  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.
