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July 24th

Lessons Learned: Dissecting Viral Marketing

Lessons Learned: Dissecting Viral Marketing
Article Cliff Notes

Truly going viral is quite a difficult thing to achieve for any company or product.

Digging underneath the surface helps us understand the keys to going viral are your Viral Coefficient, and your Viral Cycle Time.

Armed with this knowledge you can develop products more successfully, and once this is done the marketing becomes much easier.

Viral marketing is a term everyone is familiar with. But what does virality actually mean?

Virality definition: a piece of content generated by a person or business that inspires consumers to eagerly share it with their expanded social circle, viral marketing can help build brand recognition instantly and exponentially.

More importantly though, why does viral marketing matter? It matters because it allows businesses (especially startups) to acquire a large number of customers at fractional costs. For as much as buzz as viral emarketing gets it turns out to be difficult to achieve. Most companies and products fail to truly “go viral”.

Because it’s so difficult to pull off, viral marketing warrants a closer look. In the following post, we’ve pulled out our microscope and examined what factors drive viral growth and what it takes to achieve it. Through this process, 2 key variables have emerged as indispensable for viral growth. These are the Viral Coefficient, and the Viral Cycle Time. We may go a bit into the weeds, so feel free to skip ahead to the “lessons learned” section.

Our interest in breaking these models down is getting a handle on how a customer base can change over time. The first model takes a brick-by-brick approach to illustrating how viral marketing works in practice.

The Viral Coefficient

Imagine you’ve just launched a product and want to get some buzz going for it. You first invite friends and family to check it out. They, in turn, invite a few contacts, and those contacts start inviting contacts, etc.

At this particular moment your inputs are going to look roughly like this:

Variable Name Description Example Value
Custs(0) Initial set of Customers 10
i No of invites sent out be each new customer 10
conv% The percentage of invites that convert into customers 20%

The first order of business is figuring out the percentage of customers each existing customer is able to convert. This is one of the most critical pieces of information a virtual marketing company can calculate (when it comes to viral marketing), and it’s known as the viral coefficient. The formula is not complicated: to get the viral coefficient you simply multiply the number of invitations by the number of successful conversions.

Or

K = Viral Coefficient
K = i * conv%

The first stage of a viral marketing campaign is often referred to as the “infection” stage, let’s see how “k” affects customer growth at this point. Our first 10 customers each reached out to 10 more people, who converted at a rate of 20%. So after the first stage, you will have your initial 10 customers plus the additional 20 for a total of 30.

In this case, the viral coefficient is 2% and as you go through each iteration this number grows exponentially 40/70, 80/150 and on and on. 

To fully grasp how this model explains the viral loops it’s important to follow the numbers through a few different cycles. The first cycle was based on the first group being family and friends who likely sent out invitations at a higher rate than the general population will. This can drastically affect the viral coefficient and a number of people seem to forget or overlook this.

What is the Impact of the Viral Coefficient?

The viral coefficient is a data point that tells us where we are and informs future decisions. Now that a model is in place we can go back and change the numbers and see what impacts this may have. For example, if the conversion rate dropped from 20% to 5% this reduces the viral coefficient to less than 1. This is important to note and tells us that a lower viral coefficient may completely halt customer growth.

In order to achieve “viral growth,” the viral coefficient has to be greater than 1.

The inverse is of course also true. If your conversion rate goes up or the number of invites sent out spikes you can cheerfully watch your customer base follow in kind. Later we’ll discuss some strategies on how you can grow these numbers.

Viral Cycle Time: The Second Key Variable

An entrepreneur named Antonio Rodriquez built a company called Tabblo right around the same time Youtube was started. Both businesses were viral. But while Tabblo enjoyed modest success, Youtube launched into the stratosphere. Why the two different outcomes?

To get to the bottom of this question we need to take a look at the virtual cycle time (referred to as CT).

A viral cycle has various steps that, when combined, create a loop. The result looks something like this.

Customer first sees application>customer decides to try application>customer decides they like it enough to invite friends>creates an invitation and sends it to friend>friend sees the invitation and the cycle starts over.

When we use the Viral Cycle Time, we’re talking about the total time taken for this process to complete. When it came to Youtube, this cycle was extremely short. A user would arrive on the site, see a funny squirrel video and immediately send it to a friend. Conversely, Tabblo’s CT was much longer than Youtube’s. A user might see photos on Tabblo’s site, like them, and mentally think I’ll use Tabblo next time I want to share. The problem was that the timeframe for a user sharing could be anywhere from a few days to a few months.

How Does the Viral Cycle Time Affect Growth?

Glance around the internet and you’ll find it surprisingly devoid of a standard formula that shows (numerically) the impact of the viral time cycle on customer growth. The one exception is a formula devised by Stan Reiss a VC with Matrix Partners. Below is a description of the best model for viral growth that we’re aware of.

Here is what the math looks like. 

Don’t worry you needn’t fully understand this equation to get viral time cycle. 

You also needn’t look at the cycle time equation long before you realize that the length of the CT has a dramatic effect on customer growth. In other words, the shorter it is the better the viral growth curve. To use an example, if you look at a period of 20 days with full CT of 2 days you end up with a respectable 20,470 users, but if you cut the CT in half (to 1 day) you could have over 20 million users.

It also follows logic to say that the more cycles you have the more growth will occur. This may be true, but what is less evident is the impact each variable has individually. Another run through the formula shows the writing all over the wall. The Viral Coefficient K is raised to the power of t/ct, so finding a way to make ct smaller has far more impact than increasing K.

In a nutshell, this is why Youtube grew at a rate never seen before and Tabblo at a more “normal” rate.

Take Home Lessons

There are quite a few important lessons that can be learnt from the models discussed above.

  • The most important factor to viral growth is the length of your Viral Cycle Time. Making this shorter can often have an earth shattering effect on growth.
    The second most important factor is going to be your viral coefficient. Increasing the number of invitations sent out and the rates at which these invitations convert is going to help.
    If your viral coefficient is less than 1 you will not experience viral growth (especially not of the Youtube variety).
  • These are the fundamental take homes from our models but they are certainly not the only conclusions that can be drawn.
  • Virality is a buzzword, and as such people get a false notion that it’s a strategy that your marketing department can employ. The viral appeal needs to be married with your product from day one — this starts with your product and engineering teams.
  • The products with the greatest viral potential are those that get better when shared. In the beginning, Whatsapp was better for users the more their friends downloaded it and were hence available to chat. The incentive to share and add value was right there, baked into the application.
  • In an effort to shorten CT we can apply the same thinking as used for building a sales and marketing machine (here), where customer motivations and negative reactions are carefully monitored as they move through the cycle. For example, how hard it is to share has a huge impact on whether many people choose to do it or not. If a user has to look up each friend’s address individually they may bounce right out. The fix for this problem is easy, integrate Facebook or provide an email contact import function, there are also tons of handy plugins. Gmail is very easy for example, while Outlook is slightly trickier. Kovair has created a great adaption tool for this very purpose. You should also be encouraging users to share at reasonable intervals. And of course underlying all of this is a value proposition that is strong enough people can resist sharing it (if this isn’t the case you need to revisit immediately). It also doesn’t hurt to provide incentives for shares or invitations, but be careful. People don’t like to feel as if they’re profiting off the backs of friends. One workaround is to offer an equal benefit to the user and their friend, Airbnb has done this extremely effectively.
  • Try a few things out with A/B testing. This will help you determine which approach or set of tactics are getting you the best conversion rates.
  • If you manage to get the viral model just right (high viral coefficient and low CT) be prepared for what can happen. More than a few companies has struck gold only to get overwhelmed by the need to scale.

Hybrid Viral Marketing Models

Many marketers and business owners reading this may realize that true viral growth is beyond their reach. That’s okay, rather than being discouraged by this fact you can take a multi-pronged approach to viral marketing combined with more traditional outreach, SEO, and paid advertising.

Limitations of Our Models

The models outlined in this article are somewhat reductive and fail to consider more than a few real world phenomena.

Sometimes growth occurs at such a fast rate that a market quickly becomes saturated. Unsurprisingly, this has happened to a few Facebook app developers. Their app goes viral and then the growth hits a wall. If you’re curious in exploring this topic more a great blog post can be found here. The article discusses viral growth and “jumping the shark”.

What happens if your site, application or product begins to “leak” users. This can easily be accounted for by adding an attrition rate as a percentage of the entire user base at each cycle. Once this is done you can simply subtract this percentage from the total population at each cycle increment.

The initial customer base may also exceed expectations and send out more invitations. While this is a good problem to have our models have not accounted for it.

I'm Dan, and I live in the back end. I’m always busy figuring out the behind-the-scenes tools and systems that allow us to serve as many clients as possible, as effectively as possible. It might not be sexy, but I’m all about the little details that can really make a difference when it comes to winning online.

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