Improvement of Customer Satisfaction and Operational Efficiency by Data-Driven Management — a Case Study of Uber

With the development of the digital economy, sharing economy platforms are facing challenges of supply and demand matching and the improvement of efficiency. As the representative of sharing economy platforms, Uber’s management model possesses vital research value. This research analyzes the improvement of customer satisfaction and operational efficiency by using data-driven management at Uber. The research uses the case study method, focusing on critical technologies of Uber such as dynamic pricing, intelligent scheduling and a two-way rating system. The analysis shows the data-driven management mode helps corporate operations through algorithms and data support. However, the research also finds that there is still controversy remaining in algorithm management, for instance, the order dispatching algorithm lacks transparency, and drivers are forced to accept low-profit orders to maintain their performance levels. The conclusion points that data-driven increases its operational efficiency conspicuously, but still lacks balancing efficiency and equity. In the future, efforts should be made to explore the transparency of algorithms and the establishment of a driver rights protection mechanism to achieve sustainable development.