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Customer Churn Analysis Prediction Based on Cluster Analysis and Machine Learning Algorithms
- 1 School of Economics and Management, Minjiang University, Fujian, Fuzhou, 350100, China
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Abstract
This paper focuses on the impact of customer churn on a company or organization and how to predict it. Customer churn refers to the loss of a company or organization's original customers, which can have a serious impact on a company's or organization's long-term growth and profitability. Therefore, it is important for a company or organization to understand the causes of customer churn and how to predict it. This paper statistically analyzes the customer churn rate for each category and finds that the difference between the churn rate and the non-churn rate for category 2 is very significant. The results of these analyses can help companies or organizations to better understand customer churn and take appropriate measures to reduce it. To predict customer churn, this paper uses two common machine learning models - decision tree and random forest model for prediction. The results show that the prediction accuracy of decision tree is 99%, while logistic regression is 90%. This indicates that the decision tree model has better performance in predicting customer churn. However, this paper also points out that the prediction results of different models may be different, so when predicting customer churn, multiple models should be considered and analyzed in context. In conclusion, customer churn is a serious problem faced by companies or organizations. This paper introduces some useful analytical methods and prediction models that can help companies or organizations better understand customer churn and take appropriate measures to reduce customer churn, thus improving the long-term growth and profitability of the company or organization.
Keywords
Cluster analysis, Decision tree, Machine learning algorithms
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Cite this article
Jiang,Y. (2024). Customer Churn Analysis Prediction Based on Cluster Analysis and Machine Learning Algorithms. Advances in Economics, Management and Political Sciences,77,192-198.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 3rd International Conference on Business and Policy Studies
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