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:定价决策需要了解价格变化对需求的因果效应。当实际的定价实验不可行时,基于替代数据源(例如购买历史记录和共同研究)的数据驱动决策必须依赖于。我们提出了一种将人口统计学,购买历史和共同数据以系统方式结合起来的价格优化方法。我们基于最近因果推断的进展,识别和量化在客户层面上价格变化对购买概率的影响。识别任务是一种运输问题,其解决方案需要在共同研究和实际购买之间的差异上做出参数假设。使用贝叶斯方法估计因果效应,并考虑数据源的不确定性。通过比较不同价格的毛利后验分布来做出定价决策。该方法以类似于真实数据的模拟数据为例进行演示。