  Millions of drivers worldwide have enjoyed financial benefits and work
schedule flexibility through a ride-sharing economy, but meanwhile they have
suffered from the lack of a sense of identity and career achievement. Equipped
with social identity and contest theories, financially incentivized team
competitions have been an effective instrument to increase drivers'
productivity, job satisfaction, and retention, and to improve revenue over cost
for ride-sharing platforms. While these competitions are overall effective, the
decisive factors behind the treatment effects and how they affect the outcomes
of individual drivers have been largely mysterious. In this study, we analyze
data collected from more than 500 large-scale team competitions organized by a
leading ride-sharing platform, building machine learning models to predict
individual treatment effects. Through a careful investigation of features and
predictors, we are able to reduce out-sample prediction error by more than 24%.
Through interpreting the best-performing models, we discover many novel and
actionable insights regarding how to optimize the design and the execution of
team competitions on ride-sharing platforms. A simulated analysis demonstrates
that by simply changing a few contest design options, the average treatment
effect of a real competition is expected to increase by as much as 26%. Our
procedure and findings shed light on how to analyze and optimize large-scale
online field experiments in general.
