When I’m running Google Ads in 2026, one setting I always check carefully is “Search Partners.” It often appears in campaign settings as a simple way to extend reach beyond Google Search, and on the surface, that sounds useful.
But more reach does not automatically mean better reach. In my experience, Search Partners can bring traffic, but the quality of that traffic is usually the problem.
For most advertisers, I would not leave Search Partners enabled by default. I’d rather start with the main Google Search results page, prove the campaign can convert, and only then consider whether extra volume is worth testing.
What are Google Search Partners?
Google Search Partners are third-party sites that use Google-powered search results. When someone searches on those sites, your ad may be eligible to show there. This network can include YouTube, directories, other search experiences, and even parked domains.
That sounds like a broader opportunity, but I usually see a familiar pattern: lots of impressions, plenty of clicks, and cheaper CPCs than Google Search. The issue is that cheaper clicks are not always useful clicks. Real conversions and meaningful business value from these placements are often limited.
If I’m using conversion-focused Smart Bidding, I often see Search Partner spend fall naturally over time. The bidding system eventually learns that those placements are not producing the conversions it wants, so it stops pushing budget there.
How Search Partners differ from the Google Display Network
I see advertisers confuse Search Partners with the Google Display Network all the time. Some websites can be involved in both, but the intent and placement logic are different.
The Google Display Network is made up of websites and apps that use AdSense, where ads can appear while people browse content. It can show up as a placement option in Demand Gen, Video campaigns where it is called “Video Partners,” and Performance Max campaigns.
Search Partners are tied to search-based activity. That is why they apply to Search, Shopping, and Performance Max campaigns rather than standard Display placements.
How I audit Search Partner performance
I do not recommend taking anyone’s word for it, including mine. The better move is to check what Search Partners are actually doing inside your own Google Ads account.
Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.
For Search or Shopping campaigns
In Google Ads, I go to the campaign view, select Segment, and choose Network (with search partners). This splits performance into separate rows for Google Search and Search Partners, which makes the difference much easier to see.
What I usually find is a lot of Search Partner impressions and clicks, often at lower CPCs than Google Search. But when I look for true conversions, the results are usually weak unless the account is tracking something shallow or easy to manipulate, such as a page view or a low-friction form fill.
For Performance Max campaigns
Performance Max works differently. Search Partners are required for this campaign type, so I cannot simply opt out. What I can do is monitor the activity through the Channel Performance report.
If I see heavy Search Partner spend in a Performance Max campaign, I treat it as a signal to review conversion tracking, bid strategy settings, and the quality of the conversion actions being used for optimization.
Check the Content Suitability report
For more transparency, I also check the Content Suitability report under Insights and reports. This report can show the actual websites or YouTube channels where ads appeared on the Search Partner network.
That list is often enough to make the decision clear. Once I see where the ads have been running, I usually find many placements that look low quality, irrelevant, or simply not worth the spend.
In Google Ads, many decisions really do depend on the account, the market, and the goal. This is one of the few areas where my starting recommendation is straightforward.
If I’m building a new Search or Shopping campaign, I leave Search Partners unchecked. After the campaign is performing well and has strong conversion data, I may test Search Partners for added volume. Until then, I keep the budget focused on the main Google SERP.
This article is part of the ongoing Search Engine Land series, Everything you need to know about Google Ads in less than 3 minutes. In each edition, Jyll highlights a different Google Ads feature and explains what advertisers need to know to get better results in a quick 3-minute read.
When I see Microsoft Advertising campaigns struggle to scale, the issue is often not the platform itself. It is usually that the account is being treated as a copy of a strategy built somewhere else.
Importing campaigns can get me live quickly, but it is only the beginning. Real performance comes when I add human judgment, Microsoft-specific structure, clean measurement, business-specific controls, and enough creative assets to help AI understand what I am actually selling.
The strongest accounts I see have a shared pattern: import is the starting point, visual creative opens more demand, and AI works best when I give it the right structure, signals, measurement, and guardrails.
Here is how I approach Microsoft Advertising when I want more than a simple campaign import.
Note: I’m a Microsoft employee, and I have written this as objectively as possible. I have also included community-sourced hidden gems where they help highlight useful features.
1. I start with import, but I do not stop there
Import is useful because it removes friction. It can bring over campaign structure, assets, and settings from Google, Meta, or Pinterest so I can launch faster. The mistake is assuming that a successful import means the Microsoft Advertising strategy is finished.
Imported campaigns often preserve yesterday’s assumptions. I still need to make Microsoft-specific decisions about budget, bidding, audiences, creative, measurement, reporting, and AI-powered opportunities.
Decide whether sync helps or holds the account back
One of the first choices I review is whether future changes from the source platform should keep syncing into Microsoft Advertising. If I only want to mirror another platform, automatic sync can reduce maintenance. If I want to build a Microsoft-specific strategy, automatic sync can quietly overwrite the optimizations I make after launch.
To see the full list of import settings, I go to Manual import > Advanced settings. From there, I review which settings should stay, which should change, and which Microsoft-specific opportunities were never part of the original structure.
Review budgets, bids, currency, and Microsoft-only options
Imported budgets may not match the opportunity or efficiency available in Microsoft Advertising, especially when I can consolidate campaigns and use ad-group-level controls instead.
Imported bids can also carry assumptions from another platform. I want Microsoft Advertising to have room to optimize for its own auction dynamics, audiences, and conversion data.
A PPC expert highlights LinkedIn Profile Targeting as a Microsoft Advertising hidden gem, especially for B2B campaigns that need to reach senior decision influencers.
Review Microsoft-specific settings after import
Import cannot choose Microsoft-specific opportunities for me. After launch, I review the settings that can materially change performance.
LinkedIn profile targeting: I can bid up or down, observe performance, and use LinkedIn profile data as a Performance Max audience signal across Company, Industry, Job Function, and Seniority.
Ad-group-level scheduling and location targeting: I can override campaign-level schedules and location targets at the ad group level, including whether ads serve in the user’s time zone or the account’s time zone.
Impression-based remarketing: I can target, exclude, or adjust bids based on someone seeing my ad. It does not require an existing email list or pixel, and members can remain on the list for up to 30 days after a single impression.
Multimedia ads: These visual-heavy ads have their own auction, can appear on the same SERP as my text ad, and may also serve in Copilot.
Cross-account portfolio bidding: If I need to launch a new account for the same brand, I can let it benefit from conversion data in an existing account.
Microsoft Clarity: I can use this free behavioral analytics tool to understand how people and AI engage with my site, where landing pages create friction, and which grounding queries may connect AI systems to my content.
Creative and editorial considerations: Microsoft has stricter advertising policies than many platforms, but it also allows useful capabilities such as exclamation points in headlines and disclaimers of up to 500 characters that do not take up ad space. If I enable disclaimers, my ads will only serve when the disclaimers can appear alongside them.
2. I build the signal foundation before optimizing
Account-level settings can look overly technical, but I treat them as the foundation for AI performance. They determine whether automation learns from clean data or from messy, misleading signals. Settings such as business attributes also help me communicate why customers should choose the business.
Verify conversion tracking and attribution before changing bids
Even the best bidding strategy cannot make up for incomplete conversion data. Before I blame bids, keywords, audiences, or creative, I verify that conversion and attribution data are flowing correctly.
Microsoft Click ID (MSCLID): This helps connect ad clicks to conversion activity.
View-through conversions: These help me understand the role visual creative plays before a conversion happens.
Simplified conversion setup: This enables intelligent conversion action creation.
Without verified tracking, it is easy to diagnose the wrong problem. What looks like a bidding issue may actually be incomplete or inconsistent conversion data.
If the organization relies heavily on UTM parameters, I also validate how auto-tagging and manual tagging interact. My goal is clean reporting, not duplicated parameters or attribution confusion caused by mislabeling.
Treat creative inputs as signals
When enabled, Microsoft Advertising can use images from landing pages to create more relevant ad experiences. If the site has strong, brand-safe, well-maintained imagery, this can improve creative coverage without forcing me to manually build every variation for every campaign type.
AI-optimized creative works best when the site already gives it good material. If the pages include images I would not want in ads, or if the imagery is sparse, text-heavy, or poorly matched to the offer, I upload the assets I want the system to use. Auto-retrieved images reduce friction, but they do not replace creative strategy.
Use account-level negatives carefully
Account-level negatives can eliminate unwanted traffic patterns across the account. Microsoft supports phrase and exact match negatives. If I need to remove a root problem, phrase match is often the better option. If I need to block a specific search term, exact match may work better. Neither negative match type accounts for close variants.
I only use account-level negatives for terms I am confident should not serve anywhere in the account. More nuanced exclusions belong at the campaign or ad group level.
3. I use structure and controls to help AI perform
Microsoft Advertising gives me useful controls, but my goal is not to micromanage every lever. I want to give AI cleaner inputs, stronger guardrails, and fewer structural problems to solve.
Microsoft reports a 20% reduction in low-quality Search Partner Network impressions, crediting earlier invalid activity detection, stronger quality signals, and tougher enforcement.
Concentrate signals instead of fragmenting them
Ad-group-level location and ad schedule settings can reduce the need to create duplicate campaigns or split budgets across multiple accounts.
I have seen advertisers create separate campaigns only to support different geographies or schedules. In many cases, I can manage those settings at the ad group level, simplify the structure, and concentrate conversion volume.
That matters because automated bidding usually performs better with stronger, more consistent signals. When possible, I aim for at least 30 conversions in 30 days. That level of signal gives automated bidding a better chance to make stable decisions than a fragmented structure with thin conversion volume.
Use scheduling, location, and disclaimers as guardrails
I always review location targeting. Microsoft Advertising supports geographic targets, radius targeting, and exclusions, but city-, county-, metro-, or DMA-level strategies may be more practical than forcing ZIP codes.
If Microsoft does not support a specific location target, it defaults to the next-highest level, such as ZIP code to city or city to DMA. If I need narrow targeting, I look closely at exclusions.
Avoid unnecessary learning volatility
Large bid or budget changes can create volatility while the system adjusts. As a general rule, I try to keep bid or budget changes below 15% over a 14-day period when I want to avoid unnecessary learning disruption. Larger changes may still be necessary, but I make them intentionally.
Seasonality adjustments help when I expect a temporary conversion rate change because of a sale, event, promotion, or other short-term spike. Data exclusions help when conversion tracking breaks or reports misleading data that I do not want automated bidding to learn from. These tools are not bidding hacks. They protect automation from learning the wrong lesson.
Use conversion value rules whenever possible
The cleanest way I can communicate value to the bidding algorithm is through conversion value rules grounded in accurate conversion tracking. These rules let me create if/then logic for devices, audiences, and locations, then add a monetary amount or multiply conversion value.
Microsoft supports bid adjustments across audiences, devices, demographics, locations, and time. Multiple adjustments can compound. If a user qualifies for several categories at once, the bid may become more aggressive than I intended.
Before I add another layer, I ask whether I truly want to spend more to reach that audience, in that location, on that device, at that time. If I want the algorithm to understand value, meaningful conversion values and conversion value rules are usually stronger signals. If values are not reliable, CPA-oriented bidding with carefully chosen adjustments can still work.
Microsoft Advertising reports network-level gains, with indexed conversion rates up 45% and indexed cost per conversion down 1.5%, tied to cleaner traffic quality.
4. I use audiences, inventory, and creative to shape demand
Microsoft’s differentiated audiences, inventory, and creative formats can help me generate and shape new demand instead of only capturing demand that already exists.
Use LinkedIn profile targeting intentionally
LinkedIn profile targeting is still one of the most distinctive audience capabilities in Microsoft Advertising. I can apply bid adjustments based on company, industry, job function, and seniority.
Multiple targets within the same LinkedIn profile category act as “or” statements, while targeting across categories narrows the signal. A company target plus a seniority target is more restrictive than two company targets. That can be powerful when intentional and expensive when accidental because bid adjustments compound.
For B2B advertisers, this can be especially useful, but it is not limited to enterprise brands. Any business selling to specific professional audiences can use these signals to prioritize valuable traffic.
For example, if I am trying to reach someone traveling for work with local experiences or travel gear, I might bid up on a “Business development” job function in an industry with a conference happening in the next two to three weeks.
Build audiences from exposure, not just site visits
Traditional remarketing depends on someone visiting my website. Impression-based remarketing gives me another option: building audiences from people who have been exposed to my advertising.
A prospect may not click the first time they see the brand, especially in formats such as Audience ads, Premium Streaming, or Multimedia ads. Impression-based remarketing lets me continue the conversation later instead of treating the first exposure as a failed interaction. An impression can become the starting point for an audience strategy.
Reevaluate search partners and exclusions
Many advertisers disable search partners because they assume the inventory behaves like display network expansion on other platforms. I do not start with that assumption. Search partner inventory is still search inventory, and Microsoft provides publisher visibility, so I can evaluate it directly.
Recent Microsoft studies have shown a 45% improvement in conversion rates and a 20% reduction in low-quality impressions tied specifically to Search Partner inventory, independent of advertiser optimization.
If specific publishers are not performing, I use the available controls. I can manage unlimited exclusion lists at the MCC account level, and each list can exclude up to 2,500 URLs. If I need to protect a campaign’s ability to target a placement, such as when Performance Max and Audience ads run together, I exclude domains surgically instead of cutting off useful inventory.
A PPC strategist highlights a practical Microsoft Advertising tactic: run multimedia ads separately from branded search to expand visibility without self-competition.
Use Multimedia ads to expand SERP presence
Multimedia ads participate in their own auction and can appear in prominent visual placements on the search results page. A traditional search ad and a Multimedia ad can both appear for the same brand, increasing my presence on the SERP.
I can enable Multimedia ads at the campaign level and then use ad-group-level decisions to direct budget toward or away from the format.
They matter because they can amplify visual presence, serve as ads in Copilot, and qualify for impression-based remarketing. Their value is not limited to direct-click performance. They can connect search visibility, visual storytelling, and remarketing strategy.
Use Audience ads to expand reach
I use Audience ads, including display, native, and video, as a controlled way to expand reach, support full-funnel strategy, and build remarketing inputs that inform other parts of the account.
Audience ads support audience strategies, placement preferences, content category controls, and creative preview before launch. For organizations that require legal, brand, product, or executive approval, preview capability can make review much easier.
Use creative and editorial details to reduce friction
Microsoft Advertising has editorial policies I need to understand instead of assuming every platform evaluates ads the same way. Claims such as “best,” “number one,” or other superiority language need clear landing page support.
Microsoft Advertising also allows some emphasis I might not expect, such as one exclamation point in headlines, but that flexibility does not remove the need for substantiated claims and clean final URLs.
Editorial issues are often misdiagnosed as platform friction. In many cases, the issue is one specific asset rather than the entire ad. Final URL problems are more fundamental and can prevent an ad from serving at all.
Extensions and visual assets can help brands communicate more value before users reach the landing page, especially in competitive categories where plain text may not provide enough differentiation.
5. I treat PMax, AI Max, and Copilot as AI opportunities with guardrails
I find Microsoft’s approach to AI most useful when I view it as augmentation rather than replacement. Human-centered AI should help me scale thoughtfully while preserving consent, transparency, and trust.
A marketer highlights how Microsoft Clarity surfaces real user friction, from mobile testing gaps to visitors tapping images they mistake for links, offering useful context for ad and landing page optimization.
Know what Performance Max is designed to enable
Performance Max can be powerful, but it requires a different mindset from traditional campaign structures. Asset groups are not ad groups. There is no asset-group-level equivalent to ad-group negatives, and I cannot force one asset group to take priority over another.
Performance Max is built for AI-driven allocation. If strict control is the priority, traditional Search, Shopping, and Audience campaigns may provide clearer governance. When I want to influence Performance Max, I focus on the inputs that matter most.
Strong audience signals: I include impression-based remarketing and LinkedIn profile targeting, which are unique to Microsoft.
Relevant creative: Copilot can pull creative from the landing page and adapt existing creative with tonal shifts, rewrites, or formatting improvements.
Thoughtful search themes: I avoid duplicating exact match keywords as search themes because exact match keywords take priority in the auction.
Meaningful conversion tracking: I make sure conversion tracking and conversion values are accurate because Performance Max needs conversions to perform effectively.
Clear landing pages: The landing page must communicate the offer clearly. If it does not, the algorithm may struggle to match the right queries, and people may struggle to do business with me.
If I run the same search theme as an exact match keyword, there is a strong chance the exact match keyword will serve instead of the Performance Max campaign. I prefer to use search themes as testing grounds rather than duplicates.
Performance Max website URL reporting gives me URL-level visibility into spend, clicks, impressions, and conversions. That gives me more to work with than impression-only reporting and can make automated campaign testing easier to justify.
Separate campaigns when budget separation matters
If budget separation matters, I create distinct campaigns instead of forcing multiple business objectives into one Performance Max campaign. Microsoft’s capacity of 300 Performance Max campaigns, compared with Google’s 100, can be useful when budget priorities genuinely need separation.
For example, if I have two equally important products with drastically different tROAS goals, I would not want them to share budgets because I cannot specify which asset group or product should take priority. Separate campaigns with distinct budgets and tROAS goals are usually a cleaner fit.
My rule is simple: if related assets and audiences can share a budget, I consolidate Performance Max campaigns to strengthen conversion volume. If budget separation matters, I build that control at the campaign level instead of trying to force it through asset groups.
Evaluate AI Max and Copilot for new opportunities
AI Max now addresses many of the use cases that once made Dynamic Search ads valuable. If my goal is to let Microsoft AI better match queries, creative, and landing pages, AI Max may be the better place to test.
That does not mean I abandon existing high-performing campaigns. It means I stay intentional about whether I am investing in legacy dynamic functionality or AI-powered capabilities built on Microsoft’s latest technology.
Ads can appear in relevant Copilot experiences when Microsoft determines there is clear commercial intent and the ad may help the user. Ads have served in Copilot since 2024. The goal is not to force ads into AI answers. It is to preserve a useful experience for the user.
A glowing Google search bar cuts through streams of digital data, capturing the fast-moving world of search, shopping visibility, and SEO innovation.
Copilot is not a separate campaign type I manually opt into. Performance Max, AI Max, exact, phrase, and broad match search campaigns, Multimedia ads, and Shopping ads are all eligible to serve in Copilot. Performance Max and AI Max have the easiest time serving there because they can adapt to AI-driven experiences.
Use generative AI as a creative workflow and diagnostic tool
Copilot can help me brainstorm, rewrite, refine, and adapt creative across Performance Max, responsive search ads, Multimedia ads, Audience ads, and other campaign types. It does not replace the marketer. It reduces friction between strategy and iteration.
Ad Studio can generate new creative assets and make adjustments such as background modifications, seasonal refinements, location-specific tailoring, and additional aspect ratios. I see its best use as accelerating iteration once the creative strategy is already clear.
AI-generated assets can also help me diagnose how clearly the site communicates. If the outputs accurately represent the business, the site is probably sending clearer signals. If they repeatedly miss the mark, the landing pages, messaging, or content structure may be confusing both AI systems and people. The Performance Max campaign generator can be a useful diagnostic shortcut for the same reason.
6. I use reporting and Clarity before blaming the auction
No amount of AI, bidding nuance, or audience strategy can compensate for poor measurement. Microsoft Advertising provides strong reporting visibility, and I use it before making media-only decisions.
Use transparent reporting to make better decisions
Microsoft provides visibility into every search term that generates a click as part of its transparency approach. I use that visibility to understand what is really happening behind performance changes.
Genuinely wasteful: There may be no business case for targeting that search.
An AI-driven match: The query may look questionable until I examine the customer journey with behavioral analytics.
A landing page issue disguised as a traffic problem: Before I add a negative keyword, I evaluate post-click behavior to see whether the landing page or conversion tracking is the real issue.
Use Microsoft Clarity before making campaign changes
Microsoft Clarity answers one of the most important questions in campaign diagnostics: what happens after the click? It can show whether users engage with the page, get confused, abandon forms, run into technical issues, or complete actions that are not being tracked correctly.
I want Clarity in the diagnostic process before I make major campaign changes.
If people arrive and get stuck, the issue may be the landing page experience.
If they complete the desired action but conversions do not appear in Microsoft Advertising, the issue may be tracking.
If they arrive and immediately disengage, the issue may be creative alignment, traffic quality, or the offer itself.
Clarity can also help me understand how AI systems interact with my content, including the grounding queries that led AI systems to cite the domain and recommendations for improving citation opportunities.
If AI systems cite the domain as relevant, that can validate the content strategy. If they do not, or if the queries reveal mismatches, that may point to gaps in how the content communicates value.
I apply Microsoft-specific optimizations deliberately
I can import existing campaign structures and assets while still taking advantage of Microsoft-specific capabilities. AI can play a central role, act as an occasional assist, or be used selectively, but scaling becomes harder without some level of AI adoption.
Testing Microsoft Advertising does not require a massive investment. It does require getting the fundamentals right: conversion tracking, bid-to-budget ratios, and creative that reflects the channel’s visual nature.
When I get those fundamentals right, Microsoft Advertising gives me search term transparency, GDPR-compliant impression-based audiences, and opportunities to reach people across the surfaces where they work, live, and play.
I no longer think of branded search protection as simply bidding on my own brand name. Competitors can position themselves against my brand through landing pages, ad copy, modifier keywords, and Google Ads automation, often in ways that look completely legitimate.
The real pressure often goes beyond keyword bids. Comparison pages that pass review, dynamic keyword insertion that pulls brand names into headlines, and policy gaps that allow competitors to appear beside my brand can quietly weaken performance without clearly breaking Google’s rules.
By the time I notice the pattern, the damage may already be visible in branded CPCs, impression share, or conversion rate. That is why I pay close attention to how these tactics work, how to spot them early, and how to respond without overreacting.
1. Dynamic keyword insertion
Dynamic keyword insertion, or DKI, is designed to make ads feel more relevant by automatically inserting a user’s search query into the headline. In competitive brand auctions, I see it as a tactic that can create a meaningful loophole.
If a competitor bids on my branded terms and uses DKI, Google can dynamically place my brand name in the ad headline in real time, even if the competitor never typed my trademark into the ad copy.
That distinction matters. The competitor is not explicitly writing my trademark into the ad. Google is inserting the searcher’s query. To the user, the ad may look like it directly references my brand. Inside Google’s system, it is treated as standard query matching.
The result is frustrating: an ad can appear to reference my brand, capture high-intent traffic, and send that user to a competing offer without obviously violating policy.
I have seen this happen from both sides. Sometimes competitors use it intentionally. Sometimes brands trigger it in their own accounts without realizing what is happening. In one case, a competitor’s name started appearing in a brand’s ad headlines because of DKI. No one had written that name into the ad; Google inserted it based on the query.
The bigger challenge is that I cannot reliably detect this from inside Google Ads alone. I have to audit the search results page directly. Otherwise, I may only notice the problem after branded CPCs rise or conversion rates start to slip.
Comparison landing pages sit in a gray area. Google does not evaluate landing page content the same way it reviews ad copy. If a competitor creates a page such as “[Your Company] alternatives” or “[Competitor vs. Your Company]” and bids on my branded terms, the ad can still run as long as the ad itself stays neutral.
The ad does not have to mention my brand at all. It can use broad language like “Find the right solution,” “Compare top tools,” or “See your options.” The competitive positioning happens after the click.
Once the user lands on the page, the comparison does the work. The page may include feature charts, pricing callouts, benefit comparisons, and carefully framed language such as “Why teams choose us over [Your Company].” The page may not be misleading or technically noncompliant, but the intent is obvious.
Google’s review process tends to focus on the ad rather than the full post-click experience. As long as the ad copy does not make explicit competitive claims, the system may treat it as compliant, even when the landing page is built entirely around positioning against my brand.
This works because landing page relevance can reinforce auction strength. A page built around my brand and the keywords in the ad group may align closely with the searcher’s intent. Even if the ad copy stays generic, the post-click experience can help the ad compete because it matches what the searcher is trying to evaluate.
When I respond, I do not focus only on one advertiser. If competitors are using comparison-driven experiences to intercept branded demand, I look at the broader search ecosystem around my brand.
I strengthen my presence across the full search results page, not just my own ads.
I invest in publishers, review platforms, directories, analysts, and affiliates that influence comparison and alternative searches.
I work to build a search results page where credible third-party sources reinforce my positioning when prospects search for alternatives, comparisons, reviews, or competitor evaluations.
The brands that win these moments do not rely only on their own landing pages. They shape the narrative across the entire search results page.
Brand keyword bidding is not new, but I see competitors using it in more strategic ways. Instead of bidding only on my exact brand name, they target brand-and-modifier combinations that give them more flexibility.
For example, if my brand were “Acme Project Manager,” a competitor might bid on searches like “Acme Project Manager alternative,” “Acme vs. competitors,” or “Acme pricing review.” Their ad copy can avoid mentioning Acme by name while still using the search context to position itself as the alternative.
Google allows this because the ad itself does not explicitly mention my brand. The searcher does. Modifier keywords provide enough context for the ad to compete without directly referencing a trademark in the copy.
When competitors bid on terms like “[Your Brand] alternative” or “[Your Brand] vs.,” they are targeting lower-funnel research queries. These searchers may not convert at the same rate as people searching only for my brand, but they can still change the auction dynamics.
That pressure can increase branded CPCs, force me to spend more to maintain visibility, and raise the cost of my core brand terms, even if competitors convert relatively few of those modifier searches.
I treat brand modifier queries as a separate audience. I segment them by intent, including pricing, reviews, alternatives, competitors, and comparisons, and I monitor Auction Insights for each group. Exact brand searches and comparison-driven searches need different strategies.
I also build dedicated landing pages and messaging for each modifier intent. That helps me control high-intent research moments without overpaying for every branded variation.
Manual SERP checks are useful, but they do not scale. If I have meaningful branded spend or active competitors targeting my terms, I use automated brand monitoring tools to identify activity across devices, geographies, and browsers that manual checks can miss.
This is especially important when competitors use geotargeting, dayparting, or other tactics designed to limit visibility. A competitor may not appear every time I check manually, but that does not mean the activity is not happening.
I also use a clear escalation framework. If a competitor uses my trademarked term directly in ad copy, I start with Google’s trademark complaint process. If the behavior continues after enforcement action, I document the pattern and involve legal counsel.
Most other scenarios, including modifier bidding, comparison pages, and competitive positioning, are usually better handled through PPC strategy than legal action.
Before I decide how aggressively to respond, I measure the economics. I estimate the monthly cost of competitor activity by calculating the increase in branded CPCs and the additional spend required to maintain visibility.
Then I compare that number with the cost of my response, whether that means higher bids, new landing pages, expanded monitoring, or more investment in third-party visibility. My goal is to keep the cost of defending the brand lower than the value I am protecting.
Build a proportionate response
Competitors use modifier keywords, comparison landing pages, dynamic keyword insertion, and other policy-compliant tactics to influence buyers during critical research moments. Often, they can do this while staying within Google’s policies.
The strongest defense I can build combines continuous monitoring, thoughtful audience segmentation, proportionate responses, and disciplined budget decisions.
Competitive PPC success comes from understanding the auction, shaping the narrative across search results, and investing where my defensive efforts deliver the greatest return.
I’m watching a new Google Search ad test that could change how people understand sponsored results. Google appears to be experimenting with AI-generated summaries beneath paid search ads, giving its own AI more influence over how advertiser messaging is framed.
What’s happening. Some advertisers are seeing AI-generated summaries appear directly below Google Ads descriptions in Search results. These summaries include a warning from Google that says: “Google AI responses are generated independently and can make mistakes, so double-check responses.”
I first saw this test surface through digital marketer Darcy Burk, who shared a screenshot of the experience on X. The placement is notable because the AI-generated text appears close enough to the ad that users may treat it as part of the paid result, even though Google says the response is generated independently.
Why I care. If Google expands this more broadly, these summaries could shape how users interpret ads by emphasizing the details Google considers most relevant, not necessarily the exact message the advertiser intended to highlight. That raises real questions about accuracy, brand control, and whether click-through rates could be helped or hurt by AI-written context.
Between the lines. Google has already tested AI-generated summaries for organic search listings, so seeing similar functionality move into paid ads feels like another step in bringing generative AI deeper into the Search experience. What I still do not know is how these summaries are created, what sources they rely on, or whether advertisers will get any say in the copy.
What I’m watching. Google has not publicly announced this feature or responded to requests for comment, so it is unclear whether this is a small experiment or the beginning of a wider rollout. Until Google explains the mechanics, advertisers are left guessing how much control they may have over AI-generated text attached to their ads.
The bottom line. Google is testing AI-generated summaries inside Search ads, and I see that as a sign that generative AI could soon play a larger role in paid search presentation, even when advertisers are not writing that extra copy themselves.
First spotted. Darcy Burk, understandably, was not pleased with this update.
I’m watching a small but meaningful Google Search ads experiment that could change how people notice paid results. Google is testing labels that call out the ads it believes are most relevant to a user’s search query, which could affect both user trust and advertiser performance.
What’s happening. Google has started testing new Search ads labels such as “Strongest match” and “Strong match” on select ads in search results. Google Ads Liaison Ginny Marvin confirmed the experiment and said the labels are meant to help users quickly spot ads that closely match their search intent.
For now, I see this as a limited test. Google says it is only appearing for a small percentage of users in the U.S., so most advertisers may not notice it in the wild yet.
Why I care. This kind of visual signal could influence which ads users view as the most relevant and trustworthy. If Google expands the experiment, advertisers with stronger relevance and quality signals may gain more attention, while weaker or less aligned ads could become easier to ignore.
How it works. According to Google, these labels rely on the same ad quality and relevance signals already used inside its advertising systems. In other words, Google is not introducing a new ranking factor here. It is making its relevance assessment more visible directly in the Search results interface.
I see the goal as fairly straightforward: help users identify the ads most likely to answer what they were searching for, without making them interpret relevance entirely on their own.
Why Google is testing it. Google says the experiment is designed to improve the Search ads experience for both consumers and advertisers.
For users, the label could act as another cue that a paid result may be especially useful for their query.
For advertisers, it could help highly relevant ads stand out in front of high-intent audiences, which may lead to stronger engagement and higher click-through rates if the feature performs well.
Reading between the lines. I view this test as part of Google’s broader push to make ad relevance more visible and more understandable to searchers.
Historically, relevance signals have mostly worked behind the scenes through auctions, quality systems, and ranking logic. By showing those signals more clearly, Google may be trying to build more trust in sponsored results while also rewarding advertisers that closely match their ads to search intent.
The timing also matters. Search platforms are under ongoing pressure to prove that their ad experiences are useful, high quality, and worth users’ attention. A label like this gives Google another way to frame certain ads as more helpful, not just more prominent.
What I’m watching next. Google has emphasized that this is an early-stage experiment and has not said whether “Strongest match” or “Strong match” labels will become permanent. For now, I would treat this as another reminder that ad relevance, landing page quality, and alignment with user intent remain central to Google’s direction for Search advertising.
Search advertising continued to lead the pack in 2025, although its growth took a slight dip as digital advertising landscape evolved. What really struck me was how U.S. search ad revenue soared to $114.2 billion.
Despite being the largest ad channel, growth slowed down a bit, indicating a shift towards exciting AI-driven ad formats. It’s fascinating to see how advertisers are reallocating budgets towards these new trends.
Throughout 2025, the digital advertising market in the U.S. climbed to a phenomenal $294.6 billion, even without major cyclical events like elections or the Olympics driving it. The final quarter alone brought in a whopping $85 billion.
When I delve into the growth figures, video, social, and programmatic formats emerged as the fastest-growing sectors. Digital video revenue jumped by an impressive 25.4%, reaching $78 billion, while social platforms saw a 32.6% increase to $117.7 billion.
The influence of AI is undeniably reshaping the advertising landscape. It’s not just a tool anymore; it’s transforming how we discover, purchase, and measure ads across various platforms.
What truly captured my attention is the concentration of market control. The top 10 players now hold 84.1% of the market share, leveraging AI and large-scale data to assert dominance.
For anyone involved in digital advertising, it’s crucial to adapt to these shifts. With search as a somewhat stable force, emerging formats like video and social offer more exciting opportunities backed by automation and AI.
The insights come from the IAB/PwC’s comprehensive study of U.S. internet advertising revenue, giving us a look into the future of digital marketing.
I recently stumbled upon an intriguing issue with Google’s paid search ads. Imagine my surprise when I noticed multiple competing ads displaying identical web statistics! This strange occurrence immediately made me question whether it’s a bug or perhaps a deliberate change by Google.
What’s happening? I’ve seen several paid search ads showcasing the same website statistics simultaneously, despite these metrics usually being unique to each site. This uniformity makes the data appear dubious, leaving me uncertain if it’s a display glitch, an experimental test, or something more intentional.
Why we care. Trust signals in search ads play a crucial role in helping users like us make informed decisions. They boost click-through rates by instilling confidence in the results. If identical stats appear across competing ads, it risks undermining their credibility—potentially impacting the confidence and trust advertisers rely on.
What we don’t know.
Whether Google is testing this actively or it’s an unintended bug
How widespread the issue is across different search queries or markets
Whether it’s affecting user click behavior or advertiser performance
No official word. So far, Google has not confirmed or commented on this behavior. Paid Media expert and Founder Anthony Higman was the first to notice and flag this anomaly, sharing his findings on LinkedIn.
The bottom line. If trust signals can’t be trusted, they fail to serve their purpose. As someone invested in digital advertising, I’m keenly watching whether this pattern gains momentum or fades away. Observing these developments is critical for both advertisers and users.
I’ve just discovered some groundbreaking updates from Google that could transform how automotive advertisers leverage search campaigns. Google is now empowering us by integrating vehicle feeds directly into Search ads, making our inventory more visible with a vibrant and more engaging format.
So, what’s new? Google Ads now allows for vehicle feed integration on Search ads. We can pull from Google Merchant Center to enrich our ads with specific details like make, model, price, and images, all designed to enhance the standard text ad.
Let me explain how it works. These vehicle listings appear as clickable assets alongside our usual Search ads. They can either show up below or beside the main text, offering users a seamless path to either a detailed vehicle page or a broader landing page based on their interaction.
Why should we care? This update is a game-changer. It allows us to showcase real inventory directly in our Search ads, making them more attractive and informative for high-intent users. We can achieve richer visibility and potentially gain more qualified leads by displaying key details upfront in Google Search, without the hassle of extra campaign setup.
What makes this noteworthy is how it brings Shopping-style visual elements to our Search campaigns. We can now feature real inventory without needing separate campaign types, which is a significant advantage.
As advertisers, the benefits are immense. We get a more engaging ad experience, the opportunity for higher-intent leads, and we can use our existing Merchant Center feeds effectively, eliminating the need for duplicate setups.
When it comes to measuring success, we can track performance through the “Click type” segment. This helps us understand user interactions with vehicle listings compared to standard ads, offering insights into what works best.
Matching is another area where Google shines. Their automation decides which vehicles appear based on user intent and query context, marking a shift towards less manual control and more AI-driven ad assembly.
Here’s the takeaway. Vehicle feeds in Search campaigns offer us a powerful way to integrate inventory with intent-driven queries. We can turn standard text ads into dynamic, product-led experiences, significantly enhancing user engagement within Google Search.
Let me share a few valuable lessons I’ve learned about PPC advertising from seasoned experts. Even the most experienced among us encounter pitfalls—like hastily launching campaigns or leaving automation unchecked. Recently, I joined Greg Kohler from ServiceMaster Brands and Susan Yen from SearchLab Digital at SMX Next, where we candidly discussed the mistakes that catch us off guard.
Read on to discover the blunders that even the most seasoned marketers must navigate.
Never launch campaigns on a Friday
This is a well-known pitfall, yet it continues to happen. Susan Yen mentioned that due to client demands, campaigns often go live on Fridays, leading to weekend chaos if things go awry. A minor error like an inflated budget setting can cause significant issues.
Greg Kohler emphasizes the importance of reviewing setups with fresh eyes. Wait until Monday to launch; doing so may avert unnecessary problems. Even experts can become overconfident, only to be reminded of these lessons by a Friday crisis.
Takeaway: Avoid launching before the weekend or holidays and stand firm if clients push. It protects both your peace of mind and campaign performance.
Location targeting disasters
Greg shared an experience where an error in location targeting meant campaigns ran in the wrong timezone. By Saturday, ads intended for a U.S. audience accumulated thousands of views in Europe instead.
Takeaway: Configure location settings directly within the Google Ads interface to minimize risks and ensure precise targeting.
The search term report trap
Susan stressed that search term reports are essential for every campaign. Ignoring them can lead to wasted clicks and difficult client conversations later on. She advises checking these reports monthly to avoid irrelevant traffic.
Takeaway: Routine reviews help refine what to target or exclude, enhance performance, and maintain efficient account strategy.
Google Ads Editor vs. interface: A constant battle
The gap between the Google Ads Editor and the interface often leaves teams in a bind. Susan’s team preps in Excel before using Editor for bulk edits but prefers the interface to ensure accuracy in settings.
Takeaway: Use the interface for tasks requiring precision, like responsive ads or location targeting.
The automatically created assets problem
Automatically created assets often default to ‘on,’ requiring tedious navigation to disable. New types of assets can inadvertently apply to all campaigns.
Takeaway: Regularly review these settings. Set reminders to maintain control as new features roll out.
Importing campaigns from Google to Microsoft Ads
Yen warned of the pitfalls of importing Google campaigns directly into Microsoft Ads due to discrepancies in budget assumptions and automation settings.
Takeaway: Treat Microsoft Ads independently with a tailored strategy post-import for optimal results.
The App placement nightmare
A slip in excluding app audiences can direct spend to irrelevant categories. Yen advises vigilance, as settings to exclude these are often hidden.
Takeaway: Establish comprehensive exclusion lists to guard against inappropriate targeting.
Content exclusions and placement control
Applying content exclusions from the start helps avoid placement in irrelevant or inappropriate contexts, though manual follow-up remains necessary.
Takeaway: Consistent reviews ensure Google honors your settings, preventing unwelcome surprises.
Call tracking quality issues
Susan highlighted the importance of client communication in effectively tracking call quality, advocating for monthly check-ins focused on conversion metrics.
Kohler suggested distinguishing first-time from repeat callers in analytics to optimize automated bidding systems.
The promo date problem
Litner pointed out issues with scheduled assets appearing outside their promotional windows, urging manual checks to ensure proper timing.
Kohler echoed similar concerns with automated rules potentially misfiring.
Takeaway: Verify scheduled actions on their launch dates manually to prevent mishaps.
AI Max settings and control
The issues of AI-driven campaign settings defaulting to active require diligence in monitoring and fine-tuning each setting.
Takeaway: Despite AI advancements, practice consistent oversight to manage budget spend effectively.
Account-level settings that haunt you
Susan flagged the risk of overlooking critical account-level settings that can derail campaigns silently, suggesting a standardized checklist approach.
Takeaway: Establish and follow a thorough account setup checklist to catch any hidden conflicts with campaign goals.
Final wisdom
Here are several recurring themes from our discussion:
Always double-check automation; it’s not immune to errors.
New perspectives reveal potential errors.
Effective client communication prevents misunderstanding.
Manual reviews maintain balance as automation increases.
Keep updating exclusion lists to mitigate repeated issues.
The takeaway is that everyone makes mistakes. The difference lies not in avoiding them but in swiftly addressing them, learning from experiences, and creating systems to prevent recurrence. As Kohler notes, stay vigilant, question automation, and avoid the temptation of a Friday launch.
Recently, I’ve been following a concerning development involving Google, where the tech giant is urging a federal judge to halt the Department of Justice’s antitrust remedies. The primary concern? Forced ad syndication could lay bare Google’s proprietary technology and negatively affect advertisers.
In an affidavit filed on January 16 by Google’s director of product management, Jesse Adkins, the company stresses how these measures could lead to irreversible damage. The crux of the argument is about maintaining control over proprietary ad technology, which could be jeopardized if exposed.
The big picture. In Adkins’ testimony, the likely fallout includes forced exposure of confidential technology, detrimental effects on advertisers, and a loss of authority over query and pricing data.
Mehta’s final ruling could compel Google to share its search results, features, and ads with any qualified competitor for the next half-decade under the current terms.
Google contends that employing these remedies before the conclusion of their appeal would result in immediate and unchangeable damage.
Risk to Google’s ad technology. At the center of Google’s warning is the potential exposure of its search ad auctions, developed over many years by an enormous team of engineers.
Syndication on a large scale might allow competitors or outsiders to decipher Google’s ad targeting techniques, relevance factors, and auction mechanisms, according to Adkins.
Competitors could potentially use this data to enhance their ad systems, stripping Google of its competitive edge.
Sub-syndication amplifies risk. The judgment permits competitors to further share Google ads with third parties, creating multiple layers of vulnerability to scraping and misuse.
Even the most compliant partners might lack the motivation to monitor downstream entities, effectively transforming Google’s ad system into a near-open utility with limited protection.
Advertisers could face fraud. Adkins mentions advertisers are caught in this struggle, citing tactics like “trick-to-click” that incite accidental clicks or artificially inflate expenses.
One example involves a syndicator adding names of wealthier countries to queries while diverting low-cost international traffic to ads, resulting in tens of millions in click fraud within a couple of months.
As a result, users might see less relevant ads, yet advertisers would still be charged, leading to diminished conversion rates.
Pricing uncertainty. Google is also expected to offer syndication terms no less favorable than existing agreements, which are highly customized to each partner’s traffic quality and technical setup.
Imposing these terms universally could lead to suboptimal pricing and financial uncertainty linked to unpredictable query volumes.
Irreversibility is key. Throughout the affidavit, Adkins underscores the irreparable nature of the potential harm. Once proprietary ad insights are revealed, they can’t be recaptured.
Once advertisers lose confidence, it is nearly impossible to win back. Moreover, once competitors craft products based on Google’s systems, the market’s impact becomes permanent.
Google suggests that even if their appeal succeeds, it could be too late to undo the ensuing damage.
Why we care. Any court-mandated ad syndication could potentially dilute Google’s control over ad placement and targeting, resulting in irrelevant advertising and reduced conversion rates. Essentially, this affidavit highlights the risk of higher costs, lower returns on investment, and less predictable campaign performance.
What’s next. The court is set to decide whether to temporarily halt the syndication remedies while Google’s appeal is pending. Without this stay, Google might have to start licensing search ads and results to qualifying competitors under new regulations, reshaping the search advertising landscape in unexpected ways.
Dig deeper. For further reading, I recommend checking out the following resources:
