Open AccessAnalysis of financial product purchases based on logistic regression
Author(s) -
Yuzhen Wang,
Jingqiao Qin
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1848/1/012164
Subject(s) - purchasing , logistic regression , database transaction , product (mathematics) , identification (biology) , order (exchange) , business , transaction data , marketing , regression analysis , customer relationship management , computer science , finance , database , mathematics , botany , geometry , machine learning , biology
In order to analyse the different characteristics of different users in the purchase of financial products and improve the accuracy of the identification of potential customers, this paper uses the logistic regression method to mine the data of customer historical transaction based on comparing the performance indexes of various classification methods. By analyzing the internal relationship between product consultants’ communication with customers, personal loans and deposits, and the number of on-site exchanges, we can find potential customers and make accurate predictions on customer purchasing behaviors to help wealth management companies better tap new customers.