If you only use our last example about rabbit reproduction to guide your viral engineering, you might find it difficult to visualize how it applies to you and your business.
Fear not! I will now provide a more viable real-world example of viral cycle time in action.
After all, once your loop is built, your company’s growth will be dramatically affected by the length of your viral cycle time.
So we’d better ensure all your digital ducks are in a row.
Let’s say you launch a web application, and you invite 10 of your closest friends, who all join.
u(0) = 10
Easy enough.
Now let’s assume you’re doing things like adding:
The above is obviously a huge leap since we’re assuming you’re doing all of this already at launch.
But hey, let’s assume you’ve read Viral Hero already, and you’re architecting your application with virality in mind.
Given that, let’s assume you’ve achieved a K factor of 2.0 – which is massively awesome.
Congratulations. You’ve hypothetically achieved true viral growth.
Now 20 days go by.
How many users do you have?
Coming up blank, huh?
You’d better wait to cash in all those hypothetical checks rolling in because we forgot to do something very important.
We cannot calculate viral growth over time if we don’t set our viral cycle time.
After all, if we don’t know how long it takes for users to go through our viral loops, how do we know how many can occur within a fixed time period?
So let’s assume our entire viral loop takes an average of exactly five days from start (initial awareness) to finish (sending an invite).
To recap:
(NOTE: Notice I assigned a cycle time value of 5.0 days. For the sake of projection equations we’ll be using shortly, we’ll need to convert our viral cycle time to a decimal form version of days. This means that even if it’s only a few minutes long, displaying it as a fraction in the decimal form of days is the best way to ensure the equations we use remain accurate.)
We’re now in a position to accurately answer the question raised earlier. So, assuming all of the above metrics are true, after 20 days go by, how many users would you have?
Here’s how we calculate that:
u(t) = u(0) * (K^(t/ct + 1) -1) / (K – 1)
u(20) = 10 * (2.0^(20/5.0 + 1)-1) / (2.0 – 1)
u(20) = 10 * (2.0^5 – 1) / (1.0)
u(20) = 10 * 31
u(20) = 310
Wow!
We started with 10 users, and 20 days later we’ve now got 310 users.
Not too shabby, right?
Especially since we didn’t spend ANY money to acquire those users (aside from the engineering costs to build the viral loops).
Our cycle time seems a bit long in the example above, though, doesn’t it?
Why is it taking 5 whole days to execute one loop?
Is there anything we can do to speed this up?
Onboard users more quickly?
Or get them to their "aha" moment faster?
Upon a bit of careful examination, we realize we’ve got our friendly pet snail hand-writing all our viral invites and sending them out to our users’ friends.
He means well, but we need to make some changes to try and reduce our viral cycle time a bit more.
We move our snail pal to our business development department and replace him with our pet rabbit – who can run much faster.
Making that one change reduces our viral cycle time from 5 to 2 days.
Here’s our formula again – but with a faster cycle time:
u(t) = u(0) * (K^(t/ct + 1) -1) / (K – 1)
u(20) = 10 * (2.0^(20/2.0 + 1)-1) / (2.0 – 1)
u(20) = 20,470
HOLY MOLE!
Over 20k users in the SAME time period . . . from one little change?
Yes, it is sometimes as simple as replacing a snail with a hare IF you can focus your energy and effort on the KPI that provides you with the most dramatic growth (i.e. viral cycle time).
But wait, something still isn’t right.
Why is our pet rabbit still hand-delivering all our user invites?
Umm helloooo! That is so 1994.
Why wouldn’t we have him use email instead?
That would be far faster (and adorable) and should reduce our cycle time even further!
Making that change (going from hand-delivery to digital delivery) reduces our cycle time from 2.0 to 1.0.
Now it only takes one full day on average for a new user to complete a full viral cycle.
Here’s our formula again – but with an even faster cycle time:
u(t) = u(0) * (K^(t/ct + 1) -1) / (K – 1)
u(20) = 10 * (2.0^(20/1.0 + 1)-1) / (2.0 – 1)
u(20) = 20,971,510
Over 20 million users in the same time period – all from getting our pet rabbit to use email?
That’s incredible!
I know you’re speechless. It happens when you’re mind gets virally blown.
Snails and rabbits aside, this is all actually just simple math.
Since our viral cycle time functions as the exponent in our projection equation, reducing it will result in exponential improvements to our user base.
Given cycle time’s exponential nature, the more viral cycles that take place, the more users will start going through additional cycles.
The more cycles that can take place for more users in a set period of time, the better.
However, it’s not always obvious at a glance just HOW much better it can be.
To make ct as short as possible, look at three things:
IF you succeed in massively reducing cycle time AND the rest of your metrics hold steady, be prepared for what may happen.
Specifically...
That’s right, baby rabbit levels of growth.
Starting to grow like crazy comes at a price.
It has its own set of problems, and if your churn is too high, you may be in for a rude awakening.
(More on this catastrophic crazy avalanche of suckiness later.)
For now, let’s avert our eyes away from all those horny rabbits getting it on and focus our attention on how YouTube monopolized the phrase “going viral,” and how your viral marketing campaign can benefit as a result.
When most people think of the words “going viral,” YouTube inevitably comes to mind.
But how did this simple video streaming service transform into a viral behemoth?
And what does it mean for you?
Find out in our next chapter.
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