Identifying latently dissatisfied customers and measures for dissatisfaction management
Customer satisfaction continues to be an important topic in the financial services industry. However, there is an increasing awareness that customer satisfaction as such is not enough. Distinguishes between overall satisfied customers and latently dissatisfied customers; the latter
being those customers who, although reporting satisfaction in a survey, have other characteristics (i.e. satisfaction with specific service items and/or socio-demographic characteristics) that resemble dissatisfied customers. The identification of these latently dissatisfied customers may
function as an early warning signal. Indeed, their probability to defect is relatively high and can be compared to that of dissatisfied customers. Proposes a data mining technique called "characteristic rules" to identify latently dissatisfied customers of a Belgian bank. Appropriate marketing
actions (dissatisfaction management) may help to avoid these customers leaving. Therefore, the objective of this study is to provide scholars and business managers with theoretical, methodological and managerial insights into identifying latently dissatisfied customers.