Predicting customer churn using grey wolf optimization‐based support vector machine with principal component analysis

Abstract

Customer churn is a challenging problem that can lead to a loss of organizational assets. Organizations need to predict customer churn successfully in order to get rid of potential damages and gain a competitive advantage. The aim of this study is to provide a churn prediction model by including feature selection and optimization in classification. The study performs principal component analysis (PCA) to select the best features, support vector machine (SVM) to predict customer churn, and grey wolf optimization (GWO) to optimize the parameters of SVM. In other words, this study proposes a novel hybrid model called PCA‐GWO‐SVM to enhance the prediction ability in customer churn. A comparison experiment is carried out, evaluating the proposed model with the other classification algorithms. Experimental results show that the proposed PCA‐GWO‐SVM hybrid model produces higher accuracy, recall, and F1‐score than other machine learning algorithms such as logit, k‐nearest neighbors, naive Bayes, decision tree, and SVM.

Design and Optimisation of Research Performance Allocation Based on Principal Component Analysis

“…Principal component analysis helps us to better understand the structure and patterns of data by extracting key features from the data, while simplifying the data set and retaining as much information as possible. By interpreting and applying principal component analysis appropriately, we can deal with complex datasets more efficiently and discover underlying patterns and associations, providing strong support for further data analysis and decision-making [7]. This question belongs to the evaluation category, which involves more relevant elemental indicators, and principal component analysis is used to ensure objectivity.…”

Predictive and Analytics using Data Mining and Machine Learning for Customer Churn Prediction

“…Customer churn, or customer loss, refers to a situation where customers stop using a company's products or services and switch to a competitor or even stop using them completely. [3] [8]- [11]. The factors that influence customer churn can vary, and it is important for companies to understand these factors in order to address them and retain customers.…”

“…In the era of rapidly developing technology and increasing competition, organizations in various industries realize the importance of retaining existing customers. [1][2] [3]. Customer churn, which refers to the phenomenon of customers terminating a relationship with a business, has become a critical challenge that can significantly impact the profitability and long-term success of an enterprise.…”

“…To address the adverse effects of customer churn, organizations are increasingly relying on predictive and analytical techniques supported by data mining and machine learning. [3][4] [5]. The objective of this research is to explore the potential of data mining and machine learning techniques in predicting customer churn.…”