Clustering Consumers and Cluster-Specific Behavioural Models
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @InCollection{deVries2019:chpt5,
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author = "Natalie Jane {de Vries} and Jamie Carlson and
Pablo Moscato",
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title = "Clustering Consumers and Cluster-Specific Behavioural
Models",
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booktitle = "Business and Consumer Analytics: New Ideas",
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publisher = "Springer International Publishing",
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year = "2019",
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editor = "Pablo Moscato and Natalie Jane {de Vries}",
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chapter = "5",
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pages = "235--267",
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keywords = "genetic algorithms, genetic programming, Brand,
Customer engagement, Engagement, Loyalty behaviour,
Online customer engagement, Customer engagement
prediction, Segmentation methodologies, Symbolic
regression analysis",
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isbn13 = "978-3-030-06222-4",
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DOI = "doi:10.1007/978-3-030-06222-4_5",
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abstract = "Social media has almost become ubiquitous in everyday
communications and interactions between customers and
Brand. A novel clustering algorithm, that has shown
high scalability in previous applications, is applied
to analyse and segment an online consumer behaviour
dataset. It is based on the computation of a
Minimum-Spanning-Tree and a k-Nearest Neighbour graph
(MST-kNN-kNN). Cluster-specific consumer behaviours
relating to CustomerengagementEngagementcustomer
engagement are predicted using Symbolic regression
analysissymbolic regression analysis which, in a
commercial setting, would provide the basis for
personalized marketing strategies. Five major clusters
were found in the dataset of 371 respondents who
answered questions from theoretical marketing
constructs related to online consumer behaviours. They
are labelled as follows: Brand Rationalists, Passive
Socializers, Immersers, Hedonic Sharers and Active
participatorActive Participators. For each of these
clusters, a linear model of Customerengagementcustomer
engagement was predicted using Symbolic regression
analysis symbolic regression analysis. These models
inform possible personalized marketing strategies after
proper segmentation of the customers based on their
online consumer behaviour, rather than simple
demographic characteristics.",
- }
Genetic Programming entries for
Natalie Jane de Vries
Jamie Carlson
Pablo Moscato
Citations