Anastasia Braitsik is a globally recognized authority in search engine marketing, data analytics, and performance-based content strategies. With a career dedicated to bridging the gap between technical automation and human-centric marketing, she has pioneered methodologies that allow brands to scale through AI without sacrificing brand integrity or lead quality. Her expertise is particularly sought after for navigating the complexities of Google’s evolving ecosystem, where she specializes in transforming raw data into actionable growth levers for both B2B and e-commerce enterprises.
In this discussion, we explore the tactical shifts required to master keywordless environments, the nuances of match-type performance across different data thresholds, and the critical importance of integrating CRM data to filter out low-value automation outcomes.
AI Max for Search can effectively leverage blog content as landing pages to drive conversions. How do you identify which specific blog posts are suitable for this automation, and what structural elements must be present to ensure a reader transitions successfully from informational content to a product purchase?
The shift toward using blog content as a conversion tool marks a significant evolution from the days when we strictly excluded informational pages from Dynamic Search Ads. To identify suitable posts, I look for content that answers high-intent queries—specifically those where the reader is seeking a solution that our product directly addresses. A blog post is only “conversion-ready” if it features a clear, structural bridge; this means including high-visibility call-to-action buttons or product widgets within the first 30% of the page. In our observations, successful AI Max campaigns using blogs work because the generated headlines are often longer and more compelling than traditional RSAs, drawing users into a narrative that ends with a specific product recommendation. Without these direct links to a checkout or lead form, you risk paying for high-engagement traffic that never leaves the informational “bubble.”
When managing a campaign with fewer than 30 monthly conversions, phrase match often underperforms compared to broad match. Why does this specific data threshold change the effectiveness of these match types, and how should a marketer transition their strategy once conversion volume begins to scale?
The “30-conversion rule” is a tipping point where machine learning transitions from guesswork to pattern recognition. In low-volume environments, broad match actually outperforms phrase match because it taps into deeper signals—like a user’s previous search history and landing page content—to find relevance that a rigid phrase match misses. Data shows that in these early stages, phrase match often lacks the flexibility of broad match and the precision of exact match, leaving it in a performance “dead zone.” Once you scale past 50 to 100 conversions per month, the strategy should shift; Google’s algorithms gain enough data to properly execute machine-learning pattern matching for phrase match. At that point, you can layer phrase match back in with more budget, as the system finally has the “intelligence” to use those keywords efficiently without wasting spend.
Optimizing for simple form submissions in Performance Max often results in high volumes of low-quality leads or spam. What technical steps are required to integrate CRM data into the feedback loop, and how does targeting bottom-of-funnel milestones like Sales Qualified Leads fundamentally change the machine learning outcomes?
Optimizing for a raw form submission is the single biggest mistake a B2B marketer can make today because it teaches the AI to find “clickers” rather than “buyers.” To fix this, you must technically integrate your CRM with Google Ads to import offline conversions, such as Sales Qualified Leads (SQLs) or Marketing Qualified Leads (MQLs). By feeding these specific milestones back into the system, you shift the machine learning goal from quantity to quality. In a recent B2B SaaS case study, this approach allowed Performance Max to cast a wider net while maintaining lead quality, ultimately producing 204 SQLs at a $220 CPA, which was actually lower than the $237 CPA seen in traditional search campaigns. This feedback loop ensures the AI ignores the “noise” of spam submissions and focuses its bidding power on profiles that mirror your actual customers.
Mobile traffic frequently carries a significantly higher cost-per-acquisition in B2B environments compared to desktop users. In what specific scenarios should a manager implement aggressive device-level exclusions, and what impact does this granular control have on the overall efficiency of a Performance Max campaign?
Aggressive device-level exclusions should be implemented the moment you see a sustained disparity where mobile CPA exceeds your target by more than 30-40% without contributing to assisted conversions. In B2B SaaS, we often see desktop users converting at a healthy rate while mobile users drive up costs; for example, one account saw mobile SQLs costing $319 compared to a much lower desktop average. By splitting these into separate campaigns or using aggressive exclusions, we saw the mobile CPA drop to $204 in a single month. This granular control allows you to set lower, more protective target CPAs for mobile, ensuring that your Performance Max budget isn’t being drained by high-cost, low-intent mobile “window shoppers” while you maximize the efficient desktop traffic.
Transitioning to keywordless automation involves significant risks if applied to brand-new accounts without historical data. What is the ideal framework for running a 50/50 experiment, and which specific URL exclusion rules or inclusion settings are vital to maintaining brand safety during the initial rollout?
You should never roll out keywordless automation on a brand-new account; the ideal framework starts with an existing campaign that has a solid history of data and isn’t hitting its budget ceiling. I recommend a 50/50 experiment over a minimum of six weeks to two months to allow the algorithm to stabilize. To protect the brand, you must utilize URL exclusion rules to prevent the AI from sending traffic to “About Us,” “Careers,” or “Privacy Policy” pages. Simultaneously, implementing brand inclusion settings is vital to ensure the AI doesn’t misinterpret your brand’s identity or bid on irrelevant competitors. During the first two weeks, a daily review of search queries and the application of negative keywords is the only way to ensure the automation stays within the guardrails you’ve set.
In ecommerce, exact match keywords often yield high conversion rates but smaller individual baskets. When the goal is to increase average order values, how do you manage the trade-off between the lower conversion rates of broad match and the increased likelihood of a multi-item checkout?
This is a fascinating psychological trade-off in e-commerce: when a user searches for an exact product, they are in a “surgical” buying mode—they buy that one item and leave. To increase Average Order Value (AOV), we intentionally use broad match to capture users who are still in the “discovery” phase. While this leads to a lower conversion rate overall, these shoppers are more likely to build larger carts as they explore your catalog. We manage this trade-off by segmenting our bidding; we keep exact match for high-efficiency, single-item volume, but we allocate a specific portion of the budget to broad match with a focus on Maximize Conversion Value. This tells Google to prioritize the total revenue of the basket rather than just the number of checkouts, effectively balancing the lower conversion rate with higher per-transaction profitability.
AI Max for Search allows for personalized ad copy and landing page selection based on the searcher’s intent. How do you evaluate the quality of these generated headlines versus traditional responsive search ads, and at what point should a specific ad group be disabled due to poor traffic quality?
Evaluation is no longer about just “click-through rate”; it’s about “down-funnel” performance. In a financial services test, AI Max headlines actually drove 70 approved applications at a $579 CPA, outperforming standard search in terms of lead quality. We evaluate these by looking at the percentage of form submissions that move to the next stage—for instance, noting if 42% of AI Max leads reach a “soft credit pull” compared to only 36% for traditional ads. If you see an ad group where the traffic quality consistently fails to reach these secondary milestones after three weeks, or if the search query report shows irrelevant “junk” terms, that is the point to disable AI Max at the ad group level. You have to be ruthless; if the automation isn’t beating your baseline quality within a 21-day window, it’s not working for that specific segment.
What is your forecast for the future of AI-driven PPC?
The future of PPC will see the total disappearance of the “keyword” as a primary lever, replaced entirely by audience intent signals and real-time asset synthesis. We are moving toward a reality where the landing page itself becomes as dynamic as the ad copy, morphing its layout and messaging in milliseconds to match the individual searcher’s psychological profile. Marketers who continue to focus on manual keyword lists will be priced out by those who master “Algorithm Steering”—the art of feeding the machine the correct CRM data and exclusion rules. Ultimately, the “human” element of the job will shift from execution to strategic guardrail management, where our value lies in knowing exactly which 5% of the automation to turn off to protect the client’s bottom line.
