The Application of Topological Structure of Interpersonal Relationships to Optimize Customer Segmentation

DOI

10.2991/aebmr.k.210319.011How to use a DOI?

Keywords

Topology, customer segmentation, interpersonal relationship, potential customer, business analytics

Abstract

Customer segmentation is based on a large amount of customer data, but for many new users, their customer data is zero. The similarity of the characteristics of the interpersonal relationship group can make up the shortcomings of the existing customer segmentation method, so this topic is studied. This article improves the accuracy and coverage of customer segmentation in the current main customer segmentation methods. Through the combination with topological structure, we divided customer groups by using interpersonal relationship. The method not only provided a more accurate customer segmentation method, but also established a database for potential customers to ensure that potential customers receive the best service from the first time of shopping. What’s more, the combination of existing customer segmentation method and topology structure provides a more comprehensive and accurate information foundation for enterprises to study customer data. For the unoptimized vulnerability in this method, that is, how to determine the percentage of selection of primary user, further research will be carried out in the future. This improvement has vital significance for companies to expand their customer base and increase sales and profit.

Copyright

© 2021, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Haotian Liu
PY  - 2021
DA  - 2021/03/22
TI  - The Application of Topological Structure of Interpersonal Relationships to Optimize Customer Segmentation
BT  - Proceedings of the 6th International Conference on Financial Innovation and Economic Development (ICFIED 2021)
PB  - Atlantis Press
SP  - 57
EP  - 61
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.210319.011
DO  - 10.2991/aebmr.k.210319.011
ID  - Liu2021
ER  -