In the last chapter, we started discussing viral decay. To recap, this effectively means that over the course of time the K factor per user will typically spike after the initial viral value is realized. Then it will fall very quickly and will remain at a slowly declining crawl for the life of that user.
One of the main reasons for this viral decay is network saturation.
In this chapter, I’ll not only break down what network saturation is more specifically, but I’ll also go over a simple mathematical model for network saturation and a few strategies you can enact to avoid it.
Saturation: Good for the CDC, Bad for Business
Just as each person who catches a virus has a network of people they typically come into contact with every day, each person who begins using a product has a similar network of people they could potentially directly spread it to.
Once initial exposure occurs, the first wave of “infected” people are no longer candidates for future infection, and therefore cannot be infected again. If that original user then exposes their network yet again, a few others who didn’t become infected the first time may become infected this time – and therefore are also no longer eligible for infection.
After a few exposures, most people in a network who are susceptible will have been infected already. And the remaining users will more than likely have become immune. Meaning they are not eligible for infection (at least from this original user).
When it pertains to a real virus, immunity is a friend to all. But when it comes to spreading your product, immunity caps your growth.
The Nature of Immunity
When it comes to your product, immunity may happen for a variety of reasons:
- Maybe some people in the network aren’t close enough to the original user to trust them
- Maybe they didn’t understand the value of the product from the invite
- Maybe they understand the value, but feel it’s too complicated or labor intensive for them to really derive the value from it as quickly as they want it
- Maybe they simply don’t want or need what the original user is sharing at all
No matter the reason, after a few waves of infection, a network who has been exposed will either be infected or will be immune. At that point the network is considered saturated.
“Saturated” is the same word that’s used when marketers over crowd a marketing channel, driving up the prices of marketing in that channel until they’re too high for new players to use it in a cost effective way.
As a network becomes saturated, the infection rate (or conv%) decreases until something changes.
But what needs to change for that original user to infect more people?
Building Mutations to Decrease Immunity
IF the original user chooses to continue to expose their network to the infection (e.g. their product), one of the following two things must occur:
- The original user adds new people to their network who have not yet been exposed.
- The “virus” (or product invite messaging) mutates in your favor. (More specifically, the virus changes into a new form that would make the original user’s existing network susceptible to infection through a different way.)
This viral mutation may come from a positive change like a UI and/or UX revamp, new copywriting or graphics, additional functionality, better incentives, or any number of improvements.
Given our previous example of viral cycle time using rabbits, here’s a fun example of a mutation in action . . . .
However, a viral mutation is not always positive.
For example, if a far superior competitor enters the market and begins activating a viral loop that is more infectious than yours, it’s likely that your potential network will become more and more immune to your own viral infection. You may even experience churn (which we’ll discuss and factor into our model later).
A Simple Model for Network Saturation
In order to understand the dynamics of immunity through network saturation, let’s cover a few stages of exposure for a product with an initial conv% of 10%:
- If you have infected 0% of an overall network, then your natural conv% remains at 10%.
- If you have infected 50% of that same network, then your conv% is decreased by 50% to 5%.
- If you have infected 99% of the same network, then your conv% is decreased by 99% to 0.1%.
The decaying percentages in the bullets above don’t factor in the percent of users who are immune to infection, or have been invited before but have not become users. For example, if somebody hates your product and doesn’t want to use it, they’re about as likely to become newly-infected as somebody who has already been infected before (i.e. not at all).
This also doesn’t factor in the changes to these numbers when you introduce a viral mutation. Thereby removing some of the immunity some of the users will have had.
So how does this alter our formulas so far?
Updating The Equation
While conv% as we know it so far is a static measurement, this is not totally realistic. It doesn’t factor in the saturation of a network, or the market as a whole (i.e. immunity). Therefore, we’re going to need an Adjusted Conversion Rate, or Aconv%.
Aconv% = conv% * (1 – saturation%)
(NOTE: conv% and saturation% should be in decimal form.)
Once we have this, all we must do is substitute Aconv% for conv% in each equation we’ve been working with so far – and we’ve got a more accurate measure of the current state of virality.
The Harsh Reality of Saturation
As simple as this sounds in theory, it’s another animal to implement. After all, how can one TRULY measure saturation%? To my knowledge, it can’t accurately be done.
However, saturation% is a bit more measurable during non-viral marketing efforts. (By understanding audience size and impression data for various modes of PPC marketing, you can estimate foot traffic per day past an offline ad, etc.). Given this, depending on your product, you MAY be able to loosely estimate network size and saturation% for your viral campaigns using similar strategies. Such as quantifying network/market sizes using 3rd party tools and comparing those numbers to exposure estimates.
As tricky as this may sound, the important thing to keep in mind is that most viral marketing campaigns – even the ones driving insanely viral products – are only truly viral for a little while. The subsequent plateau and inevitable decline they reach is often the result of network saturation.
Now that we’ve formed a strong foundation for understanding viral growth projections over time, let’s figure out if there’s anything we can do to speed up the process. (Hint: There is.)
Because it’s one thing to be able to look into the future, but it’s something entirely to be able to shape it.
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What did you think of this article?
- Do you need any clarification on projecting viral growth over time?
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