The user life cycle is the whole cycle from when a new user starts to use our product to finally give up using the product for some reason. Managing the entire life cycle of users allows us to understand users better and operate users in a more refined manner. Which is a problem that everyone needs to think about. When we think about the user life cycle. We refer to some classic online models. Novice stage-growth stage-mature stage-early warning period-churn period. And then we will talk about how to guide different user groups. .
How to convert. Etc. Have we thought about: In the Kenya Phone Number end how to divide the different stages of users? After dividing the operation. How to evaluate our strategy or whether our division can improve the index? Today. I will make a brief summary of the user life cycle results I have done. Share it with you. I hope it can help you a little. Growth hacking – user lifecycle calculation The logic of division Growth hacking – user lifecycle calculation The first part of the above figure is the logic of our division. We choose a core behavior of e-commerce: purchase.
Another dimension is the number of login days in the Kenya Phone Number last n days. Here we need to consider several issues: 1) why is only one core behavior selected? Because once there are too many dimensions. The subsequent monitoring and analysis will be very demanding. In addition. From experience. Sometimes too fine division does not necessarily work very well. Of course. If your data volume is large and your business is complex enough. You can consider adding another dimension. 2) why choose the last 7 days as the division node. 8-30 days as the churn. And the user who has not logged in for more than 30 days is a churn? In addition.