The death spiral of social networks
I’ll link to this article because I know I’ll want to refer to it later. I very much enjoy its dicussion of the negative effects of “network effects”. Like credit in a stock market, “network effects” exaggerate both the upward and downward swings.
You see this happen all the time at dinner parties or events. Things are great until one or two people announce the intention to leave. If those folks are fun and entertaining, there’s an immediate realization that the quality of the experience is about to go down. And yet more people announce their intention to leave, and so on, until you are left with the party hosts and a big mess ;-)
Advanced discussion: Social Network Death Spiral
Now let’s do a more advanced discussion using the concepts above - for some new readers, this discussion might completely be incoherent ;-)Let’s consider a specific scenario where a social network could easily start to “Death Spiral” - here’s some set up on the scenario:
- You have a bunch of users, let’s call the total number N
- The total number of users in the ecosystem, called the carrying capacity, is variable C
- These users all individually require some utility value on a site, let’s call this V_required
- Then there’s a retention %, called R, which depends on two factors:
- If the utility value for users is satisfied, that is, V > V_required, then R close to 100%
- If the utility value drops under V_required, then R is crappy, closer to 0%
- And to borrow Metcalfe’s Law, the value of the network is calculated at V = N^2
So the scenario is that as the total users for the application reaches the carrying capacity, you basically hit a point of maximum saturation - this is defined by the ratio N/C. Sometimes this ratio can also be referred to as the “efficiency” of a user acquisition process, which relays how many people you actually acquire versus the universe of all users. (Obviously you want this to be as large as possible)
Once you hit the carrying capacity and acquire all possible users, N is at the highest point, and thus the network value is also at its highest point, V = N_max^2. Similarly, because the network value V is at its highest, the retention reaches its highest point as well.
The question in this scenario is, at any point during the growth of the network, does the network value V exceed the required value of the site, which we call V_required? Does the network break through the critical mass of value?
If so, retention should be great, as defined by the explanation above. In fact, maybe you reach V_required early on during the growth of the site, which makes the acquisition process much more efficient. Early on, maybe the userbase wasn’t sticking, but a critical mass threshold is met, and suddenly the entire userbase sticks, which creates a long-term creation of ad impressions and company value.
However, if you don’t reach the required value in the network, then you’re pretty much screwed. Then the retention sucks, since the users aren’t finding value, and some percentage of them will leave. This will then remove more value from the system, causing yet another round of users to leave. This continual loss of users is a death spiral that collapses your network in fine Eflactem’s Law style.
A very interesting variation of this is when you apply Metcalfe’s Law not to the entire network of users, but rather think of a social network as a loosely grouped set of connections. In that case, some local networks might have achieved critical mass, and if they are big enough, they will be retained. However, if the smaller networks around any given group start collapsing, then sometimes even the large networks will get pulled down with them.