Design of Customer Churn Early Warning System Based on Mobile Communication Technology Based on Data Mining

Abstract

Customer churn is a fundamental problem faced by enterprises and an important factor affecting the operation of enterprises. Due to current market conditions and changing consumer behavior, it analyzes potential customer behavior trends by mining customer behavior data. This allows companies to set targets for looming market changes so that market movements can be predetermined. The rapid development of modern mobile communication technology makes the way of life need more new ways to adapt to the development of the new era. At the same time, with the rapid development of mobile communication technology, information management systems have been widely used. If a large amount of data can support decision-making information through data mining technology, it can drive the process of enterprise decision-making. It conducts purposeful and differentiated retention efforts on these customers. It increases the success rate of high-value customer retention, reduces the likelihood of customer churn, and reduces maintenance costs. It does this to achieve preset goals and minimize losses due to customer exit. This paper proposes and establishes a customer churn early warning system based on data mining. It uses this to find the customer trends behind a large amount of customer data. It uses the decision tree algorithm to participate in the decision-making process of the enterprise with this algorithm model. The RFT model proposed in the experiment and its results show that customer value is a key factor in the decision-making process of a firm. The accuracy rate is about 6% higher than that of the control group using the logistic regression model directly.