营销敏感度中的相关动态
Correlated Dynamics in Marketing Sensitivities
AI总结：
本文提出一种基于贝叶斯非参数高斯过程估计的分层动态因子模型框架，用于捕捉跨产品类别的营销敏感度相关动态，提升了预测准确度并实现了更精确的价格弹性估计。
摘要：
理解个体客户对价格、促销、品牌及其他营销组合元素的敏感度，是众多营销问题的根本。该问题中一个重要但研究不足的方面是这些敏感度的动态特性，它们随时间变化且因人而异。先前的工作已开发出在产品类别内捕捉此类动态异质性的方法，但忽略了跨类别间存在相关动态的可能性。在本研究中，我们引入了一个使用分层动态因子模型来捕捉此类相关动态的框架，其中个体偏好参数受共同的跨类别动态潜在因子影响，并通过贝叶斯非参数高斯过程进行估计。我们将模型应用于杂货购买数据，发现仅凭少数全局趋势即可解释令人惊讶的动态异质性程度。我们还刻画了消费者敏感度在各品类间演变的模式。在管理层面，所提出的框架不仅通过利用跨类别数据提升了预测准确度，还实现了对价格弹性等目标量更精确的估计。
英文摘要：
Understanding individual customers' sensitivities to prices, promotions, brands, and other marketing mix elements is fundamental to a wide swath of marketing problems. An important but understudied aspect of this problem is the dynamic nature of these sensitivities, which change over time and vary across individuals. Prior work has developed methods for capturing such dynamic heterogeneity within product categories, but neglected the possibility of correlated dynamics across categories. In this work, we introduce a framework to capture such correlated dynamics using a hierarchical dynamic factor model, where individual preference parameters are influenced by common cross-category dynamic latent factors, estimated through Bayesian nonparametric Gaussian processes. We apply our model to grocery purchase data, and find that a surprising degree of dynamic heterogeneity can be accounted for by only a few global trends. We also characterize the patterns in how consumers' sensitivities evolve across categories. Managerially, the proposed framework not only enhances predictive accuracy by leveraging cross-category data, but enables more precise estimation of quantities of interest, like price elasticity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
