I'm trying to see if it is possible to use some sort of survival analysis in the context of analyzing daily demand for very slow moving items (i.e. items where one or two units are sold every few weeks).
In this scenario, it seems more reasonable to try to predict "how many days until I make a sale?" as opposed to predicting "how much will I sell tomorrow?" and that the best approach to do so is a form of survival analysis where I try to figure out how long will an item survive on a shelf before it gets bought, based on an average rate of sale calculated from historical sales data.
The problem is that all of the survival functions/reliability functions I've seen assume that the event that is being predicted will happen sooner or later.
But in retail sometimes that assumption doesn't hold. Instead, for some types of item we assume that if an item doesn't get sold within a certain time limit, it will never get sold (so its lifetime on the shelf is potentially infinite), and the item needs to be moved to clearance.
- Is survival analysis the right approach for this at all?
- If survival analysis is indeed a possible approach for this problem, how would we account for the potentially infinite shelf life of an item?