I’ve recently discovered an intriguing update in Google Ads Performance Max campaigns that could significantly shift how I and other advertisers approach animated display advertising.
Imagine the possibilities with Google’s new AI image animation feature! It could be a game-changer for those of us who’ve been sticking to static images in our PMax campaigns.
What I Found. Nikki Kuhlman, Vice President of Search at JumpFly, Inc., shared her discovery of an option to generate animated video clips directly within PMax asset groups. These clips use AI to transform a single source image into engaging animations.
How It Works.
I can upload a source image like a logo, a product shot, or a property photo.
The AI works its magic, generating several “enhanced” versions of my image.
Each enhanced image then produces two animated clips.
I can choose up to five animated clips per asset group to enrich my campaign.
Note: While faces can’t be used in the source images, AI might introduce people in the enhanced versions.
Early Testing Insights. I tested it, and observing a logo turn into a spinning animation and a house image pan out cinematically was impressive. Simple inputs, but the quality of the animated outputs is certainly suitable for display advertising without the need for a video production team.
Ad Placements. While Google hasn’t officially documented ad placements, early tests show these animated clips appear in Display ad previews when integrated into an asset group.
Why I Care. Video assets have always been vital in Paid Media for their creative potential, but not everyone, including myself, has the time, budget, or resources for video production. Now, this feature offers me the chance to easily convert a single photo or logo into dynamic display content, effectively removing previous barriers.
This opens a new door for advertisers like me who’ve relied on static images in PMax campaigns. I see it as a simple but effective win.
The Bottom Line. Although Google hasn’t confirmed this feature officially, it’s advisable for anyone running PMax to check their asset groups. If this is present, it’s worth testing, especially for those of us who have solely utilized static images.
First Seen. This exciting feature was initially spotted by Nikki Kuhlman and shared on LinkedIn.
In today’s automated landscape, I’ve learned that paid search performance largely depends on the quality of signals fed into algorithms. Algorithms are like chefs—they expertly cook with whatever ingredients they’re provided. By enhancing these signals, I’ve found a reliable path to better results.
While this might sound simple, I’ve noticed that many of us still cling to signals that don’t truly reflect business outcomes. Let me share my insights into how algorithms work, how I can shape them, and where common pitfalls lie.
Modern bidding systems often evoke the image of a “black box,” shrouded in mystery. However, I’ve found that understanding their function requires breaking down their capabilities. These algorithms are vast pattern recognition systems.
Initially, these systems relied on straightforward statistical methods, like rules-based logic or regression models. Today, they’ve evolved into complex learning systems capable of evaluating countless data inputs simultaneously, such as query intent and location-specific behavior, in real-time.
Despite the technological advancements, I understand the core mechanisms remain unchanged. They identify patterns that match desired outcomes, calculate probabilities, and adjust bids accordingly. It’s crucial for me to align the feedback loops with real business values to ensure these algorithms optimize effectively.
As a marketer, I’m aware algorithms lack business context—they only see what they get. If we provide them with weak or irrelevant data, even the most sophisticated systems can’t deliver the results we need.
Therefore, I focus on the controllable signals that have the greatest influence over these algorithms. These include campaign structure, bidding strategies, and how I allocate my budget.
Most importantly, I’ve found conversion data to be the key driver of success. It’s the critical signal that guides algorithmic learning and optimization.
Whenever I experience a plateau in performance, my instinct is no longer to blame budget constraints or ineffective tactics. Instead, I analyze conversion data since it’s often the root cause of stagnation. Ensuring quality over quantity in conversions has consistently elevated my results.
Ultimately, aligning conversion signals with genuine business KPIs is vital. Platforms don’t understand business profitability; they follow the instructions given. If any conversion increase jangles alarms rather than cheers, it shouldn’t drive the primary optimization signal.
To ensure effective learning and optimization, I strengthen conversion signals with rich data sources, beyond standard browser tracking, to overcome privacy and attribution challenges.
By integrating first-party identifiers and accurate transaction values, I’ve improved how platforms recognize and learn from conversions. This method offers robust feedback loops, optimizing both accuracy and performance.
Determining the right conversion goals requires balancing volume and value precision. Often, I use proxy metrics for a faster optimization cycle without sacrificing real business value.
I’ve found setting conversion goals is not straightforward; it’s about balancing volume with value accuracy and stability. This balance helps me optimize efficiently without data becoming too sparse or too noisy.
Regularly revisiting these goals and refining conversion definitions are essential. Asking myself if I truly celebrate any increase in a certain outcome guides me toward refining my signals and enhancing performance in paid search.
I recently discovered an important update from Google affecting how I run Demand Gen campaigns using Lookalike user lists. Starting April 30, Google will block creating duplicate Lookalike lists via the Google Ads API and return an error code for any breaches.
This update might seem quiet, but its implications are significant, especially for those of us utilizing automated systems or third-party tools. Google is now enforcing a uniqueness check to prevent duplicates that have identical seed lists, expansion level, and country targeting.
Why do I care about this change? An unaddressed error could disrupt the workflow of my campaigns if I don’t update my integrations in time.
Here’s what I plan to do:
Audit my current Lookalike lists and reuse those that already align with my goals instead of creating new ones.
Update my API error handling processes to catch the new DUPLICATE_LOOKALIKE error code in versions v24 and above, or RESOURCE_ALREADY_EXISTS in older versions.
The bottom line is, while this change is housekeeping, the deadline is firm. I need to ensure my campaigns are technically prepared before the end of April to maintain stability in Google’s systems.
I’ve noticed that AI is drastically changing the landscape for marketing agencies, and it’s a pressure felt from both sides. Though we welcomed AI as a tool to enhance efficiency, it seems to be impacting our margins in unexpected ways.
In 2024, 44% of digital marketing agencies, including mine, identified AI as a potential threat. By 2025, this concern had increased to 53%, as highlighted in SparkToro’s survey of agency owners worldwide.
The real kicker? We aren’t just passive observers in the AI disruption; we’re actually participants. We’ve adopted AI to streamline tasks and reduce costs, attempting to boost our profitability. Meanwhile, our clients are following suit, using AI to cut budgets or opt to handle tasks internally.
This dual pressure has created a challenging environment for agencies like mine.
The Promise That Became a Problem
When advanced AI tools such as ChatGPT and Claude emerged, I initially saw them as opportunities. They offered ways to automate tedious tasks, ostensibly improving our efficiency and competitiveness.
Our equation appeared simple: automate more tasks with AI, reduce manpower, and profit from the savings. However, clients performed the same calculations and reached a different conclusion: why pay an agency when AI can produce satisfactory content, analyze campaigns, or generate ads on their own?
This shift prompted unwelcome questions about the value we provide.
Some services we once charged premium prices for are now being completed in-house or through automation tools. Al Sefati, CEO of Clarity Digital Agency, has frequently discussed the hurdles that boutique agencies face in this AI-driven market.
Earlier this year, I faced clients who “put marketing on pause,” despite good performance metrics. One manufacturing client even walked away from a contract due to tariff uncertainties. In tightening budget scenarios, where AI renders some marketing services commoditized, agencies like ours become easy targets for budget cuts.
The Margin Trap Nobody Talks About
We began using AI to do more with fewer team members, expecting to see higher profits. But our clients expect these savings to benefit them, not enhance our bottom line.
This has led to an unpleasant trend of shrinking retainers. SparkToro’s research indicates that sales cycles are becoming longer, with more agencies reporting delays in closing deals that extend from 7-8 weeks to over 12 weeks.
The reason? Potential clients are evaluating, “If AI makes this cheaper and faster, shouldn’t our rates be reduced as well?”
Even as efficiency through AI increases, client expectations haven’t decreased—they’ve grown. Agencies are now expected to demonstrate tangible results, link investments directly to revenue, and offer genuine ROI.
This presents a dilemma: adopt AI and risk downgrading our perceived service value, or resist AI changes and fall behind more adaptable competitors.
The Junior Talent Crisis Nobody’s Preparing For
One concerning insight from the report suggests that 66% of agency owners are worried about dwindling career opportunities for junior staff. Historically, agencies have relied on entry-level employees to perform routine tasks such as keyword research, content optimization, and campaign setup.
While not glamorous, these tasks are crucial stepping stones for junior marketers to develop skills and progress to strategy and client leadership roles. However, AI is rapidly taking over these process-oriented tasks.
This shift raises a vital question: how will we cultivate new talent if there’s no foundational work for them to learn from?
What AI Can’t Replace Yet
Despite the disruptions, some agencies are successfully navigating these changes. Larger agencies report healthier sales and stronger pipelines than smaller firms. This is partly due to their ability to weather economic changes and a focus on strategic offerings that AI cannot easily replicate.
Those of us thriving have stopped competing solely on execution. We now offer something AI can’t easily mimic: strategic insights, market experience, and storytelling that aligns with business outcomes.
“Clients desire teams that truly understand their industry,” notes Sefati.
Agencies that succeed are often those with deep expertise in specific verticals like B2B SaaS, financial services, healthcare, and ecommerce. This specialization allows us to maintain our value by offering nuanced insights and strategic thinking that AI struggles to deliver.
The Uncomfortable Truth About Commoditization
In the past, simply having the technical skills to launch campaigns gave agencies a competitive edge. But as AI and martech tools advance, more brands develop internal capabilities that rival what agencies offer.
This shift is reflected in data from SparkToro’s research, with only 14% of agencies claiming a “very healthy” pipeline, while the majority experience average or below-average pipelines.
Smaller agencies, especially those with 1-10 people, are feeling this pressure acutely. They often lack sales staff, forcing founders to juggle sales and client delivery roles, making it harder to compete when budgets shrink.
How Your Agency Can Escape the Squeeze
It’s crucial to focus on what AI can’t replicate and make strategic adjustments as client expectations rise and margins narrow.
Be Honest About What AI Has Commoditized
Embrace AI rather than shying away from it. Acknowledge what AI has commoditized and concentrate on areas it can’t;
If your agency still relies on AI-performed services such as basic content creation or standard reporting, it’s time to pivot. Focus on strategic, creative, or nuanced tasks that distinguish your agency from AI applications.
Lead with AI, Don’t Hide from It
Change the narrative around AI and lead with it in client discussions. Highlight the unique value add your agency provides beyond AI capabilities.
For instance, emphasize how only your team can fully understand a client’s market dynamics or interpret data insights contextually to improve strategic initiatives.
Rethink Pricing Models
Updating pricing strategies is essential. Outcome-based fees and performance partnerships could better align your agency’s incentives with client success, leveraging the efficiencies AI brings.
Rebuild the Talent Pipeline
Address the diminishing opportunities for junior staff by involving them in high-level strategic work alongside seasoned specialists. This approach will prepare the future frontline of agency talent as their role expands beyond AI-executed tasks.
The Old Agency Model Isn’t Coming Back
Over 64% of agencies are optimistic about revenue growth in the coming year, but this hinges on whether they innovate or wait for an outdated model to return—it won’t.
The squeeze is a lasting reality. The key to thriving is to reimagine what agencies offer and how we deliver it—making our roles indispensable, not replaceable.
Will your agency evolve to leverage AI’s capabilities and become irreplaceable, or will it be swept aside as clients discover they can handle tasks independently?
Have you ever experienced the frustration of seeing the ‘Your account has been suspended’ banner on Google Ads? I’ve been there, and I know how overwhelming it can feel. But don’t worry, I’m here to guide you through understanding why suspensions happen and what you can do to get back on track.
Account suspensions, as I discovered, are essential to maintaining a healthy digital advertising ecosystem. Google’s main aim here is to protect users. However, as advertisers, we often find ourselves navigating what feels like a minefield of policies and procedures.
Here’s what I’ve learned about what triggers these suspensions, the types you might face, and crucially, what to do if your account ends up flagged or suspended.
Why do accounts get suspended?
I’ve found that suspensions occur when there’s a violation of Google’s policies. The platform uses a mix of automated systems and manual reviews to detect these issues. From my experience, it often involves a thorough check of our business practices and website content.
In late 2025, Google announced improvements to its systems, which have since reduced incorrect suspensions by over 80%, with most issues now resolved within a day. This encourages me that there’s a fair system in place to ensure accuracy.
How Google Ads suspends accounts and what happens next
Suspensions can happen immediately upon violation detection or, in some cases, we get a seven-day warning. I’ve personally experienced the abruptness this can entail, where advertising activities suddenly come to a halt.
Ads will not run during a suspension.
No new content creation like ads, ad groups, or campaigns is possible.
We can still access account data and reports for review.
If an account I manage is suspended, linked accounts can also get affected. This is something to be mindful of, as I’ve had to navigate clearing up more than just one account at times.
What are the different types of account suspensions?
Understanding the different types of suspensions has been crucial for me. Google groups them mainly into policy violations and egregious violations. Each has distinct causes and implications.
Policy violations
These are common and usually related to Google Ads policy or terms and conditions. I’ve seen issues like inappropriate content or editorial requirement violations come up frequently.
Egregious violations
These are more severe and often tied to unlawful or harmful practices, reflecting overall business conduct rather than specific campaigns. In my experience, these tend to be permanent.
Circumventing systems, unacceptable practices, or illegal activities often fall here.
What to do if your account is suspended?
Depending on the suspension type, my next steps have usually involved resolving the issue before considering an appeal. Google’s help guides are invaluable, ensuring I’m fully compliant before making my case.
Best practices for submitting an appeal
From my experience, honesty and thoroughness in the appeal process are vital. Whether it’s submitting verification or acknowledging an error, being straightforward helps in getting fair reconsideration.
Remaining patient during the appeal process is crucial, as wait times can be long. In the meantime, I’ve learned not to submit multiple appeals, which could complicate the process unnecessarily.
I recently stumbled upon an intriguing issue with Google’s paid search ads. Imagine my surprise when I noticed multiple competing ads displaying identical web statistics! This strange occurrence immediately made me question whether it’s a bug or perhaps a deliberate change by Google.
What’s happening? I’ve seen several paid search ads showcasing the same website statistics simultaneously, despite these metrics usually being unique to each site. This uniformity makes the data appear dubious, leaving me uncertain if it’s a display glitch, an experimental test, or something more intentional.
Why we care. Trust signals in search ads play a crucial role in helping users like us make informed decisions. They boost click-through rates by instilling confidence in the results. If identical stats appear across competing ads, it risks undermining their credibility—potentially impacting the confidence and trust advertisers rely on.
What we don’t know.
Whether Google is testing this actively or it’s an unintended bug
How widespread the issue is across different search queries or markets
Whether it’s affecting user click behavior or advertiser performance
No official word. So far, Google has not confirmed or commented on this behavior. Paid Media expert and Founder Anthony Higman was the first to notice and flag this anomaly, sharing his findings on LinkedIn.
The bottom line. If trust signals can’t be trusted, they fail to serve their purpose. As someone invested in digital advertising, I’m keenly watching whether this pattern gains momentum or fades away. Observing these developments is critical for both advertisers and users.
I’ve been eagerly following the latest developments from Google, and their new Ads DevCast is truly a groundbreaking resource for developers like me. This initiative offers technical insights into Google Ads and highlights how AI-driven changes are transforming ad APIs.
The new show is hosted bi-weekly by Cory Liseno, as part of the Google’s Advertising and Measurement Developer Relations team. Ads DevCast focuses on deep technical dives across key tools like Google Ads, Google Analytics, and Display & Video 360. It feels like a direct line to the experts who are constantly innovating in our field.
What’s interesting here is that Ads DevCast complements Ads Decoded, which is more about campaign strategy, hosted by Ginny Marvin. It’s specifically designed with us developers in mind, highlighting the need for a specialized approach to understanding these platforms.
The first episode, intriguingly titled “MCPs, Agents, and Ads. Oh My!”, delves into the “agentic shift” that Google is observing. With AI agents becoming the main users of ad APIs, this shift is something we’re all keenly interested in.
For those of us deeply involved with Google’s ad tools, Ads DevCast is an invaluable resource. It helps us stay ahead of technical evolutions, discover new capabilities quickly, and build efficient integrations in a landscape increasingly dominated by AI.
I see Google broadening the horizon from a niche “Ads Developer Community” to a wider “Ads Technical Community.” This change allows marketers to carry out technical tasks without needing exhaustive development cycles.
As a pilot project, Ads DevCast is still very much in development, and Google is actively seeking feedback from us to refine future episodes. It’s exciting to know we can influence its direction.
This initiative reinforces Google’s commitment to keeping us in the loop with their latest innovations, enabling us to adapt quickly and effectively in an AI-first world. Check out Ads DevCast if you haven’t already!
I’ve noticed that Google has tightened their Merchant Center rules, now requiring a visible, but disabled buy button for products that are out of stock. This means the button should appear on the page, but users won’t be able to click it since it’s grayed out and inactive.
What’s happening. This shift represents a significant change from previous practices where retailers either let the ‘Add to Cart’ button remain clickable or removed it altogether. Both methods are now against Google’s guidelines.
How it works. The requirement is straightforward: a buy button must stay on the page but needs to be disabled. This usually involves a visual change to indicate it’s not active, making it unclickable yet visible.
The catch. It doesn’t stop at the button change. Google wants explicit availability messaging on product pages, like ‘in stock,’ ‘out of stock,’ ‘pre-order,’ or ‘back order.’ This labeling has to match perfectly with the product feed.
Mismatched information between the webpage and the product feed could lead to disapprovals.
The bigger shift. This policy eliminates a workaround many retailers used, where out-of-stock items could still be sold by keeping the buy button active. Now, if retailers wish to accept orders for unavailable products, they must list them as ‘back order’ and ensure this status is synced across the landing page and feed.
Bottom line. While this seems like a minor UI adjustment, it’s a significant policy shift. Retailers must audit their handling of out-of-stock items and make sure their pages and feeds are in perfect harmony to avoid any interruptions.
First seen. This update came to my attention thanks to a Google shopping specialist who shared the details on LinkedIn.
I’m excited to share that Microsoft is making a significant update that simplifies the way we set up automated bidding in Microsoft Advertising.
By consolidating performance targets, Microsoft aims to reduce complexity, making bidding more streamlined without sacrificing the control over critical performance metrics.
What’s happening: The platform is integrating common targets like Target CPA and Target ROAS into broader automated strategies. This means these targets will now form part of a more comprehensive bidding approach instead of standing alone.
From now on, I’ll be choosing between two main strategies: Maximize Conversions or Maximize Conversion Value, with optional performance targets that can be added as needed.
Credit – Hana Kobzova of PPC News Feed
How it works: For campaigns focused on conversions, I’ll select Maximize Conversions and may set a target CPA if desired. For campaigns aiming at maximizing value, I’ll choose Maximize Conversion Value with the option of setting a target ROAS.
Microsoft reassures that this update doesn’t change the fundamental bidding behavior — it simply makes the setup more user-friendly.
Why we care: This change enhances accessibility to Microsoft Advertising’s tools, making automated bidding more straightforward and efficient, which is especially beneficial when managing large-scale campaigns.
For us advertisers, this means faster setup times, more consistent optimization across accounts, and fewer complexities when managing campaigns focused on conversion or value.
What’s staying the same: Existing campaigns using Target CPA or Target ROAS will continue seamlessly, requiring no updates. Portfolio bid strategies are unaffected as well.
The bigger picture: This move is part of Microsoft’s larger effort to simplify automated bidding while ensuring performance control remains intact.
Bottom line:Microsoft is refining bidding options to make them more accessible without losing our ability to fine-tune performance through familiar controls.
As I explore the ever-evolving landscape of Google’s AI Mode, it’s fascinating to witness how ad formats, reporting, and control are taking shape. Google seems to have a master plan in place that competitors just can’t keep up with.
I find myself intrigued by Google’s entry into this next phase of conversational search. It’s not just about user numbers but who can effectively monetize them. Google’s mature ad systems and extensive advertiser base offer a significant edge.
The initial panic surrounding Google’s position is over. Google’s long-standing advantages and huge investments have leveled the playing field with ChatGPT in LLM search.
Back in December 2025, when Google declared code red, it became clear that they were serious. Apple’s decision to partner with Google for its AI needs is indeed telling.
Initially, it seemed plausible that Google would struggle against ChatGPT, but the market has since adjusted its views. The company’s valuation reflects renewed confidence, rivaling even Apple at a substantial $3.6 trillion.
As I dive deeper into how monetization will shape this race, I’m struck by how Google’s recent advances have significantly boosted its valuation.
It’s clear that the visibility of financial projections plays a massive role in how the company is perceived financially. Google’s approach to shifts in user behavior is crucial in maintaining its robust business model.
From my perspective, much of your digital advertising budget likely goes to Google. Its prominence demands attention, not just in search but also in emerging AI platforms like ChatGPT and Claude.
The competition in LLM conversations is intriguing. Google and ChatGPT are vying for different monetization models, a fascinating case study of differing strategies.
For those of us in advertising, it’s essential to monitor developments like ad formats, rollout pace, and public reception to ads within these platforms.
OpenAI’s current monetization model is intriguing but still nascent, reliant on a small group of major advertisers. We’ll see how they expand and fine-tune this model over time.
Outsourcing inventory to programmatic partners is a smart move for OpenAI but highlights their early stage in building an ads business.
For Google advertisers, the shift to AI Mode need not be alarming. I’m watching for the ways these LLM sessions are shaping user experiences and ad placements.
One thing is for sure; the enhancements in AI Mode continue, promising more seamless and user-friendly interactions. The potential for ads remains, though their form is still evolving.
Monitoring key areas like the extent of monetization, advertiser control, and campaign types becomes more important as we navigate this new landscape.
Ultimately, the future of advertising in AI-driven search is one of adaptability and strategic planning, aligning closely with user and advertiser behaviors in this exciting yet challenging era.
