Understand everything about the virality factor “K” of viral loops (Viral Loops)
The origin of the K factor — a component of the virality function
The K-factor can be used to describe the growth rate of websites, applications, or a customer base... This use is borrowed from the rate of reproduction in the field of epidemiological medicine.
A virus with a K factor of 1 is in a “stable” state: this means no growth or decline. A K-factor greater than 1 indicates exponential growth and a K-factor of less than 1 indicates exponential decline.
The K factor is a trend indicator
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There are forecasted growth indicators and indicators based on past events. Customer acquisition cost (CAC), retention, and monthly active users (MAU) are performance indicators based on past events that we look to to assess our success.
The K-factor is different, we can use it to understand the future effect and the trend of growth loops. Let's look at its formula and calculation method.
The K-factor formula
The K factor is an organic and free acquisition driver that is fuelled either by the growth characteristics of your product or service, or by enthusiasm for your business. If you master both at the same time, you can have the chance to reach very good levels of success and virality.
To understand the K-factor, what we're trying to determine is this: For each customer you acquire, how many new customers will they bring you?
Here is the formula:
- number of recommendations/shares = i
- conversion rate = conv%
Let's take an example now to put this formula into practice.
Example of calculating the viral coefficient K
Here is an example of how to calculate your viral K-factor or coefficient:
- Take your current number of users (say 100)
- Multiply by the average number of invites or recommendations your user base sends (100 x 10)
- Find the percentage of recommended people who have made a decision, for example by registering as a new user. (12%)
- If 12% of the 1000 invitations signed up for your product, you would have 120 new users.
- You started with 100 users and gained 120 users. So you divide the number of new users by the number of existing users to find your Viral Coefficient (120/100 = 1.2).
Any K factor greater than 1 results in exponential behavior
If each new customer invites two more people, the snowball starts. In reality, a K factor greater than 1.0 is very rare and not sustainable. However, a K-factor of less than 1.0 can still be very powerful, so don't dismiss it as not being “viral enough.”
The following are the types of behavior based on the K-factor value:
- A K factor >1.0 is exponential
- A K factor between 0.4 and 0.9 is synonymous with hypergrowth
- A K factor between 0.1 and 0.3 is linear
The K-factor generally decreases as the business develops, making it possible to reach a larger audience with lower network density. Advertising and word of mouth are easier to generate with a small audience grouped around certain sectors, interests or values.
A high K-factor allows you to spend more money on any form of acquisition.
The K-factor acts as an accelerator, giving impetus to all other acquisition channels and optimizing them. Indeed, the more customers you acquire by organic methods, the lower your overall CAC (Customer Acquisition Cost) will be.
Positive k factor = Product Market Fit?
The real problem with this statement is that it doesn't talk at all about the factors that actually determine Product Market Fit.
Yes, that is the case
A Product Market Fit (PMF) means that you have a market (that is, a group of people who have a pain or a need) and that your product or service provides solutions to it.
The k factor is an insufficient condition and explains the PMF, but it is somewhat related. In the best case scenario, you calculated k using correct assumptions. You see customers who stick with and stay on your product or service. They invite new prospects to test and realize the value of what you offer. If all of these things are true, then you have a product or service that provides real value and has the potential to go viral.
But again, the determining factor of the PMF is in fact the retention rate.
NO, not only
For example, some apps literally pay their customers to send recommendations. In case you notice a lot of recommendations that have converted and come from this type of system, that just proves that there is a market of people who want to be paid for it. All of this has nothing to do with the actual product or service and how it responds to it.
The K factor is not enough to explain virality
If you take this formula at face value, you are only measuring the effectiveness of your referral program, not your virality.
Virality depends on many other factors that we are going to see in the virality loops formula.
That's good, we'll see that right away.
Understand everything about The Viral Loops Formula
Now that we've reviewed some fundamental elements, let me give you the most basic equation that will allow you to project the impact of virality over a fixed period of time.
This may seem complex at first glance. But if you take a few minutes to figure things out and translate the formula I'm about to give you into a spreadsheet for more practical use, you'll make your life easier as we continue our exploration of virality loops.
Virality Formula
Let's start by defining a few variables. Some that you already know, others that we haven't used yet:
- The fixed time period you are analyzing = t
- The growth in the number of users over a defined period = u (t)
- The number of users or customers with whom you start the process/or the first users that you introduce into the viral engine = u (0)
- The number of recommendations sent by users or customers over a given period = i
- The conversion rate of recommendations sent by users = conv%
- The viral factor K = i x conv%
- The time that elapses between the moment a user or potential customer becomes aware of your brand and the moment they send their first recommendation, measured in days as the decimal number = ct
It may seem a bit complex at first, but you don't need to memorize this formula. All you need to do is put it into practice in a spreadsheet so that you can simply add the values of your variables measured by your analysis tools.
Download this sample online sheet to find the methods for calculating the K factor and virality loops
Don't worry I've prepared an example for you to download for free above.
Below is the influence of the ct variable - time of virality cycles - that you can find in this Spreadsheet.
As you can see, the influence of ct is potentially even greater than that of K itself, contrary to what all articles on the Web may report. We are going to see right now how to maximize our virality.
How to maximize virality
It's important to keep in mind something we've just talked about:
- Since K is raised to the power of t/ct, then reducing the value of ct has a much greater effect on viral growth over time than increasing K over time.
Using the values from our previous example, here is a graph showing the influence of the value of ct on virality.
As seen together, the shorter the duration of growth cycles and the greater the virality, this is what we are now going to see through a very well known example: that of Youtube.
How YouTube exploded thanks to Viral loops
When the viral cycle length is shorter, growth becomes more rapid.
That's why YouTube has exploded faster than any other business we've ever seen before. They wanted to create a service that allowed users to easily stream and share video without having to worry about downloading or sending files. They have therefore integrated virality at the very heart of their strategy. (If you want to know exactly how YouTube did it, I'll go deeper into that in a previous chapter.)
This is how YouTube built its product in such a way that the duration of its cycle is just over 2 minutes on average. Users thus had the “mathematical” ability to perform more than 650 viral loops in a single day.
While not realistic for a single user, with each viral loop completed, more and more people were exposed to the product and sent out their own invitations.
Viral Loops vs Viral Marketing
Confusion can sometimes occur when talking about virality because people often use the term “viral loop” to refer to viral marketing. But what is the difference?
Both techniques aim to promote sharing, but a viral loop is more focused on customer acquisition, while viral marketing aims to increase brand awareness.
While viral marketing is less predictable, anyone can create a viral loop if they follow the right methods. Let's take a closer look at it.
Types of viral loops
Although all viral loops have the same structure, you need to decide what type of virus loop you want to create and how you are going to motivate your customers to recommend you. Here are a few examples:
- Savings/Reduction : The recommendation is motivated by an offer that saves money, such as a discount or a free product.
- Help/Support : Anyone who recommends a friend is encouraged to do so to help.
- Value : The person is motivated because they and the person they recommend receive something. The viral loop of many social networks works without any motivation other than the interest of using the social network with friends, which proves that some viral loops are 100% free.
- Charité : The person is motivated to share because the brand promises to donate to a charity for each sponsorship.
Success factors in creating a viral loop
When building your viral loop, it's best to make sure you have a well-thought-out funnel and recommendation system in place before you start running and optimizing your campaign.
Here are a few things to remember:
- Make sure that the motivation is relevant and appropriate for customers, but also profitable for your business.
- Use dual-purpose benefits for the customer and their friends to encourage referrers and their friends to join you.
- Create cumulative rewards, so a customer can continue to earn benefits every time they successfully refer a friend.
- Determine the entry points to your loop (where and when users are invited to recommend). For example, will users be asked to recommend right after placing an order? How are you going to invite them to enter the viral loop?
- Ensure that customers can find and use the referral program in as few steps as possible (as they say in English: “Less is More! ⚡️”
- Analyze and test every stage of the virus loop to make your recommendation process as efficient as possible.
- Continue to optimize your loop for the best results. Remember that A/B testing doesn't have to be done just once.
If you're not sure where to start or what KPIs to analyze in your analyses, I recommend taking a look at your Pirate Funnel. (The Viral Funnel shown below is a “cyclical” Pirate Funnel)
The metrics in your funnel will be a good way to optimize all stages of the Pirate Funnel (AAARRR) in order to optimize your growth and the power of your viral loop.
How to analyze your viral loop
There are a lot of things to consider when analyzing your viral loop.
- What channels do users come from? What are the channels that produce the most loyal customers?
- Should you allow users to refer through more channels (SMS, social media, in-app, etc.)?
- Would a different choice of sponsorship program, or program positioning, lead to more recommendations?
- Could a different motivation be considered more interesting by more people?
- What types of customers or users are most likely to recommend you (for example, customers who have made more than one purchase or customers who have renewed their subscription)?
- Track KPIs: What percentage of customers have successfully completed the viral loop (referred friends who become new customers) and how long do referred customers stay?
Benefits of Viral Loops
The two main benefits of creating viral loops are that they are cost-effective and that they work through trust.
Profitable
When your customers share your brand with others, it's free advertising.
When done right, the results are almost magical. One user becomes two, then four, then eight - you get the point. All of this without spending a cent, aside from your initial investment.
Confidence
On average, 92% of people trust recommendations from their bets more than information sent directly by businesses.
Virus loops focus on optimizing this process to make sharing as easy and accessible as possible. Essentially, it's about turning new customers into trusted salespeople.
Why do viral loops fail?
Customer Retention Issues
If your product has a low retention rate, you should first focus on increasing user retention and adapting the product to the market. It's harmful and a waste of time to achieve viral growth when the same users unsubscribe after signing up. Once users leave, they may never return. An engaging product where users sign up and stay is the basis for a successful viral growth campaign.
Overused tactics
Marketing tactics often work at first and then stop. Marketers are always jumping at the chance to replicate effective tactics. The response rate falls when consumers get tired of these tactics. To be successful, you need to have an idea of what motivates your customers to share and prospects to join. Offer your users personalized and adapted incentives to encourage their sharing.
High level of friction
The virus loop fails when the process is too confusing or complicated. Seek clarity and simplicity. A tweet can go viral because retweeting is easy and immediate. Create an experimental framework to test and improve the viral loop. Strive to achieve the following goals:
- Fewer steps to follow to register.
- Show the value of the product as soon as possible.
- Ask to share the moment when users realize the value of your product (WOW moment)
- Make sharing visible and easy.
- Choose the channel where your target customers are
