Modeling and Optimization of Viral Loops: A Guide to Making Your Product Go Viral
11 min read

Modeling and Optimization of Viral Loops: A Guide to Making Your Product Go Viral

Modeling and Optimization of Viral Loops: A Guide to Making Your Product Go Viral

Have you ever wondered how some products go viral while others don't? It's not magic; rather, it's a curated process. When done correctly, a viral marketing campaign can bring in hordes of new customers and skyrocket sales figures. However, many business owners fail to create successful viral loops, often because they don't understand how to model them.

In this blog post, let’s discuss the process of modeling viral loops and ways to optimize them. By understanding how viral loops work, you can create campaigns that are more likely to go viral and achieve your desired outcome.

Basics of a Viral Loop

A viral loop is a marketing term used to describe a process that encourages people to share content or products with their friends, in the hope of creating a "viral" effect. The aim is for the content or product to be so appealing that it spreads rapidly from person to person, generating a flood of traffic and sales.

A viral loop is the key to a successful online campaign. By understanding how a viral loop works and modeling it correctly, you can create a campaign that kick starts your growth. Increased brand awareness, more customers, and increased profits are just some of the benefits of viral loops.

Engineering Virality

Virality just doesn't happen. It is engineered by tons of minor optimizations necessitating product design, a great grasp of human behavior, and a growth-hacking mentality.

The first step is comprehending the four main drivers to understand the viral model:

Factors in engineering virality
Factors in engineering virality

1. Invite and Conversion

Virality is often a numbers game. You need to have lots of people inviting their friends, and you need those friends to convert at a high rate. This is where the K-factor comes in.

The formula for K-factor calculation
The formula for K-factor calculation

This formula is used to calculate how many users you'll get from a single person.

2. Cycle Time

The cycle time is the length of time it takes for an individual to go from first exposure to conversion. You want this to be as short as possible so users are more likely to experience your product and convert. To reduce the cycle time, you need to make sure you're reaching users early and often.

3. Churn

Churn is the rate at which users stop using your product. This can be due to a number of reasons, but you want to keep it as low as possible. The formula below tells you how long it will take for your product to go viral:

Virality and churn rate formulas
Virality and churn rate formulas

You can reduce the churn by keeping users engaged and using your product. You can also improve the invite and conversion rate by making it easier for people to use your product and by enticing them with rewards. Make sure you track all of these metrics so you can continue optimizing your viral loop.

4. Market Size

When it comes to going viral, the size of the market is important. If the market is too small, the chances of your product going viral are slim. You need to find a different market or a loophole that will offer access to a market large enough to support your growth goals.

You can also increase the chances of your product going viral by targeting multiple markets at once. This will help you reach more people and convert them at a higher rate.

What Goes into Modeling Viral Loops

There are a few key elements that come into play when modeling your viral loop, such as:

Key elements of modeling viral loop
Key elements of modeling viral loop

Viral Driver Assumptions

Let's say you have a base rate of 5, assuming a user sends out five referral invitations when onboarding. If this stays consistent for six months until the release of the next feature, the new feature will see the referral invites go up by 0.5. The rate increases to 5.5.

Let's look at another variable – you want a reduced viral cycle time. With three feature releases, the days will be reduced by half a day. There will be a negative 0.5 rate, and as a result, our cycle time is reduced to 7.5 days.

You'd also want the churn to diminish over time. In this case, you begin with 40% turnover and then reduce that by making it simpler to make new friends. As a result, the churn is reduced by 3% thanks to the new functionality.

Finally, we increase market size by moving into a new market where there are new potential users.

The value of 100% growth from zero is still zero. You're going to need users from somewhere, and sometimes you'll have to pay for them.

The model's initial assumption is the users you actually obtain. Once you fill these in, the overall overview of users will be shown. This should result in a large number of free users, or perhaps a large number of users spread virally.

Input of the Viral Growth Assumptions

By using this model you get:

  • 2000–3000 paying users per month from marketing efforts
  • 5 invitations
  • A cycle time that takes nine days to complete.
  • 18% conversion rate on invitations
  • A first-month churn rate of 40%

Viral Growth Math Overview

Formulas in this model include:

Viral Growth Math Overview
Viral Growth Math Overview

Adjusted Conversion Rate and Adjusted K-Factor

With a greater market share, we essentially make the conversion rate worse. As a result, when you reach 100% market penetration, the conversion rate (AR) is 100% garbage, i.e., ZERO. You cannot acquire any more users once you've exhausted the market.

At half the market share, the R of 18% is now reduced to 9%. That cuts your K-factor in half. See what's going on?

The market saturation point is a concept used to describe poorer AR. It rises to 4.6% in year three. As you enter the market, you will reduce your K correspondingly. If you have a large market with a low K (and are acquiring fewer customers), you will not notice this change.

This is the formula:

Adjusted Conversion Rate
Adjusted Conversion Rate 

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