We rolled out AI Max for Search across campaigns for clients in multiple verticals, including eCommerce, online education, and nonprofit. In most cases, it captured 5–10% of Search spend, produced ~30% stronger ROAS than non‑AI Max traffic, and uncovered incremental volume. However, it also proved to “cherry-pick” volume from existing keywords, which masked some inefficient spend, emphasizing the need for strong conversion signals and constant guardrails.

For years, marketers have been talking about AI. At this year’s Think Leads event, it finally felt tangible and purposeful. Google pulled back the curtain to reveal how AI is reshaping everything from how consumers search to how advertisers define and predict value. The message was clear: AI isn’t coming; it’s here. And, those not yet embracing it are already behind.

Predictive models are powerful tools for making smarter marketing decisions, but as with any model, the details matter. One common consideration is whether to include source as a variable. At first glance, it makes sense since different audiences generate different types of leads. But when predictive values are used not just for reporting, but for network optimization, including source as a variable can introduce risks that undermine campaign performance.

In digital marketing, big declarations never seem to stop. We like to put permanent labels on things because it feels easier: easier to write something off, easier to believe that if you just do X, Y, and Z, you’ll win. Reality is never that simple. And yet, I still hear people confidently claim that paid search is dead. That one surprises me the most. I’m open to new ideas, but in my experience, paid search isn’t dead—it’s just harder, noisier, and far more sophisticated th

Using a strategic, low-risk testing approach, we uncover the real value of a new ad network, unlocking efficient lead generation.

By implementing a more dynamic and persona-driven creative strategy, our client's campaign saw a remarkable shift in performance.