  User intended actions are widely seen in many areas. Forecasting these
actions and taking proactive measures to optimize business outcome is a crucial
step towards sustaining the steady business growth. In this work, we focus on
pre- dicting attrition, which is one of typical user intended actions.
Conventional attrition predictive modeling strategies suffer a few inherent
drawbacks. To overcome these limitations, we propose a novel end-to-end
learning scheme to keep track of the evolution of attrition patterns for the
predictive modeling. It integrates user activity logs, dynamic and static user
profiles based on multi-path learning. It exploits historical user records by
establishing a decaying multi-snapshot technique. And finally it employs the
precedent user intentions via guiding them to the subsequent learning
procedure. As a result, it addresses all disadvantages of conventional methods.
We evaluate our methodology on two public data repositories and one private
user usage dataset provided by Adobe Creative Cloud. The extensive experiments
demonstrate that it can offer the appealing performance in comparison with
several existing approaches as rated by different popular metrics. Furthermore,
we introduce an advanced interpretation and visualization strategy to
effectively characterize the periodicity of user activity logs. It can help to
pinpoint important factors that are critical to user attrition and retention
and thus suggests actionable improvement targets for business practice. Our
work will provide useful insights into the prediction and elucidation of other
user intended actions as well.
