Beginner Data Scientist here...
I'm setting out to build a predictive model for our client in the hotel/hospitality industry to explain the factors contributing to the attrition of their Loyalty Members.
I am bending over backwards trying to shape the data correctly to be input into a logistic regression model (and maybe other models) to predict the probability of attrition, but there is an issue in how my client defines attrition.
This is my biggest mental hurdle: the client's current definition of a Member who has churned is one who "has made no reservations within the past year." This definition is always relative to today.
Imagine I have a large set of Members' reservation data over time which I need to shape for the model (program join date for the Member, check-in date, total nights, $ spent, etc). In my mind I am going to encounter perfect correlation trying to predict this binary outcome because all Members with no reservations in the past year are perfectly correlated with an outcome of "has churned."
Can anyone shed some light on how to approach a problem like this where - due to the time-based definition of my outcome - the underlying data on which to Train the model will perfectly correlate (either positively or negatively)?
Many thanks, CV.
If you're curious: I'm reading data from SQL Server, manipulating/exploring in R then eventually using RapidMiner to build the model.