Omnitracs Driver Retention Model

Struggling to Retain Drivers?

Turn to predictive analytics. Well-trained, reliable drivers are critical to growing the business and meeting the increasing demands of clients. Retaining drivers proves to be challenging, as the industry struggles with a jaw-dropping turnover rate.

Why Do Drivers Leave?

Drivers tend to leave their jobs for the same reasons as other employees: pay, schedule, time away from family, and perceived lack of opportunity for advancement, among others. Decisions to leave rarely happen instantly, and instead are typically the result of a long-term, gradual attitude shift. Omnitracs Analytics’ Retention Model picks up on the behaviors associated with this gradual shift, and enables fleets to proactively address employee issues before employees give up on the job.

Retention Predictors

Omnitracs Analytics analyzes thousands of data points to identify the subtle changes that collectively represent a pattern of driver behavior that signals a driver is considering quitting.

5000 turnover

Intelligent Decisions Require a Holistic View

Contrary to conventional wisdom, no single data point can be used to accurately predict future events on a consistent basis. Omnitracs Analytics' predictive modeling technology uses all available data across thousands of data points to build a true picture of the driver's behavior and provide the opportunity for remediation.

Center of Big Data

We’re at the Center of Big Data

Omnitracs Analytics further supplements your fleet’s data with external data sources and internal insights we’ve collected since pioneering transportation predictive analytics. Touching more than 1,500,000 mobile assets each day, our breadth of global transportation data is unmatched.

Further, this data is used to create a comprehensive image of driver performance, and more importantly, a map to detect the changes in behavior that are proven to predict a higher risk of turnover.

Turnover Reduction

The Proof is in the Numbers

Omnitracs Analytics’ predictive modeling and remediation strategies help customers reduce employee turnover by identifying the drivers that are likely to leave their current employment. The chart to the right looks at two different groups of drivers, those who have been intervened with early and those who were left to traditional retention methods. The side-by-side comparison clearly demonstrates how Omnitracs Analytics’ predictive models successfully identify at-risk employees and help fleets prevent the loss of their most valuable assets — their drivers.

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