A Working Planet client thought they had it figured out: a finely tuned predictive model delivering real-time performance insights. But after months of testing, we uncovered gaps in what looked, on paper, like a perfectly sound approach.

If you run a lead generation business, you already know not all leads are created equal. Some never respond. Some stall out. Others convert quickly into high-value deals. Yet in many ad accounts, every lead is treated the same. An Offline Conversion Tracking (OCT) model fixes that. It assigns predicted value to each conversion so platforms like Google Ads can optimize for business impact, not just lead volume.

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.

Predictive modeling equips businesses to assess lead quality in real time—accelerating campaign optimization and revenue growth.