Dual Objective Segmentation to Improve Targetability: An Evolutionary Algorithm Approach**

Abstract

Cluster-based segmentation usually involves two sets of variables: (i) the needs-based variables (referred to as the bases variables), which are used in developing the original segments to identify the value, and (ii) the classification or background variables, which are used to profile or target the customers. The managers' goal is to utilize these two sets of variables in the most efficient manner. Pragmatic managerial interests recognize the underlying need to start shifting from methodologies that obtain highly precise value-based segments but may be of limited practical use as they provide less targetable segments. Consequently, the imperative is to shift toward newer segmentation approaches that provide greater focus on targetable segments while maintaining homogeneity. This requires dual objective segmentation, which is a combinatorially difficult problem. Hence, we propose and examine a new evolutionary methodology based on genetic algorithms to address this problem. We show, based on a large-scale Monte Carlo simulation and a case study, that the proposed approach consistently outperforms the existing methods for a wide variety of problem instances. We are able to obtain statistically significant and managerially important improvements in targetability with little diminution in the identifiability of value-based segments. Moreover, the proposed methodology provides a set of good solutions, unlike existing methodologies that provide a single solution. We also show how these good solutions can be used to plot an efficient Pareto frontier. Finally, we present useful insights that would help managers in * We thank Prof. Gary Erickson, Bob Jacobson, V. "Senu" Srinivasan, and the marketing seminar participants at the University of Washington, Hebrew University, The University of Sydney, and the NASMEI conference for their helpful comments. P. V. (Sundar) Balakrishnan thanks Prof. Paul E. Green and Gary L. Lilien for their encouragement and support. Subodha Kumar thanks the Mays Business School, Texas A&M University, for partially supporting this research with a Summer Research Performance Recognition Grant. We especially thank Prof. Brusco for his generous assistance in providing us with his simulated annealing code, which made the direct comparison possible. † Corresponding author. 832Dual Objective Segmentation to Improve Targetability implementing the proposed solution approach effectively.

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