Have you ever wondered where your Performance Max ads truly run? With the latest Google Ads API v23 update, we finally have the answer!
An exciting change has arrived with the v23 Ads API launch. Now, Performance Max campaign results can be broken down by channel, including Search, YouTube, Display, Discover, Gmail, Maps, and Search Partners. Previously, all your performance data was lumped together, obscuring critical insights.
Here’s the inside scoop. In earlier API versions, I always received a MIXED value for the ad_network_type segment in my Performance Max campaigns. But with v23, these results have transformed into distinct channel enums. It’s a major step forward for those of us who crave precision in reporting and optimization.
Why this matters to us. This update isn’t just about new features — it reshapes how we comprehend Performance Max. With channel-specific reporting now on the table, marketers gain much-needed clarity on where these ads are displayed.
How we can leverage this. Now, we can access channel-level data at the campaign, asset group, and even individual asset levels. This means we can observe how each creative piece performs across Google’s array of platforms. Coupled with v22 segments like ad_using_video and ad_using_product_data, the possibilities for optimizing video performance on YouTube or Shopping ads on Search are endless.
Attention, developers. Upgrading to v23 unveils a level of reporting detail that was previously unreachable. If your system relied on the old MIXED values, it’s time to gear up for the new channel enums.
Keep an eye out for:
Channel data is accessible only for dates beginning June 1, 2025.
Remember, asset group–level channel reporting remains exclusively within the API and is not visible in the Google Ads UI.
The takeaway. The newest Google Ads API rollout quietly transforms what was once a black-box campaign category into an analyzable channel-specific type. Finally, advertisers like you and me can dive into the metrics we’ve long sought.
Ads in ChatGPT signify a major transition from focusing on keyword intent to understanding user behavior. This evolution changes how we approach relevance, creativity, and performance measurement.
Currently, ads are being tested in ChatGPT in the U.S., appearing to various users across different account types. For the first time, we see advertising stepping into an AI environment designed for answering queries, which fundamentally changes the game for marketers like me.
AI has been an integral part of ad creation and planning across platforms like Google and LinkedIn for years. However, placing advertisements inside an AI that people trust to assist with thinking, decision-making, and actions is a completely new challenge. It’s not just another channel in our existing media strategy.
The primary concern for us isn’t targeting, but understanding psychology. Replicating strategies successful in search or social may lead to disappointing performance or even damage trust.
To thrive, brands must comprehend why users engage with ChatGPT, and what implications that has for capturing attention and enhancing the customer journey.
ChatGPT is a Task Environment, Not a Feed
When people use ChatGPT, they have a purpose. Whether it’s:
Solving a specific problem.
Refining a shortlist.
Planning a trip.
Writing something.
Making sense of a complex decision.
Unlike feed-based platforms, where users passively scroll and consume content, ChatGPT users are goal-oriented.
In such a task-centered environment, behavior shifts:
Goal shielding: Users focus narrowly on finishing tasks, filtering out distractions that don’t contribute.
Interruption aversion: When focusing, unexpected distractions feel more annoying.
Tunnel focus: Clarity and speed take priority over exploration.
This means gaining clicks will be more challenging than some advertisers might anticipate. If ads don’t assist users in progressing their tasks, they’ll seem irrelevant, no matter how topically aligned they might be.
Considering trust in AI is still being established, tolerance for distracting ads is particularly low.
Behavior Over Search Volume: Designing a Strategy for ChatGPT
Traditionally, search volume has directed our planning.
Keywords informed us about what users sought, how often, and the level of demand competition. This framework informed both SEO and paid media strategies.
However, ChatGPT changes this model. Instead of searching for keywords, users describe situations, ask detailed questions, and pursue outcomes beyond mere information.
Without query data to optimize, our success depends on understanding:
The task the user aims to complete.
The journey stages they’re outsourcing to AI.
The specific help they need at that moment.
This is where behavioral insights replace keyword demand as the foundational strategy.
Transitioning from Keyword Intent to Behavioral Targeting
Instead of centering our plans around queries, we should focus on behavior modes, representing the mindset of users when they turn to ChatGPT.
We can consider these modes as follows:
Explore mode: Users seek inspiration or shape a perspective.
Ads here should ignite ideas, offer options, or reframe the problem.
Reduce mode: Users aim to narrow choices effectively.
Ads should clarify differences, simplifying decisions.
Confirm mode: When users want reassurance, trust trials such as reviews or guarantees matter most.
Act mode: Users aim to complete the task, so ads that eliminate friction, like clear pricing, will succeed.
These modes correspond with recognized human drivers in search behavior: forming perspectives, informing, reassuring, and simplifying. ChatGPT condenses these moments into one interface.
The key shift is that relevance in ChatGPT is not merely about a match but about functionality.
An ad can align with a category but still fall short if it doesn’t help users with their tasks. Anything creating extra work or that distracts from goals feels frustrating in a task environment.
High-performing ads are likely to act less like traditional ads, and more like:
Tools.
Templates.
Guides.
Checklists.
Shortcuts.
Decision aids.
Such ads integrate seamlessly into user workflows.
Generic brand ads, mere awareness messages, and content serving as detours are likely to underperform.
The assets that create compelling ChatGPT ads—guides, frameworks, and reassurance-focused content—do more than boost paid performance. They enhance authority for SEO, earn media coverage for digital PR, and strengthen brand trust across social and owned channels.
Here, silos can break performance.
Paid media teams cannot create “helpful ads” in isolation while SEO focuses on authority, PR works on trust signals, and brand teams shape voice independently. AI-driven discovery blends these signals.
The best-performing ads may rely on:
Brand voice for consistency.
Trusted voice from reviews, experts, or validation.
Amplified voice through media coverage and authority.
The line between advertising, content, and credibility is increasingly blurred.
Rethinking Measurement
Evaluating ChatGPT ads purely on click-through rates risks missing their broader influence. These ads might sway decisions without triggering immediate clicks, aiding in brand recall or re-entry through different channels.
More significant indicators might include:
Shortlist inclusions.
Brand recall.
Assisted conversions.
Branded search increases.
Direct traffic improvements.
Conversion boosts further down the line.
This underscores the need for cross-department collaboration. If performance spans the customer journey, so too must measurement and accountability.
This is not just a new ad format; it’s a shift in behavior. Brands that succeed will deeply understand:
What people use ChatGPT for.
Journey stages being shifted to AI.
How to support these moments without losing trust.
We should revisit jobs-to-be-done thinking, mapping actions leading up to a purchase, inquiry, or commitment, and identify where AI reduces effort, uncertainty, or complexity.
This approach empowers us to ask, not simply, “how do we advertise here?” but “how can we be genuinely helpful when it counts most?”
Adopting this mindset will not only shape performance in ChatGPT but influence the broader future of AI-led discovery, where understanding behavioral intent will surpass the old focus on keywords.
For years, I’ve been part of countless discussions about paid media, all revolving around the same question: should we focus on building in-house teams or outsource to agencies?
While this debate is certainly valid, it often overlooks the core issue at hand. The real challenge isn’t where paid media is placed within our organizational chart. Instead, it’s all about how we structure performance leadership.
Many companies, including the ones I’m familiar with, navigate Google Ads and other paid channels with capable teams, solid budgets, and well-documented best practices. Campaigns are active. Dashboards appear full. We keep optimizing as scheduled. Yet:
Results stall.
Pipelines flatten.
Budgets get questioned.
Confidence in paid advertising erodes.
This is hardly a talent issue. Rather, it’s often a structural one.
The Plateau Most In-House Teams Eventually Hit
Across several B2B paid media accounts, ranging from SaaS to service businesses with monthly spends in the five-figure range, I’ve noticed a recurring pattern.
Performance doesn’t just drop overnight. It slows gradually.
Campaigns continue running. Costs seem stable. We still gather leads. But growth comes to a halt. Leadership observes motion without gaining insight. Decisions turn reactive. Paid media shifts from a growth engine to a cost center that must justify its existence.
The gap lies not in effort or execution. Over time, strategy narrows when teams work in isolation.
Why ‘More Headcount’ Rarely Fixes the Problem
When performance slows, the immediate response is often to hire more staff. This could be a new specialist, a channel owner, or someone in a more senior position.
While additional resources might alleviate workload, simply increasing headcount doesn’t usually solve the actual problem.
In my experience with in-house teams, three challenges are consistently present:
1. Tracking and Leadership Visibility
Often, leadership teams lack a unified and clear view of how paid media impacts pipeline and revenue. The data is out there, but it’s scattered across different platforms, tools, and dashboards.
Without strong integrations, even well-executed campaigns operate with weak feedback loops, which limits their potential for improvement.
2. Structure and Skill Ceiling
Many teams strive to adhere to proven best practices. The problem isn’t their intent but the context. What works for one company or growth stage can be ineffective, or even detrimental, for another.
Without external benchmarks or fresh perspectives, teams struggle to determine what truly applies to our business.
3. Lack of Systematic Testing
Daily execution consumes the available capacity. Teams focus on maintaining stability instead of driving performance forward. Testing becomes intimidating despite the fact that real gains usually emerge from the few experiments that succeed.
Over time, this creates an illusion of optimization: steady activity without significant progress.
The Same Mistake Happens Before Ads Even Launch
These structural problems don’t just affect companies already engaged in paid media. They often arise earlier, before the first campaigns even begin.
In many B2B companies, paid advertising becomes relevant when growth from outbound sales, partnerships, or organic channels begins to slow.
Budgets are cautiously allocated. Execution is delegated. Results are expected to spring forth from platform defaults.
What’s typically missing is strategic ownership:
Clear definitions of success that go beyond surface-level metrics
Tracking that ties spend to pipeline, not just lead volume
A testing roadmap aligned with revenue goals
Without this foundation, initial results are often disappointing. Budgets are cut. Confidence wanes. Paid media is labeled ineffective before it gets a real chance to show its worth.
Ironically, this early phase is where an external perspective can have the greatest long-term impact. It’s also the phase when companies are least likely to seek it.
The Structural Advantage of Outsourced Performance Leadership
Outsourcing is often seen as a cost-cutting measure or a way to boost execution power. In reality, its major advantage lies in perspective.
External performance teams work across various accounts, industries, and growth stages. They:
Identify patterns earlier.
Recognize when platform recommendations favor spend growth over business outcomes.
Challenge assumptions that internal teams may no longer question.
That outside view is crucial in areas like tracking architecture, platform integrations, and account structure, where partial adoption of best practices can subtly undermine performance.
A typical scenario looks like this:
Teams adhere to platform guidance but leave underlying martech gaps unresolved.
Systems fail to communicate effectively.
Optimization signals weaken.
Budget efficiency drops, even though campaigns seem fully compliant.
When Outsourcing Actually Works — And When It Doesn’t
Outsourcing isn’t a one-size-fits-all solution. It falters when companies expect external partners to improve performance in isolation, or when strategy and execution exist in separate realms.
It thrives best as a hybrid model:
Internal teams manage execution and business context
External experts provide strategic direction, structural adjustments, and continuous challenge
In this structure, partners don’t replace teams. They elevate them.
That’s why a specialized Google Ads agency offers the most value when our goal goes beyond running campaigns to transform paid media into a predictable, scalable growth driver.
A Smarter Model: External Strategy, Internal Execution
High-performing organizations increasingly separate strategy from execution volume.
We bring in outside expertise not because something is broken, but because we desire:
Objective assessments of performance and structure.
Stronger attribution and tracking foundations.
Disciplined experimentation frameworks.
Clear accountability at the leadership level.
This method builds momentum before budgets get cut, and not after results decline. It also helps leadership comprehend why paid media performs the way it does, thereby restoring confidence in the channel.
What High-Performing Companies Do Differently
Organizations that avoid prolonged plateaus tend to:
Consider paid media a system, not a standalone channel.
Invest early in clear tracking and robust integrations.
Welcome external challenges before performance drops.
Accept that most tests will fail, knowing the few successful ones will compound.
In this context, outsourcing isn’t about cost efficiency. It’s about maintaining strategic acuity as platforms and markets evolve.
Final Thought
The in-house versus outsourced debate oversimplifies a deeper question: who owns performance direction, and how often is it challenged?
As paid media platforms continuously evolve and automate, the companies that sustain growth aren’t those with the largest teams, but those with the clearest perspective.
As I delve into Google Ads API v23, I’m excited to share this update marks the beginning of a faster-paced release cycle in 2026. With this update, I’m now able to access improved Performance Max reporting, sophisticated AI-driven audience tools, and more detailed campaign controls.
What’s new:
Performance Max Transparency: I’ve discovered that PMax campaigns now offer ad network type breakdowns, making it easier for me to analyze performance.
More Detailed Invoices: Through InvoiceService, I can retrieve campaign-specific costs, regulatory fees, and adjustments, allowing for more precise financial tracking.
More Precise Scheduling: It’s a game-changer for me to now schedule campaigns using precise start and end date-times instead of limiting to date-only fields.
Local Data Access: I’m now able to access store location details via PerStoreView, which matches the data in the Stores report accurately.
New Audience Dimension: With life-event-based audience building through LIFE_EVENT_USER_INTEREST, my Insights tools are more powerful than ever.
Smarter Demand Gen Planning: The conversion rate forecasts I rely on now vary by surfaces such as Gmail and Shorts, enhancing my strategy planning.
Generative AI Audiences: I can efficiently translate free-text audience descriptions into structured attributes, simplifying audience target creation.
Expanded Shopping Metrics: The inclusion of new competitive and conversion metrics by conversion date helps me improve my shopping ads performance.
Why I care: A quicker update cycle means I can leverage new features faster. With Google’s shift towards automation and AI-driven insights, staying on top of these updates helps me optimize campaigns effectively.
Between the lines: These updates require my team to upgrade client libraries and code, so scheduling development time is crucial to benefit fully from v23.
Bottom line: The Google Ads API v23 is setting the stage for 2026. I’m ready to embrace these improvements that introduce faster releases coupled with enhanced AI insights, refined reporting, and better campaign control for large-scale advertisers.
Working as an office manager in my early 20s, I discovered Dale Carnegie’s “How to Win Friends and Influence People.”
The timeless principles in that book have been my guiding compass through various career shifts. I’ve realized that success in most professions hinges on how we interact with others—be they clients or colleagues.
For many years, combining human touch with technical skills has been a winning formula for digital marketers. It was this ability to demystify complex machines coupled with strong relationship-building that allowed agencies to retain clients.
But now, this model is under scrutiny as AI becomes integral to PPC platforms, raising a pertinent question: why shouldn’t clients dive into an entirely AI-driven approach?
What agencies have an edge on is their relational strength—their ability to communicate effectively and understand what business owners genuinely need.
1. Ask questions
I’ve learned that one of the most effective ways to understand people and what makes them tick is by asking questions. Though it seems straightforward, communication often becomes lost in translation or obscured by assumptions.
Whenever I walk into a sales call, I arm myself with a list of questions. How much can I uncover about this potential client in a brief half-hour conversation?
Similarly, during strategy discussions, I prepare a comprehensive set of queries—some for myself, and some for the client. What are they aiming to achieve? What aspects of their current strategy need refinement? How can we enhance it?
To this day, AI can’t fulfill this role—not yet, at least. Our exchanges with AI remain predominantly one-sided.
AI doesn’t actively seek to understand us as individuals or identify our unique challenges. These discoveries only come from asking questions and actively listening, which leads to the next point.
How often do I find myself in conversations, impatiently waiting for a pause to insert my thoughts? I’m guilty of this, but I’ve found that clients crave the opportunity to be heard.
Allow them to express themselves fully, encourage them with more clarifying questions, and just keep listening. It’s remarkable what you can learn about someone when you enter a conversation with no other agenda but to understand the other person.
Fill the silences only if they become awkward, and if you have valuable agenda points to address based on what you’ve learned. This approach fosters collaboration and generates ideas more swiftly than dominating the conversation could. It solidifies agreement, which is foundational in building relationships.
Whenever possible, I aim to discover commonalities between myself and new acquaintances. By doing so, I build rapport, enriching both personal and professional relationships.
Being personal and specific, whether dealing with a friend or a client, is key. I love recalling little details about people and bringing them up in future conversations. People appreciate being remembered and valued.
Though AI is beginning to develop memory, finding shared experiences with others is a uniquely human skill that, fortunately, remains beyond AI’s reach.
In the fast-paced marketing realm, it’s easy to succumb to the all-consuming cycle of data analysis and testing. Remember, though, not to take ourselves too seriously.
After all, this profession is relatively new, and its evolution is unpredictable. Let’s not forget why we ventured into marketing—to help and connect with people. Let’s embrace opportunities to be less serious and inject humor when it fits.
We’re human, and it’s vital for those we work for to recognize this humanity as an integral part of any relationship.
In a world increasingly dominated by AI, the focus is shifting from technical prowess to personal connection. AI excels at data and analysis, available at a moment’s notice, but knowledge alone isn’t sufficient anymore.
Empathy, shared experiences, and true rapport are beyond AI’s capability to replicate. These human principles, combined with expertise, are what enabled agencies to decode machines for clients and nurture enduring relationships.
By returning to relational basics—posing insightful questions, practicing active listening, and establishing common ground—agencies can affirm their indispensable value.
These relational skills are vital in distinguishing a partner from an algorithm, ensuring that the work of agencies remains not just relevant but essential.
I’ve noticed something pretty exciting in Google’s recent update to Performance Max. They have introduced one-click ad previews, making it incredibly easy to review creatives directly from the asset group table. This update feels like a breath of fresh air to anyone who’s ever been bogged down by the previous clunky process.
What’s new? Now, with just a click on any image or video within the Asset Groups table, I can instantly see how my ads will look across different Performance Max placements, without needing to navigate away from the page.
Why we care. Before this, checking ad previews meant jumping through various hoops into different views or settings. Now, everything is streamlined, keeping my workflow smooth and efficient, which makes creative quality assurance and iteration a lot less of a hassle.
Between the lines. There has been consistent feedback about the transparency limitations of Performance Max. So, even these small UI changes that bring creatives to the forefront are a big deal for me and many others in the field.
The bottom line. While one-click previews aren’t a game-changer in terms of strategy, they are a real time-saver. This especially helps when I’m handling large asset libraries or frequent creative updates.
First seen. This handy update was first spotted by Paid Search marketer Bia Camargo, adding another reason to appreciate these nuanced yet impactful changes.
I recently discovered the potential of Google AI Max and, like many of us, wondered if my account is ready to harness its power. Google AI Max promises to unlock additional conversions if set up correctly. Before jumping in, I knew I had to ensure everything was primed and in place.
Google’s AI Max is designed to transcend traditional keyword targeting by utilizing various signals to determine ad displays. It’s a game-changer for those with a history of broad match success. However, if not optimized, it could quickly deplete your budget.
One important clarification: using AI Max is not mandatory for ad appearances in AI Overviews. Broad match keywords can place ads in AI Overviews regardless of AI Max usage. I see AI Max more as a tool to expand conversions beyond mere AI Overviews.
We’ll explore the essential steps to review before testing AI Max. These insights are crucial to ensure our campaigns are fully prepared.
What to Check Before Enabling AI Max
Accurate Conversion Tracking
Having precise conversion tracking is vital. AI Max optimizes based on our defined success metrics. Inaccurate or inflated conversions can lead to poor AI decisions. This insight made me double-check everything.
Automated Bidding with a Conversion-Focused Strategy
For broad match to function optimally, a conversion-centered bid strategy is necessary. Options like ‘Maximize Conversion Value’ or ‘Target CPA’ should align with your updated strategy. My experiments indicated more consistent results with target bids than max bids.
Using max bids without watching over budget and collected data might not yield the best results. I’ve learned to keep a careful eye on it.
Conversion Volume
AI Max needs sufficient data to perform well. With over 100 conversions monthly, its reliability has been strong, provided there’s a positive history with broad match. Based on this, I aimed to test in campaigns with at least 30 monthly conversions.
No Impression Share Lost Due to Budget
If budget constraints already hinder impression share, AI Max could exacerbate this issue. Prioritize spending on top keywords and let AI Max utilize remaining funds for experimentation.
Proven Broad Match Success
AI Max treats keywords as broad match and extends beyond them. Without past success, it could be ineffective. Preparing through ad group optimization and new ad testing has been my strategy.
Should You Use URL Expansion?
Enabling URL expansion allows Google to pick any webpage for landing when AI Max triggers an ad. However, indiscriminate use can be detrimental—excluding non-conversion-oriented pages mitigates risks.
Those who created landing pages for specific geographies should carefully manage page exclusions to avoid mismatching.
Should You Try Automatically Created Assets?
I’m hopeful about automatically created assets. They can significantly enhance messaging but require caution to avoid irrelevant sitelinks and incompatible callouts. Establishing clear guidelines ensures alignment with brand objectives.
How to Test AI Max
Because of its performance inconsistencies with brand keywords, I’ve found it best to initially focus on non-brand keywords in AI Max tests. Starting with successful ad groups rich in conversion data offers the best chance to test its potential.
Operating AI Max at the ad group level via the Google Ads Editor proved efficient in my testing experience.
Is Your Account Ready to Test AI Max?
As AI Max continues to evolve, its integration into our existing systems may provide significant advantages. But, readiness involves assessing if our accounts meet all setup criteria before diving in. By following my steps, you’ll recognize its readiness and potential for success.
Have you ever scrolled through your Facebook feed, searching for ad inspiration?
If so, you might have noticed that most ads don’t really grab your attention. Let’s be honest, scrolling through Facebook can feel oddly exhausting these days.
Here’s the reality: the top-performing ads in 2026 aren’t winning because they’re exceptionally original or going viral (does that term still hold?).
They stand out by adhering to reliable templates that savvy marketers have relied on for years.
Even today, with AI and creative strategies, these frameworks remain as relevant as ever.
In this article, I aim to bypass the conceptual buzz and focus on proven strategies.
Below, I share four Facebook ad templates to boost your results, each with real examples showcasing top brands’ creative strategies.
1. Problem? Meet solution
Pain point → Relief → Simple next step
This classic approach has stood the test of time, unchanged from 1926 to 2026.
Customers are more focused on their own problems than on your business.
They ponder their challenges:
“I’ve spent too much money.”
“I lack time.”
“I’m feeling stuck.”
“I’m overwhelmed.”
“I can’t seem to stay consistent.”
You need to meet them where they are emotionally.
Customers won’t buy if they don’t see their situation as solvable.
Even as the best solution, recognition of the problem is crucial for them to seek answers.
Example: ClickUp
ClickUp converts a common tech frustration into an actionable solution:
Fed up with juggling numerous tools? Opt for an all-in-one platform to streamline everything.
The ad transcends “project management” by offering:
Mental peace.
A unified source of truth.
Reduced transition time, increased productivity.
Team cohesion.
An alluring promise of control.
Plug-and-play copy starter
Still dealing with [problem]?
You’re not alone – and you don’t have to stay stuck.
[Product/service] helps you [benefit] without [common objection].
I’ve noticed that OpenAI is introducing premium-priced ads on ChatGPT, but here’s something interesting: the data provided to advertisers is significantly limited compared to what we’re used to.
What’s happening. Reports indicate that OpenAI is offering ChatGPT ads at around $60 per 1,000 impressions. That’s about three times the rate of standard Meta advertisements! Yet, even with this higher cost, advertisers only receive basic metrics like total impressions or clicks, without insight into actions like purchases.
Why we care. ChatGPT is becoming a fresh, highly engaging ad space, but it’s not without its challenges. The hefty CPMs and limited insights mean that early advertising efforts will lean more toward enhancing brand presence and gathering learnings than achieving performance-driven efficiency.
For marketers who are open to trying new avenues, this presents a unique chance to gain insights into how ads function within AI-driven conversations before the format becomes more widespread or measurable.
The tradeoff. OpenAI is contemplating expanding its measurement capabilities in the future, yet it remains committed to user privacy. It has pledged not to sell user data or invade the confidentiality of conversations, which limits traditional targeting and attribution possibilities that platforms like Google and Meta offer.
Who will see ads. Initially, these ads will be available to those using ChatGPT’s free and lower-cost Go tiers, but won’t be shown to users under 18 or in conversations concerning sensitive topics like mental health or politics.
Between the lines. OpenAI is branding ChatGPT ads as a top-tier, trustworthy product, banking on the idea that context, focus, and brand safety can validate the higher pricing, despite the lack of detailed performance data.
Bottom line. Brands eager for prominent visibility in a cutting-edge AI-driven environment may find ChatGPT ads appealing, but those focused on performance metrics might hesitate due to the absence of detailed measurement.
I’ve learned over the years that Google doesn’t prioritize B2B marketers when developing new products. The focus initially lies with DTC and B2C brands, as they account for the largest budgets and transaction volume.
This pattern has repeated itself time and again in my advertising career. We saw this with responsive search ads and dynamic search ads. Performance Max follows this well-trodden path.
Back in the day, I would have firmly advised B2B companies against using Performance Max. However, in 2026, the scenario has changed significantly.
Not every B2B advertiser will benefit from Performance Max, nor should they. Here, I’ll explore who could benefit and who might be better off exploring other avenues. Testing new strategies is crucial if we’re to see meaningful changes in our results.
PMax 101 for B2Bs
If you’re reading this, I suspect you fall into one of three categories: Performance Max either isn’t working for you, you haven’t tried it, or you’re seeking improvements.
Let’s clarify a key point: Performance Max is a goal-based campaign model. It allows advertisers access to Google’s entire ad inventory through a single campaign. This includes YouTube, Display, Search, Discover, Gmail, and Maps, and we’re beginning to see ads in AI Overviews too.
If your industry already features ads in AI Overviews, you should seriously consider putting Performance Max on your radar.
Although encountering ads across Google’s network might seem daunting, especially if you’re working without a shopping feed, it’s no cause for concern. It’s a significant advantage, allowing us to reach individuals within the buying group who wouldn’t usually engage via traditional search.
Nurturing prospects through complex, lengthy sales processes is one area where Performance Max particularly shines for B2B marketers. Given that B2B decisions often involve multiple stakeholders over extended periods, a sustained advertising presence can be transformative.
What needs to be in place before testing Performance Max
Several prerequisites are necessary before diving into Performance Max. Importantly, you’re targeting signals, not keywords, and this distinction is crucial.
To set up successfully, you’ll need to connect your data source—such as Salesforce or your preferred CRM—and link it to meaningful online events, like qualified lead submissions or appointments booked.
Set your bid strategy to maximize conversions or target CPA, as Performance Max focuses on optimizing around outcomes rather than mere traffic.
Providing a customer list can further help the system identify and model similar characteristics, utilizing first-party data for better performance than website remarketing audiences alone.
Performance Max isn’t a blanket solution. If your B2B strategy depends on a limited, highly controlled target list, this may not be your best option.
Account-based marketing often yields better results with manual control when dealing with a few hundred named accounts.
The suitability of your market is another factor. A large addressable market is ideal, as targeting niche groups like private equity firms may not be as effective, with too little data for Performance Max to scale efficiently.
Additionally, if conversion actions are vague or poorly aligned with revenue, Performance Max may struggle to recognize what success looks like, leading to disappointing results.
Finally, if your organization is resistant to automation or frequently intervenes with adjustments, you may find Performance Max frustrating and destabilizing.
Performance Max isn’t a one-size-fits-all answer for B2B advertising, yet it’s now a viable option for many organizations when previously it wasn’t.
With the right elements—like strong conversion signals, a large market, and a willingness to let automation take the reins—it can significantly support long and intricate buying cycles.
It’s crucial to be honest about your data, your audience, and your organization’s readiness for automation. Without these, Performance Max can falter.
When conditions are conducive, it complements existing strategies by both capturing and generating demand effectively.
Success in B2B advertising isn’t about chasing the latest trend; it’s about thorough testing, relevant measurement, and discerning where and when a tool is appropriate.
