A good part of that can be explained by the low time resolution of the graph. 1 month.
Let’s assume 1 month ago, 100 new people signed up. Let’s say 20 of those made a comment or post, which is the requirement to be counted as an active user.
Many of those 100 didn’t stay for various reasons. Of the 20 ‘active’ users, only 15 were coming back the next day.
But the graph still counts 20 active users for a whole month. Only 1 month after a user last commented or posted, this user is no longer counted as ‘active’. So now we see a drop of -5 (all numbers made up).
I think it’s perfectly normal that not everyone who signs up makes a post or comment. And that not everyone who tries out something new will stick around the next day, or the next week.
With a large number of new signups, which we had in the last months, it can be expected that another a large number is only active for a short time. Due to the low resolution, we probably see what happened 1 month ago.
A good part of that can be explained by the low time resolution of the graph. 1 month.
Let’s assume 1 month ago, 100 new people signed up. Let’s say 20 of those made a comment or post, which is the requirement to be counted as an active user.
Many of those 100 didn’t stay for various reasons. Of the 20 ‘active’ users, only 15 were coming back the next day.
But the graph still counts 20 active users for a whole month. Only 1 month after a user last commented or posted, this user is no longer counted as ‘active’. So now we see a drop of -5 (all numbers made up).
I think it’s perfectly normal that not everyone who signs up makes a post or comment. And that not everyone who tries out something new will stick around the next day, or the next week.
With a large number of new signups, which we had in the last months, it can be expected that another a large number is only active for a short time. Due to the low resolution, we probably see what happened 1 month ago.