Multicriterion Clusterwise Regression for Joint Segmentation Settings: An Application to Customer Value
Marketing and Professional Sales
The authors present a multicriterion clusterwise linear regression model that can be applied to a joint segmentation setting. The model enables the consideration of segment homogeneity, as well as multiple dependent variables (segmentation bases), in a weighted objective function. The authors propose a heuristic solution strategy based on simulated annealing and examine trade-offs in the recovery of multiple true cluster structures for several synthetic data sets. They also propose an application of the model to a joint segmentation problem in the telecommunications industry, which addresses important issues pertaining to the selection of the objective function weights and the number of clusters.
Journal of Marketing Research
Digital Object Identifier (DOI)
Brusco, Michael J., J. Dennis Cradit, and Armen Tashchian. "Multicriterion Clusterwise Regression for Joint Segmentation Settings: An Application to Customer Value." Journal of Marketing Research 40.2 (2003): 225-234.