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Customer retention is one of the primary goals in the area of customerrelationship management. A mass of work exists in which machine learning modelsor business rules are established to predict churn. However, targeting users atan 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 trendis of great interest in the industry as it helps the business to understand thepain points that customers suffer and to take early action to prevent them fromchurning. A commonly used method is to collect feedback from customers byeither aggressively reaching out to them or by passively hearing from them.However, it is believed that there are a large number of customers who haveunpleasant experiences and never speak out. In the literature, there is limitedresearch work that provides a comprehensive and scientific approach to identifythese "silent suffers". In this study, we propose a novel two-part framework:developing the downward prediction process and establishing the methodology toidentify the reasons why customers are in the downward trend. In the firstprediction part, we focus on predicting the downward trend, which is an earlierstage of the customer lifecycle compared to churn. In the second part, wepropose an approach to figuring out the cause (of the downward trend) based ona causal inference method and semi-supervised learning. The proposed approachis capable of identifying potential silent sufferers. We take bad shoppingexperiences as inputs to develop the framework and validate it via a marketingA/B test in the real world. The test readout demonstrates the effectiveness ofthe framework by driving 88.5% incremental lift in purchase volume.

A Framework in CRM Customer Lifecycle: Identify Downward Trend and Potential Issues Detection