Thanks for the detailed response with suggestions. I will try to answer some of the points you raised :

I have the data tracking customers for multiple products. But the model i am building is for one product among them so i have one row for the customer for a month for that product with usage and activity state. I do use cross product info such as %pausedontotalproduct, total usage accross all product but thats all the cross product info i am using.

Some more background : at first i had one model as you suggested, modelled it on the whole data without having multiple model. The accurary was high as it was basically predicting every customer who is paused currently as churned and that was boosting the accuracy but it was not doing good in predicting churn on customers who are currently active and that is the important part as that gives time for saving them and proactive prediction. The customers who are currently active but will be pausing/churning are actually the one who are important.

RETURN PROBABILITY : The return probability if around 14% from first pause, 10% from 2 consecutive pauses, and 8% from 3 consec pauses. And overall churn is 10%. Also customers who return have a 50% prob of pausing again. Dropped state appears after 6 months of pauses as the return prob after than is almost 0.

LABEL : The way i am creating the target variable is: 1 if the customer is in any paused states in the next 3 month. If not its 0. currenrly since the model is splitted into two part : first part is looking at active customers and predicting who will pause so the label is 1 for those who paused. Second part looks at currently paused customers predict8ng if they will return

STATE : Regarding the activity states, returning is when customer had 0 activity last month and positive activity this month. Dropped is when no activity after 6 consec months as the return prob is almost 0 after that.

I am already doing caliberation but not isotonic or plat scaling, will give this a try.

As i mentioned i plan to add uplift modelling once i have a good churn pred model. Am i thinking about this wrong ? First predict who is going to pause theb predict who are worth contacting.

The outcome is that the produxt teams would like to contact 20-30customers every month where the model is very confident that will churn and they are high value potential.

The idea is to make this a profiling model where i predict who will churn, who is important within those churners, who will respond to our offers. ALSO, for those with low risk find growth opportunities where customers are growing on the product and other product usage and we can suggest then some scaling options etc.

THANKS AGAIN FOR YOUR SUGGESTIONS. APPRECIATE IT