Market segmentation is a key component of conjoint analysis which addresses consumer
preference heterogeneity. Members in a segment are assumed to be homogenous in their
views and preferences when worthing an item but distinctly heterogenous to members of other
segments. Latent class methodology is one of the several conjoint segmentation procedures
that overcome the limitations of aggregate analysis and a-priori segmentation. The main
benefit of Latent class models is that market segment membership and regression parameters
of each derived segment are estimated simultaneously. The Latent class model presented in
this paper uses mixtures of multivariate conditional normal distributions to analyze rating
data, where the likelihood is maximized using the EM algorithm. The application focuses on
customer preferences for investment bonds described by four attributes; currency, coupon
rate, redemption term and price. A number of demographic variables are used to generate
segments that are accessible and actionable.peer-reviewe