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.

翻译：企业的定价决策需要理解价格变化对需求的因果效应。当现实价格实验不可行时，数据驱动的决策必须依赖替代数据源，例如购买历史（销售数据）和选择实验（向一组受试者在人工情境中提出假设性购买问题）。我们提出一种系统性地整合总体统计、购买历史与选择实验数据的价格优化方法。基于因果推断的最新进展，我们在客户层面识别并量化价格对购买概率的影响。该识别任务是一个可迁移性问题，其解决方案需要对选择实验与现实购买之间的差异进行参数化假设。因果效应采用贝叶斯方法进行估计，该方法充分考虑数据源的不确定性。通过比较不同价格下毛利润的后验分布估计值，做出定价决策。我们利用模拟数据（该数据模拟现实数据的特征）对所述方法进行了验证。