Some Customers Would Rather Leave Without Saying Goodbye

Ascarza, E, Netzer, O and Hardie, B G S (2018) Some Customers Would Rather Leave Without Saying Goodbye. Marketing Science, 37 (1). pp. 54-77. ISSN 0732-2399 OPEN ACCESS

Abstract

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 for 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. In contrast to previous studies that have not separated the two types of churn, we find that 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 - such as customers actively opening emails received from the firm - is not necessarily a good indicator of future engagement; rather it 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.

More Details

[error in script]
Item Type: Article
Subject Areas: Marketing
Additional Information:

© 2018 INFORMS

Date Deposited: 21 Apr 2017 12:43
Date of first compliant deposit: 21 Apr 2017
Subjects: Marketing models
Customer relations
Last Modified: 16 Apr 2024 01:27
URI: https://lbsresearch.london.edu/id/eprint/815
[error in script] More

Export and Share


Download

Accepted Version - Text

Statistics

Altmetrics
View details on Dimensions' website

Downloads from LBS Research Online

View details

Actions (login required)

Edit Item Edit Item