考虑客户背景的航空公司附加费动态定价
Dynamic Pricing for Airline Ancillaries with Customer Context
摘要：
在旅游业中，旅行社已成为收入和盈利的主要来源。然而，传统的定价策略是基于业务规则的，这些规则优化得很差，并且不能对不断变化的市场条件做出响应。本文介绍了Deepair solutions开发的动态定价模型，Deepair solutions是一家面向旅游供应商的AI技术提供商。我们提出了一个定价模型，提供动态的定价建议，具体到每个客户的互动，并优化每个客户的预期收入。个性化定价的独特性质提供了在市场空间中搜索以找到每个客户的每个辅助产品的最佳价格点的机会，而不会侵犯客户隐私。在本文中，我们提出并比较了三种用于动态定价的方法，随着复杂程度的增加：（1）一个两阶段的预测和优化模型，使用逻辑斯蒂映射函数;（2）一个两阶段的模型，使用深度神经网络进行预测，再加上一个收入最大化技术，使用离散穷举搜索;（3）推荐最优价格的单级端到端深度神经网络。我们描述了这些模型的离线和在线评估的基础上的性能。我们还通过在航空公司的互联网预订网站上进行A/B测试来衡量这些方法对现实世界的业务影响。我们发现，传统的机器学习技术在在线环境中的表现优于基于人类规则的方法，转化率提高了36%，每次报价的收入提高了10%。我们还提供了离线实验的结果，这些实验表明，深度学习算法在解决这个问题时优于传统的机器学习技术。我们的端到端深度学习模型目前正在航空公司的预订系统中部署。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
