Fleet Management News & Business Info | Commercial Carrier Journal
Issue link: http://read.dmtmag.com/i/599511
commercial carrier journal | november 2015 63 TECHNOLOGY: DRIVER TURNOVER have a 24 percent lower turnover rate. Workhound created a cloud-based management tool and a mobile app that aggregates feedback data from drivers and shares insights that com- panies can use to better manage their relationships with them. Tucker Robeson led the startup of CDL Helpers, a company that creat- ed its own cloud-based Fleet Rela- tionship Management application. The app is designed to keep a rolling log of driver interactions to gather useful information and ensure those encounters follow a consistent – and unique – process for each carrier. Over time, the app is able to benchmark conflicts entered in the system – such as a driver being frustrated with a dispatcher – against the long-term success rates of drivers. This benchmark serves as a useful predictor for how many hours the company has to neutralize the threat before it escalates and leads to turn- over, Robeson says. Predictive intelligence Ten years ago, Lafayette, La.-based Dupre Logistics tapped a startup company named FleetRisk Advi- sors to create predictive models to analyze complex data sets and identify drivers who were the most likely to be in accidents, file workers compensation claims or quit within the next couple of weeks. More fleets signed up, and the models got even better, with more experience and data fueling the engines. Advanced statistical methods are able to find the patterns in oper- ational data that are predictive of future events. The models also help determine the best countermeasures to apply to mitigate the risk. Such countermeasures may be the suggested topics of conversations for fleet managers to have with drivers about personal, professional or financial issues they are having. This insight is derived from patterns in data that otherwise might go unno- ticed. To date, expe- rience has shown that using predic- tive information is more effective in preventing driver turnover than for targeting accidents, says Dean Croke, a FleetRisk Advi- sors founder. The reasons driv- ers quit usually are the same, but circumstances that lead to accidents are more complex and less repeatable. "Drivers are very predictable – they experience the same frustrations," says Croke, who is now vice president of Omnitracs An- alytics, FleetRisk Advisors' new name following the purchase of Omni- tracs and its FleetRisk subsidiary by Vista Equity Partners in 2013. Croke says each client has unique predictors, but in most cases, the early signs of turnover are things you might expect would cause drivers to be frustrated. Common predictors include fluctuations in pay and mileage, detention time at docks and denied requests for home time. A less-ob- vious predictor is the geographical state where drivers hold their com- mercial driver's license; over-the- road drivers who live in states with low freight volumes may have more difficulty getting home. "It's obvious from the start that the driver is going to quit," Croke says. On the flip side, these and oth- er data patterns also help explain why drivers stay. Drivers who stay longer have markedly different traits than drivers who quit under the same circumstances. By identifying these traits in their profile data, it is pos- sible to know from the start the prob- ability that a job applicant will leave early or stick around, Croke says. The challenge of using predictive intelligence on the front end of the employment lifecycle, he says, is the additional hurdles it can create to keep drivers moving through the Interstate Distributor Co., a dry van and refrigerated truckload carrier based in Tacoma, Wash., has noticed that drivers who are referred stick around longer. Taylor, Mich.-based ground expedite carrier Load One has been using a rewards platform from Stay Metrics for three years.