One of the fundamental elements of marketing research is the analysis of consumer choice behaviour. Hereby, researchers aim to identify important determinants that affect the choice among different alternatives. These valuable insights can show how to stimulate brand and product choice in order to enhance brand performance. In addition, they can also strengthen the understanding of psychological and behavioural phenomena, such as reference price formation and brand loyalty. From this background, our paper gives an overview of the most important aspects and models when it comes to analysing brand choice. We hereby focus on the most prominent discrete choice model, namely the multinomial logit (MNL) model. Starting with the theoretical foundation of choice behaviour, we provide a discussion of the basic models and present the state-of-the-art extensions that account for more realistic brand choice behaviour. We supplement each model description with an empirical example to emphasise the advantage of each approach. After providing the fundamentals, we demonstrate how to estimate, test, and interpret the β coefficients of the explanatory variables that are assumed to influence the brand choice behaviour in a basic MNL model. We further show how to calculate elasticities to account for the dimension of the explanatory variables and compare the effects in terms of percentage changes. After introducing the basic MNL model, we discuss its three limitations: (i) proportional substitution patterns, (ii) no random taste variation due to common β coefficients across consumers, and (iii) no consideration of unobserved factors over time in repeated choice situations. To provide guidance how to address the three limitations of the basic MNL model, our paper summarises the state-of-the-art MNL models that relax all three limitations. The common approach of these extensions is to allow the β coefficients to vary across consumers. This variation is interpreted as consumer heterogeneity in sensitivity to changes in the marketing-mix instruments (or sensitivity in behavioural drivers, such as loyalty) and introduces the more realistic random taste variation in brand choice behaviour. In addition, the individual-level β coefficients allow estimating disproportional substitution patterns and capturing unobserved factors of consumers over the time. To illustrate the advantage of allowing consumer heterogeneity, we estimate the Latent-class/Finite-mixture MNL, the Mixed MNL and Hierarchical Bayesian MNL with normally distributed β coefficients as well as a mixture of Normals heterogeneity distribution for β. We show how accounting for heterogeneous β coefficients affects the parameter estimates compared to the basic MNL, and how the knowledge of heterogeneous marketing-mix sensitivities across consumers provides valuable insights for understanding, predicting, and influencing brand choice behaviour. From this, we emphasise the importance of using the state-of-the-art approaches, even if only interested in aggregated parameter estimates. Researchers and practitioners who want to replicate our results or analyse their own research questions using the models in this paper will find the estimation code in the web appendix. Building on the current body of knowledge in discrete choice modelling, our paper concludes with avenues for future research in marketing.
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