Case Study

A major land transport organisation mitigates revenue loss with Aicadium’s driver churn prediction modelling


A major land transport organisation employing thousands of drivers had rolled out driver contracts in an attempt to reduce the resignation rate. With the contract term end fast approaching, they wanted to predict how likely drivers would churn, and intervene before it was too late.


Using Aicadium’s churn prediction modelling, the organisation is able to generate a churn probability for every driver across their fleet and take action using a driver intervention programme developed in conjunction with their driver operations team. The models are deployed onto their AWS cloud infrastructure using Bedrock.


The model is able to identify drivers at risk of resignation. Individual churn predictions empower the operations team to set intervention thresholds and prioritise which drivers to reach out to first, resulting in a significant decrease in driver churn and prevention of vehicle rental loss.

This site uses cookies to ensure you get the best experience on our website. Please visit our privacy policy page for more details.