Nick Bell's picture

Nov 22, 2016


Nick Bell

Product Marketing Coordinator

A recent article by the Harvard Business Review takes a look at how one group of scholars is studying “pre-quitting behaviors” of employees to determine the behaviors that signal a team member is planning to voluntarily quit his or her job. However, many of the factors in this study are based on behaviors managers must be able to physically observe. Unlike many industries where there is constant face-to-face interaction, the transportation industry has employees working independently with little in-person interaction. This can make analyzing drivers’ behaviors and concerns rather complicated for driver managers. 

Managing employee retention and turnover no matter the industry can be a costly activity due to training costs and productivity losses, among other factors. However, the transportation industry is experiencing even higher training costs and productivity losses compared to other major industries due to additional factors such as the shortage of qualified truck drivers and an increase in cargo volumes in the market. With the average cost of onboarding a new driver now reaching roughly $5,000 according to a study by the Upper Great Plains Transportation Institute and driver turnover at roughly 95 percent, it’s apparent that fleets need a better driver retention solution.

For example, if you have a fleet of 100 drivers and 50 percent of them voluntarily terminate employment, your fleet will incur $250,000 dollars in onboarding costs a year — not to mention the hassle of the recruitment and onboarding process. 

While the behavior analysis done in the study does uncover some good techniques for retaining drivers through long-term observation, fleets in the transportation industry need actionable solutions that will work now. Many groups boast statistical models that can make “pre-quitting behavior” predictions based on a wealth of data, but many times fleets simply don’t have the data storage capabilities or the time to utilize those models. 

If we take a look at the transportation market today, we know that the ELD mandate has brought sweeping changes to the way fleets run their businesses. However, a great deal of fleets are unaware of the many other uses that electronic log data can provide. One of the many uses is — you guessed it — driver retention.

If I told you that your fleet could utilize minimal e-log data to improve driver retention by 63 percent in the top 20 percent of drivers, would you like to hear more?

If so, take a look at our ELD Driver Retention Model to see how your fleet can make the most of your e-log data. We have developed a model that will allow you to use your e-Log data to actively predict drivers who are likely to voluntarily quit. The model also provides fleet managers with tools to actively coach those drivers with strategies for uncovering and resolving issues before they lead to voluntary driver termination. 


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