Is there more information in best‐worst choice data?

Purpose The purpose of this paper is to apply a very simple but powerful analysis of the variance‐covariance matrix of individual best‐worst scores to detect which attributes are determining utility components and drive distinct consumer segments. Design/methodology/approach First an analysis of variance and covariance is used to find attributes which are perceived to have different importance by different consumers and which jointly drive consumer segments. Then we model consumer heterogeneity with Latent Clustering and identify utility dimensions of on‐premise wine purchase behaviour with a principal component analysis. Findings Four consumer segments were found on the UK on‐premise market, which differ in the relative strength of five wine choice utility dimensions: ease of trial, new experience, restaurant advice, low risk food matching and cognitive choice. These segments are characterised by sociodemographics as well as wine and dine behaviour variables. Research limitations/implications Attributes with high variance signal respondents’ disagreement on their importance and indicate the existence of distinctive consumer segments. Attributes jointly driving those segments can be identified by a high covariance. Principal component analysis condenses a small number of behavioural drivers which allow an effective interpretation and targeting of different consumer segments. Practical implications This paper's analysis opens new doors for marketing research to a more insightful interpretation of best‐worst data and attitude scales. This information gives marketing managers powerful advice on which attributes they have to focus in order to target different consumer segments. Originality/value This is the first study considering individual differences in BW scores to find post hoc segments based on revealed differences in attribute importance.