Abstract
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. Copyright © 2021 IEEE.
Original language | English |
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Title of host publication | Proceedings of 2021 International Joint Conference on Neural Networks, IJCNN |
Place of Publication | USA |
Publisher | IEEE |
ISBN (Electronic) | 9780738133669 |
DOIs | |
Publication status | Published - Jul 2021 |
Citation
Islam, M. R., Razzak, I., Wang, X., Tilocca, P., & Xu, G. (2021). UCBVis: Understanding customer behavior sequences with visual interactive system. In Proceedings of 2021 International Joint Conference on Neural Networks, IJCNN. IEEE. https://doi.org/10.1109/IJCNN52387.2021.9533354Keywords
- Customer behavior
- Data analytics
- Visualization
- Behavioral pattern
- Decision making