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 the way 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.
How Long Does It Take to Create a New Habit?
As you dig into your analytics, your churn cohort analysis visualization will show you 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 then 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 down slope and then quickly level off.
How dramatic that downslope is, when it happens, and when it levels off depends on three key factors. Namely:
- The type and quality of the product
- The value in contrast to the price
- How well you target your marketing channels to attract users
In addition, the retention curve also depends on how effectively you are able to create a “habit path.“
Factoring Habits Into the Retention Curve
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 adopt the use of 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.
- If your product is a game, maybe this moment occurs once a user opens the app 4 times, casts 22 spells and gets to level 3.
- If it’s a fitness app, maybe this moment occurs once they complete 12 workouts, post 2 photos and log a 2.24% reduction in body fat percentage.
- If it’s a social network, maybe this moment occurs once they follow 10 other users and upload a profile photo.
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 their habit path, and they built it into their 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 just need to find the exact moment when it happened, so we can recreate it for others again, and again, and again.
Where is Your Viral Hook Point?
You know what’s better than people using your product? People being 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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