We consider the problem of developing a clickstream modeling framework for real-time customer event prediction problems in SaaS products like QBO. We develop a low-latency, cost-effective, and robust ensemble architecture (BBE-LSWCM), which combines both aggregated user behavior data from a longer historical window (e.g., over the last few weeks) as well as user activities over a short window in recent-past (e.g., in the current session). As compared to other baseline approaches, we demonstrate the superior performance of the proposed method for two important real-time event prediction problems: subscription cancellation and intended task detection for QBO subscribers. Finally, we present details of the live deployment and results from online experiments in QBO.

翻译：摘要：我们研究了在QBO等SaaS产品中，针对实时客户事件预测问题开发点击流建模框架的方法。我们提出了一种低延迟、低成本且鲁棒的集成架构（BBE-LSWCM），该架构结合了来自较长时间窗口（例如过去几周）的聚合用户行为数据，以及近期短时间窗口（例如当前会话）内的用户活动。与其它基线方法相比，我们展示了所提方法在两个重要的实时事件预测问题（QBO订阅者的订阅取消与意图任务检测）上的优越性能。最后，我们介绍了在QBO中的实时部署细节及在线实验结果。