  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.
