# Dynamic Pricing for Airline Ancillaries with Customer Context

arXiv: https://arxiv.org/abs/1902.02236 · HTML: https://mlsanity.com/paper/1902.02236/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2019.

## Built on by (most cited first, 7 of 7)

- [Distribution Shift in Airline Customer Behavior during COVID-19](https://mlsanity.com/paper/2111.14938/) (2021)
- [DISCO: An End-to-End Bandit Framework for Personalised Discount Allocation](https://mlsanity.com/paper/2406.06433/) (2024)
- [Modeling Price Elasticity for Occupancy Prediction in Hotel Dynamic Pricing](https://mlsanity.com/paper/2208.03135/) (2022)
- [Negotiating Networks in Oligopoly Markets for Price-Sensitive Products](https://mlsanity.com/paper/2110.13303/) (2021)
- [Quantum Machine Learning hyperparameter search](https://mlsanity.com/paper/2302.10298/) (2023)
- [Online Dynamic Pricing of Complementary Products](https://mlsanity.com/paper/2511.22291/) (2025)
- [Counterfactual Optimal Action Trees (COAT): Interpretable Prescriptive Policies from Observational Data](https://mlsanity.com/paper/2607.14318/) (2026)

## Abstract (arXiv)

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. In this paper, we present and compare three approaches for dynamic pricing of ancillaries, with increasing levels of sophistication: (1) a two-stage forecasting and optimization model using a logistic mapping function; (2) a two-stage model that uses a deep neural network for forecasting, coupled with a revenue maximization technique using discrete exhaustive search; (3) a single-stage end-to-end deep neural network that recommends the optimal price. We describe the performance of these models based on both offline and online evaluations. We also measure the real-world business impact of these approaches by deploying them in an A/B test on an airline's internet booking website. We show that traditional machine learning techniques outperform human rule-based approaches in an online setting by improving conversion by 36% and revenue per offer by 10%. We also provide results for our offline experiments which show that deep learning algorithms outperform traditional machine learning techniques for this problem. Our end-to-end deep learning model is currently being deployed by the airline in their booking system.