온라인 여행상품 고객이탈 분석 연구

Online Travel Product Customer Defect Analysis Study :Using data mining logistic regression

Data mining has been steadily developed with the development of information technology, and in particular, the development of database technologies such as data storage technology, artificial intelligence, and deep learning and big data analysis technology has provided a major factor in the development of data mining. This spread of data mining is being used not only in database-based marketing such as target marketing, customer segmentation, related analysis, and shopping cart analysis, but also in various industries such as credit rating, quality control, and fraud detection. As the application of data mining expands, the use of data mining as a tool to analyze customer information in customer relationship management (CRM), which manages interactions between companies and customers, is becoming more common. In particular, in terms of customer relationship management, the interest of all companies is 'customer departure', which is important for corporate management because the cost of securing new customers is much higher than the cost of maintaining existing customers. Therefore, this paper attempted to implement online travel customer churn prediction/classification modeling with logistic regression analysis, a data mining technique mainly used for customer CRM, based on travel agency online reservation data, and to present an efficient online travel customer marketing strategy through the results.