How teams can use AI to build a churn prediction (or any prediction) model with 98% accuracy in 2 weeks

Within his first two weeks as COO at Turtl, Dave Martin and his team built a churn prediction model. Tested against historic customer data, it predicted churn with 98% accuracy.Work like that normally takes six months or more, plus an agency and external partners. Most companies still make that call on gut feel, or by putting some basic data into Claude or ChatGPT.In this episode of In The Loop, I'm joined by Dave to walk through exactly how he and his team did it, using a method he calls pattern of life analysis.We cover how they paired customers who renewed with similar ones who left, got 12,500 minutes of call recordings through Claude without blowing the context window, the landmines you'll hit if you try this yourself, and what it takes to replace the loudest voice in the room with evidence.⏭️ Episode highlights(08:15) – Why a scale-up can't wait six months(09:40) – Testing the model: 98% on historic data(11:10) – Rebuilding each customer journey, call by call(24:25) – Why Claude's first answer was "absolute garbage"(42:05) – The "rudimentary" test that proved three ideas(45:55) – Why loud opinions make terrible decisions🔗 Links & resourcesDave Martin on LinkedIn - ⁠https://www.linkedin.com/in/mrdavemartin/⁠Turtl - ⁠https://turtl.co⁠Jack Houghton LinkedIn - ⁠https://www.linkedin.com/in/jack-houghton1/⁠Mindset AI website - ⁠https://bit.ly/40lJr6B⁠