Marketers employ various online advertising channels to reach customers, and they are particularly interested in attribution for measuring the degree to which individual touchpoints contribute to an eventual conversion. The availability of individual customer-level path-to-purchase data and the increasing number of online marketing channels and types of touchpoints bring new challenges to this fundamental problem. We aim to tackle the attribution problem with finer granularity by conducting attribution at the path level. To this end, we develop a novel graphical point process framework to study the direct conversion effects and the full relational structure among numerous types of touchpoints simultaneously. Utilizing the temporal point process of conversion and the graphical structure, we further propose graphical attribution methods to allocate proper path-level conversion credit, called the attribution score, to individual touchpoints or corresponding channels for each customer's path to purchase. Our proposed attribution methods consider the attribution score as the removal effect, and we use the rigorous probabilistic definition to derive two types of removal effects. We examine the performance of our proposed methods in extensive simulation studies and compare their performance with commonly used attribution models. We also demonstrate the performance of the proposed methods in a real-world attribution application.

翻译：营销人员利用多种在线广告渠道接触客户，并特别关注归因问题以衡量单个触点对最终转化的贡献程度。个体客户级别的购买路径数据的可获得性，以及在线营销渠道和触点类型的不断增加，给这一基础问题带来了新的挑战。我们旨在通过路径级别的归因，以更细粒度解决归因问题。为此，我们开发了一种新颖的图形点过程框架，用于同时研究多种触点类型的直接转化效应及其完整的关系结构。利用转化的时间点过程和图形结构，我们进一步提出了图形归因方法，为每个客户的购买路径中的单个触点或对应渠道分配适当的路径级转化贡献，即归因分数。我们提出的归因方法将归因分数视为移除效应，并采用严格的概率定义推导出两种类型的移除效应。我们通过广泛的模拟研究检验了所提方法的性能，并将其与常用归因模型进行了比较。我们还通过实际归因应用展示了所提方法的性能。