Customer churn data analysis using data mining

Xingyuan Jiang

doi:10.54254/2755-2721/77/20240496

Research Article

Open access

Published on 16 July 2024

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Jiang,X. (2024). Customer churn data analysis using data mining. Applied and Computational Engineering,77,17-24.

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Customer churn data analysis using data mining

Xingyuan Jiang *^,1,

¹ Wuhan Britain-China School

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/77/20240496

Abstract

Churn is the phenomenon of a customer terminating their relationship or subscription with a company or service provider[1]. It represents the rate at which customers stop using a company's product or service during a specific period of time. Attrition rate is an important metric for businesses as it directly impacts revenue, growth and customer retention. In the context of the churn data set, the churn label indicates whether a customer has been churn. Lost customers are those who decide to stop buying the company's products. On the other hand, non-churn customers are those who continue to buy the company's products. Understanding customer churn is critical for businesses to identify the patterns, factors, and metrics that lead to customer churn. By analyzing churn behavior and its associated characteristics, companies can develop strategies to retain existing customers, improve customer satisfaction, and reduce customer churn. Predictive modeling techniques can also be applied to anticipate and proactively address potential customer churn, enabling companies to take proactive steps to retain at-risk customers[2].

Keywords

Data mining, Neural network, Classification, Contrastive analysis, Consumer churn

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References

[1]. Tang, Longhui. Customer Churn Warning Model Based on Data Mining Technology. 2013, https://doi.org/[1]10.3969%20/%20j.i%20SSN.%202095-6835.2011.05.070.

[2]. Muhammad shahid, azeem. “Customer Churn Dataset.” Www.kaggle.com, 2023, www.kaggle.com/datasets/muhammadshahidazeem/customer-churn-dataset.

[3]. Renjith, Shini. “An Integrated Framework to Recommend Personalized Retention Actions to Control B2C E-Commerce Customer Churn.” International Journal of Engineering Trends and Technology, vol. 27, no. 3, 25 Sept. 2015, pp. 152–157, https://doi.org/10.14445/22315381/ijett-v27p227.

[4]. Equihua, Juan Pablo, et al. “Modelling Customer Churn for the Retail Industry in a Deep Learning Based Sequential Framework.” ArXiv.org, 2 Apr. 2023, arxiv.org/abs/2304.00575. Accessed 8 Mar. 2024.

[5]. Yang, Poh Yee, et al. “EXPLORING CONSUMER BEHAVIOUR PATTERNS and MODELLING CHURN PREDICTION in the FOOD DELIVERY SERVICE INDUSTRY: A CASESTUDY.” MyBib,Sept.2023,www.researchgate.net/publication/374614083_EXPLORING_CONSUMER_BEHAVIOUR_PATTERNS_AND_MODELLING_CHURN_PREDICTION_IN_THE_FOOD_DELIVERY_SERVICE_INDUSTRY_A_CASE_STUDY.a b

Cite this article

Jiang,X. (2024). Customer churn data analysis using data mining. Applied and Computational Engineering,77,17-24.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Software Engineering and Machine Learning

Conference website: https://www.confseml.org/

ISBN：978-1-83558-513-9(Print) / 978-1-83558-514-6(Online)

Conference date: 15 May 2024

Editor：Stavros Shiaeles

Series: Applied and Computational Engineering

Volume number: Vol.77

ISSN：2755-2721(Print) / 2755-273X(Online)

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