Deakin University
Browse

File(s) under permanent embargo

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 Xu
Understanding 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 - 8

Publisher

IEEE

Location

Shenzhen, China

Place of publication

Piscataway, N.J.

Start date

2021-07-18

End date

2021-07-22

ISSN

2161-4393

eISSN

2161-4407

ISBN-13

9780738133669

Language

eng

Publication classification

E1 Full written paper - refereed

Editor/Contributor(s)

[Unknown]

Title of proceedings

IJCNN 2021 : Proceedings of the 2021 International Joint Conference on Neural Networks