Modelling consumers' heterogeneous preferences: a case study with Chilean wine consumers

Abstract

Background and Aims: Understanding consumers' preferences is key to making a successful product, but preferences are heterogeneous. We compare three approaches to consider preference heterogeneity in discrete choice models: (i) systematic preference variations based on socio-demographic characteristics; (ii) latent classes; and (iii) hybrid choice models with latent variables measuring consumers' attitudes. Methods and Results: Data from a stated choice survey of Chilean wine consumers were analysed using three different approaches; these agreed on average trends but differed in fit and implied different trade-offs. For example, socio-demographic characteristics correlate poorly with preferences. Latent classes offer a good fit but do not link preference heterogeneity to consumer characteristics. The hybrid choice model provides the best fit but requires more data, making it more difficult to use this approach in forecasting. Conclusions: The best approach might depend on the research objectives. Using latent classes on a representative sample is the best approach if forecasting is paramount. Modelling attitudes is helpful when more insight into consumers' preferences is sought. Systematic preference variations based on socio-demographic characteristics are a good choice when only average trends are relevant. Significance of the Study: We make recommendations on how to model preference heterogeneity when studying wine preferences, an issue often overlooked.

Clustering Spanish alcoholic beverage shoppers to focus marketing strategies

“…Therefore, the greater the brand awareness, the more renowned brands of alcoholic beverages are bought. In other words, shoppers of alcoholic beverages prefer good brands (Palma et al, 2017).…”

“…In this regard, an impulsive decision-making style leads the individual to buy more alcohol without having to worry about the price or whether it is really needed or not, and simply buy it without any previous planning. In contrast, a decision-making style confused by information overload implies a blocked buyer decision owing to excessive alternative options, meaning that they tend to buy fewer beverages and choose the most well-known and those in which they have greater confidence (Palma et al, 2017). This leads us to formulate the following hypothesis:…”

Towards quality consumption

“…Pomarici et al (2017) sorted the variables into four groups: socio–demographics; psychographics, especially involvement and knowledge; lifestyle; and behaviour, such as frequency of consumption and consumption occasion. They, along with Arnould et al (2002, cited in Palma et al , 2014) also named four variables that can account for market segmentation: geographic, demographic, behavioural and psychographic. Bruwer et al (2002) pointed out that demographic characteristics alone are less useful to form segments regarding wine, as “wine consumers can be demographically similar but significantly different in terms of attitudes, consumption behaviour, lifestyle” (Bruwer et al , 2002: 221).…”

“…Palma et al (2014) found that most of the applied wine market segmentation techniques rely on a psychometric approach. Hall and Winchester (1999) based their research on beliefs, attitudes, values and product preferences when purchasing wine.…”

“…In the work of King et al (2012), respondents were grouped according to their liking of white wine styles. Palma et al (2014) used a mixed segmentation approach. They combined motivations and level of involvement in their classification process.…”