyze.

When those systems are in place, less time goes toward maintenance and more time goes toward improving performance.

More Resources:


Featured Image: dae sung Hwang/Shutterstock

Google Ads Creative Tools Expand, Microsoft Simplifies Bidding – PPC Pulse via @sejournal, @brookeosmundson

Welcome to this week’s PPC Pulse. Updates focus on expanding creative tools in Google Ads and updates to bidding strategies in Microsoft Ads.

The newest version of Nano Banana Pro is now available to advertisers in Google Ads. In a separate creative update, marketers spotted an expansion to Google’s Creative Toolkit in the platform. Lastly, Microsoft Ads made changes to some of their automated bid strategies to streamline setup.

Here’s what happened this week and why it matters for advertisers.

Nano Banana Pro Version Now Available in Google Ads

While Nano Banana Pro was originally introduced back in November 2025, advertisers were alerted via email this week that its newest version is now available for free in Google Ads.

Screenshot from author, March 2026

Now that it’s in Google Ads, advertisers can do all of these things in one platform:

  • Generate new visuals using prompts
  • Edit existing assets conversationally
  • Create multi-product scenes
  • Produce more detailed, photo-realistic imagery

Here’s a peek at what it looks like once you navigate to Asset Studio in Google Ads.

Screenshot taken by author, March 2026

Why This Matters For Advertisers

Embedding Nano Banana Pro directly into Google Ads removes a lot of potential friction between create generation and campaign execution.

This means that for advertisers who have more creative control, creative becomes part of the optimization loop, not a completely separate workflow. Instead of planning creative updates in batches like a traditional process, advertisers can generate and test assets in response to performance changes.

Additionally, cost is not a barrier to entry. Making this available for free inside Google Ads lowers the threshold for advertisers who may not have been able to invest in external creative tools or AI platforms.

Lastly, creative volume can quickly scale. This is something that I’ve experienced personally working with my Google rep this quarter. They seem to be pushing creative volume across the board.

When the tool becomes easier to generate assets, most accounts will naturally start testing more variations.

However, brands still need to check the outputs of these AI-generated assets to make sure they adhere to any brand guidelines, product accuracy, and compliance requirements.

Google Expands Creative Toolkit Inside Google Ads

In another possible related creative update, Bia Camargo took to LinkedIn to share an update she got in Google Ads about creative assets.

In her post, the Google notification says: “More rich media available for your Google Ads. In addition to Google-owned images, Google-owned rich media (including photos, videos, icons, 3D assets, text and more) will be available for use in Google Ads.”

It looks like the goal is to allow advertisers to build and assemble more creative directly inside the platform rather than relying entirely on external tools. Whether this is completely tied to the launch of Nano Banana Pro in Google Ads is unclear.

Why This Matters For Advertisers

This update continues Google’s push to bring more of the campaign workflow into Google Ads.

For advertisers, this can reduce the time between identifying a creative gap and launching new variations.

It can also help smaller teams or advertisers without dedicated design resources produce a broader set of assets.

What PPC Professionals Are Saying

Most comments were in favor of this move. Brian Lasonde called this a “genuine win” while Virgil Brewster commented “How cool is that? Bring on the toolbox.”

Bryan Shue had an interesting take around the influence of creative production in the platform:

This feels like a bigger shift than just creative convenience. Once production moves inside the ad platform, the system gains more influence over the signals entering the campaign from the start. Faster testing is the obvious upside, but it also means the line between creative development and platform optimization keeps getting thinner.

Microsoft Ads Simplifies Automated Bidding Setup

This week, Microsoft Advertising introduced an update to how automated bidding is structured for new campaigns.

Target CPA (tCPA) and Target ROAS (tROAS) are now available as optional target settings within conversion-focused bid strategies:

  • Choose Maximize Conversions and optionally set a tCPA
  • Choose Conversion Value and optionally set a tROAS

Microsoft confirmed that existing campaigns using tCPA or tROAS remain unchanged, and portfolio bid strategies are unaffected.

Microsoft has positioned this as a simplification of bidding setup rather than a change to how the strategies perform.

It was originally announced last year, but this week’s rollout makes it global to all advertisers.

Why This Matters For Advertisers

This change does not alter how campaigns optimize, but it does change how decisions are made during setup.

The choice of bid strategy is now more implied. Instead of selecting between multiple strategies, advertisers are guided into a smaller set of options with targets layered in.

That shifts the focus toward how targets are set and adjusted over time.

For advertisers managing performance closely, this reinforces the importance of:

  • Setting realistic CPA or ROAS targets based on actual performance
  • Allowing enough time for campaigns to stabilize before adjusting targets
  • Avoiding overly aggressive constraints early in the campaign lifecycle

Theme Of The Week: Less Friction In Setup, More Responsibility In Execution

This week’s updates focus on two different parts of campaign setup, but both change how much effort is required to move from idea to launch.

Google expanded what advertisers can do inside the platform by adding more built-in creative assets and making Nano Banana Pro accessible directly in Google Ads.

Microsoft simplified how bidding is applied in new campaigns by restructuring how targets are set.

Both are meant to reduce friction, but from an execution standpoint, it requires more upfront thought and attention from advertisers.

More Resources:


Featured Image: Gorodenkoff/Shutterstock; Paulo Bobita/Search Engine Journal

PPC Automation Layering: How Smart Advertisers Combine Automation With Strategy via @sejournal, @brookeosmundson

Automation has been part of PPC management for longer than many marketers realize.

Bid adjustments, keyword expansion, and audience targeting have been guided by machine learning inside platforms like Google Ads for years. What has changed is the depth of automation now influencing campaign performance.

Smart Bidding, automated assets, dynamic targeting, and recommendation engines now handle many tasks that used to require daily manual management.

That shift has changed the job of a PPC manager.

This is where PPC automation layering becomes useful. Instead of relying on a single automated feature, marketers combine multiple tools and signals to shape how campaigns perform.

Read on to learn more about automation layering and helpful use cases to make your job easier.

What Is Automation Layering?

PPC automation layering is the strategic use of multiple automation tools and rules to manage and optimize PPC campaigns.

The main goal of PPC automation layering is to improve the efficiency and effectiveness of your PPC efforts.

This is where automation layering comes in.

Instead of relying on one automated feature, advertisers use several layers of automation working together. Each layer contributes different inputs, signals, or guardrails.

Some examples of automation layering include:

  • Smart Bidding strategies: Ad platforms take care of keyword bidding based on goals input within campaign settings. Examples of Smart Bidding include target CPA, target ROAS, maximize conversions, and more.
  • Automated PPC rules: Ad platforms can run specific account rules on a schedule based on the goal of the rule. An example would be to have Google Ads pause time-sensitive sale ads on a specific day and time.
  • PPC scripts: These are blocks of code that give ad platforms certain parameters to look out for and then have the platform take a specific action if those parameters are met.
  • Google Ads Recommendations tab: Google reviews campaign performance and puts together recommendations for PPC marketers to either take action on or dismiss if irrelevant.
  • Third-party automation tools: Tools such as Google Ads Editor, Optmyzr, Adalysis, and more can help take PPC management to the next level with their automated software and additional insights.
  • AI-Powered analysis tools: Platforms like ChatGPT, Copilot, Claude, and Gemini all have different capabilities, from campaign analysis to keyword research, that can streamline your workflow and efficiency.

See the pattern here?

Automation and machine learning produce outputs of PPC management based on the inputs of PPC marketers to produce better campaign results.

How Has Automation Changed PPC Management?

Automation has gradually reshaped how paid media accounts are managed.

Ten to fifteen years ago, many PPC managers (including myself) spent most of their time adjusting bids, expanding keyword lists and negatives, and refining campaign structures. Success often came from tightly controlling every lever in the account.

Today, many of those levers are controlled by algorithms and automation.

Platforms automatically adjust bids in real time, assemble ad combinations dynamically, and expand targeting beyond the parameters advertisers originally set. These systems are designed to find conversions more efficiently than manual management.

In many cases, they do.

But automation introduces a new challenge. Algorithms are only as effective as the signals they receive.

For example, a few automation features built into the Google Ads platform include:

  • Keyword and campaign bid management.
  • Audience expansion.
  • Automated ad asset creation.
  • Keyword expansion.
  • And much more.

Automation has essentially taken over many of the day-to-day management tasks that PPC advertisers were used to doing.

While everyone can agree that easier paid media management sounds great, the learning curve for marketers has been far from perfect.

This leads us to the next big question: Will automation replace PPC marketers?

Does Automation Replace PPC Experts?

Job layoffs and restructuring due to automation are certainly a sensitive topic.

In reality, automation has already replaced many repetitive tasks that once filled a marketer’s day. Bid adjustments, keyword expansion, and ad rotation are increasingly handled by machine learning systems.

But it’s time to settle this debate once and for all.

Automation will not replace the need for PPC marketers.

What we have, and will continue to see, is a shift in the role of PPC experts.

Since automation and machine learning take the role of day-to-day management, PPC experts will spend more time doing things such as:

  • Analyzing data and data quality.
  • Strategic decision making.
  • Reviewing and optimizing outputs from automation.
  • Identifying growth opportunities.

Automation and machines are great at pulling levers, making overall campaign management more efficient.

But automation tools alone cannot replace human touch in creating a story based on data and insights.

This is the beauty of PPC automation layering.

Lean into what automation tools have to offer, which leaves you more time to become a more strategic PPC marketer.

PPC Automation Layering Use Cases

There are many ways that PPC marketers and automation technologies can work together for optimal campaign results.

Below are just a few examples of how to use automation layering to your advantage.

1. Make The Most Of Smart Bidding Capabilities

As mentioned earlier in this guide, Smart Bidding is one of the most useful PPC automation tools.

Google Ads has developed its own automated bidding strategies to take the guesswork out of manual bid management. These have been around since 2016, so this isn’t necessarily a “new” automation tool compared to others.

However, Smart Bidding is not foolproof and certainly not a “set and forget” strategy.

Smart Bidding outputs can only be as effective as the inputs given to the machine learning system.

So, how should you use automation layering for Smart Bidding?

First, pick a Smart Bidding strategy that best fits an individual campaign goal. You can choose from:

Whenever starting a Smart Bidding strategy, it’s important to put some safeguards in place to reduce the volatility in campaign performance.

This could mean setting up an automated rule to alert you whenever significant volatility is reported, such as:

  • Spike in cost per click (CPC) or cost.
  • Dip in impressions, clicks, or cost.

Either of these scenarios could be due to learning curves in the algorithm, or it could be an indicator that your bids are too low or too high.

For example, say a campaign has a set target CPA goal of $25, but then all of a sudden, impressions and clicks fall off a cliff.

This could mean that the target CPA is set too low, and the algorithm has throttled ad serving to preserve only for individual users the algorithm thinks are most likely to purchase.

Without having an alert system in place, campaign volatility could go unnoticed for hours, days, or even weeks if you’re not checking performance in a timely manner.

2. Interact With Recommendations & Insights To Improve Automated Outputs

The goal of the ad algorithms is to get smarter every day and improve campaign performance.

But again, automated outputs are only as good as the input signals it’s been given at the beginning.

Many experienced PPC marketers tend to write off the Google Ads Recommendations or Insights tab due to perceptions of receiving irrelevant suggestions.

However, these systems were meant to learn from the input of marketers to better learn how to optimize.

Just because a recommendation is given on the platform does not mean you have to implement it.

The beauty of this tool is you have the ability to dismiss the opportunity and then tell Google why you’re dismissing it.

There’s even an option for “this is not relevant.”

Be willing to interact with the Recommendations and Insights tab on a weekly or bi-weekly basis to help better train the algorithms for optimizing performance based on what you signal as important.

Regularly reviewing recommendations, rather than ignoring them completely, creates another layer of automation feedback inside the account.

3. Automate Competitor Analysis With Tools

It’s one thing to ensure your ads and campaigns are running smoothly at all times.

Next-level strategy is using automation to keep track of your competitors and what they’re doing.

Multiple third-party tools have competitor analysis features to alert you on items such as:

  • Keyword coverage.
  • Content marketing.
  • Social media presence.
  • Market share.
  • And more.

Keep in mind that these tools are a paid subscription, but many are useful in many other automation areas outside of competitor analysis.

Some of these tools include Moz, Google Trends, and Klue.

The goal is not simply to keep up with your competitors and copy what they’re doing.

Setting up automated competitor analysis helps you stay informed and can reinforce your market positioning or react in a way to help set you apart from competitor content.

4. Using LLM Platforms To Accelerate PPC Analysis

A newer layer of automation is emerging through large language model platforms such as ChatGPT, Claude, Gemini, and Copilot.

It’s important to note that these platforms do not control campaign delivery. Instead, they help marketers process and interpret information faster.

LLM platforms can assist with tasks such as reviewing exported performance data, identifying patterns across campaigns, or summarizing performance changes between reporting periods.

For example, marketers can upload campaign reports and ask targeted questions about cost trends, conversion performance, or impression share shifts. The model can quickly highlight patterns that might otherwise require significant manual analysis.

LLMs can also support areas like keyword expansion, creative brainstorming, and reporting summaries. When paired with platform automation features such as Smart Bidding or responsive ad formats, this approach helps advertisers produce stronger inputs for the algorithm to evaluate.

These tools should not replace human analysis, but they can accelerate many of the workflows surrounding campaign management.

In Summary

Automation now shapes nearly every part of paid media management.

Because of this, the role of the PPC practitioner continues to evolve.

Instead of managing every setting manually, marketers increasingly guide how automation systems operate. That guidance comes through better signals, stronger inputs, and thoughtful campaign structures.

Automation layering helps bring those elements together.

By combining platform automation, scripts, rules, external tools, and AI-driven analysis, advertisers can create a system where automation improves efficiency without losing control over their accounts.

The platforms may be running the mechanics of campaign delivery, but the direction still comes from the marketer.

More Resources:


Featured Image: Anton Vierietin/Shutterstock

FAQ

What are some key benefits of PPC automation layering?

PPC automation layering enhances the efficiency and effectiveness of PPC campaign management. It combines multiple automation tools and strategies like Smart Bidding, automated PPC rules, PPC scripts, and third-party platforms. By leveraging these technologies, marketers can focus on higher-level strategic tasks while the system manages routine tasks, such as keyword bidding, campaign bid management, and data analysis.

Will automation replace the need for PPC experts?

Automation will not replace PPC experts, but it will shift their role over time. While automation can handle many day-to-day management tasks like bid adjustments and ad scheduling, PPC experts should shift their focus to strategic decision-making, data analysis, and optimizing the outputs from automation tools. Human oversight remains crucial for effective campaign management.

What are some practical use cases for PPC automation layering?

Practical use cases for PPC automation layering include:

  • Smart Bidding strategies: Choosing the best bidding strategy (e.g., Target CPA, Target ROAS) and setting up rules to monitor performance volatility.
  • Recommendations & Insights: Regularly interacting with the Google Ads Recommendations and Insights tab to refine automated outputs.
  • Competitor Analysis: Using third-party tools like Semrush, Moz, or Google Trends to automate competitor analysis, staying informed on market positioning without manually tracking competitors.

These strategies help optimize campaign results while allowing more time for strategic analysis and decision-making.

The 10 Best PPC Ad Networks via @sejournal, @LisaRocksSEM

Choosing the right pay-per-click (PPC) ad network is a core strategy impacting the success of your advertising program.

Each network reaches distinct audiences, offers different ad formats, and suits different campaign objectives, from capturing high-intent search demand to driving awareness through video and social feeds. With AI-powered automation now embedded across most major platforms, understanding what each network does well (and where it falls short) matters more than ever.

In this article, we compare 10 of the leading PPC ad networks available today, covering each platform’s reach, audience demographics, ad formats, unique features, AI integration, and advertiser best fit to help you decide where to invest your budget.

Note: While we refer to the following as “PPC” ad networks, each offers multiple pricing options for pay-per-click, impressions, video views, or conversions. We are exploring popular paid media ads.

1. Google Ads 

Google Ads is the most popular ad network due to the immense reach of its ads and its broad range of users. As the world’s leading search engine, Google offers a variety of opportunities for advertisers.

It uses search and the power of the websites on the Google Display Network (GDN), which consists of more than 2 million websites, videos, and apps on which display ads can appear.

Audience targeting on the display network is commonly used for brand awareness, retargeting, and top-of-funnel lead generation.

Both search and display campaigns allow demographic targeting by age, gender, parental status, and household income.

Adding in demographic targeting narrows the available reach for ads, but makes the targeting more relevant.

  • Reach: Largest PPC network with billions of daily searches and extensive reach through Google Search, YouTube, Discover, Maps, and the Google Display Network.
  • Demographics: Broad and diverse, all-age groups, genders, and interests globally.
  • Ad Formats: Text ads, Responsive ads, Image ads, App Promotion ads, Video ads, Product Shopping ads, and Call-only ads.
  • Unique Features: Extensive reach through Google Search, YouTube, and Google Display Network, robust targeting and analytics, deep AI integration, and optimizations.
  • AI Integration: Unified machine learning powers Smart Bidding, Performance Max automation, real-time auction optimization, and AI Max for Search across Google properties.
  • Advertiser best fit: Best for reaching a broad audience with high-intent traffic, flexible targeting, and detailed performance insights.
Screenshot by author, February 2026

2. Microsoft Ads

Bing comes in as the second-largest search engine worldwide, behind Google. Despite being in second place, it has an impressive 23.36 billion monthly PC searches on the Bing search engine.

The Microsoft Audience Network serves display and native ads. You’ll find remarketing, in-market, customer match, similar audiences, LinkedIn audiences, and more opportunities in the Microsoft Audience Network.

Through its partnership with Yahoo, Microsoft Advertising powers search ads across Bing, Yahoo, and other syndicated partners. Its search network also extends to Microsoft-owned properties such as Edge, Windows, and Ouredtlook, and it supports LinkedIn-based audience targeting, including company, industry, and job function data. Bing also powers web results for some voice assistants.

Microsoft Ads offers advertisers campaign import capabilities from Google Ads, simplifying the process of getting started and managing campaigns across platforms while maintaining consistency.

  • Reach: Significant volume through Bing, Yahoo and AOL search engines, reaching users across Microsoft-owned and partner properties.
  • Demographics: Microsoft Advertising reports a broad search audience, with a large share of users (73%) under 45 and a relatively balanced gender split. According to Microsoft, over one-third of users hold a college degree, more than one-third fall into the top household income quartile, and many are part of family households.
  • Ad Formats: App Install ads, Expanded Text ads, Dynamic Search ads, Microsoft Advertising in Bing Smart Search, Audience ads, Multimedia ads, Product ads, Responsive Search ads, and Vertical ads.
  • Unique Features: Integration with Bing, Yahoo, and AOL, competitive cost-per-click rates, and LinkedIn profile targeting.
  • AI Integration: Machine learning supports automated bidding, audience expansion, and delivery optimization across Search and the Microsoft Audience Network, with Copilot assisting campaign creation and optimization workflows.
  • Advertiser best fit: Advertisers targeting working professionals and household decision-makers, including families with higher disposable income. Performs well for B2B, services, and considered purchases, especially in desktop-first environments and Microsoft-owned products.
Screenshot by author, February 2026

3. Meta Ads

Meta Ads allows businesses to reach highly targeted audiences across Facebook and Instagram, using large-scale engagement and intent signals to support precise ad delivery. The platform has increasingly shifted away from manual targeting toward automation that optimizes delivery based on user behavior and conversion likelihood.

Audience targeting includes demographics, interests, behaviors, and engagement signals. Meta supports retargeting through on-platform activity and off-site actions using the Meta Pixel and customer list uploads.

  • Reach: Meta’s advertising ecosystem spans Facebook, Instagram, Messenger, and WhatsApp. Facebook alone reports over 3.07 billion monthly active users, while Instagram reports around 3 billion monthly active users, offering large-scale reach across Meta’s platforms.
  • Demographics: According to DataReportal, Facebook’s ad audience includes 2.28 billion people globally. The analysis suggests that the average age of Facebook users in 2025 falls between 25 and 34 years old, with male users aged 25-34 representing the largest share of active Facebook users during that period.
  • Ad Formats: Image ads, Video ads, Carousel ads, Collection ads, Stories ads, and Ads in Explore.
  • Unique Features: Broad placement coverage across Meta properties, creative flexibility designed for mobile-first environments, and automation through Advantage+ Shopping and campaign optimization tools.
  • AI Integration: Machine learning powers Advantage+ automation, optimizing audience expansion, placements, budget allocation, and creative delivery in real time across Meta properties.
  • Advertiser Best Fit: Well-suited for ecommerce, direct-to-consumer, and brand-led advertisers seeking scale through short-form video and feed-based experiences, particularly for upper- and mid-funnel demand creation.
Screenshot by author, February 2026

4. LinkedIn Ads

LinkedIn reports that over 1.2 billion professionals use LinkedIn (including 98% of Fortune 500 CEOs) and that 78% of B2B leaders say that “demonstrating ROI is more critical now than ever before.”

LinkedIn reports that 75% of B2B buyers use social media to make purchasing decisions, with 50% using LinkedIn as a trusted source in that process. This provides advertisers with access to a verified professional audience that possesses twice the average web audience’s buying power.

  • Reach: Global network of professionals across nearly every industry, company size, and seniority level, with strong penetration among decision-makers and influencers.
  • Demographics: Primarily professional audiences, including business decision-makers.
  • Ad Formats: Sponsored Content, Sponsored Messaging, Lead Gen Forms, and Text and Dynamic ads.
  • Unique Features: Professional targeting by job title, company, industry, seniority, and skills, along with native lead generation and account-based marketing capabilities.
  • AI Integration: Machine learning supports automated bidding, audience expansion, and delivery optimization, with AI-driven relevance scoring and performance prediction across Sponsored Content and Lead Gen campaigns.
  • Advertiser Best Fit: Best suited for B2B marketers focused on lead generation, account-based marketing, and reaching verified decision-makers for high-consideration products and services.
Screenshot by author, February 2026

5. TikTok Ads

TikTok has quickly become one of the most influential social media platforms, particularly among younger audiences. The short-form video app has reshaped how users discover content and has created new opportunities for brands to reach audiences through immersive, entertainment-driven ads.

With its emphasis on creativity, trends, and algorithmic discovery, TikTok offers advertisers a paid ads platform built around engagement rather than explicit intent.

  • Volume: Over 1.6 billion monthly active users worldwide.
  • Demographics: Skews younger, with a strong concentration among Gen Z and Millennials, and a highly engaged, diverse global user base. According to the Pew Research Center, TikTok usage is especially high among younger adults in the United States, with roughly half of 18- to 29-year-olds using the platform daily.
  • Ad Formats: In-Feed ads, TopView, Branded Mission, Spark Ads, and Promote.
  • Unique Features: Algorithm-driven content discovery, trend-based ad formats, and native short-form video experiences designed for mobile engagement.
  • AI Integration: Machine learning drives content recommendation, ad delivery, automated bidding, and Smart Performance Campaigns, optimizing ads based on engagement and conversion signals.
  • Advertiser Best Fit: Best suited for brands targeting Gen Z and Millennials through awareness and demand creation, especially those able to lean into short-form video, trends, and creator-style creative.
Screenshot by author, February 2026

6. Amazon Advertising

Amazon Advertising is a powerful paid ads platform for ecommerce and retail brands that leverages Amazon’s massive shopping ecosystem. It reaches consumers at the point of purchase, making it especially effective for driving direct sales and product visibility.

  • Volume: Amazon reported $213.4 billion in net sales in Q4 2025, indicating substantial ecommerce transaction volume. This provides advertisers with access to high-intent shoppers actively researching and comparing products.
  • Demographics: Gen Z is a key demographic for the platform.
  • Ad Formats: Sponsored Products, Sponsored Brands, Brand Stores, Amazon Live, Video and Audio ads, Display ads, Out-of-home ads, and Device ads.
  • Unique Features: Product and keyword-based targeting tied directly to shopping behavior, with ads appearing alongside search results, product detail pages, and related placements.
  • AI Integration: Machine learning powers automated bidding, shopper relevance modeling, and performance optimization, adjusting bids and delivery in real time based on conversion likelihood and purchase signals.
  • Advertiser Best Fit: Ideal for ecommerce and retail advertisers focused on driving direct sales, particularly brands with established product listings seeking to capture high-intent shoppers close to purchase.
Screenshot by author, February 2026

7. X Ads (Formerly Twitter Ads)

X Ads provides advertisers with opportunities to reach users through its real-time social platform, which is heavily centered around news, live events, and cultural conversations.

Campaigns on X are structured around objectives such as awareness, consideration, and conversions, and ads are delivered across both desktop and mobile environments. Targeting includes demographics and audiences, even with the option to target conversion topics.

Promoted ads are highly flexible, supporting combinations of text, images, video, and carousels, with optional calls to action such as app installs or website clicks embedded directly within the ad creative.

  • Volume: 561 million monthly active users globally.
  • Demographics: As of February 2025, X’s global audience skews younger, with 37.5% aged 25-34 and 32.1% aged 18-24.
  • Ad Formats: Promoted Ads, Vertical Video Ads, X Amplify, X Takeovers, X Live, Dynamic Product Ads, Collection Ads, and X Ad Features.
  • Unique Features: Real-time conversation targeting, trend-based placements, and the ability to promote posts, accounts, and events as they happen.
  • AI Integration: Machine learning supports automated bidding, interest and conversation targeting, and delivery optimization to align ads with real-time engagement signals and trending topics.
  • Advertiser Best Fit: Best suited for brands promoting timely content, live events, launches, and cultural moments, where real-time visibility and conversation-driven engagement are critical.
Screenshot by author, February 2026

8. Pinterest Ads

Pinterest is a visual discovery platform where users actively search for inspiration, ideas, and products. Unlike traditional social networks, Pinterest users often arrive with planning and purchase intent, making it a strong environment for discovery-driven advertising.

Pinterest is a strong performer for lifestyle and planning-focused brands, with success stories from advertisers in home decor, fashion, beauty, and food.

  • Volume: Over 600 million monthly active users worldwide.
  • Demographics: Pinterest’s audience is 70% female, with strong representation among 18-44 year-olds and a growing Gen Z segment, which now makes up 42% of users.
  • Ad Formats: Standard Image ad, Quiz ad, Showcase ad, Premiere Spotlight, Idea ad, Collections, Carousel ad, Max-width Video ad, and Standard Video ad.
  • Unique Features: Visual search and discovery, intent-driven browsing, and native shopping integrations that surface products during inspiration and planning moments.
  • AI Integration: Machine learning powers personalized recommendations, automated bidding, and shopping relevance, matching ads to user interests based on search, save, and engagement behavior.
  • Advertiser Best Fit: Well-suited for brands in lifestyle, retail, and ecommerce categories looking to influence consideration and purchase through visual inspiration and discovery.
Screenshot by author, February 2026

9. Reddit Ads

Reddit Ads allows advertisers to reach highly engaged audiences within over 100,000 topic-specific communities where users actively discuss interests, problems, and purchasing decisions. With subreddits covering nearly every industry and niche, Reddit offers a context-driven environment that is fundamentally different from traditional social platforms.

Rather than passive scrolling, Reddit users participate in conversations, making the platform especially valuable for brands that want to align messaging with authentic discussions and intent signals.

  • Volume: 116 million daily active unique visitors across thousands of interest-based communities.
  • Demographics: Reddit’s audience skews younger, with the majority of users aged 18-34. According to Pew Research, adults under 30 are among the platform’s most active users, and its audience is known for strong interest in tech and niche communities. 
  • Ad Formats: Free-form ads, Image ads, Video ads, Carousel ads, Conversation ads, Product ads, and AMA.
  • Unique Features: Community-based targeting through subreddits, keyword and interest targeting, and placements that blend into discussion feeds.
  • AI Integration: Machine learning supports automated bidding, contextual ad placement, and delivery optimization by aligning ads with relevant conversations, topics, and engagement patterns.
  • Advertiser Best Fit: Best suited for brands seeking awareness, consideration, and engagement within specific interest communities, particularly for products or services that benefit from education, discussion, or social proof.
Screenshot by author, February 2026

10. Apple Search Ads

Apple Search Ads allows advertisers to promote apps directly within the App Store, reaching users at the moment they are actively searching for and discovering new apps. The platform is built around high-intent queries, making it especially effective for driving app installs and user acquisition on iOS devices.

Because ads appear natively within App Store search results, Apple Search Ads offers a brand-safe environment with clear user intent and strong performance for mobile-first advertisers.

  • Volume: Global reach across the App Store, with 800 million weekly visitors searching for apps across iOS devices.
  • Demographics: iOS users span a broad age range. Apple’s platform policies prevent targeting users under 18, and advertisers often associate iOS users with high mobile engagement and above-average purchasing power.
  • Ad Formats: Today Tab ads, Search Tab ads, Search Results ads,  and Product Pages ads.
  • Unique Features: Native App Store placements, keyword-based targeting, and direct integration with app metadata and search behavior.
  • AI Integration: Machine learning supports automated bidding, relevance matching, and Search Match, which uses AI to align ads with relevant search queries based on app metadata and user intent signals.
  • Advertiser Best Fit: Ideal for app developers and mobile advertisers focused on driving high-quality installs, subscriptions, or in-app actions within the iOS ecosystem.
Screenshot by author, February 2026

Choosing The Best Ad Platforms For Your Business

Choosing the right paid advertising platforms directly impacts business growth. Each of these PPC ad networks we’ve explored in this article offers unique audiences, ad features, and opportunities to engage with your audience across the web. The key is understanding where your audience shows intent, how they engage with content, and what influences their decisions at each stage of the funnel.

The right choice for your business will depend on your business type, target audience, and marketing goals. Some platforms excel at capturing high-intent demand, while others are better suited for discovery, consideration, or demand creation.

As you evaluate your options, focus on matching platforms to user behavior, campaign objectives, and the level of automation you are prepared to manage. Once campaigns are live, ongoing optimization based on performance data is what drives long-term success.

More Resources:


Featured Image: Darko 1981/Shutterstock

Google Ads Surfaces PMax Search Partner Domains In Placement Report via @sejournal, @MattGSouthern

Some advertisers are now seeing Performance Max placement data populate in Google Ads reporting, including Search Partner domains and impression counts that had previously been absent from the report.

PPC marketer Thomas Eccel flagged the change on LinkedIn, noting the report had been empty for his PMax campaigns until now.

“I finally see where and how Pmax is being displayed!” Eccel wrote. “But also cool to see finally who the real Google Search Partners are. That was always a blurry grey zone.”

What’s New

Google has documented a Performance Max placement report intended for brand safety review, and that report is now showing data for a wider set of accounts. The data includes individual placement domains, network type, placement type, and impression volume.

The Search Partner visibility is the detail getting attention. PMax campaigns have distributed ads across Google’s Search Partner Network since launch, but many advertisers saw an empty report when they looked for specifics. That’s now changing for at least some accounts.

Google hasn’t issued a formal announcement tied to this change. Google’s help documentation notes that starting in March 2024, the PMax placement report supports Search Partner Network sites. What’s new is the data appearing where it didn’t before.

The rollout is uneven, though. Some commenters on Eccel’s LinkedIn post said the report is still empty in their accounts.

What The Report Doesn’t Show

Google describes this placement reporting as a brand safety tool, not a performance report. The data shows impressions at the placement level but doesn’t break out clicks, conversions, or cost for individual placements.

You can see where your ads appeared and how many times, but you can’t calculate the return on any specific placement. Search Partner Network costs are reported as a single line item in channel performance reporting, rather than being attributed by domain.

Advertisers can use the data to make exclusion decisions for brand safety reasons. But tying outcomes to specific placements inside this view isn’t possible, which limits its use as an optimization tool.

This fits a pattern in how Google has rolled out PMax transparency over the past two years. Channel-level reporting launched in mid-2025 with performance data by surface type, and deeper asset segmentation followed in the fall. Each update has added visibility without giving advertisers full placement-level performance data.

Why This Matters

PMax placement visibility has been one of the most persistent requests from paid search practitioners since the campaign type launched. The placement report existed in the interface but returned no data, frustrating advertisers who wanted to know where their budgets were going.

The Search Partner detail matters because PMax doesn’t offer the same Search Partners toggle as standard Search campaigns, though advertisers can use exclusions. Seeing which partner domains are getting impressions and cross-referencing that against overall Search Partner performance in the channel report gives you a data point you didn’t have in practice before, even if the report itself isn’t new.

The brand safety framing is worth keeping in mind. Google’s documentation describes this report as a way to check where ads appear, not to evaluate performance. That distinction matters for how you use the data and how you talk about it with clients or stakeholders who may expect more granularity than it provides.

Looking Ahead

Google has steadily expanded PMax reporting over the past year, moving from limited channel visibility to surface-level breakdowns to the placement-level impression data now appearing for more accounts.

Whether placement-level performance metrics follow is an open question. Google hasn’t confirmed plans to add clicks, conversions, or cost to the placement report. For now, checking whether the data is available in your account and reviewing the Search Partner domains to get your impressions is the practical next step.

New Meridian Tool, Performance Max Learning Path – PPC Pulse via @sejournal, @brookeosmundson

Welcome to this week’s PPC Pulse, where this week’s focus is on scenario-based planning in both Google and Microsoft platforms.

Google introduced a new Scenario Planner within Meridian, giving marketers the ability to model budget allocation shifts before committing spend. Microsoft launched a scenario-based Performance Max learning path designed to walk advertisers through practical campaign situations.

Both updates point to a growing emphasis on improving decisions before campaigns go live.

Here’s what happened this week and why it matters for advertisers.

Google Introduces Scenario Planner For Meridian

Google announced a new Scenario Planner within Meridian, its Marketing Mix Modeling platform. The tool allows marketers to test budget allocation scenarios and forecast potential outcomes using Meridian’s modeled insights.

Instead of waiting for quarterly MMM reports or static insights, advertisers can now simulate how shifting spend across channels might impact performance metrics like revenue, conversions, or return on investment.

According to Google, the goal is to make MMM insights more accessible and actionable for marketers who need to defend budgets and make planning decisions in real time. It also reiterated that coding isn’t required to use this tool.

It looks to be a promising planning tool built for higher-level strategy conversations between advertisers and key decision-makers.

Why This Matters For Advertisers

Marketing Mix Modeling has traditionally been handled at a higher level of the organization. It tends to show up in quarterly reviews, annual planning decks, or conversations led by finance and analytics teams. Most PPC managers are not sitting inside MMM tools on a weekly basis.

What makes this update notable is that Google is moving those insights closer to the teams actually managing budgets day to day.

PPC marketers are being asked more frequently to justify budget increases or reallocations with something stronger than last-click performance.

A tool like this could influence how those conversations happen. Instead of pointing only to recent return on ad spend (ROAS) trends, teams may start leaning more on modeled projections and incremental impact estimates when proposing changes.

What PPC Professionals Are Saying

Ginny Marvin, Ads Liaison for Google, shared the announcement on LinkedIn. Here’s what she emphasized about the Scenario Planner:

“No technical MMM experience needed to go from ‘what happened?’ to ‘what’s next?’”

Advertisers like Ivan Walker are “very excited!” about the update, while others like Ashley V. are curious about hearing feedback from others who have started using it.

Microsoft Launches Scenario-Based Performance Max Learning Path

Along the same lines of planning, Microsoft Advertising announced a new Performance Max learning path within its Learning Lab.

Unlike standard certification modules, this path walks advertisers through real-world scenarios designed to build hands-on expertise. The training focuses on practical decision-making across campaign setup, optimization, and troubleshooting.

I appreciate how Microsoft is positioning – that Performance Max success requires understanding, context, and strategy instead of focusing solely on what settings to toggle.

The learning path is designed to help advertisers think through situations they are likely to encounter in live accounts. For example, how to approach budget allocation, how to evaluate asset performance, and how to troubleshoot underperformance.

Why This Matters For Advertisers

Performance Max is not new at this point. Most advertisers have at least tested it, and many are running it at scale. What has changed is the level of thinking required to run it well.

There is still a misconception that PMax runs on its own once you flip it on. In reality, outcomes are heavily influenced by how campaigns are structured, what signals are being fed into the system, and how clearly conversion goals are defined.

Microsoft is leaning into the idea that automation does not remove the need for strategy. It shifts where strategy shows up. Instead of spending time adjusting bids manually, advertisers are spending time making decisions around inputs, segmentation, creative quality, and measurement alignment.

For agencies and in-house teams, scenario-based training could be useful for onboarding or leveling up junior team members. It provides context around the types of situations teams actually encounter, rather than just explaining what each setting does.

Theme Of The Week: Planning Before Spending

Both updates this week center around the same idea, which is trying to improve the quality of decisions before money is spent.

Google is giving marketers a way to test budget allocation scenarios before shifting spend to other platforms. Microsoft is walking advertisers through realistic campaign situations before they are live in their accounts.

While many industry updates focus on optimizations after campaigns are running, these ones focus on the earlier stage. How confident are you in the structure? How confident are you in the allocation? How confident are you in the assumptions behind the strategy?

Especially with budgets under tighter scrutiny than ever, and automation handling much more of campaign execution, the planning phase definitely carries more weight than it used to.

More Resources:


Featured Image: Kansuda2 Kaewwannarat/Shutterstock; Paulo Bobita/Search Engine Journal

Why Do Budgets Overspend Even With A Target ROAS or CPA? – Ask A PPC via @sejournal, @navahf

This month’s Ask a PPC explores a common advertiser question: Why budgets sometimes overspend even when a target ROAS or target CPA is in place.

Understanding this behavior requires separating two concepts that are often conflated: budgets and goals. While they work together, they serve very different functions within auction‑based ad platforms. In this post, we’ll walk through how budgets and goals operate, why target ROAS can sometimes increase spend, and which levers advertisers can use to keep budgets under control.

Disclaimer: I am a Microsoft employee. The examples below reference Microsoft Advertising, but the underlying principles apply to any platform that uses automated or goal‑based bidding.

The Difference Between Budgets And Goals

When you set a daily budget, the ad platform averages across approximately 30.4 days. While there are daily fluctuations, the platform’s objective is to meet that average over the course of the period rather than strictly adhere to the number each day.

As a result, a daily budget of $50 can spend up to $100 on a given day. Here are the core reasons for “over” spending:

  • Under spending too many days during the 30.4-day period.
  • Average CPCs don’t align with the daily budget.

Goals function differently. A target ROAS or target CPA is not a spending limit. Instead, it is an optimization instruction.

A target ROAS asks the platform to achieve a specified return based on the conversion values being passed in. A target CPA instructs the platform to drive conversions at or below a certain cost, regardless of differences in conversion value.

Because goals are optimization signals rather than caps, the platform may spend more budget if it believes that doing so will help reach the target.

Why Target ROAS Can Increase Spend

Target ROAS is often perceived as a conservative bidding approach, but in practice, it can drive higher spend under certain conditions.

One common scenario involves high CPCs relative to budget size. If the average CPC exceeds roughly 10% of the daily budget, the platform may need to stretch spending in order to secure enough eligible clicks to meet the ROAS goal.

Overspending can also occur when there has been underspending earlier in the month. Since budgets are averaged, the platform may increase spend later in the period to compensate for missed opportunities. This behavior can look abrupt from an advertiser perspective, but it aligns with how budget pacing operates.

Image from author, February 2026

Accurate conversion values are critical in these situations. When incorrect or inflated values are passed to the platform, the system may believe it is driving strong returns when it is not. That misunderstanding can lead to increased spend in pursuit of perceived performance.

Another important consideration is how conversion actions are classified. Primary conversions influence bidding and reporting, while secondary conversions are observed but excluded from optimization logic. When too many conversion actions are set as primary, particularly if they overlap, the platform may double-count success and bias spend toward certain keywords, audiences, or signals.

Microsoft Conversion View (Image from author, January 2026)
Google Conversion View (Image from author, February 2026)

How Advertisers Can Protect Against Overspending

Advertisers do have meaningful controls available to manage spend behavior.

The first is aligning budgets with auction realities. A practical guideline is ensuring that a daily budget can support at least 10 clicks at the average CPC. For non‑branded search, a 10% conversion rate is unusually strong. Without sufficient click volume, the platform may either restrict spend to high‑cost opportunities or over‑allocate budget to lower‑quality traffic to meet pacing expectations.

The second lever is being realistic about conversion trust. Many advertisers have inconsistent attribution models or partial tracking implementations, which reduces confidence in reported conversion data. When conversion data is not reliable, aggressive ROAS or CPA targets can be counterproductive.

In those cases, advertisers may choose to set more conservative goals or opt for a bid strategy that better matches the quality of available data. For example, if conversion values are inconsistent, target CPA may be more appropriate. Conversely, if certain conversions are significantly more valuable than others, a purely CPA‑based approach may lead to inefficient spend allocation.

A final lever that is often underutilized is ad scheduling. Restricting campaigns to specific hours of the day can reduce volatility and improve budget efficiency. When budget pressure exists, running ads during a focused three‑to‑six‑hour window rather than all day can provide stronger control without turning automation off entirely.

Closing Thoughts

When budgets overspend in goal‑based bidding strategies, it is rarely the result of a platform error. More often, it reflects a mismatch between budgets, goals, and the quality of data being supplied.

Careful attention to conversion accuracy, realistic budget sizing, and thoughtful use of controls such as ad scheduling can significantly reduce unexpected spend behavior. Automated bidding is most effective when inputs are intentional and aligned with actual business value.

More Resources:


Featured Image: Paulo Bobita.Search Engine Journal

Are Your Google Ads Gen Z Proof? Strategies To Win The 18-24 Segment

When the average customer age increases for a brand, it’s rarely a platform failure. It’s usually a signal that younger audiences are discovering, evaluating, and buying in different places, and older established brands haven’t kept pace.

As of 2026, Gen Z spans ages 14 to 29. They’re the first generation raised in a digital online world. Moving from smartphones to social video to AI without ever experiencing a world without them. Their expectations for advertising reflect that upbringing. Traditional creative formats, linear funnels, and keyword‑centric strategies simply don’t match how they navigate the internet.

Many PPC practitioners built their instincts during the 2010-2016 era, when search behavior was more predictable and creative requirements were narrower. Those instincts don’t translate cleanly to a generation that jumps between platforms, verifies claims through peers, and expects ads to feel like the content they already consume.

This article looks at why standard Google Ads approaches fall short with the 18-24 segment, how Gen Z actually discovers products, and what advertisers can adjust to stay relevant.

The “Skip Ad” Generation

Gen Z grew up with pre‑roll ads, sponsored content, and ad blockers. They learned early how to ignore anything that feels like an interruption. Studies show their active attention for digital ads drops after about 1.3 seconds, which is a number that explains a lot about their behavior with ads.

Authenticity As A Baseline Expectation

For Gen Z, authenticity isn’t a marketing trend; it’s the baseline expectation. They gravitate toward brands that feature real people instead of polished models, communicate in plain, natural language rather than corporate phrasing, and embrace imperfect, lo-fi visuals over highly produced studio creative.

84% of Gen Z say they trust brands more when they see real customers in the ads.

Girlfriend Collective is a good example. Its product imagery features real people, not traditional models, and the approach mirrors what Gen Z expects to see in their feeds.

Authenticity isn’t a differentiator anymore. It’s table stakes.

Real people featured in Girlfriend Collective advertising campaign.
Girlfriend Collective uses real people in its advertising, aligning with Gen Z’s preference for authentic, human‑centered creative. (Screenshot from girlfriend.com, February 2026)

Discovery Habits: Beyond Google Search

Google Search still matters, but it’s no longer the first stop for many younger users.

Recent data shows:

  • 64% of Gen Z use TikTok as a primary search engine.
  • 77% identify TikTok as the top platform for products.

Their discovery path often starts with a short‑form video, not a search bar. They move through:

  • TikTok.
  • YouTube Shorts.
  • Instagram Reels.
  • Reddit.
  • Creator content.

Only after that do they turn to Google to verify what they’ve seen. Queries like [best running shoes 2026] often begin on TikTok and end on Google, not the other way around.

The Role Of Performance Max And Demand Gen

Google’s push toward Performance Max and Demand Gen reflects this shift. These formats reach users across YouTube, Discover, Gmail, Display, and Search, which are the same surfaces Gen Z moves through naturally.

But PMax can only perform as well as the creative inside it. Legacy assets built for static search campaigns rarely translate well to visual placements. Gen Z scrolls past anything that looks like an ad, especially if it’s overly polished or logo‑heavy.

The Shift Toward Intent‑Based Matching

Keyword matching is evolving. During a January 2026 PPC Chat session, Google Ads Liaison Ginny Marvin noted that appearing in AI Overviews and “AI Mode” inventory requires broad match or keywordless targeting.

This aligns with how Gen Z searches. Their queries are conversational, fragmented, and context-driven, which mirrors Google’s increasing emphasis on intent, context, and meaning rather than strict keyword matching.

Advertisers who avoid broad match risk losing visibility in the surfaces where younger users spend their time.

The Nonlinear Buyer Journey

Gen Z doesn’t move through a funnel. Their path looks more like a loop:

  1. Short‑form video discovery.
  2. Google Search verification.
  3. Social proof on Reddit or Instagram.
  4. Long‑form YouTube reviews.
  5. More short‑form content.
  6. Conversion.

Social proof carries significant weight. 77% say UGC helps them make decisions, and unboxing‑style clips can lift conversion rates by up to 161%.

The offer doesn’t change, but the format of the proof does.

Privacy And The Value Exchange

Gen Z is cautious about privacy but not unwilling to share data. They simply expect a clear value exchange. When that exchange is obvious and transparent, they are more open to participating. Incentives that work include early access, exclusive drops, loyalty rewards, and insider content.

Transparency matters. They want to know what they’re giving and what they’re getting.

Tactical Adjustments To Future‑Proof Your Google Ads Account

The following adjustments can help advertisers align with Gen Z behavior.

1. Rewrite RSAs for Tone and Context

Many RSAs still rely on keyword‑stuffed templates:

  • “Blue running shoes”
  • “Best blue running shoes”

RSAs can generate over 43,680 combinations. Use that flexibility to test tone, not just keywords. Use that range to experiment with conversational phrasing, modern language, benefit-driven messaging, social-proof elements, and UGC-inspired copy that better reflects how audiences actually search and engage.

This approach allows Google to assemble combinations that better match user intent.

How RSAs Handle Text Variation

RSAs assemble headlines and descriptions dynamically. The inputs determine the tone Google can test.

The following two examples illustrate how different brands approach RSA‑style messaging and how those choices affect relevance and emotional resonance.

Example 1: Glossier

Headline: Glow With Glossier® Today – Feel Your Glowy, Dewy Best

Description: Shop Accessible Luxury Products Inspired By Our Community To Make You Look And Feel Good. Shop Glossier Skincare Essentials For Glowy, Dewy Skin + Makeup You’ll Actually Use.

Analysis:

  • Conversational, emotional, community‑driven.
  • This style aligns with Gen Z’s expectations.
Sponsored Glossier skincare ad featuring a headline about glowing skin and promotional text highlighting community‑inspired products.
Glossier’s ad uses emotionally driven language and community framing, aligning with Gen Z’s preference for authentic, benefit-led messaging. (Screenshot by author, February 2026)

Example 2: COVERGIRL

Headline: COVERGIRL® Official Site – Available Online & In‑Store

Description: Explore Our New Makeup Products, Best Sellers, & Trending Tutorials to Enhance Your Look.

Analysis:

  • Structured, brand‑led, availability‑focused.
  • Clear and informative, but less emotionally resonant.
Sponsored COVERGIRL makeup ad with a headline promoting online and in‑store availability and text highlighting new products and tutorials.
COVERGIRL’s ad uses structured, brand-led messaging focused on product availability and category breadth. (Screenshot by author, February 2026)

Key Takeaway For RSAs

Both ads are valid inputs for RSAs, but they serve different strategic purposes:

Brand Tone Focus Gen Z Alignment
Glossier Conversational Emotional <+ Community High
COVERGIRL Informational Product + Availability Moderate

A mix of both styles gives Google more flexibility across AI‑driven surfaces like AI Overviews and AI mode.

2. Refresh Creative Assets

Gen Z doesn’t like advertising that interrupts content, which means asset groups should feel native to the environments where they appear. That includes lifestyle imagery, lo-fi video, real customers, UGC-style clips, and visuals that blend naturally into the feed rather than stand out as overt advertising.

Organic‑looking creative performs better across PMax and Demand Gen.

3. Leverage Smart Bidding

Smart bidding is designed for nonlinear, multi-touch journeys. It adapts to device switching, platform hopping, and privacy-centric signals, allowing campaigns to respond more effectively to the way users move between channels and interactions before converting.

This makes it well‑suited for Gen Z’s browsing behavior.

4. Test Gen Z‑Specific Variants

Use Google Ads Experiments to compare:

  • Control: Standard corporate creative
  • Variant: Conversational, UGC‑style creative

This approach provides clear performance insights without requiring a full account overhaul.

5. Use Data‑Driven Attribution (DDA)

Last‑click attribution hides the impact of upper‑funnel channels. DDA provides a clearer view of how YouTube, Demand Gen, and PMax contribute to conversions, which is essential for understanding Gen Z behavior.

Adapting To The New Standard

Gen Z is not opposed to advertising; they are opposed to interruption. They respond to messaging that feels honest, human, relevant, and aligned with their expectations in the spaces where they spend their time.

Brands that adapt their full funnel and not just their headlines will be better positioned to reach this demographic in 2026.

Advertisers should review their current Google Ads campaigns and assess whether Gen Z can see themselves in the messaging. If not, a strategic refresh is warranted.

Final Thoughts

Gen Z isn’t rejecting advertising outright. They’re rejecting anything that feels out of place in the spaces where they spend their time. When brands adjust their creative, targeting, and proof to match how this generation actually discovers and evaluates products, the results tend to follow.

The shift doesn’t require a full rebuild. It just requires intention, testing, and updating the parts of your Google Ads strategy that still assume a linear funnel or a polished, brand‑first message.

If your current campaigns don’t reflect how Gen Z searches, scrolls, and decides, this is the moment to rethink the approach. Small changes go a long way when they match the way people actually behave.

More Resources:


Featured Image: Stock-Asso/Shutterstock

International PPC: Why Consistency Is So Hard To Maintain via @sejournal, @brookeosmundson

With PPC becoming more automated every day, managing PPC accounts in one country is challenging enough.

Your campaign structure may stay the same, but once you add in different countries, languages, regulatory nuances, and different agency partners, PPC management gets messy quickly.

If you currently manage paid media for international brands, you probably see that scaling isn’t an issue. Typically, it’s more likely to be a coordination and consistency issue.

Not only are you launching campaigns in each region, but you’re also keeping up on different market expectations, aligning with separate teams per region, and possibly even different agency partners.

For example, you could launch the exact same campaign structure and bidding strategies in the United States and the United Kingdom and get completely different results.

Each of those probably have their own style, processes, and priorities.

This article breaks down tips on how to keep your campaigns on track across regions without losing brand consistency.

The Realities Of International PPC Management

In a perfect management relationship, every agency partner would follow your brand guidelines to a T, campaign messaging would be accurately localized, and all markets being advertised would operate under the same strategy.

The reality of this scenario? That rarely happens.

Consistency, or lack of, is a real problem. Creative assets, bidding strategies, or keyword targeting often vary widely between markets. This leads to a disjointed user experience and potentially diluted brand impact.

Then, there’s the overlap problem. Without clear global oversight, multiple agencies may accidentally compete in the same auctions or target the same audience, driving up costs unnecessarily.

Reporting visibility becomes an issue, too. Reporting formats may differ from agency to agency, or depending on the region. Some agencies might use custom dashboards, while others may send static PDFs. This can make comparing performance across the board a nightmare.

Speaking of agencies, if you’re working with multiple agencies across regions, their level of expertise may vary. Some have deep experience in a particular market, while others simply learn as they go.

Lastly, there are likely regulatory hurdles you haven’t thought of if you’re used to marketing only in the United States. Different countries have different rules around data collection, targeting methods, and ad content. It’s easy to miss a compliance detail if you’re not on top of local policies.

Managing all of that on top of the actual PPC campaigns is a lot for one person to handle.

Aligning Global Strategy With Local Execution

It’s tempting to create a single PPC strategy and roll it out globally, but that rarely works.

For example, what resonates in the U.S. may fall flat in Germany or Australia. Your job as a marketing manager is to set the strategic foundation while giving local teams enough flexibility to adapt.

Here are a few tips on how to find that balance while managing multiple PPC regions:

  • Create a global brand playbook: Define your core objectives, brand voice, performance metrics, and non-negotiables. Make it clear which elements must be consistent across markets (e.g., logo usage, value propositions) and which can be localized (e.g., promotions, tone, CTAs).
  • Set up centralized tracking and reporting: Use tools like Looker Studio, Funnel, or Tableau to consolidate data from different platforms and agencies. A unified reporting view helps you spot inconsistencies and optimize faster.
  • Spell out roles and responsibilities: Who owns budget allocation? Who reviews creative? Who has the final say on the copy? Spell this out. Confusion around ownership often slows campaigns down.
  • Use regular syncs to stay aligned: Host monthly or bi-weekly meetings with all agency partners. Even if the agendas are light, the face time builds accountability.

For example, say you’re a global hotel chain that operates on multiple continents. A great place to start is to create a shared creative playbook, but allowing each region to tailor their offers like ski packages in Switzerland or beach getaways in Spain.

A shared creative playbook helps keep brand visuals consistent while making local campaigns relevant.

This reinforces that your global strategy is the blueprint, but you still need localization to tailor what actually works in each market.

Choosing And Managing Agency Partners

If you’re working with multiple agencies across regions, things can quickly get siloed.

One agency might perform strongly in Canada while another underperforms in France. Your role is to manage these relationships without getting stuck in the weeds.

Below are some recommendations to keep things streamlined:

  • Standardize onboarding: No matter what type of agency or vendor you’re onboarding, start with a structured checklist. This can include items like tech stack access, brand guidelines, reporting templates, key contacts, etc.
  • Evaluate based on shared key performance indicators (KPIs): Hold every agency accountable to the same high-level metrics (e.g., return on ad spend, cost per acquisition, conversion volume), even if market-specific tactics differ. This makes it easier to identify outliers.
  • Encourage cross-agency collaboration: Set up a shared communication channel or quarterly town halls where agency teams can exchange learnings. One partner’s success story could inspire a breakthrough elsewhere.
  • Avoid micromanagement, but stay involved: Agencies need room to operate, but that doesn’t mean you go completely hands-off. Review ad copy regularly. Ask questions about performance drivers and what sort of experiments or tests they’re running.
  • Consider a lead regional agency model: Some brands appoint one agency as the lead for a particular continent or region. This partner acts as a point of coordination, helping to roll out strategies more efficiently.

Say you’re running a consumer electronics brand’s PPC efforts, and the company is looking to expand into Europe, the Middle East, and Africa. It may be easy to give all that work in-house, but that can essentially double your workload, which can make your existing campaigns’ performance drop since your focus has shifted.

Instead, consider hiring an agency for the EMEA region, where your responsibility may be overseeing their operations across Europe.

This frees up your time to still focus on the core markets, but still have visibility in the expansion region to understand what’s working and what’s not.

This can lead to reduced duplicated efforts, standardized reporting, and improved speed-to-market.

Tailoring Localization Without Losing Brand Consistency

One of the biggest risks in international PPC is watering down your brand, or creating an inconsistent brand. When you allow each market to fully customize messaging, your consistency issue will continue to show up.

However, localization doesn’t mean reinventing your brand. It means adapting the core message to fit cultural norms, search behavior, and language nuances.

The first way to accomplish this is to provide flexible brand guidelines. Instead of a rigid and hard-to-follow rulebook, create a toolkit. Include items like brand values, tone of voice examples, and explicit dos/don’ts. Make it clear that it leaves space for creativity.

When it comes to translation, translating ads word-for-word often leads to awkward or irrelevant messaging. Instead, invest in native-language copywriters who understand local search intent.

Be sure to test and/or vet creative with local experts. Even if your agencies are global, ensure that someone close to the market signs off on copy and visuals. One poorly placed phrase or image can derail an entire campaign or brand image.

Don’t be afraid to test and learn in each market. What works in France might not work in Spain. Build in budget and time to A/B test creative and offers in each country before scaling.

For example, say you’re running back-to-school ads for an apparel brand across the United States and Japan. You think that everyone has a back-to-school need, right?

You’d be correct, but it’d be incorrect to run them at the same time due to Japan’s school year starting in the spring, whereas the United States typically starts in the fall.

Adjusting campaign timing based on regions can help lead to an uplift in engagement.

When it comes to localization, every ad should feel like your brand, even if it says something slightly different.

Managing Regulatory And Platform Differences

The compliance side of international PPC often gets overlooked until it becomes a problem.

Before you even begin expanding your PPC efforts in other regions, start with these guardrails in place:

  • Work with legal early: Involve your legal or compliance teams in the planning process. Get clarity on what’s allowed in each region before campaigns launch.
  • Stay up-to-date with platform policies: Google Ads, Meta, and Microsoft all have country-specific ad restrictions. Review them regularly. This goes beyond demographic targeting or ad copy. How you track users once they get to your landing page is extremely important to understand what’s allowed and what’s not.
  • Use regional ad accounts: If you’re running large-scale campaigns, separate ad accounts by region. This makes it easier to manage billing, user access, and compliance settings. Google now has an account setting where admins need to check a box if they are going to run ads in the EU. For this reason alone, it’s good to keep each region in its own separate account.
  • Document your approach: Create a shared doc outlining how your team handles regulatory compliance, consent tracking, and ad policy enforcement. It helps new team members and agencies get up to speed quickly.

When in doubt, err on the side of caution. It’s better to delay a campaign launch and get it right than clean up a PR or legal mess later.

When To Consolidate Vs. Decentralize

One of the biggest international strategic decisions you’ll face: Should you centralize all campaigns under one global agency, or let each region work with its own partner?

There’s no perfect answer, but here’s a framework to help you decide:

  • Consolidate if:
    • You need unified reporting and brand control.
    • You operate in fewer countries with similar languages or cultures.
    • Your internal team is small and needs a streamlined workflow.
  • Decentralize if:
    • You’re in highly diverse markets with unique buying behaviors.
    • Local teams have strong relationships with trusted regional agencies.
    • You want to test different approaches and compare outcomes.

Some brands use a hybrid approach, which includes a central strategy with local execution. The key is to revisit your setup as you grow. What worked at five markets may not work at 15.

Managing International PPC Without Losing Control

The reality of managing international PPC campaigns is that it’s oftentimes messy and chaotic. This is especially true if you don’t have the right foundations to go off of.

If you’re struggling to understand where to start, your first priority should be working on your brand and messaging framework. Make sure that’s solid before you try to scale, whether that’s being done in-house or having an agency take that work on. Trust me, this step will make everything easier in the long run.

Your second priority should be defining clear ownership. If you’re working in a hybrid model with an agency and in-house teams, set clear expectations with everyone upfront. This reduces duplicate work and makes your teams more efficient.

Once those are in play, then you can tackle centralizing reporting and visibility.

Not everything can be optimized at once. Otherwise, you won’t know what’s working or not working. Be patient as you scale to new regions, but don’t be afraid to test the waters to see if you can find some clear winners along the way.

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Featured Image: Roman Samborskyi/Shutterstock

Shopping Ads Testing In AI Mode, Microsoft’s AI Search Guide & Keyword Strategy Shift – PPC Pulse via @sejournal, @brookeosmundson

Welcome to this week’s PPC Pulse: updates all revolve around how AI is being woven directly into search monetization and campaign structure.

Google is testing Shopping ads inside AI Mode conversations. Microsoft published a practical guide on how AI search surfaces brands. The Google Ads Decoded podcast made it clear that keywords are no longer the strategic starting point for campaign structure.

Here’s what happened this week and why it matters for advertisers.

Google Testing Shopping Ads Inside AI Mode

In a blog post from Google this week, Vidhya Srinivasan, vice president/general manager of Ads & Commerce at Google, confirmed they’re testing a new ad format in AI Mode.

Specifically, it’s a Shopping ad format that recommends products based on a user’s query within AI Mode.

In addition to this announcement, Google also said it’s testing similar formats in other verticals beyond retail, such as the travel category.

What may be the most interesting part of the announcement was the framing of ads. Srinivasan stated:

“We aren’t just bringing ads to AI experiences in Search; we are reinventing what an ad is.”

This could signal a shift in the existing ad formats in Google Ads or the possibility of adding in new formats down the road.

Why This Matters For Advertisers

This feels less like a “new placement” and more about how Search monetization is changing.

In AI Mode, the user journey is compressed. People are not scanning a page of results in the same way. They’re asking, refining, comparing, and making decisions inside a conversation.

That matters because it changes what “being present” looks like. It’s not only about ranking, or even being the first paid result. It’s about being one of the options the AI experience is willing to put in front of someone while they’re comparing.

For Shopping advertisers, this puts more pressure on feed strength. If AI Mode is assembling recommendations based on product attributes, availability, pricing, and retailer options, your data has to be clean enough to compete in that environment.

It also raises a practical question that I think a lot of teams are going to feel quickly. If AI Mode surfaces fewer visible commercial options than a traditional results page, those slots get more competitive. Winning may depend more on eligibility and relevance than on brute force bidding.

What PPC Professionals Are Saying

The initial reaction across PPC LinkedIn has mostly been “this was inevitable,” with people focusing on how this might work operationally.

Thomas Eccel, founder/managing director at AdSea Innovations, shared the announcement and called out that this format will be eligible through existing Shopping and Performance Max setups, which is the part advertisers will care about first. Are you going to need a net-new campaign type, or is this a distribution expansion of what already exists?

In a post shared by Andrew Lolk, founder of SavvyRevenue, comments around how Google is going about monetizing AI Mode vs. other players like ChatGPT and Claude were discussed.

Martin GroBe, head of SEA and Programmatic Display at Suchmeisterei GmbH, stated:

“Google has the advantage of having established AI Mode as a version of Gemini that users perceive as an evolution of Google Search and therefore accept advertising quite naturally. This allows Google to conduct monetization tests in AI Mode without negatively impacting the user experience of ‘Pure Gemini.’ Regarding Pure Gemini, Google can sit back and watch how successful Claude/ChatGPT are with their ad strategies – and then start monetization with the winning strategy.”

A lot of the chatter is less “cool new thing” and more “OK, what’s the eligibility, and what data do we need to tighten up now.” Others were questioning what attribution will look like if these ads are being shown in a discovery-first phase.

Microsoft Releases AI Search Playbook For Marketers

Microsoft Advertising published an updated edition of its AI search playbook, positioned as a practical guide for how AI-powered search and assistants are reshaping discovery.

Microsoft’s angle in this focuses more on being understood, trusted, and surfaced inside of AI-generated answers and less on simply ranking links.

It also directly addresses the overlap and difference between SEO and what it calls generative engine optimization, along with guidance on creating clearer, more structured content that AI systems can interpret confidently.

Why This Matters For Advertisers

On the surface, this looks like an SEO conversation. But paid teams should care for two reasons.

First, Microsoft is putting a flag in the ground that AI-driven discovery is not theoretical. It is treating it as the current operating environment, and it wants marketers to adjust how they show up.

Second, the “structure” theme is the part that connects directly to paid performance. In AI experiences, brands do not get pulled into answers because the copy is clever. They get pulled in because information is clear, consistent, and easy for machines to interpret.

Even if you live in Microsoft Ads or Google Ads all day, this should sound familiar. The industry keeps moving toward fewer manual levers, and more dependence on clean inputs. Content quality, feed quality, and landing page clarity are part of those inputs.

This guide is basically Microsoft saying: if you want visibility in AI discovery, you need to treat your information architecture like performance infrastructure.

What Professionals Are Saying

The response to Microsoft’s playbook has been positive, mostly because it aims to explain the mechanics without turning it into hype.

International SEO Consultant Aleyda Solis, who contributed to the guide (along with other professionals), praised Microsoft for “leading the way” and sharing practical resources for search marketers they can actually use.

Navah Hopkins, Microsoft Ads liaison, also shared the update with her take on why it’s useful for paid media folks, including topics like budget focus, landing page insights, and communication styles.

That theme of “finally, someone wrote this down in plain language” shows up in a lot of the reactions.

“Keywords Are A Means To An End” In Ads Decoded Podcast

In the latest Ads Decoded episode focusing on Search campaign structure, Google made a direct point that will land differently depending on how long you’ve been in this industry.

This week’s guest was Brandon Ervin, director of Product Management for Search Ads at Google. He and the host, Ginny Marvin (Google Ads liaison), discussed multiple topics, including account structure and the role of keywords now.

Ervin stated that the role of keywords in 2026 was that “keywords are a means to an end” and not the end itself, and that advertisers should start with the business goal and go-to-market approach first. Keywords become a thematic layer that supports that strategy.

They also discussed the ongoing shift toward semantic matching, why exact match still has a role for tighter control, and how query matching continues to evolve with frequent backend improvements.

Ginny Marvin also shared the episode on LinkedIn, framing it around modern Search structures and the role of the keyword in today’s environment.

Why This Matters For Advertisers

This topic matters because it is essentially Google validating what many advertisers have had to learn the hard way.
For years, the gold standard was granularity. Tight ad groups. Tight keyword lists. Maximum control.

And to be fair, that approach worked for a long time. I was firmly in that camp. SKAG structures made sense in the era they were built for. Broad match felt like an unnecessary gamble. Campaign consolidation felt like you were asking for wasted spend.

But the reality is the system changed. User behavior changed. And the way Google interprets intent changed.

So when Google says “keywords are a means to an end,” the real message is: stop treating keyword architecture as the strategy. Treat it as one layer of a strategy that starts with business outcomes, messaging, and intent.

It also reframes how people should think about search terms that “don’t look right” at first glance. Sometimes, those queries are noise. Sometimes, they are a discovery behavior that your account can either learn from, or completely miss because you filtered too aggressively.

I don’t think this means everyone should throw structure out the window. But it does mean segmentation should have a job. If two ad groups have the same intent, same landing page, and same creative approach, splitting them may just be creating artificial walls that the system has to work around.

What PPC Professionals Are Saying

The PPC conversation around this topic tends to split into two camps.

One group hears “keywords are a means to an end” and translates it as “Google wants us to have less control.” The other group hears it and says, “finally, this is how the system has been behaving anyway.”

The comments on Ginny Marvin’s post about the episode reflect that interest, especially around modern structure decisions and what still deserves separation in 2026.

Brad Geddes, co-founder of Adalysis, thanked Ervin and Marvin for their candidness, stating:

“I suspected that Google was using conversion data from across the account for bidding and other optimization, but I could never get anyone to confirm this. You finally confirmed it, so now I can confidently say this is true 🙂 TY.”

Alexandr Stambari, performance marketing specialist, showed support for the message overall, but expressed a concern about segmentation. He stated:

“However, there’s one point that concerns me slightly: moving too far away from segmentation can reduce control. In highly competitive niches (e-commerce, B2B lead generation), segmentation by intent, margin, and query type still plays an important role. Full consolidation without deep analytics can average out performance and hide growth opportunities.”

Marvin responded to his comment and reiterated that Ervin makes it clear that “advertisers should use segmentation where it makes sense and ground their analysis and their structure in their business goals.”

It’s also notable that Google is choosing to have this conversation in public, in a format designed for marketers, not engineers. That tells you it expects more advertisers to be wrestling with restructuring decisions this year.

Theme Of The Week: Tightening AI Search Infrastructure

This week’s updates all reinforce the same underlying shift. AI is not adding a new layer to Search. It is exposing whether your existing structure holds up.

Google is testing Shopping ads inside AI Mode, which means product visibility depends on how well your data can compete inside a summarized answer. Microsoft is explaining how brands are surfaced in AI responses, and structured, trustworthy inputs are central to that process. Google is also reminding advertisers that keywords are simply one input. The real foundation is business intent.

When discovery happens inside generated answers and fewer placements carry more weight, structure stops being a preference. It becomes performance leverage.

If your feeds are clean, your content is clear, and your campaigns are aligned to real intent, that leverage works in your favor. If not, AI environments tend to surface the gaps quickly.

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Featured Image: beast01/Shutterstock