I’m seeing Google Ads roll out a redesigned All Campaigns selector, and the goal is clear: make it easier to move through large, complicated account structures without wasting time hunting for the right campaign.
What’s happening is that Google is refreshing the All Campaigns selector across Google Ads with a cleaner layout and better navigation tools. For advertisers who manage bigger accounts, this should make day-to-day campaign work feel more organized.
The selector has also been moved to a new location in the interface, which means I’d expect some advertisers to need a short adjustment period before the new placement feels familiar.
The biggest improvement I notice is the new expandable hierarchy view. Campaigns now appear in a structure that makes campaign groups and nested setups easier to browse, especially when an account has grown beyond a simple list of campaigns.
Google has also added search inside the selector, which should help advertisers quickly find specific campaigns or campaign groups instead of manually scanning through long account lists.
Why I care: this update could save meaningful time for anyone managing large Google Ads accounts. When campaigns are split across multiple groups or complex organisational structures, faster navigation can make daily optimization work less frustrating.
The bottom line is that Google’s redesigned All Campaigns selector is meant to streamline campaign management with a clearer hierarchy and built-in search, helping advertisers navigate complex accounts more efficiently.
The update was first spotted by performance marketer Vivek Gupta on LinkedIn. Since the rollout is gradual, I would not expect it to be available in every Google Ads account immediately.
I’m seeing Microsoft bring experimentation into Performance Max campaigns, giving advertisers a more practical way to test campaign changes and measure incremental impact without disrupting live performance.
What’s new: Microsoft is adding two Performance Max experiment types designed to help advertisers understand whether their campaigns are truly driving better results.
Uplift experiments help me measure the incremental impact of Performance Max campaigns by comparing results against a control group.
Upgrade experiments give me a way to compare an existing campaign with an upgraded Performance Max version before I fully roll out the change.
For eligible accounts, both experiment types are available under Campaigns > Experiments.
Why I care: Until now, Microsoft Ads experiments were limited to Search campaigns. Bringing testing into Performance Max gives advertisers a safer path to validate changes, improve performance, and make more data-driven decisions before committing budget.
Between the lines: As Microsoft expands experimentation, it has also renamed its existing experiment offering to Search optimization experiments. That distinction helps separate traditional Search testing from the new Performance Max testing capabilities.
I see this as part of Microsoft’s broader push to give advertisers more advanced optimization tools across automated campaign formats.
The bottom line: Microsoft is closing an important gap in its Performance Max offering. With dedicated uplift and upgrade experiments, advertisers can test with more confidence and get a clearer view of the real impact of automated campaigns.
First spotted: The help docs were spotted by PPC News Feed founder Hana Kobzová.
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 know competitive brand bidding is now a common PPC tactic, but that does not mean I treat it as harmless background noise. When competitors, affiliates, coupon sites, or misleading advertisers show up on branded searches, they can inflate CPCs, divert high-intent traffic, and confuse people who were already looking for my brand.
I have seen how much difference visibility can make. Industry examples show that brands often uncover meaningful CPC inflation once they start tracking competitor bidding, affiliate activity, and trademark misuse. In documented cases, brands reduced branded CPCs by 25% to 75% after identifying infringing advertisers and enforcing their policies.
In this guide, I walk through how I monitor branded keywords, identify who is advertising on them, and decide what actions may be available based on the evidence I find.
Choosing Keywords So I Do Not Miss Hidden Activity
When I want to find out who is using my brand in search ads, I start by deciding which keywords I need to monitor.
The biggest mistake I try to avoid is watching only my exact brand name. That is a useful starting point, but it rarely shows the full picture. Some advertisers deliberately target brand-related coupon, discount, review, or alternative queries because those searches often come from high-intent users and attract less scrutiny.
For example, someone searching for “Brand coupon” or “Brand discount code” may be much closer to buying than someone searching for the brand alone. Those queries often attract coupon affiliates, loyalty sites, and unauthorized advertisers trying to intercept branded traffic.
I also pay attention to searches that include terms like “reviews” or “alternatives,” because those queries can bring in competitors and comparison sites that position themselves directly against my brand.
Misspellings matter too. Some advertisers target spelling variations because they are less likely to be monitored and may face less competition.
For a solid monitoring setup, I include my core brand name, “official page” and “login” variations, coupon and promo-code searches, review and alternative searches, commercial terms such as “buy,” “order,” and “sign up,” common misspellings, and localized versions of my brand name.
If I am using Bluepear, its built-in AI assistant can generate keyword suggestions from this kind of list and help me expand coverage faster.
The number of terms I monitor depends on the size of the brand portfolio, including trademarks, local branches, and product names. For many small to medium-sized brands, I would start with about 20 keywords and then expand as new risks, markets, and opportunities appear.
Choosing Locations and Monitoring Frequency
I do not rely on a single search from my office, on my device, at one moment in time. Search results are too dynamic for that. Two people searching the same branded keyword can see completely different ads and organic listings depending on their location, device, timing, and other variables.
I also assume that some advertisers may be trying to hide their activity. A fraudster or an affiliate violating my PPC policy might run ads outside normal business hours to reduce the chance of being caught. If I only check manually during the workday, I may never see those ads.
When I monitor branded search results, I look across the countries and markets where my brand operates, regional differences within those markets, mobile and desktop results, different times of day, and weekday versus weekend activity.
Frequency matters just as much as coverage. Some violations appear briefly and then disappear. Running checks multiple times throughout the day gives me a better chance of capturing activity that would otherwise go unnoticed.
Tracking all of these variables manually can become tedious, especially when a brand operates across multiple markets. Bluepear accounts for locations, devices, time zones, and redirects that can obscure the true destination of traffic. I can set the parameters once and gain continuous visibility without turning monitoring into a weekly time sink.
Reviewing Search Results and Recording Evidence
I do not assume every advertiser bidding on my branded keywords is breaking a rule. Competitors may be allowed to bid on branded keywords if they do not use my trademark in their ad copy. Affiliates may also be authorized to promote my brand under specific program conditions.
Still, I need to know when an advertiser’s behavior crosses the line from legitimate brand bidding into trademark misuse, policy violations, or customer deception.
The first signal I investigate is trademark use in ad copy. If the ad mentions my brand name in the headline or description, and my trademark rules or affiliate policies restrict that use, I treat it as a possible compliance issue.
I also look for misleading claims. Phrases that imply the advertiser is “official,” references to exclusive offers, or language that suggests authorization when none exists can confuse users and deserve review.
Coupon and discount promotions need special attention. I verify whether the advertised discount, promo code, or offer is legitimate, because some affiliates use expired, misleading, or fabricated offers to win clicks.
I also watch for impersonation signals. Some ads and landing pages are designed to resemble a brand’s official website. Even if the advertiser does not directly claim to be my company, that kind of presentation can still confuse users and divert branded traffic.
Because advertisers can change ad copy, pause campaigns, or remove landing pages at any time, I collect evidence quickly. I record the ad copy, SERP position, triggering keyword, location, URLs, redirects, landing page content, and timestamps.
Bluepear can handle this automatically by compiling a report with the relevant details, which makes follow-up easier when I need to contact an affiliate, review a competitor’s behavior, or escalate a trademark issue.
Identifying Who Is Behind the Activity
Sometimes I cannot immediately tell whether an advertiser is a competitor, an affiliate, a coupon site, or something riskier. Branded search results often include multiple participants with different motivations, so I need to understand who I am dealing with before I decide what to do next.
I look for patterns. A direct competitor domain usually points to competitor bidding. A coupon or cashback page may indicate an affiliate, coupon site, or loyalty site. Affiliate network tracking links often suggest affiliate activity, although they can also appear in more questionable setups. Product comparison pages often point to competitors or comparison publishers.
Other signals raise the risk level. If an ad uses my trademark, claims to be “official,” sends users through multiple redirects, promotes coupon codes I cannot verify, or lands on a page that imitates my brand’s design or messaging, I investigate more carefully.
No single signal gives me a definitive answer. I combine multiple pieces of evidence before drawing conclusions. Once I know who is advertising on my brand terms, I can move beyond detection and decide whether their activity aligns with my policies and business goals.
What I Do Next
After I identify who is advertising on my brand terms and review their ads, the next step is choosing the right response.
Competitor Brand Bidding
Not every competitor bidding on my branded keywords requires immediate intervention. Before acting, I ask how often the competitor appears, which keywords they are targeting, whether they are using trademarked terms in ad copy, and whether they are sending users to comparison content or direct offers.
In many cases, I monitor the activity and evaluate its business impact over time. Documenting patterns helps me establish a baseline, which can support future compliance reviews or legal conversations if escalation becomes necessary.
Affiliate Violations
If an affiliate is bidding on restricted branded keywords or violating program rules, I gather evidence and contact the affiliate or network. My workflow is straightforward: document the violation, verify the affiliate ID, share the evidence, request removal or corrective action, and apply program enforcement measures if needed.
Screenshots, timestamps, and redirect data make those conversations much easier because I can show exactly what happened, where it happened, and when it was detected.
Trademark Misuse
Trademark-related issues require careful review. I look for unauthorized trademark use in ad copy, ads that create confusion about brand affiliation, impersonation attempts, and misleading claims that the advertiser is an official brand representative, partner, or reseller.
The right response depends on the circumstances, internal policies, and applicable laws. In many jurisdictions, competitors are generally allowed to bid on trademarked keywords. However, ads that confuse users about the advertiser’s relationship with my brand may raise trademark or unfair competition concerns, depending on the facts and local law.
The advertising platform’s policies matter too. Google allows advertisers to bid on trademarked keywords, but it may restrict trademark use in ad text when a valid trademark complaint is submitted. Google also prohibits ads that use trademarks in a confusing, deceptive, or misleading way.
Before I take action, I collect as much evidence as possible, including screenshots, detection timestamps, URLs, redirects, and landing page content. Once the facts are documented, I may contact the advertiser directly, submit a trademark complaint to the advertising platform, send a cease and desist letter, or escalate through legal channels if necessary.
Why I Keep Monitoring Brand Search
The main lesson is that branded search protection is not a one-time audit. Affiliates can activate and pause campaigns throughout the month. Some violations appear only on weekends, outside business hours, or in specific markets. An advertiser that disappears today may return next week with new ad copy, a new domain, or a different affiliate account.
That is why I treat brand protection as an ongoing process. Occasional searches are not enough. I need consistent monitoring and a repeatable investigation workflow that shows who is appearing on my brand terms, how they operate, and whether action is warranted.
If I want easier visibility into my branded search landscape, Bluepear helps identify issues earlier, respond faster, and make more informed decisions about protecting traffic and advertising investments.
For the past two years, I have heard marketers ask the same urgent question: How do I show up in AI search?
I have seen plenty of conversation around AI optimization, visibility, and the way large language models decide which businesses to recommend. But I believe the more practical question is now becoming harder to ignore: How do I measure whether AI search is actually sending customers my way?
That is the challenge I wanted to understand more clearly.
After analyzing nearly 30 million inbound leads, I found that AI platforms are already shaping how customers discover businesses and decide to make contact. AI-generated leads still represent a small share of total volume, but they are growing steadily enough that I think marketers should start watching this channel closely.
In other words, the conversation is moving from visibility to measurement.
AI search is becoming a new attribution challenge
Traditional attribution models were built for channels like organic search, paid search, direct traffic, and referrals. AI search introduces a different discovery path, and I do not think most reporting systems are fully prepared for it yet.
A customer might ask ChatGPT for the best local HVAC company, use Perplexity to compare law firms, or ask Gemini to recommend a nearby dentist before picking up the phone.
From a marketer’s perspective, those customers may show up as direct traffic, or they may not be attributed at all. That creates a real blind spot.
If AI platforms are influencing customer discovery, I need a way to measure whether those recommendations are turning into real business outcomes.
What 30 million leads tell me
The data shows me that AI platforms are already generating measurable inbound leads for businesses. It also shows that this activity is growing over time and appearing across multiple industries, not just one category or use case.
One platform currently accounts for most AI-attributed calls, while other platforms contribute smaller shares that continue to change as customer behavior evolves. The data also reveals which industries are receiving more AI-driven calls than others.
At the same time, I have to be clear about what this dataset can and cannot measure. It does not explain why customers chose one AI platform over another, what prompts they used, or why a specific business was recommended. What it does measure is more concrete: when customers identify an AI platform as part of the journey that led them to contact a business.
That distinction matters. There is no shortage of opinion about AI search. What I need now is evidence that it is influencing customer acquisition.
Measurement should come before optimization
I understand why marketers are eager to optimize for AI search. But before investing in new tactics, I think it is worth answering a simpler question first: Is AI already driving customers to my business?
Without measurement, it is difficult to know whether greater visibility is translating into meaningful business results.
As AI search becomes another customer acquisition channel, I want to measure it the same way I measure other demand sources, including paid search, organic search, referrals, and social.
The goal is not to replace existing attribution models. The goal is to make sure those models evolve as customer behavior changes.
From visibility to measurement
The first wave of AI search focused on visibility. I believe the next wave will focus on proving business impact.
For marketers, that means moving beyond questions like, “Can customers find us?” and toward more outcome-focused questions like, “How many leads did AI actually generate?”
The businesses that answer those questions first will be better positioned to understand how AI fits into their marketing mix and where to invest as customer discovery continues to evolve.
Don’t just watch the shift. Start measuring it.
As AI search keeps evolving, I am focused on giving marketers the attribution they need to connect AI discovery with real customer conversations.
I see Google rolling out new Agency Admin and Standard roles in Merchant Center for Agencies, giving agencies a more centralized way to control client access while improving security and day-to-day efficiency.
What is new: I can now look at client access differently because clients are linked directly to an agency instead of being tied to individual users. That makes it easier to manage permissions from one place, especially when team members join, move roles, or leave.
I also see custom labels becoming a useful part of this update. Agency Admins can organize client accounts by brand, business vertical, internal team, or another structure that fits how the agency works.
Those labels can then be used to give Standard users access to groups of accounts in bulk. For me, that is the practical improvement: agencies no longer need to configure access one account at a time when the same permission logic applies across multiple clients.
Why I care: Agencies managing several Merchant Center accounts have often had to depend on user-level permissions, which can make onboarding, offboarding, and account management more cumbersome than they need to be. This role-based structure moves client management to the agency level, which should reduce administrative work and strengthen access controls.
How it works: Agency Admins get full administrative privileges inside the Merchant Center agency account. In that role, I can link and unlink clients’ Merchant Center accounts, add or remove Standard users, modify Standard users, manage their access to client accounts, and create custom labels for organizing clients.
Standard users receive more limited permissions, which helps agencies follow stronger security practices. I see this as a way to make sure team members only access the client accounts they actually need.
Bottom line: For agencies managing large client portfolios, I expect centralized client linking, bulk access management, and customizable account labels to reduce manual work while making Merchant Center administration more secure and scalable.
I’m looking at Google Ads API v24.2 as a practical update for advertisers and developers, especially because it brings together stronger security controls, AI transparency features, better reporting and new experiment options in one release.
What’s new. The biggest security addition I see is support for multi-party approvals, or MPA. This requires a second administrator to approve sensitive account actions, including user invitations and access-level changes, which gives agencies and larger organizations another layer of protection when managing Google Ads accounts.
I’m also watching Google’s expanded support for AI-generated content disclosures. The API now exposes new SyntheticContentInfo and SyntheticContentAttestation fields on assets and ads, so developers can identify and label AI-generated creative programmatically. This is especially relevant for advertisers preparing for the EU AI Act, which takes effect on August 2nd.
Developers can start building integrations now, although I’d note that advertiser attestation fields will remain read-only until v25 launches.
Performance Max gets more visibility. I see one of the most useful changes in version 24.2 as the added visibility for Performance Max campaigns. Advertisers can now segment performance_max_placement_view reports by ad_network_type, making it easier to understand where ads are appearing across Search, Display and partner networks.
The release also adds YouTube brand channel linking through the API, which should make video campaign integrations stronger. I’m also noting the new landing page text generation option, which can automatically create text assets from a website’s landing page.
New testing capabilities. Google is expanding experimentation tools with two new experiment types, and I see both as useful for advertisers who want more structured ways to compare campaign changes.
The new COMPARE_CAMPAIGNS workflow lets advertisers compare multiple campaigns or campaign types across as many as five experiment arms, including custom Performance Max experiments.
A second experiment type lets advertisers test text customization and final URL expansion inside a single Performance Max campaign by splitting traffic between variations.
Documentation improvements. I also appreciate that Google has reorganized its API release notes by separating breaking changes from feature updates. It has also introduced a dedicated guide for feature deprecations and unversioned changes, which should make future upgrades easier to manage.
Why I care. This release may not be a dramatic overhaul, but I see it as a meaningful step for teams that need to prepare for AI disclosure requirements, tighten account security and get more useful Performance Max reporting.
I am seeing OpenAI point to early momentum in its advertising business, with executives saying ChatGPT users are dismissing ads less often and engaging with them more. For me, that makes ad dismissal a key signal to watch as OpenAI looks for revenue beyond subscriptions and enterprise AI.
What is happening. OpenAI says ChatGPT ad dismissals have dropped by 50% since the company launched its advertising business in February. I read that decline as OpenAI’s way of showing that its ads are becoming more relevant, because the company treats dismissals as a proxy for whether users find an ad useful or intrusive.
The update came from OpenAI Chief Revenue Officer Denise Dresser, who framed relevance as a central focus for the company as it builds advertising into ChatGPT.
Why I care. If users are becoming more open to ads inside ChatGPT, I see conversational AI becoming a more serious advertising channel. A 50% drop in dismissals suggests better relevance and stronger engagement, which could give brands a way to reach people during high-intent, task-focused moments instead of relying only on interruptive ad formats.
Why relevance matters. I think ads inside AI experiences face a much higher bar than traditional display ads. People usually come to ChatGPT to complete a task, answer a question, compare options or solve a problem, so an ad that feels disconnected can quickly create friction and damage trust.
According to Dresser, OpenAI has been focused on making the format useful. “This form factor is about usefulness,” she said. “That’s great for the consumer, great for the user.”
The bigger picture. I see these results as an early look at how advertising may evolve inside generative AI platforms. Instead of interrupting content consumption, AI-powered advertising is moving toward recommendations that fit the user’s intent and the conversation already underway.
That shift means success may depend less on grabbing attention and more on being genuinely helpful. The lower dismissal rate suggests OpenAI is making progress toward that goal, even if the ad model is still early.
Competition extends beyond advertising. I also see this update in the context of OpenAI expanding its business on multiple fronts. While it builds an ads business, the company is also competing for enterprise AI spending against rivals such as Anthropic.
I’ve learned that generating demand is one of the hardest jobs in digital marketing. Measuring where that demand actually started can be even harder.
For years, I’ve seen paid search and paid social treated like separate worlds. Paid search usually gets evaluated through clicks, conversions, and ROAS, while paid social is often judged by platform-reported metrics and attributed conversions.
The challenge is that people don’t move through the buying journey in neat, channel-by-channel steps.
Someone might first discover a brand through a Meta ad, ignore it, see another ad a few days later, and eventually search for the brand or product on Google before adding something to the cart and converting. In most reports, paid search gets the credit because it captured the last click. But I don’t think that tells the full story if search didn’t create the demand in the first place.
As privacy rules, platform tracking, and attribution limits keep changing, I need better ways to understand how paid social influences search behavior. These are the practical signals and measurement methods I use to connect the two.
Signs I Look For When Paid Social Influences Search
Paid social’s impact on search is not always obvious inside attribution reports. I usually see it show up first in performance trends. These indicators help me understand whether social campaigns are building awareness that later turns into search activity and conversions.
Branded Search Volume Starts Rising
One of the clearest signs I watch for is an increase in branded search queries.
When people see a relevant, compelling social ad on Meta, TikTok, LinkedIn, or another platform, they often do not click right away. Instead, they may come back later and search for the brand name, product name, founder, or another branded term.
For example, after launching a new Meta Ads campaign, I might look for increases in searches like these:
Brand name.
Brand + product category.
Brand + reviews.
Brand + pricing.
Brand + competitor comparisons.
I monitor these branded searches over time because they can reveal whether paid social is creating awareness that later becomes search behavior.
To do that, I review data from Google Ads, Microsoft Advertising, Google Analytics, Google Search Console, Google Trends, and any third-party SEO tools available.
I also compare trends before, during, and after major paid social launches or budget changes. If branded search volume keeps rising as paid social investment increases, I take that as a strong directional sign that social is helping generate demand.
That does not mean every increase in branded search comes from paid social. My goal is not to prove perfect causation. My goal is to find a meaningful relationship I can use to make better decisions.
I also account for other factors that can lift branded search volume, including:
Influencer partnerships.
Email campaigns.
Public relations coverage.
Seasonal demand.
Product launches.
Highly engaging organic social activity.
Search CTR Improves
Another signal I watch closely is click-through rate. If paid social is increasing brand familiarity, people may be more likely to click a search ad from that brand instead of choosing a competitor.
For example, someone might see Instagram video ads for two weeks and later search for a related topic on Google. When several ads appear, they may be more inclined to click the brand they already recognize.
I see the same concept reflected in brand recognition surveys that Meta and LinkedIn sometimes show in user feeds. I often find myself recognizing brands I have never purchased from simply because I have seen their ads repeatedly on social media.
That basic familiarity can still matter. It can help lift CTR on branded search campaigns, improve CTR on non-branded campaigns, and potentially lower CPCs over time.
Whenever I launch a new paid social campaign or make a significant adjustment, I compare paid search CTR before and after the change to see whether search engagement improves.
Search Conversion Rates Improve
Brand familiarity can also affect conversion rates. When people have already seen or engaged with a brand, they may arrive on the website with more trust and confidence than a completely cold visitor.
Because of that, I look for improvements in search conversion rate, lead quality, search CPA, and revenue per visitor after periods of strong paid social activity. This effect can be especially noticeable for products or services with longer consideration cycles and multiple touchpoints before purchase.
For me, conversion efficiency is one of the most useful signs that paid social is influencing downstream search behavior.
How I Validate Paid Social’s Impact on Search
The signals above give me directional insight. When I need stronger evidence, I use more structured measurement methods to evaluate whether paid social activity is actually influencing paid search performance.
Pre- and Post-Campaign Analysis
One of the simplest ways I evaluate the relationship is with a pre- and post-campaign analysis.
Before a paid social campaign launches, I benchmark key paid search metrics. Then I compare those numbers with performance after the campaign goes live.
The metrics I usually measure include:
Branded search impressions.
Branded search clicks.
Search CTR.
Search CVR.
CPA.
Total search conversions.
This analysis will not prove causation on its own, but it can show whether increased social activity may be influencing search performance. When I run this type of analysis, I account for seasonality, compare similar time periods, and watch for changes in competitor activity.
Geotargeted Holdout Testing
When I need stronger evidence, I consider a geotargeted holdout test. In this setup, I run paid social in selected geographic markets while withholding it from comparable control markets. Then I compare paid search performance across both groups.
For example, instead of running paid social everywhere, a nationwide advertiser could split markets into two groups:
Test market(s): Paid social campaigns are active.
Control market(s): Paid social campaigns are paused or excluded.
I would run the test for several weeks and monitor the same core metrics in both groups:
Branded search volume.
Search CTR.
Search CVR.
Leads.
Revenue.
If the test markets show meaningfully stronger search performance than the control markets, I have a better basis for isolating the impact of paid social.
I like geotargeted tests because they reduce attribution bias. They let me evaluate business outcomes across similar populations instead of relying only on platform-reported conversions, which can be limited by privacy changes and tracking gaps.
If I run a holdout test, I choose comparable markets, set aside enough budget, and give the test enough time to produce statistically meaningful results. This approach usually works best for larger advertisers running regional or national campaigns. For smaller brands, I would usually start with pre- and post-campaign analysis.
Why I Measure Influence Across Channels
The relationship between paid search and paid social is often stronger than reporting platforms make it appear. I try not to evaluate these channels in isolation because they often play different roles in the same customer journey. Search captures demand, while paid social can help create it.
By digging into the data, I can find better ways to invest, build future demand, and drive conversions across platforms. Monitoring branded search, CTR, conversion rates, and structured test results gives me a clearer view of how paid social contributes to business growth.
Attribution will never be perfect. But when I measure influence across channels, I can make smarter budget decisions and build a more accurate picture of what is actually driving performance.
I’m seeing an important shift for Standard Shopping campaigns: Google is bringing Maximize Conversion Value bidding to these campaigns without requiring a Target ROAS. That gives advertisers more room to pursue value-based optimization without immediately being locked into a specific return target.
What’s happening. Google is rolling out Maximize Conversion Value bidding for Standard Shopping campaigns, and advertisers no longer have to set a Target ROAS to use it.
Before this update, if I wanted to optimize around conversion value in Standard Shopping, I generally had to use a Target ROAS bidding strategy. Now, this new option lets campaigns focus on maximizing conversion value while giving Google’s bidding system more flexibility to find the highest-value opportunities.
Why I care. This matters because I can now use Google’s value-based bidding in Standard Shopping without being constrained by a Target ROAS goal. That gives me more flexibility while preserving the control and transparency that many advertisers still prefer in Standard Shopping campaigns.
It may also reduce the need to run feed-only Performance Max campaigns just to access Maximize Conversion Value bidding. For advertisers who prefer tighter campaign control, that is a meaningful change.
Between the lines. I know many advertisers have continued to favour Standard Shopping because it offers more visibility and control than Performance Max. But when they wanted flexible value-based bidding, they often created feed-only Performance Max campaigns as a workaround.
With this update, that workaround may no longer be necessary for some accounts.
Why advertisers should care. I can now combine the structure and transparency of Standard Shopping with a more flexible automated bidding strategy. In practical terms, this could simplify campaign setups, reduce unnecessary Performance Max usage, and make account management cleaner.
The bottom line. Google is narrowing one of the biggest feature gaps between Standard Shopping and Performance Max. For me, this gives advertisers another reason to keep using Standard Shopping while still benefiting from automated value-based bidding.
First spotted. Performance marketer Yash Mandlesha spotted the update and shared the option on LinkedIn.
