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

TL;DR

This paper introduces a two-part framework for early detection of downward customer trends and identifying silent causes using causal inference and semi-supervised learning, validated by real-world marketing tests.

Contribution

It presents a novel approach combining downward trend prediction with cause identification, addressing silent customer issues often overlooked in existing methods.

Findings

Achieved 88.5% incremental lift in purchase volume in real-world testing.

Developed a causal inference-based method to identify silent customer issues.

Validated the framework's effectiveness through an A/B marketing test.

Abstract

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.