For generic propensity (purchase, churn etc.) modeling a lot of typical references / examples available use randomized splitting for train / eval / test sets. For propensity modeling in practice though is there much concern for temporal / seasonal factors and are most folks splitting data temporally as opposed to randomly, especially for rolling window approaches.
