An Analysis of Data Mining for Customers Attrition At Telecommunication Industry

Abstract

Attracting and retaining clients is the goal and aspiration of any organization. Nevertheless, the expense of attracting new customers remains considerably high than the one incurred to retain one. Furthermore, the absence of an exhaustive understanding of appropriate churn prevention approaches may possibly result to the loss of clients, which, in the long run, may eventually lead to the closure of the business. Consequently, corporations choose to employ a number of strategies, like data mining, to ensure reduction of churn rates. This study seeks to investigate the impact data mining plays in the prediction of client churn rates. Consequently, the dissertation assesses the role played by Artificial Inelegance (AI) technologies in the promotion of customer satisfaction and reduction of customer churning rates. The methodology of the study entailed quantitative research design with 50 participants (experts and employees) picked among Artificial Intelligence specialists and telecommunications companies workers. The selection of the participants involved a wide range of companies in the telecom industry. The study mainly targeted managers and employees that have skills in the applications of Al and data mining mining. An online survey was used. The main benefit of an online survey was that it enhanced convenience and allowed effective data collection. The main finding showed that data mining rely on the business data and has an automatic display of the definite structural process. A major benefit of such methods is the fact that these kinds of procedures are demonstrated in an objective manner. Accurate statistics regarding the needs of the consumers, their wishes, and varying wants are presented in an automatic manner guiding analytics for enhancement strategies. Consequently, a decision making guide is provided to the businesses on the basis of acknowledged facts to help in the reduction of business churning. data mining has another advantage of helping to study the business procedures across the whole institution on a large scale; however this is achieved with a human input that is low. Because of danger of companies losing clients, they should be armed with the greatest approach to predict and reduce customer churn hence avoiding future disasters. The management of customer churn entails the establishment of techniques to make sure that companies are able to maintain their lucrative clients in the long run. In this regard, this study mainly focusses on developing a perfect and effective predictive model meant for customer churn, particularly in pre- paid telecom division, through application of data mining. Cost incurred to attract a new client remains considerably higher than that incurred to retain one. Furthermore, it was distinguished among a number of participants that churn inhibition methods did not at all times emphasize on the whole portfolio of the client or had random selections of customers' sections. Consequently, the mitigation to such commercial challenges lies in conducting churn modeling with the use of well-organized predictive model. There was full and accurate analysis of processes on the basis of some unbiased data. Additionally, it calls for constant checking of organizational processes and makes sure that improvements are measured. Generally, this study proves that data mining is still key to customer retention as well as ensuring reduced consumer churning.