Network Effect on Uber’s Delivery and Ride Services
Links to online resources:
https://www.morningstar.com/articles/1009756/ubers-delivery-network-effect-continues-to-strengthen
https://medium.com/@heosphoros22/uber-and-the-network-effect-b6a4395c58ab
The COVID-19 pandemic has a significant impact on the operations and revenues of Uber. While quarantines and lockdowns prevent people from going outside, Uber’s mobility business, such as Uber Ride, has been adversely affected by the lack of users. Although the mobility business is better than a few months ago as more economies are recovering from the pandemic’s harms, the recent surge in coronavirus cases in Europe and North America makes its future market uncertain. On the other hand, Uber’s delivery service, namely Uber Eats, has grown drastically because of the pandemic. Since people are reluctant to visit restaurants and supermarkets in person, they often choose to order online deliveries. According to the first article, the gross bookings of Uber Eat have grown by 134% from 2019 to 2020. There is a long-term trend that more and more consumers start to favor online food delivery, of which the market is contemporarily dominated by Uber in multiple countries.
Indeed, compared to those of social media websites such as Facebook and Instagram, users of Uber and Uber’s Delivery care slightly less about the total number of users. Nevertheless, Uber’s success is crucially dependent on the user population and network effect. As drivers are also a crucial part of Uber’s network and primarily determine Uber’s supply side, the population of drivers is as important as the population of consumers. Consumers value Uber more if there are a lot of drivers available because the customers can benefit from prompt service at any given location. For a similar reason, drivers value Uber more if there are a lot of consumers available because the drivers can fulfill more orders and earn a higher income. Therefore, the number of drivers constitutes the attractiveness of Uber to the consumers, vice versa. According to the second article, the circumstance can be categorized as a cross-side network effect, that an increase in one side leads to an increase in the other side.
In which case, both riders and drivers are less willing to participate in the Uber Ride service due to the pandemic, causing a vicious cycle. The decrease in the total number of riders weakens drivers’ incentive to work for Uber Ride, thus quitting the platform. Subsequently, the decrease in the total number of drivers induces longer wait times and fewer regions available for nearby rides. At the same time, due to the increased food delivery demand, more drivers are attracted to the Uber Eats platform, shortening the delivery time and providing consumers with a more diverse range of restaurant options in different regions. As a result, more consumers start using Uber Eats because of its better service, creating a virtuous cycle with the increasing number of drivers. Furthermore, due to the network effect, an even greater number of consumers start using the platform as they observe that people around them are using it. With drivers shifting from Uber Ride to Uber Eats and consumers changing their behaviors and preferences, the revenues generated by each service thus responded differently.
The articles relate to the topic of network effect discussed in the course. First, it illustrates how the number of Uber users primarily determines the value of Uber’s service. As the number of users of Uber Eats continues to grow, it evidently surpasses the tipping point z’ illustrated on the graph above. Because of the sufficient user population, the market will continue to grow to the level of the stable equilibrium at z”. Corresponding with this theory, the article also manifests that the revenue and gross bookings of Uber Eats are projected to grow even higher in the future. However, the trend of Uber Ride seems to be less promising. Currently, Uber Ride is located near the left end of the curve between z’ and z”, meaning that it is possible for Uber Ride to recover to its original operations at z” if there is no more disturbance. However, because the situations of the COVID-19 pandemic are still persistently ongoing, there might be a possibility that additional shocks will push Uber Ride’s number of users below the tipping point z’. If this happens, the platform needs to implement extensive strategies, such as a marketing campaign, to rescue itself from moving toward the stable equilibrium at 0. Second, in addition to showcasing the theory covered by the course material, the articles also provide some new aspects and insights into the network effect, namely the cross-side network effect. Regarding Uber, consumers make decisions depending on not only other users but also the drivers. Taking Uber Eats as an example, the growth in the number of consumers leads to the growth in the number of drivers, which then leads to another growth in the number of consumers. The overall outcome is identical to the original network effect, yet the underlying process significantly varies.