I believe the most important success metric of any feature is churn rate, not the number of active users.
Let’s say Instagram introduces a new feature. It doesn’t matter if a billion users give it a shot after launch – if 90% of them end up churning after the first month, there’s a problem.
On the other hand, if only a couple thousand people try it out and less than 10% churn – that means the company has built something sticky and valuable.
There’s another reason why tracking churn is important.
High churn doesn’t always mean unnecessary feature, maybe users simply forgot about it. For example, the ‘Availability’ feature in LinkedIn messaging.
Last night, I was scheduling a call with a PM. I exited LinkedIn, opened GCal, found few available time slots and messaged him.
Then to send him a screenshot, I hit the Add icon. This is when I found the ‘Availability’ feature – which directly connects with your local calendar app, allows you to choose time slots and quickly send them in a detailed format.
I was shocked. I remember finding it super valuable when it first launched. But then I totally forgot about it.
I’m guessing this feature has a high churn rate. But that doesn’t mean it is unnecessary. I just forgot about it.
How can LinkedIn reduce churn rate of this feature?
One solution is to leverage NLP (Natural Language Processing) to notify users of the ‘Availability’ feature whenever they find words like ‘available times’ or ‘schedule call’ in the chat.
Photo Credit: https://bit.ly/2Y6Moav
Prashant Bagga 