If growth in the US is flatlining then where is Facebook's growth going to come from?

While we are back on the topic of the economic value of social networks we may as well take a quick look at what the flatlining in growth in the US may or may not be able tell us about the potential of social networks.

As you may or may not have heard Facebook’s growth path in the US has essentially flatlined at around 150 Million Members or at about 50% of the population (See TommyToy and Inside Facebook). This of course suggest limits to membership growth across all countries but it also provides us with some insight into the value of Facebook as a global advertising platform.

You see although Facebook theoretically reaches about 50% of the online population in the US it attracts less than 5% of the online advertising revenues.

2010 estimates put the monthly ARPU of a Facebook member in the US at just $0.67. This means the average monthly ARPU Facebook is achieving in the rest of the world is just $0.10.

Now if we assume that the US market represents a mature stage of the Facebook business model - and, after almost 4 years of solid growth in the So.Me industry, the extensive eco-system that has been built around the platform and ongoing problems with relatively weak advertising click through rates, one could probably build an argument to suggest that maybe it is –  then the business will be looking elsewhere to achieve growth. Not only in members but also in revenues.

If we use these figures to project the international growth potential of Facebook we discover that in 2010 the Social Network could have achieved Global Revenues of $5.64 Billion if the ROW ARPU equaled the US ARPU.

This puts the projected global growth revenues for Facebook at about 19% of Google’s $29.32 Billion in 2010.

Google’s market cap is at $164.2 Billion today so that would give us an estimated Facebook valuation of $31.2 Billion.

The problem with this calculation of course is it assume the emerging markets will deliver the same advertising ARPU as the US. So what happens if we just account for potential growth but leave the ARPU at just $0.10 per month?

If we assume a growth target of 1 Billion growth then we discover that Facebook’s estimated revenues will grow by just $40 Million per month to deliver annual advertising revenues of $2.23 Billion. Again if we do a comparative study with Google’s market cap we discover Facebook’s valuation estimate has now fallen to just $12.47 Billion.

Assuming that is a worst case scenario and that Facebook will not lose a significant proportion of its US members while it grows overseas what would a best case scenario look like?

Well if we apply the monthly US ARPU of $0.67 to a projected 1 Billion world-wide membership then we have global advertising revenues at $8.06 Billion or about 27.5% of Google’s 2010 revenues. This would put the comparative valuation at $45.2 Billion.

This of course is a lot less that the $100 Billion estimated valuation floated back in May.

But it does provide the background figures for a discussion about where Facebook’s potential growth is going to come from.

Again this differential in monthly ARPU between the US and the ROW (i.e. $0.67 vs $0.10) provides further evidence that Metcalfe’s Law is limited in its application when it comes to social networks. Even more so when you consider that LinkedIn’s premium network of professionals attracts even lower rates of advertising ARPU than Facebook’s “one size fits all model”.

Which reminds me. I have updated my calculation from the previous post to account for the “trust” factor and the number of “dead” or inactive, duplicate, pirate and robot nodes.

The trust element is the value the participants have in the health of the network and is directly related to the number of dead and rogue nodes. So I have included as an amplifier of this group of variables.

Here then is my revised equation complete with “Trust” variable.

The Value of the Social Network = (The total number of nodes -((inactive nodes + duplicate nodes + rogue nodes))*Trust)* (The average number of Blipnet nodes * The average percentage level of exchange activity across the blipnet) * (the frequency of the exchange * the value of the exchange)

or

SNV = (N-((IN+DN+RN)*T) * (BN * EBN%) * (FE * VE)

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