Comparison of preference mapping with projective mapping for characterizing consumer perception of brewed black coffees

This study compared projective mapping (PM) with check‐all‐that apply (CATA) with 24 consumers to traditional preference mapping with 257 consumers for evaluation of drivers of liking for brewed black coffees. For the PM exercise, black coffee consumers (n = 24) evaluated 11 coffees and placed them on a two‐dimensional plane based on similarities and then selected attributes from a provided list (PMCATA) to describe each coffee. These consumers also scored liking of each coffee. PMCATA and liking evaluations were completed in duplicate. A trained descriptive analysis (DA) panel (n = 8) documented properties of the coffees in quadruplicate and black coffee consumers (n = 257) were recruited for traditional consumer acceptance testing. Landscape segmentation analysis (LSA) was applied to trained panel data and consumer acceptance scores. Data from the PMCATA exercise was analyzed using multiple factor analysis (MFA). Consumer groups from the PMCATA exercise were differentiated by preferences for light or dark roast attributes. Similarly, determination of ideal points by LSA revealed that consumer groups were defined primarily by differences in preference for different roast types and roast‐related attributes. Based on the similarities in regard to product characterization and consumer segmentation, these results demonstrate that PMCATA can be an effective preliminary alternative to traditional methods for profiling and consumer preferences of complex products like coffee. Practical application Descriptive analysis (DA) and consumer acceptance testing for preference mapping can be time and cost intensive. This study demonstrates that projective mapping, combined with check‐all‐that‐apply (PMCATA) profiling by consumers, can be used as a rapid method for understanding a product landscape and acceptability of products with particular attributes. It is unlikely that PMCATA will replace preference mapping practices; however, it might serve as an effective alternative for researchers and developers when time or budget is constrained, or as an initial screening step for understanding a product category.