It’s Friday again and thus time for a new post. I promised to discuss how to recognize apps that are about to go exponential and some of that thinking will fix the issues that have been pointe out with my FB model (users never churning but rather becoming less active over time, for example). But before we can get to that, let’s talk about what is viral growth. To do this, I’ll resort to one home assignment of last summers ESD.74 class (taught by professor J. Bradley Morrison at the MIT) simply because it covers the very topic. Viruses. I’m using this model mainly because whatever I’d come up with would look exactly like this simply because of me having taken that course. There you go.
The model looks like so:
The three main stocks are pretty self explanatory the only tricksy part being that “Recovery Rate” is both the speed at which people recover from SARS is the same speed at which the population of susceptible people grows. Simply put, people who recover, become susceptible again, there is no immunity. Otherwise the model is based on the idea that infection happens when an infections person has any sort of contact with a healthy person and the number of such events depends on the likelihood of the person you are contacting being healthy and the number of contacts people have.
The model has the following key variables:
- Infectivity, that defines the likelihood of a contact leading to infection
- Total population defining the scale of the problem we are observing
- Contact frequency determining the number of contacts each person has on average per unit of time
- Recovery rate that defines both the speed at which people stop being infectious and the average time people can infect others
In order to put all this in a better context, let’s use an example. An example, hm. Wait a minute, a friend just sent me a note and there’s a link in there. Click. Oh, a KITTY! OMG!1!!! Hilarious! Gotta share it…. Nope, back to blogposting. And there’s our example.
- Infectivity now means the probability that whoever sees the video I shared is going to re-share it
- Total population is the size of my world, i.e. the number of people on the Internet that are in my n-th degree friend cloud with n being reasonable small. This, today, is effectively the entire internet population
- Contact frequency is the number of times a person on average sees something shared by their friends in any medium
- Recovery rate is slightly more complex. It is not simply the period during which I send out the links, it is the period during which people are likely to see your link. In FB context, this depends on the time your link remains visible in the news feed before it gets buried under new stuff as well as how long do you keep sharing the video for
Allrihty, let’s take it away.
Let our base case be 2% infectivity (a surprisingly small percentage of people finds kitties amusing. I don’t get these people), everybody seeing an average of 20 kitty videos a day and let the video be “infectious” for 3 days. All fairly reasonable numbers.
What is remarkable is that for the first 150 days nothing really happens. But during the next 150 all hell breaks loose and we hit 90 million cases. This is one of the reasons these things come as a surprise: the explosion takes time to ramp up and people tend to forget about a kitty video pretty fast.
Let’s now consider what would happen if our video was really really cool. What’s cooler than a kitty? Two kitties. Fighting. To the music of Burzum. This would amount to infectivity, oh I don’t know, 3 per cent? Sounds reasonable? A graph showing the current number of people rather than total tells the story:
Oh, bummer. Not only does the explosion happen five times sooner it is also much quicker and results in three times the infected population!
This is scary, right? As an antidote, let’s see what happens should infectivity be only slightly lower, 1.8% instead of 2. As a result of, say, the subject of your share failing to mention kitties. Or Burzum.
I actually had to resort to excel to get this one to even show properly. Yes, the vertical axis is now a log scale and while our base case dealt with millions of people the video with a slightly dull title deals with thousands. Zero point two percentage points difference in infectivity levels yields three orders of magnitude worse performance. By the way, isn’t it cool how the log scale makes the curves all straight outlining the exponential nature of the phenomena we are observing?
I won’t burden you with another graph but the effects of playing with the infectivity period are almost as severe: 2.8 instead of 3 days gives about 31 thousand instead of 3.6 million users within our period.
Obviously my goal was not to educate you guys on kitties, I had a couple of points to make. And lest my storytelling skills foil this mission, let’s lay them out nice and straight:
- Exponents are weird! It is really hard to grasp how exponents work, especially in complex arrangements. Human mind is just not built for this.
- Small lapses have big impact. Even minor changes in how your viral campaign is set up will have a massive impact on whether you’ll reach millions or hundreds of users. Note that the differences I’ve played with here are almost within statistical margin of error. Hence, even if your do everything perfectly but your estimate on how long your video will be visible is just a wee bit off, the campaign is going to be a big flop. Or, a moderately well-executed campaign might explode simply because the stars were aligned just right
Allright, that’s enough SD Action! ™ for today. See you next week!