I’m seeing OpenAI continue to build out ChatGPT Ads with a new round of updates for advertisers. In an email, ChatGPT Ads announced changes across ChatGPT Ads Manager and the broader ad experience, including custom audiences, a new overview tab, suggested ad drafts, a refreshed static ad card format, and expanded availability in Japan and South Korea.
Here is what stands out to me from the latest update.
Custom audiences: I can now upload audience lists with 25,000 or more users to include or suppress audiences from campaigns. OpenAI is also allowing bid multipliers for audiences at the ad group level, which gives advertisers more control over how aggressively they want to reach specific segments.
Overview tab: The new overview tab gives me a more centralized place to monitor account health, review recommended tasks that may improve campaign performance, and analyze key performance metrics in a larger, more flexible trend chart.
A before-and-after look at ChatGPT's refreshed static ad card, turning a small sponsored grocery prompt into a cleaner, more readable format with larger visuals and a clear Ad badge.
Suggested ad drafts: If a campaign needs broader content coverage to improve delivery, I may see an option to select “Add new ad” from the campaign view. This feature uses existing website metadata to prefill an ad draft with an image, title, and description, which I can then review, edit, and assign to a campaign and ad group. Importantly, OpenAI says this does not generate new copy or imagery with AI.
Japan and South Korea expansion: ChatGPT Ads are now live in Japan and South Korea. That means campaigns can target users in both markets, giving advertisers more reach if they do business there.
Refreshed static ad card format: OpenAI is also rolling out a refreshed static ad card across web and mobile. I see this as a cleaner, more compact format designed to be easier to read while giving visuals more prominence. This format had already started appearing in late June.
A bold Google logo sits atop layered, colorful digital documents, evoking the fast-moving world of search marketing, ad formats, campaign assets, and platform updates.
Why I care: ChatGPT Ads are still new, and OpenAI is clearly moving quickly. New targeting tools, reporting views, draft workflows, market expansion, and format tests all point to a platform that is still taking shape.
My takeaway is simple: I need to keep watching these changes closely, test them as they become available, and continue refining ad creative, audience strategy, and campaign structure as ChatGPT Ads matures.
I am seeing OpenAI roll out the ability to upload audience lists inside ChatGPT Ads. The new option appears under the “Tools” section and is labeled “Audiences.”
My read is that this gives advertisers a way to target campaigns based on the audience lists they upload to the platform, which should make ChatGPT Ads more useful for more precise ad targeting.
A new Audiences area appears in ChatGPT Ads Manager, inviting advertisers to upload customer lists for campaign targeting and audience filtering.
More details. I can upload raw or hashed emails and phone numbers and use them as audience filters for campaigns running on ChatGPT Ads.
A ChatGPT Ads audience upload form shows how advertisers can add customer lists, choose identifier type, and submit CSV or TXT files for campaign targeting.
What it looks like. I spotted screenshots of the feature from Craig Graham and Joss Froggatt on LinkedIn. Here is what the Audiences option looks like in the platform:
A glowing Google search bar cuts through streams of digital data, capturing the fast-moving world of search, shopping visibility, and SEO innovation.
Why I care. I see this as another sign that OpenAI is continuing to build more customization and targeting controls into its new ChatGPT Ads platform.
For advertisers and marketers, audience uploads could make the platform more practical and more performance-focused. If the targeting works well, it may help improve conversions, strengthen ROI, and make ChatGPT Ads a more serious option in paid media plans.
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.
Advertisers are projected to lose $172 billion a year to ad fraud by 2028. I have seen how quickly that problem can move from an abstract industry statistic to a very real performance issue inside a Google Ads account.
The risk is especially high in competitive industries where CPCs are expensive and every wasted click hurts. One client I worked with was in exactly that situation, and invalid click activity was dragging campaign performance below any profitable level.
After testing the usual defenses, I adjusted Google Ads audience targeting in a way that reduced invalid-click activity by 50% and brought the campaigns back to profitable performance.
Case study: How I cut invalid clicks by 50%
The client sold book editing and ghostwriting services. The search terms triggering the ads were relevant and high intent, but the traffic was not converting anywhere close to the level needed for profitability.
The warning signs appeared quickly. Google was reporting a 60% to 80% invalid click rate. Microsoft Clarity recordings showed bot-like behavior from Google Ads traffic. Many search terms had click-through rates above 80%, and some were even above 100%. GA4 and other analytics tools also showed far fewer sessions than the number of clicks reported in Google Ads.
I tested third-party click fraud tools first, but they did not produce any measurable improvement in performance.
Next, I filed an investigation with Google. Google agreed that suspicious activity existed, but said it had already caught all of it and had not charged the account for those clicks.
I was still confident that Google was not filtering out all of the invalid activity, so I decided to use the targeting controls inside the account more aggressively.
I added 540 Google-defined audiences to the Google Search campaigns and set them to Targeting.
The result was immediate. The invalid click rate dropped by 50%, and the conversion rate rose back to profitable levels.
Here is why I tested this approach, why I believe it worked, and what advertisers should understand before trying it in their own accounts.
What click fraud and invalid clicks actually are
Google defines invalid clicks as clicks on ads that do not come from genuine user interest. That includes intentionally fraudulent activity, accidental clicks, and duplicate clicks.
In practice, this can include actual fraud, such as competitors clicking ads, as well as less malicious behavior like accidental double-taps.
Google does not charge advertisers for clicks it determines are invalid. If Google initially charges for a click and later classifies it as invalid, it credits the advertiser back for that activity.
Why the usual defenses can fall short
Google catches a lot of invalid click activity, but this account showed me that the system is not perfect.
That gap is why so many third-party click fraud tools exist. Most of them try to identify suspicious IP addresses and block them before they can keep costing advertisers money.
The challenge is that fraudsters understand how these tools work. They can cycle through IP addresses with VPNs and avoid being stopped by a system that only blocks previously identified addresses.
If a tool blocks an IP address after suspicious activity occurs, that may help only if the same IP address is used again. When the source keeps changing, old IP exclusions lose much of their value.
There is also a platform limit to consider: Google allows a maximum of 500 IP address exclusions per campaign.
The tactic: Add audiences set to Targeting
I started thinking about what might separate fraudulent traffic from legitimate traffic. Google’s predefined audiences stood out because Google builds hundreds of audience segments from demographics, search behavior, and browsing behavior.
For example, someone researching private jet companies and Rolex watches might be classified by Google as a luxury shopper and added to that audience.
My hypothesis was simple: fraudsters who constantly rotate IP addresses may not also be building normal-looking online behavior profiles that fit neatly into Google’s predefined audiences.
So I added most of the available audiences to the Search campaigns. I was not trying to target only audiences that matched the ideal customer. I was using the audiences as a filter for users who carried enough Google audience signals to look more like real people.
The key detail is that I chose Targeting, not Observation.
When I use Targeting, Google limits ads to people who trigger the keywords and also belong to the selected audiences.
When I use Observation, Google simply reports how people in those audiences engage with the ads compared with everyone else. The ads can still show to anyone who triggers the keywords.
I would only test this tactic in accounts with unusually high invalid click rates. It can create real downside, including the risk of blocking legitimate searchers who do not fit inside Google’s predefined audience segments.
How to test this in your own account
In a Search campaign, go to Audiences > Edit audience segments > Targeting > Browse. Then select the audiences you want to add and click Save.
Common questions about fighting click fraud
Will Google refund clicks it identifies as invalid?
If Google identifies a click as invalid when it happens, I am not charged for that click. If Google identifies the click as invalid later, the account receives a credit toward future advertising.
How do I see how many invalid clicks I am getting?
The Invalid activity credit report in Report Editor inside the Google Ads UI provides the most detailed reporting.
I look at two key metrics there: Invalid clicks, which are clicks I was not charged for, and Credited clicks, which are clicks I was originally charged for but later credited back.
I can also add the Invalid clicks and Invalid click rate columns at the campaign level, though not at the ad group or keyword level.
What is a normal invalid click rate?
A February study found an 11.4% invalid click rate across 43,700 accounts.
Industry makes a major difference. While the average invalid click rate for StubGroup clients is very close to that study’s finding, I have seen advertisers in competitive industries with invalid click rates above 40%.
Should I file an investigation with Google?
If I have reason to believe Google is charging for invalid clicks, I consider filing an investigation here.
Why this approach worked best
Using Google’s predefined audiences as a filter cut this account’s reported invalid click rate in half. More importantly, it blocked activity that Google had said it was already catching, which turned failing campaigns into profitable ones.
Across Google Ads, Meta, and TikTok, I’m seeing platforms push advertisers toward broader, AI-driven targeting. Performance Max, Advantage+ campaigns, and TikTok’s automated audience expansion give algorithms more room to find converters, but they also reduce how much control I have over exactly who sees each ad.
That shift is changing how I think about campaign qualification.
As targeting becomes broader, creative has become one of the most important signals for both people and algorithms. I no longer see audience qualification as something that happens only inside targeting settings. More and more, it happens inside the message itself.
In other words, broad targeting is making creative my best qualifier.
The shift from audience qualification to creative qualification
For years, I treated targeting as the primary lever for improving lead quality. If I needed prospective graduate students, I could layer education interests, demographics, and remarketing audiences. If I needed patients looking for specialized care, I could build audiences around health-related behaviors and intent signals. If I needed insurance shoppers, I could narrow targeting by age, life stage, and consumer interests.
Those approaches are not disappearing, but I can see their influence shrinking. Platforms increasingly ask me to provide broad audience inputs, strong conversion signals, and compelling creative, then let machine learning determine who is most likely to convert.
Meta’s Advantage+ ecosystem, Google’s Performance Max campaigns, and TikTok’s recommendation engine all operate on this principle.
The challenge is that algorithms still need signals.
Conversion data remains the strongest signal, but I believe creative is becoming more important in helping platforms understand who should engage with an ad. Every headline, image, video, and call to action gives the system more context about the intended audience and the desired action.
Creative is no longer just a persuasion tool. I now treat it as a targeting signal.
Why broad targeting requires more intentional creative
I still see many advertisers create ads as if targeting will do all the audience qualification for them.
The messaging stays broad because the assumption is that audience settings will narrow who sees the ad. But when platforms expand delivery beyond tightly defined segments, vague creative can attract engagement from people who are unlikely to become qualified leads.
The consequences are familiar: lower lead quality, higher cost per qualified lead, less efficient optimization, and noisier conversion data.
That is why I need creative that clearly communicates who the offer is for, and just as importantly, who it is not for.
The goal is not simply more clicks or more video views. The goal is engagement from the right people.
When my creative clearly identifies the audience, users can self-select. Qualified prospects lean in. Unqualified prospects move on. Both outcomes improve campaign performance and give machine learning systems cleaner signals.
Higher education: When creative becomes the targeting layer
Higher education is one area where I see this shift clearly.
Historically, campaigns relied heavily on demographic filters, education interests, degree status, and segmented audience lists to reach prospective students.
Today, many strong-performing campaigns use broad lookalike audiences, Advantage+ audiences, or broad prospecting structures designed to maximize audience size and algorithmic learning.
But broader audiences create a real challenge.
If I am promoting an online Master of Science in Data Analytics program, I do not need just any prospective student. I need prospects who meet specific admission and career criteria. They may already hold a bachelor’s degree. They may have professional experience. They may want to move into leadership or pivot into a more technical career path.
Rather than relying only on targeting settings to communicate those distinctions, I would build them directly into the creative.
Consider the difference between a generic headline like “Advance your career with a Data Analytics degree” and a qualifying headline like “Built for bachelor’s degree holders ready to advance into leadership – earn your online M.S. in Data Analytics.”
The second example immediately signals who the program is for. Undergraduate prospects are less likely to engage, while qualified graduate prospects are more likely to click, convert, and reinforce positive optimization signals.
In that case, the creative itself becomes the qualification mechanism.
Google Performance Max: Creative guides the algorithm
Google Performance Max may be the clearest example of this industry-wide shift.
Despite the name, audience signals are not strict targeting controls. I treat them as starting points that help Google’s systems learn. Ultimately, Google decides where and to whom ads are shown across Search, YouTube, Display, Discover, Gmail, and Maps.
Because I have less direct control over audience selection, creative assets become increasingly important in helping Google’s systems understand who should respond.
Imagine I am helping a healthcare provider promote orthopedic services. A generic headline might say, “Expert Care for Your Health Needs.” While that may be technically accurate, it gives very little context about the intended audience.
A stronger alternative would be, “Persistent Knee Pain? Meet with Our Orthopedic Specialists.”
That second headline identifies a specific need, a specific audience, and a specific solution. Users immediately know whether the message applies to them, and Google’s systems receive stronger engagement signals from people actively experiencing that problem.
The same principle applies across insurance, legal services, financial services, and education.
When my Performance Max creative clearly identifies the audience and their need state, I help Google’s machine learning systems learn faster and optimize toward more qualified outcomes.
TikTok: The first three seconds matter more than ever
TikTok has always relied heavily on content signals to determine who sees a video.
As the platform continues investing in automation and audience expansion, creative becomes even more critical.
I pay close attention to the opening seconds of a video because they often determine not only whether a user keeps watching, but also how TikTok categorizes and distributes the content.
For lead generation campaigns, I want qualification to begin immediately.
A graduate program might open with, “Already have a bachelor’s degree and looking for your next career move?”
An insurance provider might start with, “Shopping for Medicare coverage this year?”
A law firm specializing in workplace injury cases could lead with, “Were you injured on the job within the last 12 months?”
These openings accomplish two things at once.
First, they quickly tell viewers whether the content is relevant to them. Second, they give TikTok’s algorithm stronger behavioral signals about who engages with the video.
Qualified prospects are more likely to continue watching and take action. Unqualified viewers are more likely to scroll past. Over time, that self-selection process improves audience learning.
Creative is now a performance lever
One of the biggest mistakes I can make today is treating creative as something that happens after strategy and targeting are finalized.
In increasingly automated advertising environments, creative is strategy.
The message, visuals, hooks, and calls to action no longer serve only a branding or conversion role. They help platforms determine who should see the ad in the first place.
That means I need creative and media teams working together more closely than ever.
When I build campaigns, I ask whether the creative clearly identifies who the offer is for, whether it communicates relevant qualifications or prerequisites, whether an unqualified prospect would immediately recognize that the message is not intended for them, and whether I am helping both users and algorithms understand the ideal audience.
If the answer is no, the campaign may be relying too heavily on targeting to solve a problem that creative is now better positioned to address.
The future of qualification is creative
As Google, Meta, and TikTok continue expanding AI-driven targeting, I expect advertisers to have even less control over audience selection than they do today.
Qualification does not disappear. It shifts into the creative itself.
What once happened primarily through audience settings is increasingly happening through messaging, visuals, and creative strategy.
To thrive in this environment, I need to write headlines that identify the intended audience, create videos that establish audience fit in the first few seconds, and build qualifications, prerequisites, and intent signals directly into the message.
Every ad speaks to two audiences at once: the user and the algorithm.
Platforms are handling more targeting than ever, but they still need direction.
Increasingly, that direction comes from creative. In a world of broad targeting, creative is not just the message. It is the qualifier.
I have seen traditional competitor campaigns turn into expensive click traps. When someone searches for a competitor’s brand, they are often already close to buying, which means my ad can become little more than a brief detour on their way to converting somewhere else.
That does not mean I have to give up on competitor-aware audiences. Instead of relying only on competitor brand bidding, I can use Demand Gen campaigns and negative-intent keywords to reach those buyers more efficiently, often at a lower cost.
Demand Gen: Reaching the right audience for less
Before I focus on negative-intent keywords, I like to look at Demand Gen because it gives me another way to reach people who may not know my brand yet but are already showing signs of interest in my market.
For Demand Gen to work well, I need two things: strong targeting and strong creative. Within that targeting, custom audience segments and lookalike audiences are essential.
Custom segment targeting lets me reach people who have searched for specific terms on Google or who show certain interests and purchase intentions. It is also one of the most practical ways I can get in front of users researching my competitors without paying the higher price of a search click.
When I create a new audience inside a Demand Gen campaign, custom segments are one of the first targeting options I see, right after the audience name.
From there, I choose the option for People who searched for any of these terms on Google and add as many relevant competitors as I can. This helps me reach a highly relevant audience across Google’s inventory at a lower cost than a traditional search network click.
If I am not sure which competitors to include, I start by typing my main product or service into Google Ads and reviewing who appears. Those businesses are usually my primary competitors, and depending on the networks I opt into, my ads can appear across YouTube, Discover, and Gmail.
Designing conquesting landing pages for Demand Gen
When I use Demand Gen for conquesting, I need a landing page built specifically for that audience. I want to highlight my key differentiators, show social proof, and make it obvious why my product or service deserves consideration.
The click is only the first step. Once someone lands on my page, the offer has to be clear, specific, and aligned with the ad they just clicked. I need to explain the value thoroughly and guide the visitor toward a call to action that matches the promise I made in the ad.
But Demand Gen is not always the right starting point. If I do not have strong image or video assets, I may be better off staying closer to the search network.
Because high-quality creative tends to perform best across Demand Gen placements, search can make more sense when those assets are not available. That is where negative-intent conquesting becomes useful.
Most advertisers understand traditional competitor search campaigns, but many overlook the people who are not simply searching for a competitor. They are searching for alternatives, comparisons, cheaper options, or signs that another company can solve the problem better.
I often see this happen during the consideration phase. A user may search for terms like “companies like X,” “companies cheaper than X,” or, for branded products, “dupe for X.” Not every variation will have enough volume to bid on, but these searches reveal where serious comparison research is happening.
Building campaigns around competitor pain points
If I know a competitor has a reputation for poor customer service, I might test keywords such as “customer service complaints for [competitor].” I would keep this focused in a single ad group with closely related keyword variations.
In the ad copy, I would focus on what makes my customer service stronger, faster, or more helpful. Because of trademark policies, I would avoid naming the competitor directly in the ad text and instead emphasize the benefit I can prove.
Traditional competitor campaigns focus on bidding against a brand name. Negative-intent conquesting focuses on the weakness behind the search. The audience already knows the competitor, but they are actively looking for a better option.
I can also pair this approach with a separate custom audience, which lets me reach people searching for these alternatives across Google’s networks.
For this to work after the click, the landing page matters just as much as the keyword and ad. If my ad promises a better solution to poor service, high prices, or another competitor weakness, the landing page has to validate that claim and present a unique value proposition that directly addresses the concern.
Target competitor audiences before the decision is made
The biggest challenge with traditional competitor campaigns is not always the competitor. It is timing.
When someone searches for a competitor’s brand name, they may have already narrowed their options and moved close to a decision. That is why competitor keyword campaigns can become expensive and hard to scale profitably.
Demand Gen and negative-intent conquesting help me approach the same audience from different angles. Demand Gen lets me reach potential customers before they commit to a brand, while negative-intent conquesting reaches them when they are actively questioning their current options.
My goal is simple: I want to reach potential customers when they are most open to considering a different choice. If I can do that with the right targeting, message, and landing page, competitor traffic becomes much easier to win without overspending on traditional brand bidding.
As I explore the potential of Performance Max for acquiring new customers, I realize that without proper setup, it’s easy to see inflated dashboard metrics that obscure the reality of your profitability.
One major pitfall is recycling traffic from Meta. Paid search and social traffic often overlap, leading to the dreaded scenario where platforms each claim credit for conversions they didn’t fully drive.
Many direct-to-consumer (DTC) brands I talk to boast about their growing numbers. But upon deeper inspection, it’s clear that those ‘new’ customers frequently originate from existing brand efforts, shared between different ad platforms.
These overlapping sales, while still revenue, can be deceiving. Their true cost is higher than often reported, eroding actual profit without proper intervention.
Rather than limiting yourself to one ad channel, utilizing an effective system to measure genuine customer acquisition is key.
Using brand and audience exclusions along with Customer Match data, I have developed a four-step framework to target genuine new customers through Performance Max, minimizing overlap across platforms.
Steps like excluding specific audiences and leveraging first-party data can help Performance Max focus on new customers instead of warm leads.
By refining these strategies, we’re optimizing how our ad spend contributes to true customer acquisition and enhancing overall profitability.
Starting in August 2026, Google will begin to automatically categorize customer types in conversion-based lists, removing some of the control we advertisers once had. I must now provide Google’s systems with clearer signals on where audiences are in their customer journey.
As someone deeply involved in advertising, I know the importance of precise audience targeting. With these changes, I’m urged to review and update my classifications in the Google Audience Manager before they kick in.
What’s Changing? From August 2026, Google Ads will automatically classify customer lists into categories like:
Existing customers
New customers
Other customer segments
Why Google’s Making This Shift. It appears that Google aims to enhance audience consistency across its tools for customer acquisition and retention. This standardization allows for better optimization decisions in Google’s automated bidding and targeting systems by clearly defining prospecting from retention audiences.
Why This Matters to Us. As an advertiser utilizing customer acquisition strategies, the precise classification of these lists is crucial. Any misclassification could impact Google’s optimization of users throughout their lifecycle, affecting campaign performance.
What We Should Do. It’s vital for us to audit our Customer Match lists—based on conversion data—before August. Consider these questions:
Are my customer lists categorized correctly?
Do they represent existing customers versus acquisition targets?
Will Google’s automatic classification align with my internal definitions?
Reviewing these settings now could prevent unexpected changes when Google enforces these classifications.
The Bottom Line. Google is taking an active role in managing audiences, further streamlining the signals powering their automated advertising systems by assigning lifecycle labels to conversion-based lists.
First Spotted. This update was noticed by Google Ads expert Bia Camargo, who shared the alert on LinkedIn.
I recently discovered some exciting news from Microsoft Ads that could be a game-changer for advertisers like myself. They’ve expanded their LinkedIn targeting capabilities to include job seniority filters. This allows me to target audiences with more precision in both Search and Audience campaigns.
This new feature means that I can now target users based on their job seniority, a wonderful addition for those of us focusing on B2B marketing. Thanks to LinkedIn data, I can reach audiences at various levels of seniority.
What’s the scoop? According to Navah Hopkins, Microsoft Advertising has added job seniority targeting to its LinkedIn Profile targeting, allowing me to utilize it within Search and Audience campaigns.
This update provides me the ability to choose from 10 different seniority levels, ranging from CXO to Volunteer. This flexibility is available at both the campaign and ad group levels, making it easier to segment my audiences effectively.
Why is this vital for us? In the world of B2B marketing, it’s often challenging to separate decision-makers from operational staff in search campaigns. With this new job seniority targeting, I can better align my messaging and bidding strategies with the right audience segments, ultimately improving my campaign performance.
Understanding who is interacting with my ads is crucial, especially in organizations with long sales cycles or multiple stakeholders. It’s not just about conversions; it’s about knowing who is behind them.
A closer look: Unlike other platforms, Microsoft’s integration with LinkedIn provides a unique perspective of professional identity that allows me to better understand user interactions.
Not only can I apply these filters directly within my campaign settings, but I can also utilize them in observation mode to gather insights without limiting my reach.
How do I benefit?
Customize messaging by seniority: I can create targeted ad groups for different audience levels, like executives or individual contributors, tailoring my messaging to each group’s expectations.
An executive-focused strategy might highlight business growth, while campaigns targeting practitioners could focus on efficiency gains.
Analyze conversions by seniority: Observation mode helps me assess conversion performance across different seniority levels, answering questions crucial to my strategy:
Where are my conversions coming from? Are they decision-makers or influencers? Is my budget effectively spent? Which seniority levels bring in high-quality leads?
Enhance audience testing: This feature offers an extra layer of reporting, helping me make informed optimization and expansion decisions. If I’m importing from other platforms, this insight is invaluable for discovering performance patterns unique to Microsoft Ads.
Availability: This powerful tool is now accessible in select markets across the Americas, EMEA, and APAC regions, including countries like the United States, Canada, Brazil, and more.
Americas: Argentina, Brazil, Canada, Chile, Colombia, and others.
EMEA: Egypt, Nigeria, Saudi Arabia, and South Africa.
APAC: Australia, India, Japan, among others.
The takeaway: Microsoft Ads continues to leverage its LinkedIn integration as a standout feature in B2B advertising. By aligning search intent with professional profiles, I gain deeper insights into not just what my audiences search for, but who the searchers are.
I’ve been following Microsoft’s latest moves in the advertising world closely, and there’s some exciting news for those in the crypto space. Microsoft is now offering more premium ad inventory to cryptocurrency advertisers, all while ensuring compliance with existing requirements.
Now, if you’re in the crypto exchange business, this move could open up new doors for you. Essentially, Microsoft has decided to expand the reach of Audience Ads for cryptocurrency exchanges in markets where crypto advertising is already allowed, which could mean greater visibility and reach.
The big picture: This update is about more than just new ad spots. It’s about giving eligible exchanges a shot at being seen across a wider network via Microsoft’s Audience Ads inventory, moving beyond the confines of traditional search placements.
You might be wondering what exactly is changing. Well, Microsoft’s ad policies have been updated, and now cryptocurrency exchanges that meet the necessary compliance checks can use Audience Ads across all approved crypto advertising markets.
The catch? This expansion is strictly for those advertisers who adhere to Microsoft’s Cryptocurrency and Related Products policies, along with any local laws and regulations that might apply.
Why we care. In the world of advertising, Audience Ads provide a valuable opportunity. They let me, as an advertiser, reach users effectively across Microsoft’s native advertising network. This includes placements on various content, news, and partner sites, providing a broader canvas to engage with potential customers.
For those of us in the cryptocurrency exchange field, this means a chance to boost awareness and connect with potential users beyond the intentions guided by search. It’s an opportunity to deeply engage and build relationships.
The fine print. Though this sounds promising, Microsoft hasn’t relaxed its stringent requirements for cryptocurrency advertising. Advertisers still need to meet all eligibility criteria, sticking to Microsoft’s policies for Cryptocurrency and Related Products, which vary depending on the market and regulatory landscape.
What to watch. I’ll be keeping a close eye on how this expanded inventory is adopted by cryptocurrency exchanges. Will this lead to more widespread use of Audience Ads? Also, I’ll be curious to see if Microsoft will eventually broaden its crypto advertising reach into additional markets. Stay tuned!
