The ubiquitous internet is a multipurpose platform for finding information, an avenue for social interaction, and a primary customer touch‐point as a marketplace to conduct e‐commerce. The digital footprints of browsers are a rich source of data to drive sales. We use clickstreams (clicks) to track the evolution of session‐level customer browsing for modeling. We apply Markov chains (MC) to calculate probabilities of page‐level transitions from which relevant topological features (persistence diagrams) are extracted to determine optimal points (URL pages) for marketing intervention. We use topological summaries (silhouettes, landscapes) to distinguish the buyers and nonbuyers to determine the likelihood of conversion of active user sessions. Separately, we model browsing patterns via Markov chain theory to predict users' propensity to buy within a session. Extensive analysis of data applied to a large commercial website demonstrates that the proposed approaches are useful predictors of user behavior and intent. Utilizing computational topology in digital marketing holds tremendous promise. We demonstrate the utility of topological data analysis combined with MC and present its merits and disadvantages.

中文翻译：

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数字营销中的拓扑数据分析

无处不在的互联网是用于查找信息的多功能平台，社交互动的途径以及主要的客户接触点，是进行电子商务的市场。浏览器的数字足迹是推动销售的丰富数据来源。我们使用点击流（点击）来跟踪会话级客户浏览以进行建模的过程。我们应用马尔可夫链（MC）来计算页面级过渡的概率，然后从中提取相关的拓扑特征（持久性图）以确定营销干预的最佳点（URL页面）。我们使用拓扑摘要（轮廓，风景）来区分购买者和非购买者确定活动用户会话转换的可能性。另外，我们通过马尔可夫链理论对浏览模式进行建模，以预测用户在会话中的购买倾向。对应用于大型商业网站的数据的广泛分析表明，所提出的方法是用户行为和意图的有用预测指标。在数字营销中使用计算拓扑具有广阔的前景。我们演示了结合MC进行拓扑数据分析的实用性，并介绍了其优缺点。