How to define Activation for your product?

📝 Why do you even need a definition of activation?
• Activation is a leading indicator of customer retention.
• Defining activation helps streamline efforts that improve product FTUX, which helps drive conversion.


Unfortunately, many product teams struggle to define activation and default to using MAU or WAUs to indicate active customers. This approach is flawed because MAUs and WAUs do not suggest whether customers derive the promised value and therefore fail to predict long-term customer retention.

If you are struggling with where to begin, use the following framework.

📝 First up, what is activation?
Activation is when the customer sees the value in your product that has been promised. It is also defined as the ‘aha’ moment or the moment where the customer builds a habit around your product. This is how Reforge defines it.

1️⃣ Analyze your product usage data!
This step helps you understand how your existing customers are using the product and helps lay the foundation of activation.
• Pull a list of all the features of your product.
• Against each feature, plot the percentage of your existing customers who used that feature in the past year.
• Arrange these features in descending order of their percentage value.

2️⃣ Create your first definition of activation!
Now, Step 1 will give you the most used features of your app. However, that represents overall product engagement.

To come up with the definition of activation, you need to filter these features further into
• Features that are available to all customers and not locked away into higher plans
• Features that you believe will enable the delivery of value that's being promised at the outset
• Features that are actionable and predict conversion and retention.

3️⃣ Put it into action!
The first iteration of this definition will not be perfect, but it's a good start.

Once you have this definition, it's time to run AB tests that help optimize activation. You could measure conversion as a secondary metric to see if the experiments targeted at improving activation worked.

You could also measure cohort-on-cohort activation and retention to see the long-term effects of your definition.
These experiments will help guide whether the initial definition makes sense or definition needs further tweaking.

Note: you can break down this process for different end-user personas if you want to personalize user journeys.

Recommended Reading:

What is a good activation rate by

Lenny Rachitsky