We’ve created a pretty awesome model to project viral growth over time so far.
We’ve factored in things like viral magnitude, cycle time, churn, and network saturation.
Pretty slick, right?
Well, yes . . . but our model is still flawed.
While we’ve talked about how virality can decay over time due to things like network saturation, we haven’t touched on how some of our other KPIs may change over time.
For example, our churn equation is relatively static – which isn’t how churn is measured.
As we discussed in a prior chapter, churn is typically measured using cohort analysis.
This is essentially a visualization of the percentage of users who churn over the course of intervals of time relative to their first exposure to the product.
Thus far, our model hasn’t factored this in yet because we haven’t yet touched on things like habit creation.
As you dig into your analytics, your churn cohort analysis visualization will show that just as a user’s i value changes over time, their likelihood of churning will also change.
Typically over the course of time, users will be less likely to churn.
More often than not, you’ll be able to pinpoint an exact moment in the user journey where all churn stops.
Nine times out of ten, users will exhibit a retention curve in a descending hyperbolic fashion.
Meaning the curve will start with a sharp downslope and then quickly level off.
How dramatic that downslope is, when it happens, and when it levels off depends on three key factors.
Namely:
In addition, the retention curve also depends on how effectively you can create a “habit path.“
In his book Hooked (which I love), author Nir Eyal describes a data-driven framework you can work through to identify the exact moment when users use your product as a new habit - thereby bringing churn to a complete halt.
While you won’t be able to get every user to this point, it’s critical to know exactly where it is located.
In other words, you need to determine what needs to happen over the course of your user journey for a user to get hooked on your product and precisely at what moment it occurs.
The last example above actually comes from Twitter.
You may now notice that when creating a new Twitter account, users are essentially forced (or at least very strongly encouraged) to follow a specific amount of users before their account is fully set up.
This is because Twitter identified its habit path and built it into its new user onboarding process.
To sum things up, your retention curve is important because, so far, we’ve only assumed users will invite others through the first month.
However, as retention improves, you should see an overall increase in viral growth since your product will more frequently be top-of-mind, users will have more time to send invites, and users will simply be more satisfied overall.
In short, as retention improves, so does K’ (i.e. your lifetime viral factor).
So what are we really getting at here with all this talk of retention and habits?
Quite simply, the first step towards engineering your viral hook point. Because once you’ve generated a new habit for the user in using your product, you’ve essentially gotten them addicted to it.
Now we need to find the exact moment when it happened, so we can recreate it for others again and again and again.
Do you know what’s better than people using your product?
People are addicted to it.
So the goal of every great growth engineer should be to pinpoint that one ecstatic moment when the user gets high on your product. Let’s find out how in our next chapter.
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