Abstract
A key issue of making upgrade decisions is to match the most relevant upgrade offers to the right customers at the right time. To optimize upgrade strategies and profitability, companies seek to break “data silos” between themselves and other business partners for a more holistic view of customers’ consumption experiences. However, multi-source data fusion may lead to potential privacy leakage. To overcome these two challenges in data silos and privacy protection, this study introduced a privacy-preserving federated learning (FL) approach and explained the process of using FL in modeling airline passengers’ willingness to pay for upgrade offers. Using a case study of an airline company, this study demonstrated how FL-based upgrade algorithms using multi-source data can be developed to improve upgrade prediction accuracy while preserving customers’ personal data privacy. This study offers significant theoretical and practical implications for upgrade optimization in the contexts of airlines and other hospitality-related businesses.
Original language | English |
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Title of host publication | 2022 AMA Summer Academic Conference |
Place of Publication | Chicago, USA |
Publisher | American Marketing Association (AMA) |
Number of pages | 14 |
Publication status | Unpublished - Aug 2022 |
Keywords
- Marketing Analytics
- AI
- Machine Learning