## Viral growth: How ‘going viral’ works

In comparison to metrics like customer churn rate, CAC, and MRR, the viral coefficient (k) has been positioned as a key metric to gauge potential growth trajectory. The viral coefficient compares the number of referrals (e.g. invitations or shares) sent by current users to their network to the rate these referrals become new users. Simply put, going viral is defined as the number of users who sign up for a product or service as a direct result of a referral from a current user. _Related: __The Role of Growth in Mobile Product Development The thinking is, the closer the number is to K > 1, the more likely you are to experience viral growth. This means that every newly converted user will send out additional referrals, which will lead to an exponential increase in users. The more users you can get to send referrals that lead to successful conversions, the better. There are a few ways to calculate your viral coefficient, but this is the most common method:

K = i x conv%
(“K” is current users, “i” is the referrals they make, and “conv%” is the rate referrals are accepted)

A value greater than 1 means that for every user you get through organic means (e.g. a Google search), they’ll bring in one new user. So you can see, when K > 1 your users are bringing more and more new users to you. Source This graph shows the trajectory of growth based on changes to K. When K < 1, growth will eventually become flat. Users are inviting new users at a constant rate, and this means growth will level off within some amount of cycles. Example #1: K < 1 Going Viral Scenario where K = 0.5 In this scenario growth fails to take off. Despite adding more users, growth remains constant. Over the course of five cycles, new users only increase by a little over 200 customers. If you have a viral coefficient of say, 2, the story is very different: Example #2: K > 1 Going viral scenario where K = 2 A few mistakes people commonly make while calculating their growth with _k:_

• Current users won’t continue to send referrals to new users indefinitely. Including them in your calculations can mean wildly overestimating how many users you’re attracting.
• With a higher viral coefficient, growth takes off quicker in the same number of cycles. The drawback to this is that you might not be measuring growth at all. With poor retention, you have nothing.
• A shorter viral cycle time is better. Viral cycle time is the amount of time it takes for prospective users to find and use your product before sending their own referrals. The thinking is that a short cycle time leads to more conversions— you grow quickly because it takes users less time to share your product with others and for others to come on board.
An example of a short viral cycle time is early stage YouTube, when they were a nascent sharing platform. The secret to its viral growth is directly related to its short viral cycle time. Once users visit the site and see a video they like, they immediately share it with friends who then click on the link to the video. Once on the site, they start sharing with others. This could take a matter of minutes. Despite the success YouTube has built based on this concept, it might not always work the way you expect it to. Some products have a longer viral cycle time— sometimes several months— than others simply because potential users need more time to experiment with the product and try to understand it before sharing it with others.

## When you grow too fast too early for your own good

There’s no shortage of startups that saw early traction, built “waiting lists” and then fizzled out. What happened? Their team was focused on how to manage that rapid growth rather than build features that would retain their early adopters and find product-market fit. Even if you had a strong feature set, a poor user experience due to an ill-equipped platform means you’ll lose users. In the case of Peach, a messaging app, their sleek design and impressive features meant they were able to attract thousands of users the day they launched the product. Talk about exploding onto the social networking scene! But just as quickly as they arrived, they saw a drastic decrease in users within a week. Their platform just wasn’t ready to handle the rapid growth. They had no time to develop their infrastructure before their growth took off. Before they could use their user data to improve their product, their users had already left to find the next big thing. Despite the downside of viral growth, there are businesses who’ve made it work and are still hugely successful. Here are companies that were able to survive beyond initial user interest. While their initial growth was based on going viral, their continued growth is based on strategic planning and using a variety of unique marketing approaches.

1. AppSumo: To build a large user base quickly, AppSumo put considerable effort towards building their email list. By implementing tactics like product giveaways and contests, they were able to boost their email list to well over 700,000 in a short amount of time. They knew that the more subscribers they had would eventually translate into skyrocketing sales. Since they already had user attention, they began to expand their product to meet user needs.
2. Dropbox: With other cloud storage competitors available, DropBox needed to attract as many users to its service as possible. It did this by offering a referral program that gave new sign-ups who referred someone else an extra 500MB of data. By offering multiple ways to take advantage, they were able to grow their signups by 60%. They were able to build user trust and grow consistently to become a leader in cloud storage.
3. Airbnb: Used free marketing platforms to reach users.