The Growth Operator with Fexingo · 2026-08-02 · 8 min
In Episode 147 of The Growth Operator, Lucas and Luna dive into a fresh angle in AI-powered revenue operations: predicting deal velocity. They explore how forward-thinking sales leaders are moving beyond static forecasting to model the speed at which deals progress through the pipeline, and why the typical 20 percent step-wise stage probability model is falling short. The conversation centers on a concrete case: a mid-market SaaS company that cut its average sales cycle by 18 percent by feeding historical win data, engagement signals, and economic indicators into a machine learning model. Lucas breaks down the nuts and bolts of feature engineering, from 'stage dwell time' to 'email response lag', while Luna challenges the practicality for smaller teams. They also touch on the ethical tightrope of over-automation and the risk of over-indexing on predictive scores. If you're in B2B sales, marketing, or revenue operations, this episode offers a practical roadmap and a cautionary tale. Tune in for a dense but accessible breakdown of a real-world application of AI that goes beyond the hype.
Other episodes covering the same guests and topics, from across The B2B Podcast Index.
How AI Is Personalizing B2B Sales Sequences That Actually Convert72 / 100
Why B2B Brands Are Using AI for Lead Scoring75 / 100Why B2B Brands Are Using AI for Account Prioritization84 / 100Why Pipeline Velocity Trumps Deal Size Every Time95 / 100How B2B Brands Use AI for Churn Prediction83 / 100
