Bayesian Methods for Conjoint Analysis-Based Predictions: Do We Still Need Latent Classes?

Abstract

Recently, more and more Bayesian methods have been proposed for modeling heterogeneous preference structures of consumers (see, e.g., Allenby et al., J Mark Res 32:152–162, 1995, 35:384–389, 1998; Baier and Polasek, Stud Classif Data Anal Knowl Organ 22:413–421, 2003; Otter et al., Int J Res Mark 21(3):285–297, 2004). Comparisons have shown that these new methods compete well with the traditional ones where latent classes are used for this purpose (see Ramaswamy and Cohen (2007) Latent class models for conjoint analysis. In: Gustafsson A, Herrmann A, Huber (eds) Conjoint measurement – methods and applications, 4th edn. Springer, Berlin, pp 295–320) for an overview on these traditional methods). This applies especially when the prediction of choices among products is the main objective (e.g. Moore et al., Mark Lett 9(2):195–207, 1998; Andrews et al., J Mark Res 39:479–487, 2002a; 39:87–98, 2002b; Moore, Int J Res Mark 21:299–312, 2004; Karniouchina et al., Eur J Oper Res 19(1):340–348, 2009, with comparative results). However, the question is still open whether this superiority still holds when the latent class approach is combined with the Bayesian one. This paper responds to this question. Bayesian methods with and without latent classes are used for modeling heterogeneous preference structures of consumers and for predicting choices among competing products. The results show a clear superiority of the combined approach over the purely Bayesian one. It seems that we still need latent classes for conjoint analysis-based predictions.