TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
The Impact of Transforming Unstructured Data into Structured Data on a Churn Prediction Model for Loan Customers |
| 영문제목(English Title) |
The Impact of Transforming Unstructured Data into Structured Data on a Churn Prediction Model for Loan Customers |
| 저자(Author) |
Hoon Jung
Bong Gyou Lee
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| 원문수록처(Citation) |
VOL 14 NO. 12 PP. 4706 ~ 4724 (2020. 12) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
With various structured data, such as the company size, loan balance, and savings accounts, the voice of customer (VOC), which is text data containing contact history and counseling details was analyzed in this study. To analyze unstructured data, the term frequency–inverse document frequency (TF-IDF) analysis, semantic network analysis, sentiment analysis, and a convolutional neural network (CNN) were implemented. A performance comparison of the models revealed that the predictive model using the CNN provided the best performance with regard to predictive power, followed by the model using the TF-IDF, and then the model using semantic network analysis. In particular, a character-level CNN and a wordlevel CNN were developed separately, and the character-level CNN exhibited better performance, according to an analysis for the Korean language. Moreover, a systematic selection model for optimal text mining techniques was proposed, suggesting which analytical technique is appropriate for analyzing text data depending on the context. This study also provides evidence that the results of previous studies, indicating that individual customers leave when their loyalty and switching cost are low, are also applicable to corporate customers and suggests that VOC data indicating customers’ needs are very effective for predicting their behavior.
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| 키워드(Keyword) |
Churn Prediction Model
Text Mining
Unstructured Data
Voice of Customer
Convolutional Neural Network
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| 파일첨부 |
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