I recently met the CEO of a company who claim to be one of the most popular social networks in Turkey with several million monthly visitors from Turkey. This happened by accident – the founders are Americans who have no prior connection to Turkey.
This is just one of many examples of how difficult it can be to predict or control the growth of viral social media. Google’s Orkut, is a better known example – a social network started by a Turkish engineer working in the US that now dominates in Brazil and India. Friendster and hi5 fall into this bucket as well. As I’ve noted before, the online advertising market in the US is bigger than that in the rest of the world combined. The senior management of these companies know this, and all would love to see more US traffic, but it is now beyond their control.
The reason is the mathematics of viral growth. If the viral coefficient (the number of additional members a new member brings) in a population is less than one, it grows but eventually hits a ceiling. But if the viral coefficient is greater than one, it grows unbounded. Although your social media property may start in many different populations, it will come to be dominated by those with the highest viral coefficients.
This is best demonstrated by example. Consider a new social media property with 30 members, 10 each from three distinct populations; call them Pinks, Purples and Greens. Suppose that by luck and because of the initial users, the viral growth coefficient for the Pinks is 0.6, for the Purples is 0.9 and for the Greens is 1.2. Watch what happens to the populations over time:
All three groups are initially equally represented, But already by time period 4 the population is more than 50% Green. By time period 10 it is more than 75% Green and probably considered a “Green social network”. By time period 16 less than one member in 10 is not Green. For Green now substitute whatever national, language based, religious, racial or other demographic grouping that you choose.
These evolutions can happen very fast since a time period is the time it takes for a new member to invite more members. 2-8 weeks might be a reasonable assumption.
This example is vastly simplified, of course. Viral coefficients vary over time within and between groups. Viral coefficients also don’t vary quite as much between groups; the same underlying feature set is being exposed to all groups. But it illustrates the point that randomness can play a significant role in the eventual makeup of a social media property’s user base.
I posted about a similar finding in May, how you don’t necessarily get the wisdom of crowds, but sometimes just the crowdiness of crowds. It was based on a NY Time’s article that showed how randomness can have a big impact on the most popular songs for a crowd when popularity information is public.