How predictive driver insights can enhance driver onboarding criteria

Onboarding the wrong drivers can lead to frustration on all sides, leading to poor rider and driver experiences along with lengthy and expensive claims.

When you’ve had the perfect evening that ends with a poor rideshare ride home, what do you remember the most? Unfortunately, it often ends up being that one ‘crazy’ driver that leaves the lasting impression of the night.

That’s the thing about drivers. While apps can calibrate perfectly the technology to connect people with rides and cars, deliveries, and groceries – it’s the driver that dictates the customer experience once the rubber literally hits the road.

Unfortunately, knowing which drivers will provide good experiences as reliable, safe drivers is both critical for success and difficult to optimize. Onboarding the wrong drivers can lead to a lot of frustration on all sides, leading to poor rider and driver experiences along with lengthy and expensive claims for the business.

So how can we get ahead of these problems? They’ve existed since the concepts of sharing and on-demand services began, and they’re only going to get bigger as these companies keep scaling. Combined with growing market demand as we ease out of the pandemic and activity returns, we can expect that decisions to onboard drivers may focus more on quantity over quality.

Focusing on volume means making decisions quickly. There are a lot of good ways to do this already, and existing practices to use business rules to onboard drivers works to maintain a healthy supply of drivers that are generally safe. But, the trouble with business rules is that they can only answer yes/no questions, and they can only be based on events that happened in the past. What will happen once the driver’s actually on the platform is far more important to ensure a good customer experience. What if you had insights on which drivers are more likely to get into accidents and have higher claims costs? 

Companies can set themselves up for success early when they have predictions on which drivers will be safer once they’re driving for the platform. This allows them to filter out undesirable drivers early, preventing wasted time, money, and effort that would be required otherwise. Moreover, for customers, it also means that the rideshare ride home is less likely to be the most memorable moment of the night.

To learn more about using predictive insights for onboarding, visit

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Arity is a mobility data and analytics company. We provide data-driven solutions to companies invested in transportation, enabling them to deliver mobility services that are smarter, safer, and more economical.