  Customer retention is one of the primary goals in the area of customer
relationship management. A mass of work exists in which machine learning models
or business rules are established to predict churn. However, targeting users at
an early stage when they start to show a downward trend is a better strategy.
In downward trend prediction, the reasons why customers show a downward trend
is of great interest in the industry as it helps the business to understand the
pain points that customers suffer and to take early action to prevent them from
churning. A commonly used method is to collect feedback from customers by
either aggressively reaching out to them or by passively hearing from them.
However, it is believed that there are a large number of customers who have
unpleasant experiences and never speak out. In the literature, there is limited
research work that provides a comprehensive and scientific approach to identify
these "silent suffers". In this study, we propose a novel two-part framework:
developing the downward prediction process and establishing the methodology to
identify the reasons why customers are in the downward trend. In the first
prediction part, we focus on predicting the downward trend, which is an earlier
stage of the customer lifecycle compared to churn. In the second part, we
propose an approach to figuring out the cause (of the downward trend) based on
a causal inference method and semi-supervised learning. The proposed approach
is capable of identifying potential silent sufferers. We take bad shopping
experiences as inputs to develop the framework and validate it via a marketing
A/B test in the real world. The test readout demonstrates the effectiveness of
the framework by driving 88.5% incremental lift in purchase volume.
