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.
Just as each person who catches a virus has a network of people they typically come into contact with daily, 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).
Regarding a real virus, immunity is a friend to all. But when it comes to spreading your product, immunity caps your growth.
When it comes to your product, immunity may happen for a variety of reasons:
No matter the reason, after a few waves of infection, a network that 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 overcrowd 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?
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:
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).
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%:
The decaying percentages in the bullets above don’t factor in the percentage 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 introducing 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?
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 will 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.
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 remember 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.
That’s actually a trick question.
There’s no such thing as a “hack” to create better growth.
Great growth comes from a great process.
No shortcuts.
By using the right data, you CAN predict and change your company’s future for the better.
Want to see how? Join me in our next chapter.
SIDE NOTE: if you want to hear me talk about all things growth, startups, and inspiration, hit me up on Twitter, Instagram, and LinkedIn!
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