Recently, several macro trends have converged to provide airlines new opportunities for one-to-one digital customer engagement and personalization. Airlines have more types and volumes of data available than ever before: shopping-behavior data, data providing context on booking decisions, social media data enriching the information available on travel trends, and more. All of these can play a critical role in defining the right offers and setting the right prices for each shopping request. A plethora of advanced AI and ML techniques have become available on open-source platforms, letting players generate actionable customer insights and leverage vast amounts of existing data. New distribution technology is being deployed to allow airlines to implement real-time retailing capabilities. Consumers have been trained by the likes of Amazon, Netflix, Alibaba, and Starbucks to expect products and services tailored to their individual needs along with superior and engaging content. This paper presents different approaches to price-product personalization that have been tested in airline cases globally. It also explores how the concept of experiential learning is nicely suited to tackling scenarios in which the purchaser is well-identified as well as cases in which not much is known about the visitor except the context of the shopping session.

最近，几种宏观趋势汇聚​​在一起，为航空公司提供了一对一数字客户参与和个性化的新机会。航空公司拥有比以往更多类型和数量的可用数据：购物行为数据、提供预订决策背景的数据、丰富旅行趋势可用信息的社交媒体数据等等。所有这些都可以在为每个购物请求定义正确的报价和设置正确的价格方面发挥关键作用。开源平台上已经提供了大量先进的人工智能和机器学习技术，让玩家能够生成可操作的客户洞察并利用大量现有数据。正在部署新的分销技术，以允许航空公司实施实时零售功能。消费者已经接受过亚马逊、Netflix 等公司的培训，阿里巴巴和星巴克期望为他们的个人需求量身定制的产品和服务，以及优质和引人入胜的内容。本文介绍了不同的价格产品个性化方法，这些方法已经在全球航空公司案例中进行了测试。它还探讨了体验式学习的概念如何很好地适用于解决购买者身份明确的场景，以及除了购物会话的背景之外对访客知之甚少的情况。