Capturing the evolution of customer–firm relationships: how customers become more (or less) valuable over time
Capturing the evolution of customer–firm relationships: how customers become more (or less) valuable over time
Few studies have examined the influence of marketing activities while accounting for customer dynamics over time. The authors contribute to this growing literature by extending the hurdle model to capture customer dynamics using a hidden Markov chain. We find our dynamic model performs better than static and latent class models. Our results suggest the customer base can be segmented into four segments: Deal-prone, Dependable, Active, and Event-driven. Each segment reacts differentially to marketing activities. Although catalogs influence both purchase incidence and the number of orders, this marketing activity has the largest impact on purchase incidence across all four segments. In contrast, retail promotions are more likely to influence the number of orders a customer will make for all of the segments except for the Deal-prone segment. For this segment, retail promotions have the strongest impact on purchase incidence.
Mark, Tanya
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Lemon, Katherine
89064eb7-d7e8-43e5-96e9-02d7ce8a10a2
Vandenbosch, Mark
642817e8-57dc-44ef-96d2-1f800d8a06dc
Bulla, Jan
4d80e568-b212-49e9-b0c9-a6f21430224e
Maruotti, Antonello
7096256c-fa1b-4cc1-9ca4-1a60cc3ee12e
16 May 2013
Mark, Tanya
61502394-d2f0-4082-b185-b933e619939a
Lemon, Katherine
89064eb7-d7e8-43e5-96e9-02d7ce8a10a2
Vandenbosch, Mark
642817e8-57dc-44ef-96d2-1f800d8a06dc
Bulla, Jan
4d80e568-b212-49e9-b0c9-a6f21430224e
Maruotti, Antonello
7096256c-fa1b-4cc1-9ca4-1a60cc3ee12e
Mark, Tanya, Lemon, Katherine, Vandenbosch, Mark, Bulla, Jan and Maruotti, Antonello
(2013)
Capturing the evolution of customer–firm relationships: how customers become more (or less) valuable over time.
Journal of Retailing.
(doi:10.1016/j.jretai.2013.04.001).
Abstract
Few studies have examined the influence of marketing activities while accounting for customer dynamics over time. The authors contribute to this growing literature by extending the hurdle model to capture customer dynamics using a hidden Markov chain. We find our dynamic model performs better than static and latent class models. Our results suggest the customer base can be segmented into four segments: Deal-prone, Dependable, Active, and Event-driven. Each segment reacts differentially to marketing activities. Although catalogs influence both purchase incidence and the number of orders, this marketing activity has the largest impact on purchase incidence across all four segments. In contrast, retail promotions are more likely to influence the number of orders a customer will make for all of the segments except for the Deal-prone segment. For this segment, retail promotions have the strongest impact on purchase incidence.
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Published date: 16 May 2013
Organisations:
Statistics, Statistical Sciences Research Institute
Identifiers
Local EPrints ID: 352692
URI: http://eprints.soton.ac.uk/id/eprint/352692
ISSN: 0022-4359
PURE UUID: 55fa95a3-4851-4252-9a35-da278c63292e
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Date deposited: 20 May 2013 11:50
Last modified: 14 Mar 2024 13:55
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Contributors
Author:
Tanya Mark
Author:
Katherine Lemon
Author:
Mark Vandenbosch
Author:
Jan Bulla
Author:
Antonello Maruotti
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