Hey there! I’ve got exciting news for advertisers like me who are constantly looking for better ways to fine-tune our Google Ads campaigns. Google has just introduced new Performance Max asset testing tools that make it easier to analyze creative performance and make data-driven decisions.
Google’s latest update is all about expanding our ability to experiment with Performance Max. Now, I can test creative assets and measure campaign performance more effectively before committing to large-scale changes.
What’s new? Google is enhancing how I run asset experiments in Performance Max campaigns. This update lets me test different creative assets to see which ones drive the best results.
The new feature allows me to compare entirely new asset groups, assess the impact of adding individual assets, or even measure how seasonal content stacks up against evergreen creatives.
I can also test assets generated through Google’s Asset Studio, opening up even more possibilities for creative experiments.
The bigger picture. While Performance Max has automated many aspects of campaign optimization across Google’s inventory, the real challenge has been understanding how creative changes impact results.
The new experiments provide a more controlled environment for evaluating creative decisions before rolling them out across all my campaigns.
Cutting through the noise. With an additional success metric, I can balance multiple objectives—like maximizing conversions and maintaining efficiency targets—by evaluating broader campaign performance rather than relying on a single KPI.
What to look out for:
All experiments, including conversion lift studies, are centralized under one Experiments page.
More experiment and measurement capabilities are on the way.
Support for manager accounts (MCCs) and the Google Ads API will start rolling out soon.
Why it matters. Creative assets are crucial in Performance Max campaigns, but testing new assets always carries some risk. With these new tools, I can validate my creative decisions using data before fully committing any budget.
Stay ahead of the curve. As Google continues to invest in automation and AI-generated creative, asset testing becomes even more vital. Being able to compare human-crafted, seasonal, and AI-generated assets provides deeper insights into what excels in Performance Max campaigns.
The takeaway. Google is empowering Performance Max advertisers like myself with sophisticated testing capabilities. I find it easier than ever to evaluate creative changes, measure results across multiple KPIs, and manage experiments from one place.
First sighted by. This update was first spotted by PPC News Feed.
I’m thrilled to share some exciting news from Microsoft Advertising. They’ve made a significant leap in Performance Max reporting by adding conversion and spend data to PMax placement reports. This means I now have a much clearer understanding of how my ad placements are performing, which is fantastic for optimizing my campaigns.
What’s happening. According to Microsoft Ads Product liaison Navah Hopkins, the PMax Website Publisher URL report now includes conversion and spend metrics. This update takes us beyond just seeing where our ads appear; it lets us see actual performance data in action.
This new visibility allows me to pinpoint exactly which placements are driving meaningful results, not just impressions or clicks. It’s a game-changer for understanding what really works.
Why we care. Having this level of detail means I can make smarter decisions about where to allocate my budget. It helps me scale successful inventory and eliminate waste, providing a stronger foundation to trust Performance Max’s capabilities with tangible data rather than estimates.
How advertisers can use it. This update opens several practical doors. I can leverage high-performing placements to shape my Audience Ads strategies, like building remarketing campaigns or targeting audiences based on successful inventory.
At the same time, I can spot placements that aren’t a good fit and exclude them using account-level URL exclusion lists. This not only protects brand safety but also boosts efficiency.
Between the lines. This development further enhances the transparency of automated campaigns. It’s evident that while automation handles much of the heavy lifting, platforms are keen on giving us advertisers clearer insights into what’s effective and where we need to intervene.
What to watch:
Will this transparency extend even further in PMax reporting?
How will advertisers balance the power of automation with manual tweaks?
Could similar reporting features be rolled out across other platforms?
In the evolving world of B2B marketing, Performance Max has emerged as a powerful, yet often misunderstood, tool. Over the years, I’ve witnessed its transformation from an uncertain trial to a crucial part of my B2B marketing toolkit.
The core principles still hold true: skepticism is essential, first-party data remains invaluable, and experimentation is a must. Google has improved in integrating these elements, making it important for me to adapt my strategies accordingly.
Let me share five best practices that have helped me enhance my Performance Max campaigns effectively.
1. Guide AI with the Right Inputs
In 2022, as Google aggressively promoted automated PMax campaigns, I predicted a surge in AI integration. This shift has indeed occurred, driven by competitors like ChatGPT. AI Max for Search and PMax have taken center stage, with improvements making PMax more viable for the B2B landscape.
Some updates I’ve embraced include search themes for precise targeting, brand exclusions to control costs, and account-level channel reporting, which allows me to see performance across all campaigns. By segmenting conversion metrics, I can identify and optimize on overperforming channels.
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2. Address Persistent Lead Quality Issues
B2B lead quality has always been a concern in search campaigns. PMax’s lack of control has made it even more challenging. To combat this, I’ve relied heavily on offline conversion tracking (OCT). It’s a vital element for successful B2B campaigns.
In addition to OCT, I’ve been using enhanced conversions for leads, along with reCAPTCHA, to reduce low-quality leads from my PMax campaigns.
3. Build Stronger Audience Signals
With the end of third-party cookies and the phasing out of Similar Audiences, I’ve focused on leveraging PMax’s audience signals. By feeding high-quality first-party data to the AI, I’ve managed to target the right prospects efficiently.
Cleansing and segmenting CRM data to create robust audience lists close to revenue points are pivotal to capturing valuable new users.
4. Make Creative a Performance Lever
Creative content plays a crucial role in engaging the right audience. Given YouTube’s significance in PMax campaigns, producing quality video content is more critical than ever. Google’s new tools for AI-generated assets and creative A/B testing have made this process much easier.
Testing these elements helps me identify what truly resonates with my audience and optimize accordingly.
5. Use Reporting to Drive Decisions
Transparency in results has been a sticking point with PMax, but recent reporting updates from Google offer more insights than before. Utilizing search term insights and auction insights provides me with clarity on performance metrics, enhancing my optimization capabilities.
With asset-level reporting, I can see how creative assets perform and make data-driven decisions to boost my campaigns’ success.
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Make Performance Max Work for You
These updates have made PMax a more practical tool for B2B marketers like me, especially when equipped with strong first-party data. I always strive for more control and transparency, balancing Google’s tools, and leveraging every resource available to optimize my campaigns.
Stay ahead by exploring the latest Google releases that add visibility and control, making Performance Max truly work for you.
Recently, I discovered Google’s latest addition to their Google Ads arsenal: the Association metric in Brand Lift Studies. This innovative feature reveals how consumers connect brands with essential attributes, bridging the gap between awareness and consideration.
Google is addressing a critical gap by providing advertisers with a clearer view of how their brand is truly perceived—not just recalled.
What’s new. With this update, Google Ads introduces a fresh “Association” metric within Brand Lift Studies. As advertisers, we can specify a concept, category, or attribute, and Google will survey users to determine which brands they associate with these ideas.
How it works. This revolutionary metric evaluates whether audiences link our brand to a desired positioning—such as “premium” or “sustainable”—offering a sophisticated perspective on brand perception.
Why we care. This new metric allows us to measure brand positioning, not just surface-level awareness or recall. It’s crucial to understand if our campaigns genuinely influence how consumers perceive our brand—vital for those targeting specific attributes or categories.
Between the lines. Previously, Brand Lift focused on awareness, recall, and consideration. Now, Association dives deeper, illuminating whether our messaging shapes how people perceive our brand, beyond mere recognition.
The catch. However, there’s a catch: we can only choose three Brand Lift metrics per study. Adding Association requires us to balance the existing KPIs.
The bottom line. Association provides a strategic perspective on brand building, enabling us to measure whether our intended messages resonate with consumers.
First seen. This update was first spotted by Google Ads expert, Thomas Eccel, who shared the news on LinkedIn.
I’m thrilled to share that Microsoft is simplifying the process of expanding Google PMax campaigns into Microsoft, allowing us to enjoy greater visibility and control over our campaign performance.
Microsoft Advertising is launching several updates to make managing, measuring, and migrating Performance Max campaigns more straightforward, especially for those of us already familiar with Google Ads.
Driving the news. Microsoft now allows us to import Google PMax campaigns with new customer acquisition (NCA) goals, a feature that’s been part of Microsoft since earlier this year.
The update is live for all advertisers now, enabling us to transfer campaigns focused on first-time buyers more seamlessly, without having to start from scratch.
What’s new. Microsoft ensures that when we import Google PMax campaigns with NCA goals, they will be retained if they don’t already exist in our account. Our existing settings won’t be overwritten.
Regarding audience lists:
Google website visitor segments transform into Microsoft remarketing lists.
Google’s “all visitors” and “all converters” lists map to similar lists on Microsoft.
For unsupported lists like Customer Match, we may need to use alternate options.
I’ve also noticed that Microsoft takes a cautious approach with “unknown” customers, categorizing them as existing customers to avoid inflating new customer conversion counts.
Why we care. This initiative could streamline cross-platform campaign expansion and reduce the hassle of rebuilding, making it simpler to test Microsoft’s PMax inventory. Plus, enhanced landing page reporting and search term insights offer a clearer picture of campaign performance, aiding our optimization and budget decisions.
More visibility for PMax. Microsoft is integrating landing page (Final URL) reporting for PMax campaigns, allowing us to review spend, clicks, impressions, conversion value, and ROAS by landing page.
We can also break this information down by campaign, asset group, and other dimensions.
Additionally, Microsoft stated that search term reporting will become more apparent by default, with more transparency updates such as auction insights and publisher URL metrics rolling out soon.
Other key updates:
Seasonality adjustments now support portfolio bid strategies, aiding short-term promotions.
Campaign name limits have increased, enabling up to 400 characters for easier management.
Autogenerated assets are improving ad relevance and performance by filling in underused Responsive Search Ads.
Merchant Center users can directly update store names and domains without needing support.
The bottom line.These updates simplify scaling across platforms, save time on campaign setups, and enhance our visibility into campaign performance, giving us greater control over efficiency and outcomes.
As someone deeply invested in the world of digital advertising, I’ve noticed that Google is making a significant change. They’re moving away from impression-based planning and encouraging us to adopt more conversion-focused strategies.
Recently, I learned that Google’s Performance Planner tool has refined its scope. They’re now emphasizing conversion-focused campaign types, leaving behind the traditional impression-based planning style.
What’s happening? Last month, Performance Planner stopped supporting planning for Display and Video campaigns. This adjustment also means that metrics like impression share, top impression share, or absolute top impression share are no longer viable on their platform.
Why this matters to us. This shift away from impression-focused planning affects how we forecast and optimize campaigns concentrated on brand awareness. Google’s push towards conversion-focused and automated strategies challenges us to rethink our approach to upper-funnel tactics.
The bigger picture. It’s evident that Google Ads is prioritizing automation and performance-driven results. They are aligning their tools more with campaign types like Search, Shopping, App, Demand Gen, Local, and Performance Max.
How it’s working now. We can continue using the Performance Planner for supported campaign types, but any plans that included Display or Video campaigns, based on impression share metrics, are no longer editable or viewable.
What I’m watching. I’m curious about how we’ll adapt our planning and forecasting strategies for upper-funnel channels like Display and Video now that they lack native support in Google’s tools.
Bottom line. Ultimately, Google’s focus on performance-driven planning means that impression-based strategies might soon be a thing of the past. It’s time to embrace the shift towards conversions.
Embrace audience engineering to influence AI decisions, manage ad spend wisely, and connect with high-value customers through creativity and data.
I’m witnessing a significant transformation in the paid media landscape as platforms shift from manual targeting to AI-driven audience discovery. This change is redefining how we approach advertising, with automation tools consolidating campaigns, obscuring data, and favoring prediction algorithms over manual selection.
This transition requires me to innovate by mastering the art of audience engineering. By doing so, I ensure I’m equipped with strategies to thrive in this evolving landscape.
The End of Manual Targeting as I Knew It
Previously, I depended on detailed keyword lists and demographic filters to pinpoint my ideal audience. I directed platforms about where to focus and paid to access the desired market.
However, these options are now outdated:
Google has transitioned to Performance Max, which eliminates keyword-specific targeting in favor of more fluid groups and signals.
Meta’s Advantage+ automates demographic focus, turning my role into that of a signal provider instead of an audience selector.
Microsoft’s inclusion of this model confirms this is an industry-wide evolution.
While traditional targeting seems to have vanished, it has merely moved to the internal structures of the platforms where algorithms dictate the direction based on their indigenous data.
The Rise of Audience Engineering
My role shifts from targeting to engineering as it becomes more about guiding algorithms than manually selecting audiences.
From Targeting to Teaching
The distinction is crucial. Traditionally, targeting emphasized choosing audiences, but now it’s about educating AI with comprehensive conversion data, targeted creativity, and insightful first-party data.
Previously, I might have targeted CFOs with job filters, but now I feed the AI robust data (e.g., “deal closed” signals) to characterize valuable prospects and devise creative content tailored to their needs.
The New Competitive Discipline
Embracing this transformation gives me an edge. By finetuning conversion signals, honing creative content, and fortifying data systems, I ensure our performance remains robust.
The performance gap now relies on the quality of signals, making audience engineering pivotal for success.
The Three Levers that Now Drive Targeting
I focus on optimizing these three crucial AI inputs to ensure effective audience segmentation:
1. Conversion Signal Quality
By providing the algorithm with relevant business outcomes rather than superficial metrics, I encourage it to find results that truly matter.
Using tools like Offline Conversion Imports (OCI) and the Conversions API (CAPI), I ensure our data highlights genuine sales by leveraging value-based bidding techniques.
2. Creative as a Targeting Mechanism
With no demographic filters, my creative content now acts as the primary targeting tool, filtering users through its message.
If my creative targets niche pain points, the AI connects with users aligned with that perspective, even without traditional filters.
3. First-Party Data as Competitive Moat
Our customer lists and engagement signals become core learning elements for the algorithm, replacing third-party signals and offering a competitive edge.
Essentially, I’m arming the AI with a guide to discover the most profitable audiences.
How This Plays Out in Real Campaigns
The journey to AI-led targeting isn’t just theoretical. Within our agency, managing over $215 million in media spend annually, we have evaluated this approach across different platforms, witnessing its power firsthand.
Advantage+ Audiences in Practice
One long-standing client had a specific perception of their audience based on a vast history of accurate data. Initially, our campaigns ran with tightly controlled targeting to maintain efficiency.
Transitioning to Advantage+ allowed for data-driven optimization, revealing an unexpectedly lucrative older demographic, improving their click-through rates by 37% and conversion rates immensely.
Broader AI-optimized targeting cut costs and raised revenue — outperforming past manual methods.
By aligning goals with data and creative, we found valuable segments conventional targeting schemes previously overlooked.
Microsoft PMax Placement Transparency and Advanced Audience Signal Targeting
Another client benefited from a Microsoft PMax test, effectively targeting high-intent prospects using internal data across several Microsoft networks, seeing notable increases in performance metrics each month.
This trial highlighted the importance of combining strategic oversight with smart AI deployment, enhancing the algorithm’s reach while maintaining disciplined campaign direction.
The balance between scale and strategic input preserved efficiency and bolstered overall performance.
The Risks Nobody is Talking Enough About
While automated targeting offers significant advantages, it’s essential to understand its limitations. Here’s what I strive to avoid:
Garbage In, Garbage Out
Poorly defined conversion objectives, weak data quality, or junk data hinder performance and mislead the algorithm. Feeding it quality information and focused outcomes is crucial.
An overly broad goal without distinct signals results in quantity over quality, which doesn’t necessarily translate to business success.
The Self-Reinforcement Trap
If the seed data has biases, the AI will continuously optimize for those biases, possibly neglecting valuable audience segments.
These underrecognized biases present inherent risks in leveraging automated systems without mindfulness.
Automation Without Oversight
Platforms promote broad automation, but I recognize the need for continued oversight to realign campaigns with business goals.
Constant monitoring is essential to ensure objectives are met, avoiding a passive management style.
Creative Complacency
As automation advances, creative strategy becomes a crucial differentiator and shouldn’t be neglected.
Crafting compelling creative that addresses core customer issues is vital in distinctively standing out.
How to Put Audience Engineering into Practice
Here’s how I integrate audience engineering into everyday operations:
Restructure Creative: Focus on intent signals, addressing what beliefs inspire conversion.
Predefine Guardrails: Establish performance boundaries before unleashing the algorithm, allowing for better campaign control.
The Future Belongs to Audience Engineers
The era of manual targeting is closing, but precision remains crucial. Audience engineering acts as an invaluable skill, unlocking AI’s full potential to achieve maximum results in this dynamic landscape.
Recently, I discovered that Google has launched an exciting new feature for Performance Max campaigns. As an advertiser, I’m always on the lookout for tools that provide clearer insights, and this new channel performance timeline view does just that. It offers a comprehensive breakdown of how different channels like Search, YouTube, and Display contribute to my campaign results over time.
What’s New
The latest update introduces a timeline graph that showcases channel-level contributions over a selected period, complete with investment and performance filters. This means I can quickly identify which channels are excelling and which ones might need a bit more attention.
The chart features helpful visual cues—like a yellow box highlighting channel performance evolution over time, and a pink box indicating different ad types, such as All Ads, Ads Using Product Lists, and Ads Using Video.
Why I Care
Managing Performance Max campaigns across multiple channels often left me guessing about where my budget was working best. This new view provides valuable insights into channel-level trends, allowing me to adjust strategies or budgets more efficiently. If I notice YouTube underperforming while Search is thriving, I can now make informed decisions without relying purely on guesswork or exported data.
The Big Picture
This new view empowers me to evaluate PMAX performance more effectively, without relying solely on Google’s automated decisions. Now, I can see consistent underperformance or excellence across channels, which guides my budget and asset strategies moving forward.
The Bottom Line
Though it’s not full transparency, this update is a significant move in the right direction. I now have a more structured way to detect trend anomalies in PMax campaigns early and make necessary adjustments to optimize performance.
First Spotted
This feature was first noticed by Axel Falck, Head of Search at Le Mage du SEA, who shared his insights on LinkedIn.
B2B buyers start their journey long before they even search for us. I’ve learned that AI-powered Google Ads campaigns can ignite early demand and reward patience over time.
If I’m relying solely on brand and non-brand keywords in Google Ads, my growth becomes limited. A decline in performance isn’t due to the platform but the strategy behind it.
Discovering a brand doesn’t begin with a non-brand search. Buyers are researching on platforms like Reddit, ChatGPT, Facebook, LinkedIn, and YouTube. They watch demos, read testimonials, and become familiar long before actively searching for us.
For complex sales processes with lengthy customer journeys, this transformation is crucial, demanding a strategic shift. Here’s how I can make it effective in B2B.
AI-powered Campaigns: Your Growth Treasure
Over the years, Google has innovated with multi-channel, multi-asset campaigns like Performance Max and Demand Gen. These campaigns place my brand front and center as audiences research and evaluate options.
When my audience is ready to choose vendors, they’ve already built trust in my brand. They’ll search specifically for me because of the trust I’ve cultivated through consistent visibility.
A well-rounded Performance Max campaign includes diverse ad types, like image and video ads displaying demos or testimonials on YouTube. These ads also engage audiences across the web via the Display Network and retarget them as they continue their research. This process naturally leads to branded searches that ultimately convert.
Such campaigns are cost-effective, allowing me to leverage customer data alongside keywords as intelligent signals, not replacements. It’s about smarter keyword usage.
As AI Overviews and AI Mode transform Google’s search results pages, it’s time I reconsider my ad strategies to align with these changes.
I’m fond of the 4S framework: search, scroll, stream, and shop.
Adding “ask” captures how people now engage with AI tools. They consult ChatGPT or Gemini, search on Google, scroll through LinkedIn, stream videos on YouTube, and shop across numerous platforms. If my strategy focuses on only a couple of these behaviors, I’m missing the full growth opportunity.
Solely targeting keywords means missing the larger narrative. Brand keywords undoubtedly convert better, but how do people arrive at searching my brand? Consistent visibility ensures they notice my brand in their feeds.
Embrace Testing and Learn with Patience
This strategy requires time, especially in B2B settings with protracted sales cycles.
For example, it took almost a year to appreciate how Performance Max contributed to one of my life science client’s success, whose deals typically take months to finalize. There was a moment where our account manager nearly paused the campaign because initial data wasn’t promising.
Integrating sales data changed the perspective. As revenue figures rolled in, the campaign’s value became transparent.
If I can sync beyond MQLs with data like Proposal Sent, it keeps Google well-informed and offers reassurance until the sales data solidifies our insights.
Patience is key when providing the system quality data. I must remain steadfast and avoid quitting prematurely, accepting the complexity of B2B cycles.
An event might draw 100 people, some catch a webinar email later, and months pass before they search for us and request a proposal, eventually becoming customers. With long sales cycles, phenomena like this unfold subtly.
If testing funds are limited, I can designate 5% to 10% for AI-forward campaigns. Strategic testing without major commitments at peak times allows room to maneuver while the system adjusts.
Investing time in this strategy ensures sustainable growth. Those who master it gain an enduring competitive edge, unlike those focused on diminishing demand.
I’ve recently discovered an exciting update from Google that makes managing seasonal campaigns a breeze. Their new Asset Group Theming feature is a game changer inside Performance Max, allowing me to quickly apply seasonal themes to existing asset groups without having to start from scratch.
Here’s How It Works: I can clone a top-performing asset group and apply a theme. Google then takes care of generating themed image variations and suggesting headlines and descriptions that match, all while keeping the original group intact. This way, I can safely test new themes without any risks.
The Themes Available:
Promotional: Sale, Studio/Editorial
Seasons: Winter, Spring, Summer, Fall
Cultural moments: Christmas, Black Friday/Cyber Monday, Halloween, Valentine’s Day, Easter, Mother’s Day, Father’s Day, Hanukkah, New Year, Lunar New Year, and Back to School
Where to Find It: I find the theme application option inside Asset Groups ahead of major holidays, or by selecting “Apply theme to existing asset group” while setting up a new one.
Important Note: This tool is a starting point, not a complete solution. It uses existing images and adds themed backgrounds without replacing videos, and only updates a few headlines. Everything still requires review to ensure it fits the campaign before going live.
Why This Matters: Seasonal creative refreshes used to consume a lot of time, especially when factoring in design resources and the risk of performance drops with asset changes. This feature minimizes that hassle, allowing me to adapt my best-performing strategies quickly.
The Bottom Line: Think of this as a creative assistant, rather than a designer replacement. For those of us juggling multiple seasonal peaks, the time savings alone make it worth exploring.
First Spotted: Google Ads specialist Bia Camargo first noticed this update and shared it on LinkedIn.
