A two stage machine learning approach for Modeling Customer Lifetime Value in the Chinese Airline Industry

Sien Chen, YINGHUA HUANG, Dong-Ling Xu, Wei Jiang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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This study proposed a two-stage machine learning approach for modeling customer lifetime value (CLV) by integrating the advantages of three traditional machine learning methods (Stage 1) and an innovative method of evidence reasoning (ER) modeling (Stage 2). Unlike previous studies of developing a single CLV model, this study adopted the ER approach (Stage 2) to fuse the results obtained from the Stage 1 (i.e., logistic regression, gradient boosting decision tree, and neural network models) to improve the prediction accuracy. We illustrated the proposed two-stage approach using a case study of Chinese airline passengers. We analyzed a dataset of over 100,000 Chinese airline passengers, which includes 327 variables related to passengers’ travel experience with major airline companies and online travel agents in China. The case study showed that the proposed two-stage approach performed better in comparison with other machine learning algorithms. This study provided airline companies a useful data-driven method to identify high-value passengers and allowed airlines to allocate the CRM resources more effectively.
Original languageEnglish
Title of host publicationAMA Summer Academic Conference 2020
Subtitle of host publicationBridging Gaps Marketing in an Age of Disruption PROCEEDINGS
EditorsSimon Blanchard, Amber Epp, Girish Mallapragada
Place of PublicationUnited States of America
PublisherAmerican Marketing Association (AMA)
Number of pages18
ISBN (Electronic)978-1-71382-224
Publication statusPublished - 18 Aug 2020


  • machine learning
  • evidential reasoning
  • customer lifetime value
  • airline industry


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