Hi experts out there -
I have a user behavior log (e.g. # of logins, send post..etc) and trying to come up with a churn prediction model. A part of the request that I was asked was to find the value, or a moment that a user is about to churn. For example, when a user's post is under 5 in day 2 then a user will likely to churn (so the marketing can do something). The retention drops severely on the 3rd day, so I organized the data by behavior+timestamps(every 6hour) like below and ran the logistic regression
churned(0 or 1)|userid|event A count_at_6h)|...|event B_count at timeline at 72h
- event count here was cumulative by timeline
The issue that I faced is...
1) Performance is really low: AUC is under 0.7. 2) Is it possible spot "the moment" that the user will churn from this(e.g. users with less than 5 posts will churn) from any modeling exercise?
I will appreciate if anyone can advise how I can possibly proceed from here.. thank you in advance...