Proceedings of the 2023 3rd International Conference on Business Administration and Data Science (BADS 2023)

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Predictive Analysis of Customer Churn in Community-Supported Agriculture Based on RFM Modeling

Authors
Xiaoying Yan1, *, Mei Yin1, Renren Li1
1Software Engineering Institute of Guangzhou, Guangzhou, 510300, Guangdong Province, China
*Corresponding author. Email: 93847153@qq.com
Corresponding Author
Xiaoying Yan
Available Online 30 December 2023.
DOI
10.2991/978-94-6463-326-9_39How to use a DOI?
Keywords
RFM model; community-supported agriculture; customer churn; early warning models
Abstract

High customer turnover has been a significant challenge in China's community-supported agriculture (CSA) industry. Establishing a customer churn prediction and intervention management mechanism based on consumption data analysis is of great significance for the sustainable and healthy operation of many Chinese CSA family farms. In this paper, we utilize RFM models (Recency, Frequency, and Monetary) and algorithms to rank and classify the consumption ability of CSA customers on a regular basis. This is done by analyzing their recent purchase time, number of times of consumption, and consumption data. In order to determine the reasons behind CSA customer loss and intervene early, it is important to continuously enhance the knowledge and level of intelligent management in the CSA industry. This will effectively support the healthy and stable development of the community-supported agriculture industry.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 3rd International Conference on Business Administration and Data Science (BADS 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
30 December 2023
ISBN
978-94-6463-326-9
ISSN
2589-4900
DOI
10.2991/978-94-6463-326-9_39How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Xiaoying Yan
AU  - Mei Yin
AU  - Renren Li
PY  - 2023
DA  - 2023/12/30
TI  - Predictive Analysis of Customer Churn in Community-Supported Agriculture Based on RFM Modeling
BT  - Proceedings of the 2023 3rd International Conference on Business Administration and Data Science (BADS 2023)
PB  - Atlantis Press
SP  - 380
EP  - 387
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-326-9_39
DO  - 10.2991/978-94-6463-326-9_39
ID  - Yan2023
ER  -