A New Dimension Reduction Approach for Data-Rich Marketing Environments: Sliced Inverse Regression

Abstract

In data-rich marketing environments (e.g., direct marketing or new product design), managers face an ever-growing need to reduce the number of variables effectively. To accomplish this goal, the authors introduce a new method called sliced inverse regression (SIR), which finds factors by taking into account the information contained in both the dependent and independent variables. Sliced inverse regression objectively identifies appropriate factors through simple statistical tests for determining the number of factors to retain and for assessing the significance of factor-loading coefficients. The authors make conceptual connections between SIR and several existing approaches, including principal components regression (PCR) and partial least squares regression (PLSR). Using Monte Carlo experiments, the authors demonstrate that SIR performs better than these approaches. Two empirical examples—designing a new executive business program and direct marketing by a catalog company—are presented to illustrate the application of SIR and to show that it outperforms both PLSR and PCR in these cases. In addition, the authors discuss how direct marketers can apply SIR to analyze large databases and to thus target customers effectively. In conclusion, SIR is a promising methodology in data-intensive marketing environments.