Assessing the Impact: Does an Improvement to a Revenue Management System Lead to an Improved Revenue?
收益管理系统的改进能否提高收入？AI 解读 ↓
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收益管理系统的改进能否提高收入？
- 研究问题
- 收益管理系统更新后，如何估计保持原系统时不可观测的收入，并区分系统改进带来的小幅收入变化与其他变动？
- 方法与思路
- 作者将影响评估转化为反事实收入预测，使用航线起讫点对的每日收入数据，并比较计量模型与深度神经网络模型。
- 材料陈述的贡献
- 摘要报告两类模型的反事实预测误差分别为1%与1.1%，并称可估计约2%的小幅影响，展示加拿大航空的大规模数据应用。
- 阅读关注点
- 应核对反事实预测的验证设计、误差指标和比较样本，以及系统变更与同期其他因素能否区分、影响估计如何量化不确定性。
- 阅读边界
- 这是arXiv预印本，不能据此声称已同行评审或期刊接收。摘要未说明反事实识别条件；所报预测误差本身不足以证明因果效应准确。
原文摘要
Airlines and other industries have been making use of sophisticated Revenue Management Systems to maximize revenue for decades. While improving the different components of these systems has been the focus of numerous studies, estimating the impact of such improvements on the revenue has been overlooked in the literature despite its practical importance. Indeed, quantifying the benefit of a change in a system serves as support for investment decisions. This is a challenging problem as it corresponds to the difference between the generated value and the value that would have been generated keeping the system as before. The latter is not observable. Moreover, the expected impact can be small in relative value. In this paper, we cast the problem as counterfactual prediction of unobserved revenue. The impact on revenue is then the difference between the observed and the estimated revenue. The originality of this work lies in the innovative application of econometric methods proposed for macroeconomic applications to a new problem setting. Broadly applicable, the approach benefits from only requiring revenue data observed for origin-destination pairs in the network of the airline at each day, before and after a change in the system is applied. We report results using real large-scale data from Air Canada. We compare a deep neural network counterfactual predictions model with econometric models. They achieve respectively 1% and 1.1% of error on the counterfactual revenue predictions, and allow to accurately estimate small impacts (in the order of 2%).