Developing practical models for capturing competitive effects in revenue management and pricing systems has been a significant challenge for airlines and other industries. The prevalent mechanisms of accounting for competitive effects rely on changing the price structure and making manual adjustments to respond to dynamically evolving competitive scenarios. Furthermore, micro-economic models have also not become popular in practice primarily because of the simplistic mechanisms proposed for modeling consumer behavior in a competitive setting. In particular, many of these models assume that the customers always seek the lowest price in the market, that is they are fully flexible. In practice, customers may display some degree of affinity or loyalty to an airline and may pay a premium for their preferred choice. On the other hand, almost all early revenue management models did not explicitly consider competitive effects and assumed that an airline’s demand only depends on their prices i.e., demand is fully dedicated to an airline (loyal). This paper develops a model to capture more realistic competitive dynamics by including both these types of customer behavior. We also develop a Bayesian machine learning based demand forecasting methodology for such models with explicit competitive considerations and show the benefit of this approach over traditional models on a real airline data set.
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We are grateful to Dr. Karl Isler for his insights on Bayesian Variational Inference which helped us develop the methodology for class-free models. We are also grateful to the referees for their valuable comments that helped improve this paper.
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Kumar, R., Wang, W., Simrin, A. et al. Competitive revenue management models with loyal and fully flexible customers. J Revenue Pricing Manag 20, 256–275 (2021). https://doi.org/10.1057/s41272-021-00311-4
- Bayesian inference
- Dynamic pricing
- Competitive modelling
- Continuous pricing