A Machine Learning Approach to Predict Customer Churn of a Delivery Platform

被引：1

作者：

Liu, Qing ^{[1
]}

Chen, QiuYing ^{[1
]}

Lee, Sang-Joon ^{[2
]}

机构：

[1] Chonnam Natl Univ, Interdisciplinary Program Digital Future Converge, Gwangju, South Korea

[2] Chonnam Natl Univ, Sch Business Adm, Gwangju, South Korea

来源：

2023 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE IN INFORMATION AND COMMUNICATION, ICAIIC | 2023年

关键词：

machine learning; customer churn; delivery platform; prediction;

D O I：

10.1109/ICAIIC57133.2023.10067108

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The use of delivery platforms has become widespread due to the impact of the Covid-19 and the O2O industry. However, the ELEME delivery platform, a subsidiary of Alibaba Group, which represents China, has recently been losing market share. This means that companies need to constantly look at strategies to attract new customers and maintain existing ones. In general, it costs at least five times more to attract new customers than it does to manage existing customers. This paper attempts to predict customer churn using the ELEME customer dataset to develop strategies to identify and prevent churn in advance. The results of the analysis using machine learning approach found that the most influential feature that can predict churn is the number of clicks made by the user. This paper presents the process and explanation of applying various algorithms for predicting customer churn on a distribution platform. It also proposes strategies for dealing with customer churn.

引用

页码：733 / 735

页数：3

共 11 条

[1] A Survey on Churn Analysis in Various Business Domains
Ahn, Jaehyun
Hwang, Junsik
Kim, Doyoung
Choi, Hyukgeun
Kang, Shinjin
[J]. IEEE ACCESS, 2020, 8 (08): : 220816 - 220839
[2] Customer churn prediction in the telecommunication sector using a rough set approach
Amin, Adnan
Anwar, Sajid
Adnan, Awais
Nawaz, Muhammad
Alawfi, Khalid
Hussain, Amir
Huang, Kaizhu
[J]. NEUROCOMPUTING, 2017, 237 : 242 - 254
[3] Kelleher J. D., 2020, Fundamentals of Machine Learning for Predictive Data Analytics, Vsecond
[4] Machine learning applications in cancer prognosis and prediction
Kourou, Konstantina
Exarchos, Themis P.
Exarchos, Konstantinos P.
Karamouzis, Michalis V.
Fotiadis, Dimitrios I.
[J]. COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 2015, 13 : 8 - 17
[5] Combined rough set theory and flow network graph to predict customer churn in credit card accounts
Lin, Chiun-Sin
Tzeng, Gwo-Hshiung
Chin, Yang-Chieh
[J]. EXPERT SYSTEMS WITH APPLICATIONS, 2011, 38 (01) : 8 - 15
[6] Moon S., 2016, INFORM COMMUNICATION, V33, P49
[7] Customer relationship management research (1992-2002) An academic literature review and classification
Ngai, E.
[J]. MARKETING INTELLIGENCE & PLANNING, 2005, 23 (06) : 582 - +
[8] Artificial intelligence, machine learning and health systems
Panch, Trishan
Szolovits, Peter
Atun, Rifat
[J]. JOURNAL OF GLOBAL HEALTH, 2018, 8 (02)
[9] Raeisi Shamim, 2020, Proceedings of the 10th International Conference on Computer and Knowledge Engineering (ICCKE 2020), P055, DOI 10.1109/ICCKE50421.2020.9303661
[10] A comparison of machine learning techniques for customer churn prediction
Vafeiadis, T.
Diamantaras, K. I.
Sarigiannidis, G.
Chatzisavvas, K. Ch.
[J]. SIMULATION MODELLING PRACTICE AND THEORY, 2015, 55 : 1 - 9

← 1 2 →