Year: 1993
Type: article
Abstract: List segmentation refers to the set of techniques employed by direct mail marketers to attempt to predict those specific individuals who are more likely than others on a list to respond to a specific direct mail solicitation. Regression analysis is a commonly used list segmentation technique which yields managerially useful results. However, the commonly accepted criteria for evaluating the “goodness” of regression equations (such as adjusted or unadjusted R2) are inappropriate in the list segmentation context. The authors describe a superior evaluation criterion—the Pareto Prediction Criterion—and report on a study in which full forced entry regression is compared to stepwise techniques with varying criteria for entry and removal of a variable. It is shown that the stepwise technique with a very strict criterion for entry of a variable far outperforms the full forced entry technique.
Source: Omega
Authors: T.L. Magliozzi, P.D. Berger
Institutions: Suffolk University, Boston University
Language: English
Subfield: Marketing
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $4,030
OpenAlex ID: W2016085654