About: Behavioural User Identification from Clickstream Data for Business Improvement

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #91697 Behavioural User Identification from Clickstream Data for Business Improvement
sameAs	Behavioural User Identification from Clickstream Data for Business Improvement
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http://eprints.org/ontology/hasDocument	Behavioural User Identification from Clickstream Data for Business Improvement (PDF) Behavioural User Identification from Clickstream Data for Business Improvement (Other) Behavioural User Identification from Clickstream Data for Business Improvement (Other) Behavioural User Identification from Clickstream Data for Business Improvement (Other) Behavioural User Identification from Clickstream Data for Business Improvement (Other) Behavioural User Identification from Clickstream Data for Business Improvement (Other)
dc:hasVersion	Behavioural User Identification from Clickstream Data for Business Improvement (PDF)
Title	Behavioural User Identification from Clickstream Data for Business Improvement
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
Date	2021-12-06
Creator	Fernando E.B. Otero Gaurav Misra Matteo Migliavacca
status	peer reviewed published
Publisher	Springer
abstract	One of the key elements for businesses to succeed is to get to know their customers. Traditionally this task has been performed through user studies, however, over the last few years clickstream analysis has been proposed as a potential way of conducting automated behavioural studies at scale. In this paper, we explore the use of a recently-proposed unsupervised data-mining technique to identify common behavioural patterns from a clickstream and use them to automatically group users into clusters. In particular, our goal is to validate the potential of behavioural user identification with respect to a key business-level objective. We consider to which extent it is possible to link overall user in-application behaviour to the completion of a particular business-relevant action. Identifying behavior patterns resulting in such business-relevant actions can enable businesses to make changes to their interface, target relevant user groups or trigger actionable insights, all with the objective of maximizing the likelihood of preferable user actions. We analyzed a realworld dataset from a mobile application deployed on both the iOS and Android platforms for this experiment.
Is Part Of	https://kar.kent.ac.uk/id/repository
Subject	Q335 Artificial intelligence
list of authors	https://kar.kent.ac.uk/id/eprint/91697#authors
presented at	Forty-first SGAI International Conference on Artificial Intelligence
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #91697 Behavioural User Identification from Clickstream Data for Business Improvement

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