Viral Infection: How the CDC Can Make You a Viral Marketing Savant

These days, the word “viral” is strongly associated with something spreading like wildfire online. That is to say, it’s something desirable. But that wasn’t always the case. In fact, just a few years ago, folks likely would have responded in a much more fearful way when asked about viral infection. Primarily because they would have associated it with the spread of – you guessed it – a virus.  As obvious as this may seem, ask any kid walking around with their face glued to their phone what “viral” means, and they’ll immediately go straight to “something on YouTube.”

Back When “Viral Infection” Was Scary

  The science and mechanics of virality can be tough for startup founders and business people to implement (or even understand). However, it’s something that has been studied at great length by virologists at the CDC and other infectious disease prevention centers and medical research organizations. These organizations make the spread of disease their business. So in creating a user-generated viral growth engine, it can be helpful to look at their contagion models to help quantify viral “infection.” Specifically, these models demonstrate the propensity for certain viruses to spread from one to person to many. In other words, if you want to better understand viral marketing, it helps to start thinking like a germ. That said, once you’re able to model viral infection mathematically (which trust me isn’t as weird as it sounds), I highly recommend you leave the world of infectious diseases behind. It won’t help you in a practical, business sense to continue to think through this lens. And no one will probably want to hang out with you anymore if all you talk about is catching diseases. Now that we’ve settled that, let’s get sickly.

A Viral Use Case

  Let’s assume you’re building a live chat tool – such as Olark or Intercom.  The goal is to help brands engage prospective and existing customers in a more efficient way. So how could this tool be viral?

• It could use viral collaboration marketing by adding a feature that lets users incorporate their team members in providing customer service through the tool.
• It could use viral communication marketing by adding a feature that lets users email a transcript of a conversation to friends or coworkers after it’s over.
• Almost certainly it WILL use embeddable viral marketing as companies will typically embed a tool like this within their own website or app, exposing it to that site’s visitors.

There are likely other viral marketing engines that can be implemented with a tool like this. For this reason – AND because you’ve spent ample time developing the product and making it legitimately valuable in contrast to the other solutions available at the moment – let’s assume you’ve built what you believe is a strong viral loop. But wait, there’s a problem. You haven’t figured out how to quantify that growth yet. Why is this important? Because without these numbers you won’t be able to optimize your viral loop and assess how to improve. Thankfully, you’ve discovered Viral Hero in time. We have cure for that.

The First Bit of Viral Math

  So, you’ve worked hard on your product and your viral loop. Finally, you’ve decided that it’s time to expose your amazing product to the world. You start by inviting 10 of your closest friends. Since they know and love you, they all sign up as your first 10 users. (It pays to have friends, amirite?) This kicks off our math with a base level of 10 users, or . . . . u(0) = 10 That wasn’t so hard was it? See, math can be easy and fun. Our initial 10 users seem to totally get the product. They all love the experience, and you’ve made the additional value they receive for inviting others both obvious and compelling. As a result, they each send out an average of 10 invitations to their friends, generating 100 invites total. Now, let’s create a new variable called i. This metric equates to the total number of invites sent out per user on average during a selected period of time. So for us . . . . i = 10 Still with me?

The Second Bit of Viral Math

  For every batch of 10 invitations that get sent out, say two of the people who received those invites responded favorably and signed up to use your product. Let’s calculate this using another variable called conv% (aka the conversion rate on invites). conv% = 2 new users / 10 invites sent = .2 (or 20%) Now we’re really churning!

Summing It All Up

  So to recap, we started with a base amount of 10 users (u(0) = 10), who wound up sending 10 invites each (i = 10). Those invites had a 20% average conversion rate (conv% = .2). This means that the total number of customers at the end of the first full “cycle” (or the amount of time for all this to take place) would equate to the initial 10 users, plus the new 20 (calculated from 100 total invites * 20%). This leaves us with 30 total customers, and a K of 2.0. But wait . . . what is K?

Do You Have the Viral Factor?

  K is a measure of potential viral magnitude. It’s also known as the viral factor (or viral coefficient). Remember those CDC contagion models I spoke of earlier? K follows the same principles, and can be used to predict the future spread of your product. It’s calculated by using the following equation: K = i * conv% So continuing with the use case above: K = 10 * .2 = 2.0 This tells you that on average, for every user you acquire through non-viral means, they’ll bring another two additional users via your viral loop. In other words, K quantifies the level at which these users will “infect” others around them. (Note: A K factor of 2.0 is absolutely incredible, and also very rare.) Since we initially seeded our viral engine with 10 non-viral users (aka your awesome friends), applying this K to them gives us 20 MORE users, for a total of 30 users. Piece of cake so far, yeah?

Finding the Holy Grail of Viral Marketing

  Now in all likelihood, those new 20 customers will send out a similar number of invitations themselves, beginning brand new viral loops. Those users they recruit will then similarly recruit new additional users from their own viral loops, and so on and so on. Hence, the phrase “going viral.” The original 10 users you seeded your viral engine with may continue to sporadically send invites. However, the invites they send will drastically drop off as they both max out their perceived viral value and run out of other people they want to invite. This is called viral decay, and is something we’ll go into later in a lot more detail. Likewise, it’s highly unlikely (scratch that – impossible) that your entire population of users will continue to send out invites during every cycle. Bank on seeing a quick spike when users initially see viral value, and then a dramatic drop to a slow trickle after that. With a K of 2.0, you’ll see true “viral growth.” This is a compounding, exponential process that’s as rare and difficult to achieve as it is lucrative and powerful. Call it the “Holy Grail of Viral Marketing.” It may be incredibly hard to obtain, but it’s not impossible. With a substantial viral education (like the one you’re currently receiving) you can gain a massive edge in the probability of reaching it. And become a veritable viral Indiana Jones. What’s cooler than that? See, I told you math could be fun.

What’s Next

  Now for some of you this all may seem like just a review. Especially if you just came off my guide on Creating Your Viral Engine in 15 Steps. But whereas before we merely touched on the math behind analyzing virality, now we’re going to get knee deep it. That might sound intimidating (and a little messy), but don’t worry it’s actually very straightforward. Once you get the hang of applying these equations to your marketing campaigns the impact they’ll have will blow your mind. Which is a perfect segue into our next topic . . . .   

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  What did you think of this article?

• STILL having trouble figuring out your K, i and conv%? 
• Share some numbers below, and I’ll be more than happy to help.
• True or false, Viral Panda deserves a role in the next Indiana Jones film?

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