In Q1 2018, the American Trucking Associations (ATA) found that the annualized driver turnover rate for large fleets was 94 percent. This high rate of turnover not only reduces the productivity of a fleet, but also has a direct impact on its bottom line. The cost to replace a single driver can range from $5,000 to $10,000, without factoring in the lower productivity or reduced safety performance of a new driver.
To help fleets address this ongoing challenge, we recently introduced Trimble’s Driver Retention Analytics platform. This solution analyzes data from across a fleet’s driver base, helping the fleet to proactively address driver retention, ensuring at-risk drivers don’t leave.
Transforming a manual process
Traditionally, fleet managers have relied on a gut feeling to identify and address those drivers likely to leave. This intuition is based on metrics like number of miles driven, load type and personal interactions with the driver. While this intuition can be valuable, tracking this information is a time-intensive and manual process, not including the time it takes to actually intervene with the driver who is deemed “at-risk” of leaving. Trimble’s Driver Retention Analytics platform automates this manual process, using data to predict the specific factors behind why a driver might leave a fleet. The platform can help fleets identify drivers who are at-risk for departure over a seven-day horizon with 90-95% accuracy.
Harnessing the power of machine learning
The Trimble Driver Retention Analytics platform relies on our extensive reach into the data ecosystem to pull essential information such as Hours of Service (HOS), pay and home time to help drive the process. Drivers typically don’t leave an employer based on a single event, so data is normalized and transformed through data science to distill and accurately capture inter-relationships between events.
Once this data is transformed, the platform develops predictors, which are fed into a predictive engine that identifies at-risk drivers and scores risk factors. While predictions are important, it is is also crucial to develop solutions to help remedy the situation. The Trimble driver retention platform includes a prescriptive engine that provides a fleet with actions to help keep a driver. For example, a fleet can proactively address a driver who may be frustrated about home time or has an issue with payroll before it has the potential to cause the driver to leave.
Trimble’s driver retention model turns driver data into predictors of why a driver might leave and prescribes solutions to proactively address a driver’s issue.
Supplementing your driver retention initiatives
Using our Driver Retention Analytics platform won’t replace a good driver-manager relationship but will provide a great starting point for retention conversations. Fleet managers and dispatchers working with drivers on a daily basis bear the responsibility to stay engaged with drivers to ensure they are satisfied in their jobs.
Are you interested in learning more about how our Driver Retention Analytics platform can help support driver retention in your fleet? Be sure to check out our webinar to learn more about how this new solution can help you make data-driven decisions to keep your drivers happy, efficient and on the road.