Network Effects in Lyft
Last week, Lyft released the filing for its IPO; the filing’s ‘risk factors’ section notably mentions the company’s dependence on network effects. Specifically, Lyft discloses that “network effects among the drivers and riders on [its] platform are important to [its] success” and furthermore that “if [it is] not able to continue developing [its] … network effects, [its] business, financial condition and results of operations could be adversely affected.” The strategy question at the center of the Lyft IPO, as well as other headlining questions about whether Lyft can sustain growth and can eventually turn a profit, focuses on these aforementioned network effects.
Network effects occur when, for some decision, one incurs an explicit benefit when aligning one’s behavior with the behavior of others. In the context of products, a product is valuable to a customer to the extent that other people are using the product as well. In other words, the addition of a new user increases the value of the product for other users. The success of many well-known tech companies, including Google and Facebook, have relied on network effects and have thus cemented it as a critical part of strategy in tech. One of the main reasons that startups place a significant emphasis on growth has to do with network effects — only with more people signing on to use the product can the product truly take off.
One of the challenges that new companies often face is getting enough interest in the initial stages. Particularly with a product that depends heavily on network effects, this is especially important. If the company cannot garner enough interest and get people to use the product, the product has little value to potential users. For Lyft, if it cannot get enough riders to use its platform, then drivers will not have incentive to use it (i.e., there are few people looking for rides); on the flip side, if Lyft cannot get enough drivers to use its platform, then riders will not have incentive to use it (i.e., there are few people providing rides). To get over the initial barrier of getting users to sign up, Lyft has used several strategies, including offering first-time users a free ride. In doing this, Lyft is not simply giving away rides for free; by gaining a potential new user, Lyft’s value has increased (i.e., more riders means more drivers will sign up, which in turn means more riders will sign up).
It is important to realize that different kinds of networks rely differently on network effects. Specifically, ride-sharing networks like Lyft’s are not as advantageous as search or social media networks. One property of networks that determines the platform’s potential for success is clustering. The more fragmented a network is (into local clusters), the more vulnerable the network is to competition. For example, Lyft, which has a highly localized network (i.e., riders and drivers only care about the drivers and riders in their city), is more vulnerable to competition than a global network like that of Google search. Another property that determines a platform’s success is multi-homing. When multi-homing (i.e., when users form ties with multiple platforms at the same time) is pervasive in a platform, it is difficult to generate a profit. For Lyft for example, many of its users also utilize Uber — and compare prices and wait times for the two applications — which means Lyft constantly has to compete with Uber for riders and drivers. These are only a few of the concerns that Lyft and its investors have had to keep in mind as the company IPOs.
