Prediction of Customer Churn based on Social Behavior Analysis and Topic Model with Big Data

• Main Authors: Yung-Lin Fu, 伏泳霖
• Other Authors: Chin-Hui Lai
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/z3cqwe

碩士 === 中原大學 === 資訊管理研究所 === 106 === Because the convenience of the network, so that customers in the online shopping conversion costs are lower. For enterprises, how to use limited resources to prediction of customer churn and customer retention, is a very important issue. In the past research, customer churn was mainly predicted by customer value. Today''s online shopping platform provides a platform for customers to write reviews and social. Therefore, the past customer churn prediction method, because the different data characteristics, has gradually become not applicable. Customers share and exchange information about purchase, including product reviews, ratings as a consideration for purchasing decisions and influence the possibility of continued consumption in the future. Such decisions may be influenced by the opinions of their friends who have a relationship. Purchase records and product reviews will be with the accumulation of time, so that enterprises can analyze the amount of data gradually increased. Therefore, this project proposes a prediction of customer churn model based on social behavior analysis and topic model with big data. It considers the social behavior of customers on the Internet and the information implied in the reviews written by the customers. And through the topic model of the building, customers often express the words can be topic classified, and in order to build the customer’s own preferences, and to Hadoop platform for the experimental infrastructure, saving a large amount of data required to calculated the amount of time. The experimental results show that our prediction of customer churn model has better prediction results compared with the other customer churn prediction method, and the execution efficiency on the Hadoop platform is obviously higher than that on the general computer implementation efficiency.