Segmentation into predictable classes

We describe a strategy for assigning objects into classes based on characteristics which have not (yet) been measured—for example, assigning people into classes according to how they are predicted to behave in the future. The standard approach would take a training set on which predictor variables, that is variables which can be measured in the present, and behavioural variables, that is the ones to be used to define the classes, had both been measured. The space of behavioural variables would then be partitioned, perhaps using cluster analysis, and a model would be built to predict cluster membership from the predictor variables. New cases, for which only the predictor variables could be measured, would then be assigned to clusters in the space of the unobserved behavioural variables using the predictive model. However, when determining the behavioural classes by this approach no account is taken of future predictive accuracy. We describe a method which combines class homogeneity with predictive performance, yielding a natural single criterion which can be optimized. We illustrate on two real data sets, one arising from a travel survey, and one from a retail banking application.