Predicting Customer Churn Using Data Analytics: A Mixed-Methods Approach for Improving Retention Strategies

Description

Predicting Customer Churn Using Data Analytics: A Mixed-Methods Approach for Improving Retention Strategies

Abstract

The major issue in the organizations is customer churn because it negatively impacts their market share and revenue. Moreover, various factors involving issues in the pricing, and poor services to customers lead to customer churn. However, understanding the customers’ problems and implementing actions is helpful to mitigate the churn problem. To overcome the problems that are associated with churn rate needs to be predicted. Additionally, the research aims to predict customer churn using mixed methodology and data analytics to improve retention strategies.

Furthermore, qualitative and quantitative analysis are the crucial aspects. These analyses take place as part of research to understand data analytics’ role in retention plans and find churn rates. Whereas quantitative analysis is on consumer data that involves approaching patterns and buying history. It provides information regarding the customers who are ready to leave the company and reasons for churn. In addition, analysis delivers strategies to develop the predictive models that are necessary to find consumer churn. To evaluate these models’ accuracy, statistical techniques involving machine learning algorithms and logistic regression will be used.

Moreover, qualitative analysis is on arranging interviews with customers and market professionals. In the interview, the major discussion relates to the customer’s perceptions and experiences. Market professionals explain the usage of data analytics and their importance in churn prediction. Additionally, they discuss the cruciality of outcomes of data analytics in designing and implementing retention strategies. Furthermore, the mixed method combines two analysis results and delivers the knowledge of the prediction model validation. In addition, the finding provides the factors that help in retention strategy and data analytics importance in the prediction. Overall, such insights are useful for firms to create strategies that involve data analytics to retain customers. Finally, it helps to enhance consumer loyalty, sustain the competition, and increase business growth.

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