Fleet Management News & Business Info | Commercial Carrier Journal
Issue link: http://read.dmtmag.com/i/795447
34 commercial carrier journal | march 2017 P redictive analytics are part of the websites and apps we use daily. Online activities are tracked and fed into computer models and algorithms that learn our behaviors to make recom- mendations to us on what to read, watch or buy. In the trucking industry, predic- tive analytics are being applied to common business challenges such as driver turnover. Powerful models can find patterns in data to identify which drivers are likely to leave and why. Chicago-based startup Enlistics has developed an online driver applica- tion management system for fleets to use in their recruiting and hir- ing. Drivers who apply for a job at a carrier fill out the online application, which includes the option for them to streamline the process by using their Facebook profile. While the Facebook feature is com- mon in many websites, the Enlistics application uses a driver's Facebook identification to gather data on their technology social media activity. e application also collects drivers' work history records and conducts background checks. Enlistics founder Austen Mance says that with these combined data sources, the model will predict the likelihood of the driver staying for six months. is prediction is the only information that fleet customers would see from drivers' social media data. Enlistics is soliciting customer carriers and providing its system at a discount. e company previously launched a predictive model for car dealerships. As an example, a common social media phrase that correlates with sales- person turnover is "I'm sick of … ," Mance says. "at tends to show they get stressed easy." PREDICTIVE ANALYTICS: Powerful models can find data patterns to see which drivers will leave and why. SOCIAL MEDIA: Online information and usage can predict the likelihood of a driver staying for six months. TRANSPARENT RESEARCH: There is an inherent value when drivers see managers act on their feedback. Predicting driver turnover Methods used for collecting data send a message HOS data might indicate that drivers are dissatisfied with their jobs due to fatigue or variability in their work schedule. MAKING THE LATEST TECHNOLOGY DEVELOPMENTS WORK FOR YOUR FLEET BY AARON HUFF