Managing B2B customer churn, retention and profitability

Tamaddoni Jahromi, Ali, Stakhovych, Stanislav and Ewing, Michael 2014, Managing B2B customer churn, retention and profitability, Industrial Marketing Management, vol. 43, no. 7, pp. 1258-1268, doi: 10.1016/j.indmarman.2014.06.016.

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Title Managing B2B customer churn, retention and profitability
Author(s) Tamaddoni Jahromi, AliORCID iD for Tamaddoni Jahromi, Ali orcid.org/0000-0002-5943-3172
Stakhovych, Stanislav
Ewing, MichaelORCID iD for Ewing, Michael orcid.org/0000-0002-2260-2761
Journal name Industrial Marketing Management
Volume number 43
Issue number 7
Start page 1258
End page 1268
Total pages 11
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2014-10
ISSN 0019-8501
Keyword(s) B2B customer churn
Data mining
Non-contractual setting
Profit
Retention campaign
Summary It is now widely accepted that firms should direct more effort into retaining existing customers than to attracting new ones. To achieve this, customers likely to defect need to be identified so that they can be approached with tailored incentives or other bespoke retention offers. Such strategies call for predictive models capable of identifying customers with higher probabilities of defecting in the relatively near future. A review of the extant literature on customer churn models reveals that although several predictive models have been developed to model churn in B2C contexts, the B2B context in general, and non-contractual settings in particular, have received less attention in this regard. Therefore, to address these gaps, this study proposes a data-mining approach to model non-contractual customer churn in B2B contexts. Several modeling techniques are compared in terms of their ability to predict true churners. The best performing data-mining technique (boosting) is then applied to develop a profit maximizing retention campaign. Results confirm that the model driven approach to churn prediction and developing retention strategies outperforms commonly used managerial heuristics. © 2014 Elsevier Inc.
Language eng
DOI 10.1016/j.indmarman.2014.06.016
Field of Research 150504 Marketing Measurement
Socio Economic Objective 910403 Marketing
HERDC Research category C1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30067454

Document type: Journal Article
Collections: School of Management and Marketing
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