Abstract
Several documented weaknesses in current market segmentation techniques exist that may reduce the accuracy and optimization of marketing decision-making supporting cruise ship passenger onshore excursion choice. These weaknesses relate to the data analysis techniques used as part of the market segmentation of cruise ship passengers. This paper presents a new analytical method to overcome some of the weaknesses of the existing approaches, the Chain Event Graph (CEG). Adopting a Bayesian approach, the CEG model offers a market segmentation method to aid the identification of specific groups of passengers, based on their behavioral tendencies and demographic characteristics, and match them with targeted marketing communications promoting specific onshore excusion options. Ultimately, the CEG enables the design of marketing programs to improve the distribution of customers within a tourism operator's portfolio of offerings. This capability is important for those businesses and DMOs in cruise tourism and supporting transport sectors whose offerings are often perishable, inflexible and have uncertain customer demand. In this study, we use the CEG approach to segment cruise ship passengers' choice of train excursion for their onshore visit to Ōtepoti Dunedin, Aotearoa New Zealand.
Considering the limitations of existing methods of market segmentation, in this paper CEG has been demonstrated to elicit parsimonious models that can illuminate the process dynamic of consumer behavior and optimize the use of available data. As we show using the example of cruise-ship passengers booking a train excursion, three viable segments were identified based on their choice of choice of train excursion related to two variables (age and propensity for cruise travel). Cruise passengers can also be segmented into four groups based on their information search and booking location behaviour and preference for public or cruise trains. Furthermore, the results of the study demonstrate that a comparison with another approach using a Bayesian Network model showed superiority of the CEG method. Overall, CEG provides destination and tourism operators’ marketing managers with a tree-based graphical model, which depicts the steps and behavior patterns in the process of booking a train excursion and the probability of each possible pathway.