# A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation
## 9 Conclusions  
Telecom customer churn is a central issue for telecom companies, since it decreases profits [1]. Furthermore, preventing customer churn is imperative. As the global telecom industry is becoming more saturated and companies are increasingly struggling to retain customers [41]. Currently, most companies invest heavily in marketing to attract new customers. However, keeping existing customers is cheaper than acquiring new customers [2]. Thus, it is becoming more critical and a significant concern for telecommunication companies to prevent customer churn. This study inventively builds a discriminant model and a logistic regression model to predict telecom client churn using customer segmentation data from three major telecommunication Chinese companies. Moreover, the results of this study will give telecom managers the ability to predict customer behavior and loss accurately and to optimize their strategies to improve customer retention rates. Meanwhile, the findings will help companies reduce costs and optimize their budgets. Furthermore, for telecom managers, it will be possible to improve customer targeting through the results of this paper and to increase the profits of telecom companies. There is very little knowledge about how telecom customers' opinions regarding the services provided by their telecom company impact customer churn. We aimed to cover this research gap using a Fisher discriminant analysis and a logistic regression analysis of telecom customer churn related to diverse factors. Moreover, the discriminant function and logistic regression analysis are proven to predict telecom customer churn [42]. In this study, through a Wilks' lambda discriminant test, we be concluded that the discriminant equation is valid and can explain the reasons for churn. Furthermore, through the accuracy test, the logistic regression equation was also proven to be valid and can explain the reasons for churn. [PERSON] et al. [43] highlighted that previous telecom customer churn studies have mainly applied factor analysis, cluster analysis, and other methods, while telecom customer churn studies conducted using Fisher discriminant analysis and logistic regression analysis remains scarce, even in top journals. This new investigation should solve this problem. According to the results of this paper, the recommendations are for telecom companies to decrease their monthly fixed costs and local costs to increase the possibility of retaining their telecom customers. Additionally, the managers of telecom companies have already realized the value and importance of improving the service quality of the Internet, fixed-line, and CRBT products, as well as the call time for long-distant calls and the numbers of SMS and MMS in the telecom package, which has previously been proven to have a positive influence on telecom customers retention. Research Limitations and Future DirectionsThe dataset includes the information for 4126 clients from 2007 to 2018. However, it has been nearly four years since then. Because of the COVID-19 pandemic, the telecom market and customer consumption habits may be significantly different from before. Therefore, more current data will be gathered to further improve the model's accuracy and move the model more in line with the current market situation. Furthermore, the model can be further improved using the repeated data testing approach. Moreover, data were collected from three operators. Data from other operators may increase the reliability of the model. Finally, additional variables could be applied to improve its predictability. **Author Contributions:** Conceptualization, T.Z. ; methodology, T.Z. ; software, T.Z. ; validation, T.Z., S.M. and R.F.R. ; formal analysis, T.Z., S.M. and R.F.R. ; investigation, T.Z., S.M. and R.F.R. ; resources, T.Z. ; data curation, T.Z. ; writing--original draft preparation, T.Z. ; writing--review and editing, S.M. and R.F.R. ; visualization, T.Z., S.M., and R.F.R. ; supervision, S.M. and R.F.R. ; project administration, S.M. and R.F.R. ; funding acquisition, S.M.