For over 15 years, Anastasia Braitsik has navigated the evolving landscape of digital advertising, witnessing firsthand how new technologies are adapted for the unique challenges of B2B marketing. As a global leader in SEO and data analytics, she brings a seasoned, pragmatic perspective to the often-hyped world of automation. Today, she joins us to demystify one of Google’s most powerful, and often misunderstood, tools: Performance Max. We’ll explore its journey from a B2B non-starter to a viable strategic tool, discussing the critical signals required for success, the pitfalls of poor data, and the importance of patience in an automated world. Anastasia will share her insights on when to embrace PMax and, just as importantly, when to stick with more traditional methods.
New Google products often struggle with B2B applications at launch. Thinking about Performance Max, what specific evolutions have made it a more viable option for B2B now, and what key challenges from its early days have been addressed? Please share a practical example.
It’s a pattern I’ve seen repeat for over a decade: Google releases a shiny new tool optimized for high-volume B2C, and it completely falls flat for the nuanced, long-cycle world of B2B. Performance Max was no different. Three years ago, I wouldn’t have recommended it to any B2B client because it was a black box that optimized for volume over value. The biggest evolution has been its ability to ingest and learn from deeper, more meaningful business signals. In the early days, if you told it to get “leads,” it would find the cheapest form fills possible, which are often worthless. Now, with robust CRM integrations, you can tell it to find not just a “lead,” but a “Sales Qualified Lead” or a “Booked Demo.” This shift from optimizing for a top-of-funnel action to a real business outcome is what has fundamentally changed the game for B2B advertisers.
Performance Max shifts the focus from keywords to audience signals. For a B2B advertiser ready to test it, what are the three most critical signals they must have in place, and how does each one directly influence the algorithm’s ability to find qualified leads?
Absolutely, the pivot from keywords to signals is the core concept here. First and foremost, you must import your source of truth by connecting your CRM, like Salesforce. This isn’t optional. It’s how you teach the algorithm what a truly valuable lead looks like after the initial click, moving beyond a simple form submission. Second, your primary conversion action must be a meaningful event, like a qualified lead submission or an appointment being set. If you optimize for a generic “contact us” form fill, the system will just find more people who like filling out forms, not actual buyers. This signal provides a clear, high-value target for the AI to aim for. Lastly, you need to upload a first-party customer list. This gives the algorithm a rich, high-fidelity blueprint of your ideal customer, allowing it to build powerful lookalike audiences and find net-new prospects with similar characteristics far more effectively than a simple website remarketing list ever could.
Let’s talk about lead quality. If a B2B company only tracks top-of-funnel form fills as conversions, how will that likely sabotage a Performance Max campaign? Describe the steps they should take to provide the system with more meaningful, down-funnel data from their CRM.
It would be a complete and utter disaster. You’re essentially telling one of the most powerful machine learning algorithms in the world to find you the cheapest, lowest-quality leads imaginable. The campaign will look “successful” on paper, generating lots of conversions at a low CPA, but your sales team will be buried in junk leads. The algorithm is doing exactly what you asked it to do: find people who will fill out a form, not people who will eventually buy your product. To fix this, you must connect your CRM to Google Ads and set up offline conversion tracking. The first step is to import key milestones from your sales cycle—like when a lead is marked as “Sales Qualified” or an “Opportunity Created.” You then set these down-funnel events as your primary conversion goals in the PMax campaign. This completely reframes the objective from “get a form fill” to “find a prospect who will likely become a paying customer.” It’s the only way to align the campaign’s optimization with actual revenue.
A key benefit mentioned is reaching stakeholders in a buying group who don’t use traditional search. How does Performance Max accomplish this across Google’s inventory, and can you share an anecdote where this wider reach helped nurture a lead through a complex, multi-month sales cycle?
This is where PMax really shines for B2B. A traditional search campaign only captures the person in the buying committee who is actively tasked with researching solutions. But what about the CFO who needs to approve the budget, or the end-user who will influence the decision? They might not be searching for your keywords, but they are watching YouTube, browsing news sites on the Display Network, or checking Gmail. Performance Max serves tailored ads across all that inventory, keeping your brand top-of-mind for the entire group. I recall a client in the enterprise software space with a six-month sales cycle. Their search campaigns would bring in the initial lead, but then things would go quiet. Once we launched PMax, we saw that our video assets were being served on YouTube to people from the same target accounts. The sales team reported that later-stage conversations became easier because the wider team was already familiar with our brand and value proposition, which they had absorbed passively over months. It transformed the campaign from a simple lead-capturing tool into a powerful nurturing engine.
Performance Max is not a fit for every B2B business. What specific characteristics of a company’s target market, such as a small addressable market or an account-based marketing approach, would make you advise them against using this campaign type and stick to more manual controls?
It’s crucial to be honest about whether this tool fits your strategy. I would strongly advise against Performance Max for any company running a true account-based marketing program with a small, named-account list of, say, a few hundred businesses. The algorithm needs a reasonably large total addressable market to learn and find patterns effectively. If you’re only targeting private equity firms in a specific region, for example, there simply isn’t enough data for the system to work its magic; you’ll get much better results with the precision of manual targeting. Similarly, if your audience is extremely niche and restricted by very specific firmographics, PMax will struggle to scale. In these scenarios, you’re better off maintaining tight control over your targeting and messaging with traditional campaign types where you can dictate every variable.
Patience is required for automation to succeed, yet many marketing teams feel pressured to intervene constantly. What common mistakes do you see teams make when they get impatient, and how does this “over-optimizing” disrupt the campaign’s learning phase and ultimately harm long-term results?
The pressure for immediate results is the biggest enemy of a successful PMax campaign. The most common mistake I see is teams panicking after a few days of fluctuating results and immediately changing assets, audience signals, or bidding strategies. Every time they do that, they essentially hit the reset button on the algorithm’s learning phase. The system needs a stable environment and clean data inputs to understand what works over time, especially in B2B where the path to conversion isn’t linear. This constant meddling creates a vicious cycle: the campaign never gets enough data to stabilize, performance remains volatile, and the team gets even more anxious and intervenes again. This “over-optimizing” directly prevents the campaign from ever reaching its full potential, ensuring that long-term results will be mediocre at best. You have to trust the process and give it the time and space it needs to learn.
What is your forecast for B2B advertising on Performance Max?
My forecast is one of cautious but definite optimism. Performance Max will become an increasingly standard component of the B2B marketing mix, but it will never be a “set it and forget it” solution. Its success will be directly proportional to the quality of the data we feed it. We’ll see Google roll out more B2B-specific features, like better firmographic targeting signals and more direct integrations with B2B data platforms. The advertisers who succeed will be those who move beyond surface-level metrics and master the art of feeding the algorithm high-quality, down-funnel signals from their CRMs. The line between marketing operations and paid advertising will continue to blur, and the most valuable skill for a B2B advertiser will be the ability to translate deep business data into a language that Google’s AI can understand and act on. It’s an exciting, and challenging, time.
