Grow a viral loop and reduce churn – Part 2 of 3

Apr 1

Grow a viral loop and reduce churn – Part 2 of 3

Last time…..

 

In the last post we covered:

  • the basics of a viral loop: engage, share, view, click.
  • why viral loops are for everyone.

 

In this post we cover: k-factorreachresponse-rateengagement rateshare rate and the impact of “cycle time

Quantifying viral loops with the k-factor

How can our simple loop (in the previous post) help us in constructing a tightly integrated perpetual acquisition engine? (sounds sexy right????)

First we have to get specific and quantify just how much virality you have. This is done with the

k-factor kicks

k-factor kicks

concept of k-factors, or the viral coefficient.

We’ve already used k-factors in the previous section. It is simply the number of secondary users each new user you acquire brings in over their lifetime.

If your k-factor is greater than 1, that means every new user that comes in attracts at least one new user and so on. Congratulations, your App is now growing exponentially without any extra input. Your acquisition costs can essentially go to 0.

If your k-factor is between 0 and 1, you still benefit, as shown in the previous section.

 

So how is the k-factor calculated?

k-factor = reach x response rate x engagement rate x share rate

Basic Viral Loop with k-Factor

Basic Viral Loop with k-Factor

Let’s look at each of these.

  • Reach: Once a share is made, its reach is the number of people that was shared to, or sent an invite. This depends on how easy it is to find and select all the people you would like to share to and the type of network it is made to. Sometimes there is a trade off with response rate: a share to social media will have a larger reach but lower response rate than direct invites sent via email.

  • Response rate: The proportion of people who click through on the share once they see it. This depends on the relevance and interest of the shared message. Personalising the messages with the value the sharer found is one method of encouraging a high response rate.

  • Engagement rate: The proportion of click through visitors who engage with the App and becomes a new user. This depends on how intuitively useful the App is and the overall quality it exhibits. The first user experience should be carefully engineered.

  • Share rate: The proportion of users who share to others. This is affected by how easy it is to share and when the prompts to share appear in the engagement process.

These four elements can all be individually tracked. As well as this, these elements can all be individually optimised. Details and tips for optimising each component will be discussed in Part 2.

Considering the engagement rate and share rate, these elements in particular are heavily affected by the user experience in the App. This shows that creating a viral loop within the App is not only a marketing consideration but also a product decision. It cannot simply be an afterthought, but should involve the support of multiple teams.

The impact of cycle time

The k-factor says nothing about how long it takes to go around the loop for one generation. This comes from the concept of cycle time. The cycle time takes into account the time between one share and the next generation of shares. It is affected by a delay in each step in the cycle above. For example, if the user takes a long time to engage with the App before they decide to share it, or if there is a lag between when someone first receives an invite and when they decide to act on it, this leads to a longer cycle time.

Since the effects of a viral loop lasts over many generations, a faster cycle time decreases the overall time it takes for the effects to compound and allows you to recover your CPA more quickly.

Multiplicity

Metcalfe's Law

Metcalfe’s Law

Each run of the viral loop is not the same in the long run. The goal is to construct a product with a viral loop that benefits from the network effect, where more people makes it more useful. An example of this comes from the extraordinary success of communications Apps such as WhatsApp and Snapchat. As you invite more contacts to the network, the App inherently becomes more useful. This offers a natural reward for users to invite more and more people to join. Another example is social games, where players frequently run into situations where they need help. The more friends who are actively playing the game, the more fun the experience is.

The reverse of the network effect is a product where more people creates more noise, so excessive use actually discourages the further spread of the product.

Key lessons:

  • A carefully constructed viral loop is behind the growth in Apps like FaceBook, Instagram and WhatsApp

  • Viral marketing is about compounding viral growth rate, so a small improvement leads to a huge difference in outcome. Even Apps that are not inherently viral can use the idea of a viral loop to lower customer acquisition costs

  • k-factor = reach x response rate x engagement rate x share rate. Each component can be measured and optimised

 

What to look for in Part 3:

  • Optimising each component of the k-factor formula

  • How to integrate retention and viral loop strategies

  • Double viral loops

 

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