Modeling Multimodal Consumer Heterogeneity in Conjoint Analysis – A Sparse Learning Approach

A realistic specification for consumer heterogeneity should allow for both multiple market segments and within-segment heterogeneity. Such a flexible heterogeneity specification, which we term as multimodal consumer heterogeneity (MCH), raises considerable modeling challenge. We propose an innovative sparse learning approach for modeling MCH and apply it to conjoint analysis, where an adequate modeling of consumer heterogeneity is critical. The unique perspective of our approach is to characterize MCH via a special form of structured sparsity defined on conjoint partworths that can be recovered efficiently using recently developed optimization techniques. The proposed approach is intuitive and easy to implement in practice. We use extensive simulation experiments and two empirical conjoint data sets to demonstrate the performance of our sparse learning approach in modeling MCH and recovering both accurate individual-level partworths estimates and managerially relevant market segmentations