Driver Intelligence: Why Data Gathering is Key to Driver Retention

July 22nd, 2020    3 Min Read   

Technological advancements have changed the logistics and transportation industry’s landscape in recent years. For decades, fleet managers had to rely on paper logs and manual tracking but this all changed with the entry of automation.

Artificial intelligence and machine learning are automated solutions that are impacting profoundly the industry. These two have the ability to gather information in real-time and make the necessary adjustments to workflows. The algorithms in machine learning and artificial intelligence perform daily tasks without specific instructions thus allowing operational processes to become more productive and intelligent.

Artificial intelligence and machine learning enable companies to reduce financial burden, streamline processes, and enhance driver safety. Plus, they also help in driver retention, a recipe for a more profitable business.

The Persistent Problem with Driver Retention

Driver retention has always been a major concern of industry players. A study conducted by the American Transportation Research Institute (ATRI) in 2019 showed that driver shortage is likely around 60,000. In the next five years, the number is expected to rise to about 100,000.

Driver attrition is quite costly for companies since hiring and training new ones are more expensive than retraining present drivers. Maintaining the current workforce and reducing the high turnover rates of drivers across the industry have always been the companies’ goal.

The Role of Artificial Intelligence and Machine Learning in Driver Retention

In fleet management, maintaining a productive and happy workforce requires understanding the needs of the drivers. Unfortunately, gaining more insights into driver's preferences is challenging because of the distributed workforce.

These days, fleet managers along with the Human Resources department can leverage artificial intelligence and machine learning to the information gathered in real-time by various tools such as in-cab videos, electronic logging devices, and telematics systems, among others. Driver-related data like family structure, shift difficulty, time away from home and salary can be used by artificial intelligence and machine learning tools for the following purposes:

  • Alerting management of at-risk or unhappy drivers, allowing Human Resources personnel or fleet managers to proactively talk to drivers to find the source of dissatisfaction and prevent them from leaving the company.
  • Identifying unmet driver's needs and preferences so managers can determine the issues that make them want to quit their jobs such as current pay level, schedules, and workload, to name a few.

Driver Fatigue is Also a Challenge

Driver fatigue is one of the primary reasons for the high attrition rates across the industry. All drivers, after all, suffer from fatigue regardless of the roads or routes they traverse. Driver fatigue is a serious condition as major accidents can happen if a driver is having difficulties concentrating while driving.

Companies can use artificial intelligence and machine learning to protect their drivers, fleet, and other road users. Organizations can utilize the granular data generated to gain more insights into the root cause of driver fatigue.

Artificial intelligence and machine learning can also be used to automatically highlight drivers who are likely to be already suffering from fatigue by analyzing the following information.

The time they’re driving

Number of hours on the road

Number of breaks taken

Speed variation

Time-to-lane crossing

Predicting which drivers are already fatigued allows the Human Resources team and fleet managers to adjust driver's schedules, and re-assign workloads to well-rested drivers as necessary. Implementing these changes can help ensure that all drivers on the road are alert and focused on driving.

Gone are the days when personnel involved in fleet management have to rely on manual record-keeping and processes to run the operations. These days, technological advancements, particularly artificial intelligence and machine learning are paving the way for automation and data-driven decision making. Transportation companies can use these technological tools to address the growing problem of driver retention. Artificial intelligence and machine learning, after all, can help minimize driver fatigue, increase driver's safety and protection, and identify the root cause of driver's dissatisfaction.

DispatchTrack is a leading provider of SaaS solutions that enable end-to-end optimization of operations and customer experiences in last-mile delivery. The company's platform includes modular tools for self-scheduling, route optimization, customer communication, real-time tracking and ETA, proof of delivery, and delivery network intelligence and analytics. With customers across North America, Europe, South America, and Asia, DispatchTrack is used by thousands of businesses of all sizes and many multi-billion-dollar enterprises across a wide range of industries, including furniture, appliances, building supplies, food, and beverage. More than 60 million scheduled delivery experiences are powered by DispatchTrack each year. For more information, visit

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