Faced with the increasingly fierce competition in the aviation market, the strategy of consumer choice has gained increasing significance in both academia and practice. As ever-increasing travel choices and growing consumer heterogeneity, how do airline companies satisfy passengers' needs? With a vast amount of data, how do airline managers combine information to excavate the relationship between independent variables to gain insight about passengers' choices and value system as well as determining best personalized contents to them? Using the real case of China Southern Airlines, this paper illustrates how Bayesian belief network (BBN) can enable airlines dynamically recommend relevant contents based on predicting passengers' choice to optimize the loyalty. The findings of this study provide airline companies useful insights to better understand the passengers' choices and develop effective strategies for growing customer relationship.
Part of the book: Bayesian Inference
With the rapid development of civil aviation industry, high-quality customer resources have become a significant way to measure the competitiveness of the civil aviation industry. It is well known that the competition for high-value customers has become the core of airline profits. The research of airline customer lifetime value can help airlines identify high-value, medium-value and low-value travellers. What is more, the airline company can make resource allocation more rational, with the least resource investment for maximum profit return. However, the models that are used to calculate the value of customer life value remain controversial, and how to design a model that applies to airline company still needs to be explored. In the paper, the author proposed the optimised China Eastern Airlines passenger network value assessment model and examined its fitting degree with the TravelSky value score. Besides, the author combines China Eastern Airlines passenger network value assessment model score with loss model score to help airlines find their significant customers.
Part of the book: Data Mining
Airline customer demand has plummeted since the COVID-19 pandemic, with about two-thirds of the world’s fleet grounded. Under such circumstances, the airline needs to adjust its market strategy. Mining the value of passengers and providing differentiated services for passengers with different values are key to the differentiated competition of airlines. In the case of ensuring data privacy, this study introduces a privacy-preserving federated learning method, which combines airline internal data with external operator data, comprehensively considers multiple dimensional characteristics of passengers. This study compares a unilateral model using airline data with a joint model combining airline internal data and operators through federated learning. The result shows that the joint model based on federated learning is more accurate than the unilateral model. Based on this result, this study puts forward the thinking about passenger mining and insight in the construction of MaaS under the epidemic situation, constructs a customer journey map according to the characteristics of the segmented population, and proposes the idea of providing different transportation services for the segmented population. This research provides important theoretical and practical implications for the airline digital transformation and MaaS construction under the epidemic.
Part of the book: A New Era of Consumer Behavior