Struggling to Retain Drivers?
Drivers tend to leave their jobs for the same reasons as other employees: pay, schedule, time away from family, and perceived lack of opportunity for advancement, among others. Decisions to leave rarely happen instantly, and instead are typically the result of a long-term, gradual attitude shift. Omnitracs Analytics’ Retention Model picks up on the behaviors associated with this gradual shift, and enables fleets to proactively address employee issues before employees give up on the job.
Benefits of Predictive Analytics for Driver Retention
Driver Retention Infographic
The Proof is in the Numbers
Omnitracs Analytics’ predictive modeling and remediation strategies help you identify the drivers who are most likely to voluntarily quit so you can limit driver turnover, reduce related expenses, and enhance overall productivity.
The graph above shows the efficacy of the ELD Driver Retention Model when run across live data points from over 450,000 drivers’ hours of service data logs to determine which drivers are at risk of early termination. The ELD Driver Retention Model identifies similar outliers in a driver’s Hours of Service data that correlate with the data points of drivers who prematurely terminate employment. This allows Omnitracs to predict 63% of the quits within the top 20% of fleet drivers.
Learn From Your Best Drivers When Looking For New Ones
Within any fleet, some drivers are long term, productive employees and others are gone before HR has finalized the paperwork. It doesn’t have to be this way. Predictive analytics can take your fleet’s pre-existing data and develop a model that provides insight into the attributes that make drivers successful. When evaluating a specific applicant, this model can be applied to show the likelihood of success before you hire and invest in them.
Omnitracs Predictive Analytics analyzes thousands of data points available from the current driver workforce to predict which applicants will be successful.