A Statistical Learning Approach to Personalization in Revenue Management
A Statistical Learning Approach to Personalization in Revenue Management
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DOI：
10.2139/ssrn.2579462
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发表时间：
2015-03
期刊：
影响因子：
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通讯作者：
Xi Chen;Zachary Owen;Clark Pixton;D. Simchi-Levi
中科院分区：
文献类型：
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作者：
Xi Chen;Zachary Owen;Clark Pixton;D. Simchi-Levi
我们考虑了一个基于Logit模型的框架，用于建模考虑客户特征的联合定价和分类决策。当一个人对任何一个客户没有足够的数据，并且希望将关于一个客户的偏好的学习推广到整个人群时，这个模型提供了一个显着的优势。在该模型下，我们研究了从静态存储的预先收集的客户数据进行模型拟合的统计学习任务。与流行的学习和收入范例不同，这种设置代表了许多业务团队遇到的情况，即他们的数据收集能力已经超过了他们的数据分析能力。在这种学习环境下，我们建立了模型参数的有限样本收敛保证。然后，将参数收敛保证扩展到在收入方面的样本外业绩保证，其形式是关于在估计参数下采取的最佳行动的预期收入与充分了解选择模型的决策者产生的收入之间的差距的高概率界限。我们进一步讨论了这些界限的实际意义。我们使用航空公司的机票购买数据演示了个性化方法。本文被《数据驱动规范分析》特刊J.George Shanthikumar接受
We consider a logit model-based framework for modeling joint pricing and assortment decisions that take into account customer features. This model provides a significant advantage when one has insufficient data for any one customer and wishes to generalize learning about one customer’s preferences to the population. Under this model, we study the statistical learning task of model fitting from a static store of precollected customer data. This setting, in contrast to the popular learning and earning paradigm, represents the situation many business teams encounter in which their data collection abilities have outstripped their data analysis capabilities. In this learning setting, we establish finite-sample convergence guarantees on the model parameters. The parameter convergence guarantees are then extended to out-of-sample performance guarantees in terms of revenue, in the form of a high-probability bound on the gap between the expected revenue of the best action taken under the estimated parameters and the revenue generated by a decision maker with full knowledge of the choice model. We further discuss practical implications of these bounds. We demonstrate the personalization approach using ticket purchase data from an airline carrier. This paper was accepted by J. George Shanthikumar, special issue on data-driven prescriptive analytics