Research on the Influencing Factors of Precision Marketing in Commercial Banks from The Perspective of Data Mining

Abstract

With the rapid development of financial technology, modern data mining techniques are becoming increasingly widespread in the banking industry. Telephone marketing, which offers interactivity, measurability, extensive spatial coverage, and database support, is an effective way for banks to promote their financial products. This study focuses on how to use existing customer data to explore the potential promotion value of bank financial products while meeting customer needs and to develop a telephone marketing model that is suitable for the era of big data. We begin by analyzing the data of a Portuguese bank to predict whether customers will sign a long-term deposit agreement and identify which features have a significant impact on the response variable. Univariate analysis is used to identify these features, and then the logistic regression model is employed to confirm them. Finally, the random forest model is used to identify the variables that have the greatest impact on customer response. After identifying these key variables, we analyze the reasons and provide improvement measures and optimization strategies for the telephone marketing promotion of bank financial products.