Market segmentation strategies for complex automotive products

Abstract

With the advent of 'big data' the purpose of this empirical study was to take the opportunity to rethink conventional market segmentation strategies. This is particularly relevant for the automotive industry which is going through a period of rapid change with advanced technologies such as electric powered and autonomous vehicles, creating increased concerns as to how this complexity is communicated effectively. A mixed methods approach was utilised to collect data from multiple sources, incorporating in-depth discussion groups, semi-structured interviews, an online survey and data collection of communication processes through the attendance of new car product launches. The results suggest that marketing departments should rethink their data capture methods to collect more relevant consumer information, not the contemporary trend of needs, attitude and motivation variables that are difficult to identify and collect, but basic information on their level of familiarity with products through previous experience and exposure. The basic dimensions identified are characterised by a consumer's expertise, involvement and familiarity with a product. The findings are synthesised into a theoretical framework to define differing levels of product complexity, which would enable manufacturers to provide more closely defined market segmentation strategies when communicating new product information.

A multi-analytical approach to studying customers motivations to use innovative totally autonomous vehicles

“…Subsequent investigation of causal recipes further specifies personas of potential users by demonstrating their multidimensional personality traits. Such information will help manufacturers to design and develop distinctive models of AVPs that fit the personalities of specific consumer groups and therefore overcome the confusion the consumers seeking to adopt AVPs are currently facing (König and Neumayr, 2017;Taylor-West, Saker and Champion, 2018). For example, when integrating traits at the elemental, compound and situational levels (see Table 8), Recipes R1 and R2 have in common conscientiousness, openness to experience, agreeableness and selfidentification expressiveness.…”

The big data-business strategy interconnection: a grand challenge for knowledge management. A review and future perspectives

“…For example, by using BDA the firm is able to effectively analyse the heterogeneity of preferences within different market segments and thus better personalise its value proposition ('one-to-one' approach). Several contributions refer to the opportunity of using customer-generated BD to determine new segmentation criteria, based on variables such as price sensitivity and customer engagement (Kumar et al, 2017), customer opinions regarding the relevance of certain product or service attributes (Ahani et al, 2019) and the degree of knowledge and familiarity towards the product or service (Taylor-West et al, 2018). The development of a clear segmentation strategy represents an essential precondition to identifying and satisfying consumer needs.…”

The Effect of Attribute Alignability on Product Purchase: The Moderating Role of Product Familiarity and Self-Construal

“…Consumers with different level of product familiarity show great differences in mindset, preference formation, and behavior tendency. Furthermore, Taylor-West et al (2020) suggest that marketing departments should review their data capture methods to collect more basic consumer information on their level of familiarity. Therefore, it is meaningful to further explore the moderating role of product familiarity in consumers’ dependency on attribute alignability.…”