Network Effects in Real, Complex Markets
This article presents an interesting discussion of the ways that the consumption of certain products rise and fall, with a strong emphasis on network effects. The network effects we studied in class provide a good model of this phenomenon for isolated products; a low market share can lead to a product’s failure as demand drops, and a strong market share can lead to an even stronger market share as demand soars. This effect is certainly observed in real life scenarios; the article describes the success of Facebook as more users led to more prolific and interesting content, encouraging even more users, while the drop in popularity of the Chinese rideshare service Didi spelled out its eventual failure. In some aspects, real-life follows the network effects model well. One such aspect is in the varying strength of network effects; the article states that for social media sites like Facebook, the network effects tend to be quite strong, since there is a strong correlation between an increased user base and wider user appeal. However, for products like video game consoles, network effects tend to be weaker; only some successful video games are needed to make video game consoles dominate the market typically, so there is a weaker effect between a change in the number of users and a change in the overall demand. If we relate this to the network benefit function f(z) used to determine a consumer’s effective reservation price with network effects (p = r(x)f(z)), f(z) effectively determines the strength of the network effect with changes in market share. Social media sites like Facebook may have an f(z) with a large coefficient, and/or an f(z) with a larger power, like a quadratic function. Products like video game consoles likely have an f(z) with a smaller coefficient, and/or may be related to a slower growing function like a log.
However, the fluctuation in demand for a product is almost always more complex than the network effects model we studied in class. This article also discusses many of these complications, citing real-life case studies. One such complication is the fact that products are often competing for market share; when choosing between competing services like Uber and Lyft, to give an example from the article, customers choosing one detracts from the business of the other. Network effects become much more complicated with this in mind. The article gives an interesting perspective on the differences between market competition. If you represent the users as nodes in a network (shown in visual), companies like Airbnb tend to have strongly connected, dense networks, with hosts/users connecting across many countries and continents. However, companies like Lyft and Uber tend to have users/drivers clustered in geographic areas, with rare connections between users and drivers in different geographic areas. The result is that companies like Airbnb have a strong network of consumers, making the barrier to entry more difficult for competitors, and lowering competition as a result. However, since dominating market share is determined more by geography, it is easier for competitors to dominate the market in certain geographic areas; this is what happened when Chinese ride sharing service Didi outcompeted Uber in the Chinese market. As a result, the network benefit function f(z) is not static like we assume in our class exercises, but rather very dynamic and prone to changing over time. As competition in a product market increases, the benefit consumers have from more people using a service may be diluted; this effect, along with the fraction of users decreasing due to other competitors, makes it difficult to maintain a dominating market share. This means that companies rarely have equilibrium market share values, especially those with significant competition, and the market is volatile in this regard. Then, companies must consider not only the actions of their users but other competitors, and try to act in a way that maximizes the value of their product with more users, even in the face of competition. An example of this is when Uber and Lyft both implemented loyalty programs where customers were rewarded for a certain number of consecutive rides within a certain time period; both companies had to develop competing strategies. Then, real-life markets are a delicate balance between game theory (trying to outcompete the competition), network observations (like the strength and interconnectedness of networks indicating the likelihood of competition in a market), network effects, and more. It is interesting to see how all of the seemingly isolated concepts we learn in class interconnect to explain an underlying structure between the highly complex markets we see in real life.
Visual User Networks of Airbnb vs. Uber
https://hbr.org/2019/01/why-some-platforms-thrive-and-others-dont