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Discovering customer value for marketing systems: an empirical case study

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  • Wen-Yu Chiang

Abstract

Data mining technologies have been employed in a variety of business managements for discovering useful commercial knowledge or marketing model for many years. Hence, the major marketing issue for airlines is to identify and analyse valuable air travellers recently, so that airlines can attract them for enhancing the profits and growth rates. However, growth rates are always an important issue for airline industries. An empirical case of air travellers’ markets in Taiwan is implemented in this research. This research proposes a model (FSLC model, RFM model based) via the data mining technologies to discover valuable travellers for airlines. This study partitions the market of air travellers in Taiwan, and the paper generates useful association rules to find an optimised target market for dynamic marketing or CRM systems. Nevertheless, the results of this research can be applied on marketing or CRM systems of the airline industry for identifying valuable travellers. Finally, the purpose of this research is to find high-value markets for marketing or CRM systems of airlines in Taiwan, and the framework can be applied to other industries as well.

Suggested Citation

  • Wen-Yu Chiang, 2017. "Discovering customer value for marketing systems: an empirical case study," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5157-5167, September.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:17:p:5157-5167
    DOI: 10.1080/00207543.2016.1231429
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    Cited by:

    1. Dora Simões & Joana Nogueira, 2022. "Learning about the customer for improving customer retention proposal of an analytical framework," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(1), pages 50-63, March.
    2. Jianmin Jia & Mingyu Shao & Rong Cao & Xuehui Chen & Hui Zhang & Baiying Shi & Xiaohan Wang, 2022. "Exploring the Individual Travel Patterns Utilizing Large-Scale Highway Transaction Dataset," Sustainability, MDPI, vol. 14(21), pages 1-13, October.

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