- Solo apps: applications whose main goal is to be used on your own, even if the score (of a game) may be shared eventually.
- Communication apps: applications which are used to synchronously communicate with other people. The value of the service grows with the number of correspondents that may be reached.
- Social apps: application which use asynchronous communication to become content publishing platforms. The distinction between “communication & social” will become clearer later on, but we may state right now that the value depends on the amount of available content, which depends on the total amount of time spent by social partners on the social app.
1. Metcalfe Law for Communication Software
The value for one individual is linear in the number of user (O(N)), but both the total value and the virality is quadratic. The virality, which is linked to the growth rate, may be seen as the product of the “infected” population (number of users) and the probability for one customer to “infect” another person (that is, recommend the service), which is liked to her or his satisfaction (hence, to the value).
The second idea is that all correspondents are not equals and that the communication time distribution usually follows a law similar to Zipf’s Law. This leads to the result that the value grows in a O(N logN) fashion. The whole issue boils to the question of knowing is the distribution of the communication tool among your possible correspondents is homogeneous (randomly distributed) or not. This is actually a debate about strong ties versus weak ties, one of my favorite topic. If the communication tool is used to communicate with your close friends, then the propagation model follows the strong ties social graph and we may assume that the value for each customer grows in O(log N) because of Zipf’s law. On the other hand, if the communication tool is used to reach a larger set of people, then the probability of one of these contact to be equipped with the same communication tool is roughly linear with respect to the usage rate, hence the individual value grown in O(N).
2. Social Software and Cumulative Valuation of Time
We now consider an application, like Facebook, that acts as an asynchronous content publishing platform. The key observation is that the value of a Facebook session does not depend on how many friends you have, but on how frequently they visit and contribute.
- Total Value = N x Average Value
- Average Value = Average Degree x O(Average Time Spent) x Filtering Factor
- Average Time Spent = O(Average Value)
- The formula that describes the value obtained by a social app user is complex, hence the virality percolation model is complex. It does not compare at all with an epidemiology model since the probability of “infecting” someone depends both on (a) the number of your infected friends (b) how deeply infected they are.
- There is not simple model for understanding the spread of social network platforms : there may exist multiple solutions with similar customer bases (N). The example of Google Plus and Facebook springs to mind: They have both large customer bases (1230 Millions monthly active users for Facebook and 300 Millions montly active users for Google Plus) and average time spend stats which are totally different (8 hours per month for Facebook versus 7 minutes for Google Plus). Nothing in the percolation models tells if Google Plus should grow closer to FB in the future, it all depends on much finer details (value provided to the user per unit of time and per unit of meaningful social content). The non-linear nature of the equation (re-entering loop) means that a tiny difference in this value-creation function may lead to a radical difference in customer usage (i.e., the presentation difference produces different time allocation patterns that, in turn, amplify the perceived value difference).
3. Why Facebook’s Doom Cannot Be Predicted with Epidemiological Models
Early this year there was a lot of excitement about a paper that predicted that Facebook would almost disappear before 2017. This information was printed and commented in many famous news sites and newspapers. The origin for this information is an "archive" (i.e., submitted for publication) paper from two Princeton PhD students, John Cannarella and Joshua Spechler.
4. Percolation Models for Social Software are Unstable
The previous “model” of section 2 is crude because it does not introduce the connection frequency. To understand and to model the behavior of a social app user, one need both the average frequency and the average time spent per users (20 mins for an average Facebook session and slightly more than once a day). I have tried to build a computational model two years ago, and failed because I did not have enough connection frequency data. This means that I could have used my model to predict almost any possible outcome … somehow like the Princeton computational experiment.
- A new app appears, that is more efficient for a new group of users (most likely, an aged-based group, but not necessarily, it may be a matter of geography or culture). WhatsApp is a great example since it has reached 500 M users in record time.
- Because the app is significantly better (from the point of view of new users), it eats away the “free time budget” : the time spent on the new app is taken away from the time spent on Facebook. This is clearly true for WhatsApp with more than 10 hours of monthly use (here also, statistics vary, but the tally is still impressive).
- This decreases the perceived value of Facebook for other users, who open an account and then spend some of their SNS time onto the new app. This has yet to appear for the WhatsApp case; for instance, in Spain where WhatsApp is very strong, Facebook is still growing, even if adoption rate is slower than other European countries. Also, the fastest growing segment of Facebook users is people over 55, it will be hard to get them away as a community.
- Eventually the new app becomes the place where the majority of users go (there is a winner take all system dynamic, which has been very profitable for Facebook since it started).
The previous curve shows that social apps have a stronger percolation capability than simpler communication apps.
Rather than drawing a conclusion from this difficulty to efficiently model percolation of social software, I will simply point out a few directions for developing social and viral adoption of applications:
- One must “pick the right fight”: it does not make sense to fight for usage time if the usage frequency is not high enough. If the frequency is too low, it’s a different game : how to use other SNS for “signaling” (letting people know that theirs friends have used your app).
- “Surf the wave instead of racing it” : profit from existing SNS which are created as platforms, to leverage existing social networks to grow you own app's social usage.
- Make it easy to share your content on competing platforms (a good example being LinkedIn which allows easy sharing with Twitter, while the reciprocate exchange, that is, sharing from Twitter on any other SNS, is not true).
- Empower your users to do whatever they please with your app, making it a true "platform". This follows from the observation that increasing time spent will increase value, hence adoption. This is something that Facebook has been quite good at (although this is a subject of debate), and that Snapshat or Instagram are also good example of.
- Think about “value / effort” all the time and focus on simplicity, usability and speed. Especially, to the previous point, sharing/publishing must be as effortless as possible. We are back to the “maximize the value per unit of time and unit of content” principle stated in Section 2. The dynamics of content/time percolation means that a small efficiency competitive advantage can accumulate rapidly into a larger content & customer base sustainable advantage.