Characterising network effects

Met a bunch of people for drinks this evening. Most of the conversation was just okay. But there was this little bit about network effects. Where I figured out how to calculate whether network effects are present in an industry. It all came out of Kingsley claiming that the age of network effects is over, and there are no more network effects left.

The discussion presently moved to how you discover whether there exist network effects in different industries. Does the fact that  Amazon’s marketshare is nowhere close to that of a monopoly mean that there are no network effects in e-commerce marketplaces? Doesn’t Google have network effects in that given the larger number of people searching on the platform, there are more clicks and more opportunity for learning (for Google) and hence better results?

At a point of time in the conversation, I made the statement that Google (in particular and search engines in general) has “partial network effects”, in that more users means more learning and hence more results. And that for this reason Bing or any other competitor can’t match up.

So how can we characterise whether an industry has network effects, and if so, to what degree? Thinking about it, it’s rather simple. In a “normal” non-networked industry, the value of the user base is directly proportional to the number of users. Going by Metcalfe’s Law, in a fully networked industry, the value of the user base is directly proportional to the square of the number of users. An industry with “partial network effects” should surely have its value a power between 1 and 2 of the number of users?

Here’s how we figure out how networked an industry is. Take all the players in the industry and tabulate the size of the user base and the value of each of the players (excluding very small players). Plot them on a log-log plot, and measure the slope. If the slope of this log-log plot is close to 1, it means that the industry is not networked at all. If the slope is close to 2, it means it has “full network effects”. And the numbers in between represent the spectrum of possible values.

Rather simple, isn’t it? This is why I love drinking sessions, for they allow you to unleash such thoughts. Oh, and I “recorded” this thought by sending a WhatsApp voice message with the gist of the above content to Hariba. He replied with “keep them coming” or some such thing, but this was all it was for this evening.

Put Comment