Ten years ago, a startup company named FleetRisk Advisors created a predictive model for Dupre Logistics. This was the first of its kind in the transportation industry.
The model analyzed hundreds, even thousands, of data elements to identify drivers who are most likely to be in accidents during the next few days and weeks. FleetRisk then created models to predict which drivers would file workers comp claims or quit.
As more fleets signed up the results got better. As more data fueled the analytical engines, more patterns in the data emerged as harbingers of future events.
The models also identified the countermeasures to stop risky events from happening. They come in the form of suggested topics for conversations between managers and drivers about personal, professional or financial issues that the data suggested drivers were having.
Most of the patterns identified by the models are not apparent just by looking at the raw data.
To date, experience has shown that use of predictive intelligence is more effective at preventing driver turnover than preventing accidents, says Dean Croke, one of the founders of FleetRisk Advisors. The reasons drivers quit are by and large the same, he says. Accidents by comparison are much more complex and far less repeatable.
“Drivers are very predictable. They experience the same frustrations,” says Croke, who today is vice president of Omnitracs Analytics, the name given to the company after the purchase of Omnitracs and its subsidiary, FleetRisk Advisors, by Vista Equity Partners in 2013.
