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Article: Double Mover-Stayer model on customer switching in telecommunications industry

TitleDouble Mover-Stayer model on customer switching in telecommunications industry
Authors
KeywordsMarkov chain
Mover-Stayer model
maximum likelihood estimation
brand switching
multiple Mover-Stayer model
operations research in telecommunications
Issue Date2012
Citation
Naval Research Logistics, 2012, v. 59, n. 8, p. 663-674 How to Cite?
AbstractCustomer acquisition and customer retention are the most important challenges in the increasingly competitive telecommunications industry. Traditional studies of customer switching always assume that customers are homogeneous, and thus that model customer switching behavior follows a Markov formulation. However, this postulation is obviously inappropriate in most instances. Blumen et al. (Cornell Studies of Industrial and Labor Relations, Cornell University Press, Ithaca, NY, 1955) developed the Mover-Stayer (MS) model, a generalization of the Markov chain model, to relax the requirement of homogeneity and allow the presence of heterogeneity with two different types of individualsâ"stayers," who purchase the same kinds of products or services throughout the entire observation period; and "movers," who look for variety in products or services over time. There are two purpose of this article. First, we extend the MS model to a Double Mover-Stayer (DMS) model by assuming the existence of three types of individuals in the market: (1) stable and loyal customers, who have stable usage within the same company; (2) instable but loyal customers, whose usage varies within the same company over time; and (3) disloyal customers, who switch from one company to another to seek for new experiences or/and benefits. We also propose an estimation method for the DMS model. Second, we apply the DMS model to telecommunications data and demonstrate how it can be used for pattern identification, hidden knowledge discovery, and decision making. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012 Copyright © 2012 Wiley Periodicals, Inc.
Persistent Identifierhttp://hdl.handle.net/10722/276938
ISSN
2021 Impact Factor: 1.806
2020 SCImago Journal Rankings: 0.665
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorNg, Michael K.-
dc.contributor.authorChung, Yuho-
dc.date.accessioned2019-09-18T08:35:06Z-
dc.date.available2019-09-18T08:35:06Z-
dc.date.issued2012-
dc.identifier.citationNaval Research Logistics, 2012, v. 59, n. 8, p. 663-674-
dc.identifier.issn0894-069X-
dc.identifier.urihttp://hdl.handle.net/10722/276938-
dc.description.abstractCustomer acquisition and customer retention are the most important challenges in the increasingly competitive telecommunications industry. Traditional studies of customer switching always assume that customers are homogeneous, and thus that model customer switching behavior follows a Markov formulation. However, this postulation is obviously inappropriate in most instances. Blumen et al. (Cornell Studies of Industrial and Labor Relations, Cornell University Press, Ithaca, NY, 1955) developed the Mover-Stayer (MS) model, a generalization of the Markov chain model, to relax the requirement of homogeneity and allow the presence of heterogeneity with two different types of individualsâ"stayers," who purchase the same kinds of products or services throughout the entire observation period; and "movers," who look for variety in products or services over time. There are two purpose of this article. First, we extend the MS model to a Double Mover-Stayer (DMS) model by assuming the existence of three types of individuals in the market: (1) stable and loyal customers, who have stable usage within the same company; (2) instable but loyal customers, whose usage varies within the same company over time; and (3) disloyal customers, who switch from one company to another to seek for new experiences or/and benefits. We also propose an estimation method for the DMS model. Second, we apply the DMS model to telecommunications data and demonstrate how it can be used for pattern identification, hidden knowledge discovery, and decision making. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012 Copyright © 2012 Wiley Periodicals, Inc.-
dc.languageeng-
dc.relation.ispartofNaval Research Logistics-
dc.subjectMarkov chain-
dc.subjectMover-Stayer model-
dc.subjectmaximum likelihood estimation-
dc.subjectbrand switching-
dc.subjectmultiple Mover-Stayer model-
dc.subjectoperations research in telecommunications-
dc.titleDouble Mover-Stayer model on customer switching in telecommunications industry-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/nav.21511-
dc.identifier.scopuseid_2-s2.0-84869090828-
dc.identifier.volume59-
dc.identifier.issue8-
dc.identifier.spage663-
dc.identifier.epage674-
dc.identifier.eissn1520-6750-
dc.identifier.isiWOS:000310985500007-
dc.identifier.issnl0894-069X-

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