Network effects explained.
In 1991, Nobel Prize winner Gary S. Becker wrote a paper in which he explained what may seem to be a paradox: in our daily lives, we see businesses that are extremely popular. It may be a restaurant, a food store, a liquor store, or even a tailor. If these places are so popular and if there is a long line outside, meaning that they can have more customers than they can handle, why not just raise the prices?
According to the basic rules of supply and demand, rising the prices would make the demand go down, the line will thin out, the only people in line will be the people that can afford the new price, and everybody would be happier. In the example with a restaurant, the restaurant would make more money and the customers would have to wait less, however, for some reason this just doesn’t happen in real life and Becker decided to figure out what was happening.
In the paper, Becker explains that it is not happening because of the network effects. Essentially, the choice of the restaurant is the choice between all or nothing. People will be lining up in front of the restaurant only if there already is a line of people who want to go to the restaurant. And if the restaurant were to raise its prices, then the line would disappear and the restaurant would have a bigger supply of seats that it would have customers.
Before the Internet and the artificial intelligence, the powers of network effects were severely limited because a business could only have a small number of network effects. It could be the word of mouth and customers telling each other about how great the service is or a physical line of people in front of the store.
With Internet, businesses can grow faster because the Internet removes friction and offers unprecedented opportunities for scalability.
In the example with the restaurant, the costs of adding tables and seats stay low until the restaurant reaches its capacity. This means that if a restaurant can have 100 seats and currently has 80 because the owners did not want to buy all the tables right away, then adding 20 seats would be relatively inexpensive comparing to the costs of building a new restaurant. However, when the seating does reach the maximum of 100, then adding new seats would become impossible and scaling would only be possible if the owners build a new restaurant.
Historically, all monopolies were able to get to a point where adding a new user or a new item would cost very little, which is not the case with most brick-and-mortar businesses. A telephone company is a classic example of such a monopoly and the network effect in action. Originally, adding a landline to a home was expensive, yet it did create a network effect because the more people had phones, the more people were interested in getting one. Eventually, adding a phone number cost next to nothing because of the rules of economies of scale.
A popular restaurant may only have one hundred seats, but there is almost no limit to how many search queries a search engine like Google can process or a number of profiles that users can create on a social network such as Facebook. The Internet has removed the barriers that most brick-and-mortar businesses struggle with to this day.
What’s more, the Internet and technologies also provide businesses with plenty of data and metrics about their users. For example, for a restaurant to know how many people there are outside waiting in a line, the restaurant would need to have a person actually counting the people in line. With technology businesses, it is possible to see how many users are using a service in the moment, without having to add steps or perform any kind of manual labor.
The definition of network effect. How Cindicator can help businesses increase network effects.
In simple language, a network effect is when one user using a product or a service adds value to all the subsequent users of the product or the service. Cindicator as a platform can help businesses streamline network effects in a variety of ways. For example, top management of a company could use the Cindicator platform to gather ideas, proposals and signals from employees and users of the product, including software developers, sales people, and others. Because the platform is decentralized, all the thoughts and opinions will be independent and not have an impact on one another. Then, Cindicator could combine all this data obtained from people with its big data processing algorithms and mathematical models, allowing top management of the company get valuable insights they would not be able to get otherwise.