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UCBVis: Understanding Customer Behavior Sequences with Visual Interactive System
conference contribution
posted on 2021-01-01, 00:00 authored by M R Islam, Imran RazzakImran Razzak, X Wang, P Tilocca, G XuUnderstanding customer behaviour (UCB) sequences with the multi-dimensional and temporal of the data is necessary for any competitive and global business aiming to provide interesting insights and to improve business strategies. While existing researchers have applied various data analytics approaches to understand and analyze behaviors of customer, they often failed to allow the analysts including business management, product marketing and development, and decision making, etc. Achieving these goals in collaboration with domain experts, we conducted a design study contributes to address a known problem with a novel solution and to provide data-driven visual decision support in collective policy data. We determine core study demands and then use a Visual Interactive System for Understanding Customer Behavior, named UCBVis that enables decision makers to gain detail insights into customer activities. In this study, we present customer behaviour pattern of multidimensional relationship through the visualisation system based on interweaving the pattern mining and querying with a designed encoding scheme. We use a large number of customer claim records and present visual outcomes to facilitate the exploration of customer behavior. Furthermore, we provide a concise set of insights and challenges associated with the use of UCBVis in the life insurance industry. We show the robustness of UCBVis through a user study with five participants shows that UCBVis is perceived to be more useful and provides actionable insights.
History
Event
Neural networks. International joint conference (2021 : Shenzhen, China)Pagination
1 - 8Publisher
IEEELocation
Shenzhen, ChinaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2021-07-18End date
2021-07-22ISSN
2161-4393eISSN
2161-4407ISBN-13
9780738133669Language
engPublication classification
E1 Full written paper - refereedEditor/Contributor(s)
[Unknown]Title of proceedings
IJCNN 2021 : Proceedings of the 2021 International Joint Conference on Neural NetworksUsage metrics
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