Social Value is the amount of behavior that one person generates among their friends. An anology might be the ripple on the social pond. Let's say this person goes to see a movie, listens to a song, or plays a game. Now let's say that this person is influential. How much more likely are their friends to go see the movie, hear the song, or play the game?
Each person has a unique amount of influence in their social network, and on each friend. Maybe you are highly influential on Bob, but Steve doesn't care what you do. What the Social Value algorithm does is to add up all of your influence and put it in units we care about--like sessions, time, or dollars.
1) An asocial, future behavior number. Marketers call this lifetime value (LTV), but they don't strip out the social impact. This approach does.
2) Social value. An amount, independent of the product, service or activity (i.e. independent of the movie, song or game), caused by the social connections of the person.
Put together and viewed in the aggregate, these values show how much behavior is attributable to the service, and how much is attributable to the community around the service. We're finding that the typical split is about 75/25, where 25c on the dollar is due to community, rather than the product. This number goes up when there's robust community, and down when there isn't.
In gaming, we're seeing a 10-40% range for Social Value.
Knowing these numbers and who they are attached to has a strong and clear value proposition for the developers--find the high Social Value people and a) get more of them, b) retain them, & c) leverage them to cause others to spend more. People with big Social Value numbers can be thought of as "Social Whales."
Conversely--and this I hope to blog on in the future with some shareable data--there's a fun side effect of the scores when a person has a negative Social Value. That's a person who's presence in the network depresses the activity of others. The scientific term is "asshole," or in the game context, troll. I'm looking forward to seeing how firms use this number.
So, that's the commercial side of things. On the research side, I think this algorithm has a lot of applicability in the social sciences. After all, it doesn't care what the behavior is--voting, shopping, writing, singing, whatever.