E-tourism - Applying structural pricing strategies to tour operator industry

OBJECTIVES OF THE STUDY: The study contributes to the neglected topic of tour operator pricing in the digital era. The primary aim of the study is to manipulate the decision-makers in tour operator industry to realize the potential and need for structural pricing strategies. DATA AND METHODOLOGY: The study has a constructive and practical step-by-step approach. Firstly, a comprehensive understanding of the topic is obtained by going through the present and future role of tour operators after the advent of internet and other information and communication technologies (ICTs). A solution idea is constructed by addressing the outcomes of ICT revolution by a structural pricing framework, which better addresses the current buying behavioral patterns. In the empirical part, a data mining technique called cluster analysis is applied to reveal the customer buying behavior from a case company's transaction data. Finally, a structural pricing framework is applied to match the pricing structure with the current pricing environment. FINDINGS AND CONCLUSIONS: The thesis gives a clear signal of tour operators' changing role in the tourism value net. Tour operators are becoming internet brokers and have to understand the weaknesses of homogenized one-size-fits-all product portfolio, cannibalization effects of the price-led marketing, as well as the influence of web-based aggregation sites. The study states that digitalization has made the consumers more heterogeneous and the cluster analysis results in three distinct customer types: bargain hunters, regulars and valuables. In the end, the paper gives guidelines for the design and implementation of a suitable pricing structure. In the academic point of view, the study provides comprehensive classifications of both the current pricing challenges faced, as well as the current pricing strategies applied in the tour operator industry. Moreover, the thesis contributes to the service pricing literature in general by applying cluster analysis to real-life data as well as by introducing a novel framework for structural pricing strategies