The Role of Network Effects on the Rideshare Experience
The influence of network effects, or any situation where the value of a given service, product, or platform is dependent on the number of users, buyers, or sellers who leverage it, can be observed in countless aspects of our lives. From e-commerce marketplaces like eBay and Etsy that offer vastly more valuable experiences to customers as their user count increases, to social media platforms like Instagram and LinkedIn that seem to craft much more diverse and varied user experiences as more users sign up, network effects and the benefits of their influence are undeniably very present in the majority of popular and prominent companies in today’s day and age. This might bring to mind the question: what about rideshare apps? How might network effects influence, or not influence and possibly even damage, companies that provide both riders and drivers, an interaction that seems so obviously dependent on networks and managing them?
In his article “The Intentional Network Effects of Uber,” James Currier, a General Partner at a venture firm, explores this very concept of the network effects of Uber, specifically looking into how the company’s biggest asset is quite literally their network effects. Interestingly, Currier argues that Uber’s massive network, which the company even reported as being “the foundation of [their] platform,” has some major cracks, namely due to the fact that Uber’s cross-side network effects deviate from the proper formula of a truly two-sided marketplace. More specifically, he goes on to explain how the benefits of increasing supply beyond the average wait time for an Uber rider (which he deems to be “optimal”) sees steeply diminishing returns. This article takes a unique stance of diving deep into observing how Uber might become more vulnerable as a result of its reliance on network effects to maintain its platform’s primary interactions, which offers an interesting contrast from other articles on the topic that chose to emphasize the benefits of the influence of network effects on Uber, such as by providing increased reliability and convenience to riders as more drivers join the network. In this way, the article offers a distinct lens into the role that network effects play on Uber, specifically from a business perspective and also from the perspective of how this supposedly primary asset of the company may also be the very reason their business model may be vulnerable.
Article URL: https://www.nfx.com/post/the-network-effects-map-nfx-case-study-uber/