Abstract
The traditional customer segmentation model is based on the value of the customer's consumed data, and the customer's consumption habit is obtained to predict its potential consumption value, and then the marketing strategy and customer retention strategy are determined. According to the characteristics of e-commerce enterprises and the recordability of historical network behavior of e-commerce customers, this paper constructs the AFCS customer segmentation model based on the traditional customer segmentation model customer value matrix which represents the existing value of e-commerce customers, added two potential value factors representing e-commerce customers, one is the total number of clicks of users who represent the activity of e-commerce customers, the other is the total number of user collections and shopping carts representing the potential purchase intention of users. Then the AFCS model is tested with K-Means, SOM and SOM + K-Means, the experimental results prove that the AFCS model based on the SOM + K-Means algorithm is superior to the AFCS model using the SOM or K-Means algorithm alone, and its customer segmentation results are more accurate, which can provide reference for effective customer retention strategies and targeted marketing for e-commerce.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhou, H.: Research on the application of e-commerce precision marketing based on big data technology. Econ. Manage. Abs 11 (2021)
Hu, H.: Customer value analysis of enterprise marketing platform based on RFM model improvement. Marketing 08 (2022)
Wang, K.F.: Research on application of AFH customer classification based on data mining technology. Tech. Econ. Manage. Res. 11 (2012)
Sun, J.: Research on Improvement and Application of Customer Lifetime Value Model Based on Complaint Behavior, vol. 4. South China University of Technology (2018)
Gu,Y.R., Chen, Y.Z.: Research on commodity review based on SOM-K-means algorithm. Software Guide 12 (2021)
Feng, M.: Research on e-commerce daily sales forecast based on data mining. Comm. Circul. 12 (2021)
Zhu, M.: Data Mining. China University of Science and Technology University Press (2002)
Shao, F., Yu, Z.: Data Mining Principles and Algorithms. China Water and Power Press (2003)
Fu. L.M.: Video recommendation system based on K-means optimized SOM neural network algorithm. Softw. Eng. 10 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, J., Li, J. (2023). Research on E-commerce Customer Value Segmentation Model Based on Network Behavior. In: Barolli, L. (eds) Advances in Internet, Data & Web Technologies. EIDWT 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 161. Springer, Cham. https://doi.org/10.1007/978-3-031-26281-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-26281-4_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-26280-7
Online ISBN: 978-3-031-26281-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)