• Enioluwamo Ireoluwa Obatoki

The ride-hailing industry is no longer a novel phenomenon. Since 2009, when the pioneer ride-hailing giant, Uber, was founded, other similar companies have sprung up around the world, bringing on-demand transportation to millions of people at the tap of a button. Uber is market leader in many of the countries it operates in. However, the company has seen fierce competition from other regional giants - In Asia, Grab, Ola and Didi-Chuxing have given it a run for its money and in Europe and Africa, Bolt(Formerly Taxify) and Yango are fierce contenders. As the global ride-hailing scene continues to take shape, one thing is clear, that the ecosystem is heavily reliant on the real service providers - “Drivers” who are independent contractors providing the vehicles and services to the consumer market. This paper is a study of drivers in the ride-hailing industry; what are the key drivers of driver churn? and subsequently, how can this knowledge be used to reduce churn and increase driver activity?

Driver - An independent contractor who has been accepted into the ride-hailing ecosystem to provide transportation services with his/her vehicle to app users on-demand. Active driver - a driver who has gone online on the ride-hailing app in the past week Quality - the average rating a driver has. This rating is assigned by the drivers customers after a trip is completed. Hours - the number of hours the driver is active(or online) and available to take ride requests. RPH - Rides per Hour Earnings - the amount of money the driver makes in trip fares Churned driver - a driver who has not gone online on the ride-hailing app in the past week Fully churned driver - a driver who has not gone online on the ride-hailing app in the past 4 weeks

"PREDICTING DRIVER CHURN WITH LOGISTIC REGRESSION: A case-study of drivers in the ride-hailing industry", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.615-619, June 2019, Available :http://www.jetir.org/papers/JETIR1906W82.pdf

"PREDICTING DRIVER CHURN WITH LOGISTIC REGRESSION: A case-study of drivers in the ride-hailing industry", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp615-619, June 2019, Available at : http://www.jetir.org/papers/JETIR1906W82.pdf