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

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Document pages: 35 pages

Abstract: 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.

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