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 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 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.
I’m seeing Google roll out a new set of Demand Gen updates designed to help advertisers improve creative performance, reach more potential customers across YouTube, and measure campaign results with more clarity.
For me, the bigger story is that Demand Gen is becoming less about manually adapting assets and more about using AI-assisted tools to make creative work harder across Google’s most visual surfaces.
Demand Gen campaigns are built to drive discovery and conversions across Google’s visual placements. With these latest updates, I see Google trying to reduce creative friction while giving advertisers better visibility into what is actually moving performance.
Google says the enhancements arrive as YouTube continues to show value for customer acquisition. The company cited research from Measured showing that 72% of incremental conversions on YouTube come from new customers.
What’s new. I’m watching Demand Gen add expanded video resizing capabilities, giving advertisers the ability to automatically transform creative into more aspect ratios, including vertical-to-square, vertical-to-landscape, and square-to-landscape formats.
That matters because it should make it easier to adapt existing creative for different YouTube placements without having to produce every version manually from scratch.
Why I care. Expanded video resizing can help existing assets fit more YouTube inventory, Gemini can provide AI-powered recommendations before launch, and new web-to-app measurement can give marketers a clearer view of how Demand Gen campaigns influence app installs and return on ad spend.
Gemini joins the creative workflow. Google is also bringing Gemini-powered recommendations directly into the Demand Gen campaign creation process, which makes AI guidance part of the asset selection workflow instead of a separate optimization step.
When advertisers choose image and video assets, Gemini will offer automated suggestions for optimizing creative for YouTube. I see this as a way for marketers to improve asset choices before campaigns go live, rather than waiting for performance data after launch.
Better app measurement. Demand Gen now includes Web to App Acquisition Measurement, allowing advertisers to measure when web campaigns lead users to install an app.
The new reporting gives me a more complete way to evaluate campaign performance because it attributes app installs generated through Demand Gen campaigns. That should help advertisers better understand the full impact of their media spend.
The bottom line. I see Google’s latest Demand Gen updates as a practical combination of AI-powered creative guidance, more flexible video optimization, and broader measurement tools that can help advertisers improve performance while gaining clearer insight into customer acquisition.
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.
PPC advertising for medical and mental health services comes with more restrictions than many other industries, but I still see it as one of the most effective ways to keep a steady flow of new patients and clients coming into a practice.
Whether I am managing campaigns for a client, promoting my own practice, or building a campaign from scratch, I focus on the same fundamentals: the right keywords, compliant messaging, clear landing pages, and lead-quality tracking.
Choosing keywords for medical and mental health advertising
When I choose keywords for medical or mental health advertising, I start by thinking about how real patients search. In most cases, their searches fall into three main groups.
First, some people search by symptoms or treatment options. They may not know which professional they need yet, so they search for phrases like “treatment options for depression” or “why does my ankle hurt when I run.” I do not ignore these searches, because they can still turn into new patients or clients.
Second, people often search for what they think the service is called. They may use simplified or incorrect terms, such as “therapist to manage bipolar medications” or “foot pain doctor.” These searches still show intent, even if the language is not medically precise.
Third, some searchers use the correct term because they already know what they need and are ready to contact a professional. They may search for “psychiatrist” or “endodontist near me.” Even then, I watch for confusion between similar roles, such as therapist, psychologist, and counselor.
Most of my budget usually goes toward the second and third groups, where searchers are closer to taking action and starting treatment.
If I have a larger budget, I may also test broader symptom-based or informational searches that could convert later. These can work, but I treat them carefully because informational searchers may or may not be ready to book.
I also rely heavily on negative keywords. They help me block searches for services the practice does not provide, which protects the budget and improves lead quality.
With medical and mental health ad copy, I have to be careful. I need the ad to make it clear that help is available, but I cannot write in a way that feels too direct, too personal, or too aggressive.
I expect some trial and error. An ad rejection does not automatically mean an account is in trouble. It usually means the ad was not approved, so I adjust the wording or request a manual review when appropriate.
Blunt language is often where problems happen. Instead of making strong claims, I test softer, more compliant language that still communicates the value of the service.
To stand out from competitors, I focus on practical benefits such as accepted insurance, payment options, specialized treatments, or distinctions like being family-owned, local, award-winning, certified, or licensed.
I avoid terms like “cure” and other language that implies guaranteed results. Google and Meta both have ad policies that restrict how medical, mental health, and wellness services can be promoted.
When an ad gets rejected, I rewrite it so it still explains the value of the practice without crossing policy lines.
For some psychiatrists, doctors, and other medical service providers, Google Ads may also require a LegitScript.com listing, especially for addiction treatment services.
When I build landing or service pages, I start with the information the front office already gives to patients. That is often the clearest and most useful material available.
I pull details from pamphlets, office materials, and common intake conversations. Then I highlight key points such as accepted insurance, cash payment options, payment plans, financing, and specialized treatments.
I also answer the questions patients regularly ask in person or over the phone. A strong landing page should keep improving as new questions come up.
Those questions might include whether the practice works with children, accepts Medicare, offers phone or virtual sessions, or provides a specific treatment.
I make the next step obvious. That may mean booking an appointment, scheduling an initial consultation, requesting a free phone consultation, filling out a form or questionnaire, submitting a contact request, or calling with questions.
I avoid vague forms and generic phone numbers with no instructions. Instead, I explain the process clearly from pre-treatment to treatment to post-treatment.
I also like to include a FAQ section that answers questions such as “what is the process?” and “how does treatment work?” The more uncertainty I remove, the easier it is for a patient or client to take action.
Choosing the best campaign types
For medical and mental health services, I usually build the strategy around Search campaigns.
Automated or audience-based campaign types, including Performance Max and Demand Gen, can run into privacy and targeting limits. Depending on the service, the ads may not be approved.
Remarketing is typically restricted for the same reason. Video campaigns may be possible, but targeting limits often make them better suited for local branding than direct response.
Search campaigns work well because people are actively looking for answers, treatment, or a specific type of provider. They are typing in the exact services they need.
Many providers also use directories like Psychology Today or ZocDoc for lead generation. I still like supplementing those channels with Google or Microsoft Search campaigns because they send traffic directly to the practice’s own site and give more control over patient or client flow.
My usual approach is to target very specific terms for people who are ready to hire a professional, then test broader symptom or research-related terms when the budget allows.
Meta Ads can also be useful, but privacy laws limit targeting. I also have to be careful with ad copy, images, and landing pages so the campaign stays compliant.
I review Meta’s ad policies before launching campaigns to reduce avoidable disapprovals. Meta can support larger budgets, but for most medical and mental health marketing, Google Search remains the most reliable starting point.
With any online advertising, and especially with medical and mental health services, I need to know more than how many leads came in. I need to know which leads became real patients or clients.
A simple CRM, whether generic or built for the industry, can track incoming leads and show which ones converted.
Google Ads, Microsoft Ads, and Meta Ads all offer built-in CRM connections. I can also use a tool like Zapier to connect systems without needing a programmer.
Beyond website form submissions, I also track inbound calls generated by marketing campaigns. Phone calls often represent high-intent leads, so leaving them out can distort ROI.
Call tracking tools such as CallTrackingMetrics, CallRail, and WhatConverts can integrate with CRMs and major ad platforms to measure lead quality.
They also offer call recording and are HIPAA-compliant, which matters when tracking performance in healthcare-related campaigns.
Keeping medical and mental health ads effective
To keep medical and mental health ads effective, I focus on four things: targeting the right searches, writing compliant ads, improving landing pages, and tracking lead quality.
When those pieces work together, I can build campaigns that attract the right patients and clients more consistently.
A steady, well-structured approach is what helps a practice maintain or expand its patient flow without creating unnecessary compliance risk.
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.
