Introduction
It’s often said that time is people’s most valued commodity. If you can’t quickly demonstrate to customers that you are solving a problem for them, they are not going to stick around. The average retention rate for a mobile app is incredibly only around 3%. That means that after a month the average mobile app will have lost 97% of the people that installed it.
The point at which you are able to successfully demonstrate to a user that you are solving their problem is referred to as the ‘Habit Moment’. To better understand how to move customers to that point of retention we can use a measurement called the Habit Moment Metric. In this article I’ll explore the Habit Moment Metric and how you can get started defining yours.
The Habit Moment
The Habit Moment is the point in time at which an acquired customer becomes a retained one (this is also referred to as the ‘Aha’ moment). If an acquired user doesn’t understand the value your product offers (or understands it but doesn’t have the problem you are solving) they very quickly become a churned (ex) user. It’s the job of Product Managers to make sure new customers have the product value demonstrated to them as clearly and quickly as possible.
It’s a good idea to define this for your product qualitatively as your first step. The best way to do this is by starting with the customer problem your product is trying to solve. For example Airbnb’s problem statement might look something like this:
“As a traveller it is not easy to find a nice place to stay for a fair price”
The Habit Moment definition for this problem statement might look something like this:
“After booking a trip with Airbnb I now know it's the easiest way of finding good accommodation when I travel and I can use it every time I take a trip”
Writing a habit-moment definition is a useful exercise in aligning your team behind the core focus of your product. If you find yourself wanting to write multiple definitions of habit moments for your product you might realise your product trying to solve too many problems!
The Habit Moment Metric
The Habit Moment Metric is a way of identifying when an acquired user becomes a retained one by describing the product actions that user takes in order to reach that point. These product actions are usually the ‘key’ or ‘core’ actions that create value for your customer. How straightforward it is to identify these key product actions will depend upon the nature of your product, and how ‘broad’ the set of problems it is trying to solve
Example key product actions:
Language learning app: taking a lesson
Software-analytics service: creating a report
Recipe-sharing app: sharing a recipe
The Habit Moment Metric also defines the time period in which these actions have to take place in order for that customer to become a retained user.
Here are some examples of Habit Moment Metrics that are used/have been used by well known companies:
Facebook: 7 friends connected within 10 days (7f10)
Slack: Send a message 4 out of the first 7 days (4d7)
Airbnb: 2 bookings in the first year (2bY)
Zoom: Host 4 meetings in the first 28 days (4d28)
You may notice that sometimes companies substitute the letter standing for the key action ('f' for friends at Facebook) with a letter defining the unit of the time frame in question ('d' for days at Zoom). There's no hard and fast rule here - do whatever works for you.
Defining your Habit Moment Metric
The process for defining the Habit Moment Metric involves running a retention cohort analysis for groups of users who have carried out the key product action a certain number of times within a time period. The first step is understand which time periods you should be focusing on.
To do this the easiest way is to create a usage histogram to help you identify what the natural usage cadence is for your product.
How simple it is to define this natural usage cadence will depend upon the nature of your product. For example, if your product is an app to help people with their tax returns the natural usage frequency will be yearly. If your product is a social messaging app you would expect a daily usage cadence. If the usage cadence is less clear look at your usage histogram and see where the bulk of the users are located. For the example histogram above the majority of retained users have between 4 and 6 sessions a month. This would suggest a weekly usage cadence.
The next step is to crate your retention matrix. You might want to create multiple retention matrices for a variety of key actions in your product if you want to find out which product feature delivers the most customer value.
The Y axis of your matrix should show the number of key product actions carried out in increments. The X axis of your matrix should show the time period you are measuring - the usage cadence you have defined will help you understand what this should be.
You then populate each space on the matrix with the retention rate for each cohort.
When populating your matrix, make sure you check the size of each cohort to make sure it is of a sufficient size to be statistically significant (Evan Miller has created a really useful tool to help you with this).
Once you have finished your retention matrix you can analyse it to understand your Habit Moment Metric. Ideally you will see an inflection point where retention spikes after a certain number of actions within a time period. In the example retention matrix shown above you can see that this happens when 4 product actions are carried out. In our matrix, the biggest jump in retention occurs for users who do this within a 4 day period. So using this example we can define our Habit Moment Metric as 4d4 (four key actions carried out within 4 days).
Identifying the exact time period for your metric with 100% confidence might be tricky depending upon the data you see in your matrix. It’s totally valid, and even encouraged, to use additional business context to help you here. There might be product reasons why you prefer one time period over another if the retention data is very similar. Feel free to use discretion to define something that makes sense for you.
What next?
Now that you have your Habit Moment Metric, you can start focusing product work on the onboarding / activation experience in order to encourage new users to participate in those key actions as early as possible. Product Managers will have a variety of tools at their disposal to do this, such as - notification scheduling and messaging, designing for maximum affordance on key actions and optimising the messaging of your value proposition during onboarding.
If you are an early-stage company and focused solely on growth, your Habit Forming Metric will quickly become your product team’s North Star. Much of your user research and solution design will be aimed at finding ways to move more of your newly acquired users to that Habit Moment more quickly.
Conclusion
Understanding your product’s Habit Moment is an absolutely vital step in the journey towards product growth and success. There are many good products that have failed because the value of the product was not appreciated quickly enough by customers. By taking the time to define your Habit Moment and by using retention data to define it in terms of key product actions, you will have the framework you need to focus your solution design in a way that delivers the maximum possible impact on customer growth.
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