The Dynamic Predicting Model of Individual Consuming Behavior for Online Company

• Main Authors: Suu-Han Chen, 陳思翰
• Other Authors: Shu-Chuan Lo
• Format: Others
• Language: en_US
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/369zs6

碩士 === 國立臺北科技大學 === 工業工程與管理系所 === 94 === In this thesis, we would like to establish a particular procedure for monitoring the online consuming behavior. This mechanism associates exponential CUSUM scheme with mixture Bayesian hierarchical model. Mixture Bayesian hierarchical model is used for getting objective parameters of individual active/inactive behaviors and then were applied at the optimal design of exponential CUSUM scheme. The empirical analysis of interpurchase time behavior of customers has received significant attention in recent years. Besides, a simple recency-of-last-purchase rule outperforms other complex models. Recency variable is contributive to judge whether customers still active or not. Our goal is to predict degenerate alarms by monitor the two important consuming characteristics, one is historical message and another is the latest new. As time goes by, we can depict the latest consuming behavior information on chart instantly. If there are some exponential CUSUM scores exceed the specific limit, out-of-control signals sound. It could announce a probable time point when customers’ states transfer to relative inactive and provides a friendly graphical version which can monitor customers’ states immediately and persistently. Dynamical monitor and graphical version characteristics are distinct from past consuming behavior predicting techniques which are statical analysis and numerical report only. A real-world case study from a website employ on our specific procedure that demonstrates the effectiveness of this specific mechanism. The valid experiment shows about 96% detective power while a customer’s behavior real transfer from active to inactive of this mechanism.