Remember the end of the movie The Matrix, where Neo finally embraces his true gift and can see the world around him as binary code rather than people and things?
You're not there yet.
But after this chapter, you’ll be much closer to Keanu Reeves-esque levels of “Whoa.”
(Side Note: If you don’t know what I’m talking about or you’re not a fan of the first movie in The Matrix series, we can’t be friends. Also, you probably won’t get some of my hilarious jokes in this post. But trust me, they are very hilarious.)
As part of our guide on How to Create Your Viral Engine in 15 Steps, we have dissected the basic structure and critical points of most standard viral loops.
(In case you’re wondering, we’re currently on Step #12.)
That said, KNOWING what those steps are can only get you so far. Or, as Morpheus would say:
“There’s a difference between knowing the path and walking the path.”
The theory is great, but putting theory into practice involves creating a practical method for implementation that can be sustained over time.
To do this, you’ll likely have to see a visualization of your viral loop, complete with data and context.
Here’s how I’d recommend doing this (other than by swallowing a red pill):
You obviously want to make each metric in your viral loop as awesome as possible. But how awesome is “awesome enough”?
This is never an easy question to answer. After all, if you’re getting your data for the first time, you likely have nothing to compare it to. So do your research.
In other words, don’t think you are. Know you are.
For example, let’s say your viral carrier is SMS (meaning users send out invites via text).
You might not be familiar with the average open rate of an SMS text message, but after doing a little research, you’ll find that this number is around 36%. Is this specific to your app or your industry?
No...but it will give you some benchmarks to set more accurate expectations.
You can do this type of research throughout your funnel. For the most part, you should be able to find general benchmarks of what you can expect.
If you’re severely undershooting these, you’ll likely be able to diagnose that step as a bottleneck.
You can then place more focus on lifting this metric over others that are closer to the benchmark average.
In general, if you make each step in the process incredibly short and clear and provide a very slight progressive information commitment, you’ll win.
Optimize this viral funnel as if it were a landing page or sales funnel, and test for drop off at each step.
The difference between optimizing a landing page and optimizing a viral loop is that a landing page is a static metric.
Throw people at the top, and customers come out at the bottom – plain and simple.
However, viral growth is a compounding process. So optimization can be a LOT bigger of a deal if done correctly.
Let’s delve into some basic viral math to see exactly what I mean.
Some people may have heard of the viral coefficient, but few know exactly what it is, and even fewer still can calculate it. L
ucky for you, this “secret” will be revealed in our next chapter.
Professor Viral Panda’s class on viral math is now in session.
SIDE NOTE: if you want to hear me talk about all things growth, startups, and inspiration, hit me up on Twitter, Instagram, and LinkedIn!
