Driver Churn Reduction
Predicts when drivers are considering to leave your team, suggests preventative measures and increases overall driver retention.
CHALLENGE
Driver shortages or unexpected departures can put a lot of pressure on core logistics operations.
It's unclear which drivers are likely to churn, why they're considering to churn and what to do about it.
It's not clear how to retain as many drivers as possible and how to grow the number of loyal drivers.
The process of hiring and training new drivers is costly and ineffective.
New, experienced drivers often request higher salaries and more perks than your current drivers do.
A high turnover is not a sign of a heathy fleet and could bring down team morale or have a snowball effect.
SOLUTION
By predicting which drivers are likely to churn with machine learning, you can be one step ahead and prevent disasters before they even emerge.
Targeted alerts can inform you which drivers aren't satisfied with their current occupation and which areas of your business could be optimized in order to reduce driver churn overall.
VALUE
Improved capacity planning
Improved Fill Rate
Improved On-Time, In-Full (OTIF)
Reduced planning effort and errors
Reduced training costs
Reduced inventory costs