If there was ever any doubt, we now have statistical proof: Experienced drivers are safer drivers.
A report from the Federal Motor Carrier Safety Administration issued in January shows rather definitively that for drivers with less than five years of experience who are involved in a crash, the odds of them being assigned blame for the crash — specifically, being named the “critical reason” for the crash — are 41 percent higher than for drivers with five or more years of experience.
If that’s the case, then the next questions should be how do you keep those more experienced drivers from leaving your company? And, how do you hire and retain good young drivers who show promise of becoming the same kind of experienced and safe drivers you’ll want to keep around for a long time?
Analytics as a hiring tool
The FMCSA’s mathematical analysis of data collected from 943 drivers show that the probability of being assigned primary responsibility for a crash continues to be significantly lower for experienced drivers than for those with less than five years on the road until at least the 30-years mark. While a 30-year driver’s odds of being given primary responsibility for a crash still are lower than those of a driver with less than five years’ experience, it’s around the 30-year point when the odds gap begins to narrow.
Obviously, experienced drivers are extremely valuable assets to have on your trucking concern’s roster, so how can you not just keep those experienced drivers but make sure you’re recruiting drivers who’ll stick?
- 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 historical 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.
- Use Recruiting Predictors. Sophisticated analytics programs can analyze thousands of data points available from your current driver workforce application data to predict which new applicants will be successful with your company.
- Hire Intelligently. Wed the insights you gain from your own staff about what makes a great driver to the predictive analysis done on your applicant pool to zero in on the best candidates. This involves “Personalizing the Big Data” gleaned from your current workforce and comparing with the “small data” available from your application process to find those patterns that indicate which applicants have the highest probability of being productive and safe, and, just as important, achieving tenure with your company.
Analytics as a retention tool
Of course, once you’ve hired drivers who you believe can become great drivers, keeping them is critical. Great recruiting and hiring practices alone do not guarantee that your newly-hired drivers will achieve long-term success. So how do you make sure a great new hire becomes a fantastic, and very safe, veteran driver with your firm?
Again, a big part of the answer involves mining the data you get from your own operations and your drivers. Affordable systems exist that can allow you to analyze thousands of data points to identify the subtle changes that identify well in advance which drivers are at risk to leave so that you can take action to keep them fully engaged, happy, and in the drivers’ seats of your trucks.
And the continued use and analysis of your own performance data over time can help even your best drivers get better than they would from experience alone. Your data can help you better predict when drivers are most alert and fit to drive, and when they’re not. Your data can even help you help them learn how to reduce the extent and cost of damage when circumstances make it impossible for them to avoid some sort of crash.
To learn about how Omnitracs’ predictive analytics tools can help your fleet, visit http://www.omnitracs.com/solutions/data-analytics.
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