Omnitracs Analytics, part of Omnitracs and a pioneer in applying predictive analytics and remediation applications in the transportation industry, marked its 10-year anniversary. Since 2005, Omnitracs Analytics has analyzed driver behavior over 35 billion miles and accumulated 85 years’ worth of transport data.
In celebration of this milestone anniversary, the company is offering a six month free trial of its custom Recruiting Model, designed to pinpoint the most suited driver to hire, for customers that purchase its Retention Model before September 30, 2015.
The Retention Model is an advanced platform that detects subtle changes in driver behavior to identify which drivers are likely to leave a company. The Recruiting Model can help build a true picture of which drivers to hire and focus on to maximize productivity and help reduce employee turnover.
Omnitracs Analytics began as a fledgling startup, based on the notion that in-cab technology and driver data could be used to predict drivers most likely to have an accident. What started as a big idea transpired into a technology adopted by a number of transport companies. Today, the company predicts sleep patterns, DOT-recordable accidents, ideal pairing of team drivers, the best driver to hire, intelligent routes, critical events, cargo theft, tire failure and roadside breakdowns.
“Before 2005, we looked at drivers like they were still-frame pictures, either safe or unsafe. With big data we now see drivers like moving pictures—constantly changing from safe to unsafe over time. Our models slow down the moving picture, so we can study each frame of data and pinpoint behavior predictors,” said Dean Croke, vice president of Omnitracs Analytics. “Today, we run 85,000 driver-models every month. Additionally, using our latest Text Analytics Models, we’re able to study 134 million words per month to help identify drivers most likely to have accidents or voluntarily terminate employment.”
The company says that 95 percent of the predictors of risk do not initially come from a fleet’s raw data. The predictors only surface once applied to Omnitracs Analytics proprietary algorithms.
“With Omnitracs Analytics predictive modeling, the possibilities are endless. Over the next 10 years, just as it did from 2005 to 2015, we’re confident that Omnitracs Analytics will shape how the industry evolves and how we think about safety, driver retention and big data,” said John Graham, CEO of Omnitracs.
Reggie Dupré, CEO of Dupré Logistics, LLC and the first Omnitracs Analytics customer, commented: “Ten years ago, like most companies, we thought we were doing all of the right things. We’d passed every DOT audit in our history, but were still having accidents. We knew there was more we could do, but were at a loss as to where to start. It wasn’t until we partnered with Omnitracs Analytics that the solution presented itself with laser-like precision. We learned to embrace big data using data analytics and predictive modeling. In a very short period of time we saw a dramatic turnaround in safety results and are now recognized as an innovative industry leader in managing risk.”
Longtime Omnitracs Analytics’ customer, Jay England, CEO of Pride Transport, added, “We’ve laid out aggressive future growth plans and being a multi-generational company we had a lot of experience to draw from. The problem was that experience didn’t quite line up with today’s rapidly changing driver demographic and transport task. The rate of change since the beginning of the digital age in 2002 has meant we’ve captured more electronic data in the last 10 years than in the previous 100 years, and that meant we needed to use different management techniques and tools to make sense of the terabytes of data at our disposal.”
England continued, “That’s when we contracted with the big data experts at Omnitracs Analytics to better understand what made drivers tick, but more importantly, help focus our efforts on only those drivers most likely to quit. What Omnitracs Analytics is doing with predictive models and data analytics has substantially reduced driver turnover in our at-risk population and helped us continue to grow without losing what made us successful in the first place.”