Some Customers Would Rather Leave Without Saying Goodbye
60 Pages Posted: 17 May 2015 Last revised: 28 Sep 2016
Date Written: September 19, 2016
We investigate the increasingly common business setting in which companies face the possibility of both observed and unobserved customer attrition (i.e., “overt” and “silent” churn) in the same pool of customers. This is the case of many online-based services where customers have the choice to stop interacting with the firm either by formally terminating the relationship (e.g., cancelling their account) or by simply ignoring all communications coming from the firm. The standard contractual versus noncontractual categorization of customer-firm relationships does not apply in such hybrid settings, which means the standard models for analyzing customer attrition do not apply. We propose a hidden Markov model (HMM)-based framework to capture silent and overt churn. We apply our modeling framework to two different contexts — a daily deals website and a performing arts organization. We find that, counter to previous studies that have not separated the two types of churn, overt churners in these hybrid settings tend to interact more, rather than less, with the firm prior to churning. That is, in settings where both types of churn are present, a high level of activity — as when customers actively opening the emails received from the firm — is not necessarily a good indicator of future engagement, but rather is associated with higher risk of overt churn. We also identify a large number of “silent churners” in both empirical applications — customers who disengage with the company very early on, rarely exhibit any type of activity, and almost never churn overtly. Furthermore, we show how the two types of churners respond very differently to the firm's communications, implying that a common retention strategy for proactive churn management is not appropriate in these hybrid settings.
Keywords: Churn, retention, attrition, customer relationship management, customer base analysis, hidden Markov models, latent variable models
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