The increasing range of ancillary products offered by airlines is making existing, static frameworks obsolete. The changing expectations of customers have created a need for more dynamic and reactive offers. In order to tailor an offer to an individual journey, it is possible to leverage similar journeys and the observed outcomes using a semi-supervised approach. In this paper, a multi-stage deep learning framework, namely Deep Ancillaries Prediction (DAP), is developed to understand personalised demand for airline ancillaries and enhance pricing strategies. DAP solves the overlapping distribution problem and class imbalances observed in real-world airline datasets. The framework incorporates a contrastive learning module to learn richer feature embeddings and an autoencoder for semi-supervised learning into one framework, and outperforms current ancillary prediction systems. The modules can be trained separately and hence, are suitable for an online learning setting. This framework is designed to be transferable to different prediction tasks. Significant performance enhancements are attained compared to the current state-of-the-art algorithms.

Ancillaries have become a major source of revenue and profitability in the travel industry. Yet, conventional pricing strategies are based on business rules that are poorly optimized and do not respond to changing market conditions. This paper describes the dynamic pricing model developed by Deepair solutions, an AI technology provider for travel suppliers. We present a pricing model that provides dynamic pricing recommendations specific to each customer interaction and optimizes expected revenue per customer. The unique nature of personalized pricing provides the opportunity to search over the market space to find the optimal price-point of each ancillary for each customer, without violating customer privacy.