The document discusses propensity modeling using logistic regression for various applications such as insurance, banking, and consumer purchases. It describes how propensity models use logistic regression to predict the probability of a binary outcome based on multiple independent variables. The document then provides a specific example of building a propensity model to predict the likelihood of a customer obtaining a new mortgage within 3 months using logistic regression on customer database variables. It evaluates the economic impact of the model by estimating costs, revenues, and 5-year net present value when targeting customer segments identified by the model.