Spam is inevitable when you’re generating leads at scale. From junk submissions to disposable email addresses, it’s part of the digital marketing landscape—but that doesn’t mean you can’t take steps to improve quality. Over the past few months, we’ve implemented several tactics across Google and Meta Ads campaigns to cut through the noise and help our clients get higher-quality leads while maintaining strong efficiency.

Are ad tech platforms worth It? For us, no. These tools rely on rigid, generic frameworks to automate campaign management and optimization. These companies need to make a profit and offer a single, stable product that works across clients. If they were constantly updating their software to match each client’s unique business model and every platform change, they’d have no functional product and no business. If your goal is measurable, sustainable profit, ease is not the sam

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.

You may find yourself wishing you had more data to help you understand the value of your leads. In a perfect world, every new column would be filled out consistently, giving you richer, more precise insights. But the reality? Between chasing follow-ups and keeping existing scoring systems updated, asking sales to fill out multiple new fields just isn’t realistic. The good news: you may not need more fields. The data you need could already be sitting in your CRM, just waiting

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.