From Average Customer to Individual Traveler: A Field Experiment in Airline Ancillary Pricing

Ancillaries in the travel industry are now a major stream for revenue and profitability. Ancillaries are optional products or services whose sales depend on an individual's personal preference and their trip context. Conventional pricing strategies for ancillaries based on poorly optimized or static business rules do not respond to changing market conditions or trip context. We present a dynamic pricing model developed in conjunction with Deepair solutions, an AI technology provider for travel suppliers. Our models provide dynamic, customer-interaction-specific pricing recommendations, to increase revenue. The unique nature of personalized pricing provides the opportunity to search over the market space to find the optimal price-point for each customer, without violating customer privacy. We present an A/B testing deployment framework on an airline's website. Embedded in it are three models for dynamic pricing of ancillaries, with increasing levels of sophistication: (1) a two-stage forecasting and pricing model using a logistic mapping function; (2) a two-stage model with a deep neural network for forecasting, followed by pricing using discrete exhaustive search; (3) a single-stage end-to-end deep neural network that recommends the optimal price. In an outer loop, we introduce an online adaptive model-selection framework that adaptively routes customer requests to the above models. This is modeled as multi-armed bandit problem, which we solve using Thompson sampling. We evaluate the performance of these models based on offfine and online evaluations, and their real-world business impact. Offline experiments show that deep learning algorithms outperform traditional machine learning techniques for this problem. In online testing, our AI-driven pricing outperforms human rule-based approaches, improving conversion by 17% and revenue per offer by 25%. Additionally, our adaptive model-selection approach outperforms a uniformly random selection policy by improving the expected revenue per offer by 43% and conversion score by 58% in a simulation environment.