Rick L. Andrews, Imran S. Currim
Although a variety of approaches for inferring market segments exist, little, if any, effort has been devoted to comparing the relative validity of these approaches. This study conducts two extensive simulation experiments in a scanner data setting to empirically compare and validate alternative mixture model-based procedures for segmenting households using choice behaviors and household characteristics. Compared to existing two-stage approaches, a new method known as the joint approach produced 23–27% less error in estimates of characteristics and 30–38% less error in estimates of choice model parameters. Contrary to conventional wisdom, the joint approach, which simultaneously uses household choice and characteristic data, is shown to be superior even when one is interested in recovering only characteristic-based segments.
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