I learned about your site after visiting the Stanford “Creating Apps For Facebook” group on Facebook. After learning more about the course and apps that the students developed, a central theme is clearly the use of metrics to identify and trigger iteration points.

I’m curious as to what metrics proved to be the most valuable. Did the students identify any metrics that really provided them with great data for making specific changes to their applicaitons?

All the best,
Sarah

I think the current best practice is to use incentives to elicit more invites. E.g., “send 5 more invites to unlock the ultra-cool whatchamacallit”. As you noted, this technique definitely has its limits and it places the ball squarely in the app publisher’s court to create compelling functionality and/or content in order to drive continued app activity and invitations.

Tho as an app developer who can only manage a couple of thousand installs, I can’t say i’ve got any better ideas

I was actually thinking of K-factor as a “lifetime” measurement. E.g., if you wait until a viral host either goes inactive or uninstalls an app, then measure how many new infections that host started during their “viral lifetime”, then that’s K.

But, you’re absolutely right — you could measure K for shorter timespans and that would yield measurements like “monthly K” or “daily K”. (Starts to sound like a breakfast cereal. Or maybe I’m just hungry.

One drawback — those types of time-based measurements will generally yield an inflated K-factor (because the original viral host will usually still be included in the active infected population). So, if getting a short-timespan measurement is important, then app developers can try artificially capping the measured “lifetime” of their active viral hosts. E.g., some techniques could include:
* only consider a viral host to be active for the first M days after app install
* only consider a viral host to be active during their first N visits to the app
* only consider a viral host to be active for X invitation loops

With those type of capping techniques, you can very tightly delimit the viral loop time period to days, sometimes hours, and get a quick read on virality. The obvious caveat: this will produce an artificially depressed K-factor measurement and the degree of impact will depend on how good the app is at driving repeat visits and continuous invitations.

Nice post. Missing one factor: time period. It is important that to minimize the time period over which the viral loop occurs. It’s the denominator to a fraction where you have correctly specified the numerator.

Cheers, and thanks for the post.

- Mike