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.
I think every PPC professional has at least one mistake they wish they could erase. For Danny Gavin, founder of Optidge, it was not a failed bidding strategy, a blown budget, or a campaign that never found its footing. It was something much simpler, and in many ways, much more painful.
When Danny joined me on PPC Live The Podcast, he shared the story of a technical issue that kept landing page leads from reaching the client. For one to two months, the campaigns were still generating qualified prospects, but the client believed nothing was working because those enquiries never appeared in their inbox.
The mistake that no one spotted
At the time, Danny’s agency was still small, with only a handful of people managing client accounts. One client, an autism therapy provider, appeared to be getting strong results inside Google Ads.
Clicks were rising. Cost per lead looked healthy. From inside the ad platform, everything pointed to success.
But the client was growing more frustrated because no enquiries were coming through.
The problem was not Google Ads.
It was not the landing page.
It was the email notification system.
Every form submission was being stored correctly in the database, but a technical failure stopped the notification emails from reaching the client. Because neither side realized those emails had failed, the issue went unnoticed for weeks.
By the time the problem was found, dozens of leads had already gone cold.
Why the emotional impact was worse than the technical problem
What stood out to me was that the financial loss was not the part Danny remembered most. The harder part was the feeling that his agency had let the client down. Because he knew the client personally, the mistake felt even more personal.
His team had spent weeks reporting positive campaign performance while the client saw no return from their investment. That disconnect created guilt, regret, and a real sense of helplessness.
As Danny explained it, the agency felt as if it had taken the client’s money without delivering value, even though the campaigns themselves were actually working.
Honesty became the first step
Once the problem became clear, Danny did not try to hide it. His view is straightforward: when mistakes happen, honesty is the only response that gives you any chance of repairing trust.
Instead of making excuses, the agency investigated immediately, exported every lead stored in the database, and gave the client everything they could recover. Many of those opportunities had already gone cold, but at least the client had access to the data that still existed.
From there, the focus had to move from blame to prevention.
Building systems that stop the same mistake happening twice
That experience changed the agency’s processes in a lasting way.
Instead of relying on one notification email, Danny’s team introduced multiple safeguards:
CC’ing the agency on every lead notification.
Automatically logging every lead into a shared Google Sheet.
Testing forms regularly to confirm submissions and notifications both work.
Checking with clients routinely to confirm leads are actually being received.
Those checks are now part of the agency’s standard operating procedures. They are no longer assumptions about technology working in the background.
Why communication matters as much as optimisation
Looking back, Danny sees the technical failure as only part of the issue. Communication failed too. No one had asked the simple question: “Are you actually receiving the leads?”
Today, communication is one of Optidge’s core values.
Rather than expecting PPC specialists to manage constant client communication while also running campaigns, the agency brought in dedicated account managers whose primary role is to keep clients informed.
The lesson I took from this is simple: campaign metrics alone do not define success.
Success only happens when the client experiences the results you are reporting.
Sometimes clients remember how you responded
At first, the relationship with the client ended. Danny assumed the mistake had permanently damaged the trust they had built.
Years later, though, that same client reached out again about potentially working together. In her email, she described Optidge as the most professional agency she had worked with. For Danny, it was a reminder that clients do not forget mistakes, but they also remember how agencies respond to them.
Transparency, professionalism, and a genuine effort to improve can leave a stronger impression than perfection.
Common PPC mistakes Danny still sees today
Although this happened years ago, Danny still sees agencies making similar mistakes today.
One of the biggest is focusing only on traffic instead of business outcomes. Sending visitors to a page is no longer enough.
Strong lead generation requires understanding what happens after someone clicks.
When Danny audits accounts, he often finds agencies failing to:
Feed qualified lead data back into advertising platforms.
Review search terms thoroughly and maintain negative keywords.
Build landing pages that match campaign intent.
Measure lead quality instead of simply counting conversions.
Without those fundamentals, campaign optimisation is based on incomplete information.
Where AI is genuinely helping lead generation
Danny believes AI has real potential in lead generation, but not always in the way marketers expect.
One of the most useful opportunities is phone call analysis.
Instead of manually listening to every conversation, AI can now help agencies:
Generate call transcripts.
Categorise calls by quality.
Identify whether a call became a genuine sales opportunity.
Feed qualified conversion data back into Google Ads.
That makes it possible to optimise around real business outcomes instead of surface-level metrics.
Why AI still needs human oversight
Even though Danny is using AI, he does not treat it as an infallible system.
Like automation inside advertising platforms, AI can make mistakes, miss context, and confidently reach the wrong conclusion.
For industries with strict privacy requirements, such as healthcare, AI may not be appropriate for handling sensitive customer information at all.
His advice is to trust AI enough to improve efficiency, but always verify the work.
Human expertise still matters.
The biggest lesson
I do not think any PPC professional can avoid mistakes completely.
What defines a strong agency is how it responds when something goes wrong.
That means being honest, fixing the immediate problem, building safeguards, and making sure the same issue does not happen again.
As Danny puts it, a mistake only becomes valuable when you have genuinely learned from it.
I know LinkedIn Ads has a reputation for being expensive, and at first glance, the data backs that up. Across the client accounts I analyzed, LinkedIn’s average CPC was $11.12, compared with $5.45 on Google Ads.
But that simple comparison misses the more useful story. When I compare the cost of reaching new, high-intent B2B buyers, the gap gets much smaller. Non-branded Google Search campaigns averaged a $12.48 CPC, while comparable LinkedIn prospecting campaigns averaged $13.94.
To understand how LinkedIn CPCs really compare with Google Ads across campaign types and industries, I reviewed more than $700,000 in LinkedIn ad spend and compared it with CPC data from the same accounts on Google Ads.
What I included in this analysis
I focused on CPC and performance data from clients that had active campaigns on both LinkedIn Ads and Google Ads over the past year.
The main questions I wanted to answer were straightforward: What CPCs are we actually seeing? Do CPCs change by ad objective and industry? And how do those costs compare with Google Ads?
For LinkedIn Ads, I analyzed more than $700,000 in spend across 63,000+ clicks and 8.1 million impressions.
The clients fell into two main business categories: B2B SaaS, which represented approximately 97% of spend, and professional services.
I looked at LinkedIn CPCs by ad set objective and business category. For Google Ads, I pulled CPC data from the same client accounts across branded search, non-branded search, Demand Gen, and display campaigns.
Client names are withheld. The date range for this analysis was May 2025 through May 2026.
LinkedIn looks more expensive, but the comparison needs context
LinkedIn’s blended average CPC across all objectives was $11.12. Google’s blended average CPC across all campaign types was $5.45. On the surface, LinkedIn costs about twice as much per click.
There is an important caveat. In Google Ads, a large share of those lower-cost clicks came from display campaigns, which averaged $0.89 per click, and branded search, which averaged $1.71 per click. Both are naturally less expensive because display generally reaches lower-intent audiences, while branded search captures people already looking for your company.
When I narrow the comparison to the cost of reaching new, high-intent audiences, the difference becomes much less dramatic.
Google Ads non-branded search averaged a $12.48 CPC across the clients in this study.
LinkedIn prospecting campaigns, excluding retargeting and using lead generation, website conversion, or website visit objectives, averaged a $13.94 CPC.
I used those LinkedIn objectives because they most closely represent high-intent direct-response campaigns, which makes the comparison with non-branded search more useful.
When I compare the cost of reaching a new audience, LinkedIn is still more expensive, but it is not twice as expensive. In practical terms, I am looking at roughly $12 CPCs on Google and $14 CPCs on LinkedIn.
LinkedIn CPCs change a lot by objective
One of the clearest findings in this data set is how widely LinkedIn CPCs vary by campaign objective.
Website visits: $6.75
Brand awareness: $8.34
Website conversions: $4.84
Engagement: $4.45
Lead generation: $31.29
Video views: $71.43
Lead generation campaigns, where LinkedIn lead gen forms capture contact information directly inside the platform, cost nearly five times more per click than website visit campaigns.
That higher CPC can still make sense because these campaigns often convert at much higher rates than ads that send people to a website or landing page.
Here is the full breakdown of CPCs by campaign objective:
The number that jumps out most is video views. CPCs for those campaigns look extremely high, but cost per view is the more relevant metric there, so CPC alone can be misleading.
If I were planning a LinkedIn campaign focused on click volume or site traffic, I would budget for CPCs in the $6-$8 range. For lead gen ads, which in my experience often produce stronger conversion rates and better lead quality, I would plan for $30+ CPCs.
LinkedIn CPCs also change by industry
The two business categories in this analysis showed noticeably different CPC profiles on LinkedIn.
B2B SaaS: $11.02 average CPC on $681,000 in spend
Professional services: $15.25 average CPC on $23,000 in spend
I would be careful not to overstate that comparison because the spend levels were very different. B2B SaaS had a much broader mix of campaign types, which likely affected the average CPC. The professional services campaigns also used very specific targeting, which may have pushed CPCs higher.
B2B SaaS CPCs by campaign objective:
Professional services CPCs by campaign objective:
One interesting twist is that lead gen CPCs in professional services were lower than website visit CPCs. Lead gen CPCs were also much lower for professional services than they were for B2B SaaS.
If I were budgeting for a professional services firm on LinkedIn, I would factor in $15-$20 CPCs. For B2B SaaS, I would plan for a wider range, roughly $7-$35, depending on the campaign objective.
How this compares with Google Ads
The pattern is fairly consistent across channels. Professional services had higher CPCs than B2B SaaS in this data set. Even when I compare only non-branded search between the two industries, the CPCs are closer, but professional services still comes out higher.
Here is the breakdown of Google CPCs by campaign type:
What I would budget for LinkedIn Ads
Your targeting will have a major impact on CPCs and budget needs, but I use this data as a practical planning framework.
Minimum viable budget: $3,000-$5,000 per month
Below this level, I would not expect enough traffic to drive meaningful lead volume or conversions. You may still be able to get started, but trend-spotting will be slow, and you will probably be limited to one or two campaigns.
Testing and learning: $5,000-$10,000 per month
At this level, I would expect enough budget to run two or three objectives, launch more campaigns, test creative and audiences, and generate more meaningful lead volume.
Scaling: $10,000+ per month
With this budget, I can run always-on brand awareness and thought leadership campaigns alongside lead gen and website visit campaigns. I can also support event registrations, test more advanced list-targeted campaigns, and use retargeting without starving direct-response efforts.
For B2B SaaS or professional services companies with an ACV above $20,000, I would rarely recommend starting LinkedIn with less than $5,000 per month. A single closed deal worth $30,000-$50,000 in ACV can justify meaningful investment, even at a $500+ CPL, as long as the pipeline quality is there.
The B2B channel mix I recommend
For most B2B clients, I do not see LinkedIn and Google as either-or channels. I use them for different jobs.
Use Google Ads and Microsoft Ads for intent capture
Non-branded search reaches buyers who are actively researching. Branded search and remarketing are lower-cost and essential. If someone is searching for your category keywords, I want your brand to be visible.
I also use Demand Gen and Performance Max where they make sense to fill gaps and support brand awareness.
Use LinkedIn Ads for audience-led demand generation
If the ideal customer profile is highly specific, such as VP-level decision-makers at mid-market SaaS companies, LinkedIn’s targeting is hard to replace. No other platform gives me the same ability to reach that kind of professional audience at scale.
Run both channels in parallel
The strongest setup is to run both channels together. Google captures existing demand. LinkedIn helps create new demand and keeps the brand visible to the exact buyers I want in the pipeline.
Why I still think LinkedIn is worth the higher CPCs
LinkedIn is more expensive than Google on a raw CPC basis. But when I compare the platforms more fairly, with both reaching cold, qualified B2B buyers, the gap narrows significantly.
Higher CPCs can still be worth paying if they put the brand in front of the right customers earlier in the decision-making process. Over time, that can be more valuable than relying only on high-intent keywords after buyers have already narrowed their list of options.
The best scenario is for the brand to become an active part of the buyer’s decision, shaping the narrative before competitors do it instead.
My take is simple: I use LinkedIn Ads to build intent and tell the story, and I use Google Ads and Microsoft Ads to capture intent. The right budget depends on targeting, but I want enough spend to generate at least 100 clicks per month. Anything less usually means spending money without giving the system enough data to learn from.
I used to think bad data mainly meant bad reporting. Now, in Google Ads, I see it as something much more expensive: bad delivery. When conversion data is wrong, it does not just make a dashboard confusing. It can train campaigns to spend budget chasing the wrong people.
As automation takes over more of the ad-buying process, from creative generation to bidding, data has become one of the few inputs I can still control. It may also be the most important one, because automation can only optimize toward the signals I give it.
I keep coming back to one question: what is worse, a brilliant ad shown to the wrong audience or an average ad shown to the right one? The first burns budget on people I do not want. The second may not win every click, but when someone does engage, at least they are closer to the customer I actually need.
That is why I have to ask myself a harder question before launching any automated campaign: did I spend more time verifying the data than writing the ad copy?
The cost of bad data has changed
A few years ago, bad tracking was mostly a reporting problem.
If a tag fired twice, a conversion was mishandled, a value came through incorrectly, or offline conversions stopped working for a few weeks, the main result was a dashboard that did not add up. It was frustrating, but the damage was usually limited. Someone would eventually question the numbers in a monthly review, I would trace the issue, fix it, and the next report would look cleaner.
That same data now feeds the algorithm buying paid media. Smart Bidding does not wait for me to interpret a report or sit through a monthly review. It reads conversion data and acts on it before I may even notice that something is broken.
The same wrong number now creates a very different outcome. A bad number in a report requires an explanation in a meeting. A bad number in a conversion action used for bidding costs money immediately, because the algorithm does not know the signal is wrong.
It simply optimizes toward that signal the moment it sees it, and it does so efficiently.
Google does not understand my funnel or my business
Google may let me label conversion actions as “lead,” “opportunity,” or something similar, but those labels are mainly for organization. The platform does not truly understand where each conversion event sits in my funnel.
What it sees is a conversion event with a numeric value attached to it, usually a currency value. It does not inherently know that a newsletter signup might be worth $2 in eventual value, a lead might be worth $60, and an opportunity might be worth $400. To Google, those are conversion events. Without better signals, it has no real context that one may be worth 200 times another.
The algorithm is not optimizing for my business outcome by default. It is optimizing for the data I provide. If that data is wrong, the optimization will be wrong too.
For example, if every form submission fires the same conversion with the same default value, I give the system no clean way to separate low-intent inquiries from high-value prospects. The algorithm treats them the same. And because low-quality leads are often cheaper to acquire, it can quickly flood the account with them.
The cost per lead may drop from $40 to $25, and the dashboard may make performance look more than 35% better. But behind that cleaner metric, the pipeline can dry up as genuinely qualified inquiries quietly fall by half.
Bad data can show up in different ways, but I see three issues that are especially likely to derail campaign delivery.
1. Wrong event
If I optimize for a top-of-funnel action like a page view while the real conversion events happen further down the funnel, the algorithm learns to buy more of those cheap events. The problem is that the lower-funnel activity may never follow.
2. Wrong value
If I count every conversion equally, or assign every conversion the same placeholder value, I hide the real differences in business value. When actual value can vary by 10 times or more, the algorithm will often chase the easier, lower-value conversions because they are cheaper to acquire.
3. No data
This problem does not get discussed enough. A complete break in conversion data can damage a campaign faster than almost anything else.
On Day 1, the algorithm starts wondering where the conversions went. By Day 2, it begins assuming they may not be coming back. By Day 3, it can start making serious bidding changes. Within a week, many campaigns can throttle themselves down to almost nothing.
How I pick the right signal for Google
So how do I fix this? I start by choosing the signal that best represents business value, not just the easiest action to count.
Take a typical lead generation business. Some leads will never convert, while others may be worth 10 times as much as the rest.
If the form asks the right qualifying questions, I may already know which leads are which. But if I optimize for every submitted lead using a target CPA, I am telling Google that all leads are equally valuable.
Imagine an account spending $20,000 a month at a $40 target CPA and generating about 500 leads. Only 150 qualify, and maybe just 50 are genuinely high value. A basic lead may be worth $60, a qualified lead may be worth $200, and a high-value lead may be worth $600. That is a 10 times spread in value.
In that situation, I have several ways to improve the optimization signal.
Optimize for a qualified lead: I can create a new conversion action, such as “qualified lead,” and fire it only when a lead has real value. Then I can move the target CPA strategy to that conversion action, knowing the campaign will ignore leads with no value. The advantage is that I train the campaign on a more meaningful signal. The downside is that every qualified lead is still treated equally.
Assign conversion values and use target ROAS: I can add a currency value to the qualified lead based on the potential revenue it could generate if it becomes a sale. Then I can switch the campaign to target ROAS, allowing Google to optimize for return instead of simply counting leads. The tradeoff is that it may still buy larger numbers of lower-value leads if it can acquire them at the right price.
Optimize for a high-value lead: I can create a “high-value lead” conversion event that fires only for top-tier leads, with or without a conversion value. Then I can optimize with either target CPA or target ROAS, depending on whether I care more about acquisition cost or return. The advantage is stronger lead quality. The downside is that, depending on spend and volume, the data may be too limited to support this approach until the account scales.
These are only a few possible optimization signals, and they do not even go deeper into the funnel. I can apply the same thinking to lower-funnel milestones by creating separate conversion actions for events such as contacted lead, qualified contact, or high-value contact.
Targeting and measurement can be different
This sounds simple, but the conversion event I optimize for and the one I report on are not always the same. In many cases, they should not be the same. One trains the algorithm. The other tells me how that training is performing.
In the example above, a client or internal stakeholder may still want to see cost per lead. That is a valid metric. But the campaign may be optimizing for the Qualified Lead conversion, not the original lead submission.
I can keep the original lead conversion running purely as a reporting metric, so stakeholders still get their cost-per-lead view while the campaign bids on the qualified lead signal that actually reflects business value.
Same campaign. Two conversions. Two very different jobs.
That brings me back to the question I started with: did I spend more time verifying the data than writing the ad? In an automated account, data is no longer just measurement. Data is strategy.
I see Google’s latest Google Ads API change as another clear move away from legacy automation and toward newer AI-driven campaign types, especially Performance Max.
Beginning August 3, 2026, Google says developers will no longer be able to create new Smart Campaigns through the Google Ads API. For me, the key detail is that this change is about new campaign creation only.
Existing Smart Campaigns are not being shut down. They can keep serving ads, and advertisers and developers will still be able to update and manage those campaigns through the API.
What changes is the ability to create brand-new Smart Campaigns through API workflows. If I depend on automated campaign setup, that is the part I would review now.
I care about this because it signals where Google wants advertisers to go next. Smart Campaigns may continue running, but the path for new API-based campaign creation is moving toward newer products such as Performance Max, Search campaigns, and Demand Gen campaigns.
Google is specifically pointing advertisers toward Performance Max as the primary alternative. Since Performance Max runs across Google’s advertising inventory and uses AI to automate more of the campaign process, it fits the broader direction Google has been taking for years.
I also see this as part of a wider consolidation around automated campaign formats. Google has increasingly emphasized systems that handle bidding, targeting, and creative optimization across channels, and limiting new Smart Campaign creation reinforces that shift.
For developers, the practical next step is to audit any application that creates Smart Campaigns before the August 3, 2026 deadline. The affected requests are campaign creation operations where advertising_channel_type is set to SMART and advertising_channel_sub_type is set to SMART_CAMPAIGN.
After August 3, attempts to create new Smart Campaigns through the API will fail. In version 24 of the Google Ads API, developers will receive a SmartCampaignError.CREATION_FAILED error.
In version 23 and earlier, the same type of request will return an OperationAccessDeniedError.CREATE_OPERATION_NOT_PERMITTED error.
My main takeaway is that advertisers, agencies, and software providers should not treat this as a last-minute technical cleanup. If campaign creation is built into an internal tool, onboarding flow, or platform integration, I would start mapping the replacement path now.
Google is not ending existing Smart Campaigns, but it is removing a key creation path for new ones. To me, that is a strong signal that future campaign planning should center on Performance Max and other AI-driven Google Ads campaign types.
I’m watching a small but meaningful Google Search ads experiment that could change how people notice paid results. Google is testing labels that call out the ads it believes are most relevant to a user’s search query, which could affect both user trust and advertiser performance.
What’s happening. Google has started testing new Search ads labels such as “Strongest match” and “Strong match” on select ads in search results. Google Ads Liaison Ginny Marvin confirmed the experiment and said the labels are meant to help users quickly spot ads that closely match their search intent.
For now, I see this as a limited test. Google says it is only appearing for a small percentage of users in the U.S., so most advertisers may not notice it in the wild yet.
Why I care. This kind of visual signal could influence which ads users view as the most relevant and trustworthy. If Google expands the experiment, advertisers with stronger relevance and quality signals may gain more attention, while weaker or less aligned ads could become easier to ignore.
How it works. According to Google, these labels rely on the same ad quality and relevance signals already used inside its advertising systems. In other words, Google is not introducing a new ranking factor here. It is making its relevance assessment more visible directly in the Search results interface.
I see the goal as fairly straightforward: help users identify the ads most likely to answer what they were searching for, without making them interpret relevance entirely on their own.
Why Google is testing it. Google says the experiment is designed to improve the Search ads experience for both consumers and advertisers.
For users, the label could act as another cue that a paid result may be especially useful for their query.
For advertisers, it could help highly relevant ads stand out in front of high-intent audiences, which may lead to stronger engagement and higher click-through rates if the feature performs well.
Reading between the lines. I view this test as part of Google’s broader push to make ad relevance more visible and more understandable to searchers.
Historically, relevance signals have mostly worked behind the scenes through auctions, quality systems, and ranking logic. By showing those signals more clearly, Google may be trying to build more trust in sponsored results while also rewarding advertisers that closely match their ads to search intent.
The timing also matters. Search platforms are under ongoing pressure to prove that their ad experiences are useful, high quality, and worth users’ attention. A label like this gives Google another way to frame certain ads as more helpful, not just more prominent.
What I’m watching next. Google has emphasized that this is an early-stage experiment and has not said whether “Strongest match” or “Strong match” labels will become permanent. For now, I would treat this as another reminder that ad relevance, landing page quality, and alignment with user intent remain central to Google’s direction for Search advertising.
