结合联合分析数据与购买历史的价格优化：一种因果建模方法
Price Optimization Combining Conjoint Data and Purchase History: A Causal Modeling Approach
AI总结：
本文提出一种结合总体统计、购买历史与联合分析数据的因果建模方法，通过贝叶斯估计价格对购买概率的因果效应并比较毛利润后验分布来实现价格优化。
摘要：
公司的定价决策需要理解价格变化对需求的因果效应。当现实中的定价实验不可行时，数据驱动的决策必须基于替代数据源，例如购买历史（销售数据）和联合研究，在联合研究中，一组顾客被要求在人工设置下进行虚拟购买。我们提出了一种价格优化方法，以系统化的方式结合总体统计信息、购买历史和联合分析数据。我们基于因果推断的最新进展，在顾客层面识别并量化价格对购买概率的影响。识别任务是一个可迁移性问题，其解决方案需要对联合研究与真实购买之间的差异做出参数化假设。因果效应使用贝叶斯方法进行估计，该方法考虑了数据来源的不确定性。定价决策通过比较不同价格下毛利润的估计后验分布来做出。该方法使用模拟数据进行了演示，模拟数据具有真实世界数据的特征。
英文摘要：
Pricing decisions of companies require an understanding of the causal effect of a price change on the demand. When real-life pricing experiments are infeasible, data-driven decision-making must be based on alternative data sources such as purchase history (sales data) and conjoint studies where a group of customers is asked to make imaginary purchases in an artificial setup. We present an approach for price optimization that combines population statistics, purchase history and conjoint data in a systematic way. We build on the recent advances in causal inference to identify and quantify the effect of price on the purchase probability at the customer level. The identification task is a transportability problem whose solution requires a parametric assumption on the differences between the conjoint study and real purchases. The causal effect is estimated using Bayesian methods that take into account the uncertainty of the data sources. The pricing decision is made by comparing the estimated posterior distributions of gross profit for different prices. The approach is demonstrated with simulated data resembling the features of real-world data.
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