The authors address the situation in which a researcher wants to cross-tabulate two sets of discrete variables collected in independent samples, but a subset of the variables is common to both samples. The authors propose a statistical data-fusion model that allows for statistical tests of association using multiple imputations. The authors illustrate this approach with an application in which they compare the cross-tabulation results from fused data with those obtained from complete data. Their approach is also compared to the traditional hot-deck procedure.

This article provides a descriptive analysis of how methodological factors contribute to the accuracy of customer churn predictive models. The study is based on a tournament in which both academics and practitioners downloaded data from a publicly available Web site, estimated a model, and made predictions on two validation databases. The results suggest several important findings. First, methods do matter. The differences observed in predictive accuracy across submissions could change the profitability of a churn management campaign by hundreds of thousands of dollars. Second, models have staying power. They suffer very little decrease in performance if they are used to predict churn for a database compiled three months after the calibration data. Third, researchers use a variety of modeling “approaches,” characterized by variables such as estimation technique, variable selection procedure, number of variables included, and time allocated to steps in the model-building process. The authors find important differences in performance among these approaches and discuss implications for both researchers and practitioners.