Basic Viral Marketing Math
If you’re as big a fan of the latest and greatest tech talks as I am, or you do any combination of reading books and blogs, attending growth and marketing conferences, and talking to other founders and growth engineers, you’ve probably heard the buzzword “viral factor” or “viral coefficient”.
However, most people who throw one of these terms around never actually bother explaining what it is, how it’s calculated, or how much of an impact it can make.
Lucky for you, this big “secret” is about to be revealed.
Your viral factor tells you to what degree your own users will power your growth by inviting others to try your product. While it would be a bit more intuitive to name this variable V (which I almost did – as you can see in our jazzy little panda art above), but since the majority of existing resources (however sparse or fragmented they may be) label this variable as K, let’s adopt K as our variable here so other past or future resources make sense to you.
Your viral factor = K
To elaborate more, K is needed to calculate the average number of new prospective users each existing user will successfully bring back to your site or app to enter your viral loop. In other words, K is a measure of the magnitude of virality your site or app possesses on a per-user basis.
Not quite getting it yet? Hang in there.
No sharks here…
As a quick example, let’s say K = 0.5.
This means for every user you recruit via non-viral means, that user will bring 0.5 prospective users into your viral loop for you.
Wait…a half a user? I still don’t get it. Did the other half get eaten by a shark or something?
Nope, no sharks here. Let’s try a different way of explaining this. When K = 0.5, it means for every TWO users you recruit via non-viral means, they’ll collectively bring ONE prospective user into your viral loop for you.
Make sense? Now instead of 0.5, what if K = 2.0?
This means those SAME two users you acquired via non-viral means will now bring you FOUR prospective users into your viral loop.
And that’s where sh*t gets cray. But more on that later.
K is made up of two smaller KPIs:
- The total number of invites sent out per user on average during one cycle of a viral loop. Let’s call this i.
- The conversion rate on those invites – or the percentage of those invites sent that result in a new prospective user coming to your site and starting a new viral loop. Let’s call this conv%.
K = i * conv%
As you start to think about this, you’ll inevitably want to factor in time – so it’s worth noting that K is typically calculated over a fixed time period, such as one month. Don’t use an average i value from the last 90 days with an average conv% of the last 2 weeks. That’s just silly.
As you optimize, conv% should eventually become relatively stable (unless you experience heavy seasonality). This will be great for the projections we’ll begin doing shortly. However, i will vary heavily over the life of each user, so measuring for month one is a good starting point.
If your loop has been rocking and rolling for a while and you’ve got enough data to try and measure K over the life of a user, we’ll use a different variable – which we’ll call K’…but more on that later on.
Pretty exciting so far, right?
You’ve likely just run to a calculator or a spreadsheet to see what sort of an impact a K > 1.0 would have on your product, and you’re currently flipping out.
If that’s you – take a deep breath.
I hate to be the bearer of bad news, because even if you hit K >1.0, this cannot last for long. As you’ll soon be able to see as we move forward, if it DID last for very long, you’d soon have more users than current inhabit this lovely planet of ours.
But fear not – you can still harness the incredibly-profound power of K…but believe it or not, K is not the most powerful viral KPI we’ve got at our disposal.
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