The Growth Operator with Fexingo · 2026-07-08 · 9 min
In this milestone 100th episode, Lucas and Luna dive into the story of a mid-market B2B SaaS company that used AI not for lead scoring or content personalization, but to completely rethink its customer acquisition funnel. The company, a workforce analytics platform with 200 employees, analyzed 18 months of sales and marketing data to identify the highest-intent signals across web visits, content downloads, and trial behavior. By building a custom machine learning model that prioritized accounts showing a specific combination of three behaviors within a seven-day window, they reduced customer acquisition cost by 40 percent and shortened sales cycles by 25 days. Lucas and Luna walk through the exact signals, the model architecture, the failed first attempt that cost $80,000, and what the VP of Marketing learned about keeping humans in the loop. They also discuss whether this approach works for enterprise sales, the role of data hygiene, and why this case matters for any B2B team trying to do more with less in 2026.
Other episodes covering the same guests and topics, from across The B2B Podcast Index.
How B2B Marketers Use Predictive Lead Scoring for Enterprise SalesB2B Marketing with Fexingo · on Predictive Lead Scoring85 / 100
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