Network Effects ≠ Startup Success
https://sloanreview.mit.edu/article/all-platforms-are-not-equal/
It’s always exciting to talk about transformational technology and startup companies are often the folks who bring it to us. Naturally, out of that comes a lot of analysis about what attributes lead to startup success. One of these attributes that is often talked about is something we discussed in class: network effects.
The article linked above is called Why Airbnb will always be a better business than Uber. The article talks about the narratives that surround many of the most exciting innovations of today. The article states that often times the discussion place a tremendous emphasis on these network effects, when in reality having network effects is nowhere near a sufficient condition for startup success. As we learned in class, the idea of a network effect is that the value of the service increases due to more people using the service. Clearly, many big startups like Uber and Airbnb have network effects: for example, the value of ridesharing companies is dependent on having drivers who have access to many customers and having enough drivers to quickly satisfy the demands of all riders; hosts find services like Airbnb more valuable when there are many people looking for a place to stay, while people looking for a place to stay find it valuable to have many choices for homes to stay. However, the article notes other businesses with network effects too, but ones that don’t have a success story behind them. The article mentions the P2P lending companies Prosper Marketplace Inc. and Lendingclub Corp. which are also in a business where network effects exist, but have led to struggles with profitability and disappointment from investors.
The article is making the point that throwing out the term “network effects” can be nice when detailing the meteoric arc of a legendary startup, but the fact of the matter is that having network effects just means the service is more valuable with more people using it: it does not offer any guarantees about success. Network effects are rife in many businesses, many of which are unsuccessful. If you ever go into investing yourself, armed with the knowledge of this article and the Networks class, you will know not to give too much credit to a company just because they mention network effects. Rather, you can consider network effects in the context of other attributes that the article mentions like minimum viable market share, the nature of customer relationships created by the network, and how you can use data from the network to optimize.
You can reinforce those things by using the models we learned in class. You can ask questions about how competition with many firms impacts the reservation price r(x). How does the nature of the business affect the shape of the f(z) function which acts as a multiplier on the reservation price? These questions can help you determine equilibria and actually make a determination about whether a company is worth all the hype. The point is that the devil’s in the details. The phrase “network effects” should not cause you to get ahead of yourself: as we learned in class, the behavior of the r(x) and f(z) functions really hold the key to whether the company will go boom or bust!