Customer implicit churn in the securities industry : investigating the different effects of individual business portfolio and team business structure

Abstract

This research used the customer data of a large securities company in China from the beginning of 2023 to January 2024 and designed three studies to develop a measurement and explore the business-related antecedents of customer implicit churn in the securities industry. It aimed to answer three empirical questions that may be largely ignored in past literature: (1) Can we develop an accurate and stable measurement that can be used to predict the probability of the implicit customer churn in advance? (2) Does a customer’s business portfolio affect his/her implicit churn, and if so, how? (3) Does the team-level business structure affect each customer’s implicit churn, and if so, how? Empirical results show that: (1) The observation period, login times, and asset threshold as three core indicators can jointly provide an accurate and stable prediction for the risk of customer implicit churn. The reasonable indicator level is the average asset less than 10000 (yuan) RMB and the login times no more than 3 for 3 consecutive months. (2) A customer’s different business types generate more diverse demands that can hardly be met by his/her manager, but the increase in the amount of different business is more a reflection of the strong business complementarity. As a result, the customers with a larger number of different businesses tend to have a lower risk of implicit churn. (3) A customer team with more heterogeneous business structure (i.e., more different types of business across its customers) greatly reduces the switching cost of its customers’ choice to quit, and each customer member in this team is therefore more likely to have an implicit churn. It contributes in at least three ways. (1) The research developed a new measurement for customer implicit churn in the securities industry, which is more accurate, stable and predictive than those measurements in previous studies. (2) The research drew from related theories in customer demand diversity and product complementarity to examine the effects of business-related antecedents on customer implicit churn, this research has to some extent broadened the explanatory boundaries of these theories. (3) The research not only considered each customer’s business portfolio, but also incorporated the business structure of their teams into analyses. By comparing the opposite impacts of the two business-related antecedents respectively at the individual level and team level, it provides a new cross-level explanation for the phenomenon of customer implicit churn.