Earticle

초록

영어

The availability of detailed data on customers’ online behaviors and advances in big data analysis techniques enable us to predict consumer behaviors. In the past, researchers have built purchase prediction models by analyzing clickstream data; however, these clickstream-based prediction models have had several limitations. In this study, we propose a new method for purchase prediction that combines information theory with machine learning techniques. Clickstreams from 5,000 panel members and data on their purchases of electronics, fashion, and cosmetics products were analyzed. Clickstreams were summarized using the ‘entropy’ concept from information theory, while ‘random forests’ method was applied to build prediction models. The results show that prediction accuracy of this new method ranges from 0.56 to 0.83, which is a significant improvement over values for clickstream-based prediction models presented in the past. The results indicate further that consumers’ information search behaviors differ significantly across product categories.

목차

ABSTRACT
 Ⅰ. Introduction
 Ⅱ. Consumers’ Purchase Decision-Making Processes
  2.1. Purchase Delay in Online Shopping
  2.2. Previous Studies on Purchase Prediction
  2.3. Shannon’s Information Theory
 Ⅲ. Prediction Techniques in Big Data Analysis
 Ⅳ. Empirical Analysis
  4.1. Dataset
  4.2. Analytical Procedure
 Ⅴ. Results
  5.1. Electronic Products
  5.2. Fashion Products
  5.3. Cosmetics
  5.4. Prediction Accuracy
 Ⅵ. Discussion
 Ⅶ. Implications, Limitations, and Future Research
 

저자

• Minsung Kim [ Manager, Big Data Solution Business Team, SK telecom, Korea ]
• Il Im [ Professor, Information systems at School of Business, Yonsei University, Korea ] Corresponding Author
• Sangman Han [ Professor, Marketing at SKK Business School, Sungkyunkwan University, Korea ]

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