Indian Journal of Science and Technology
DOI: 10.17485/ijst/2015/v8i25/85359
Year: 2015, Volume: 8, Issue: 25, Pages: 1-7
Original Article
Miseon Lim1 , Hyunsoo Byun2 and Jinhwa Kim1*
1 School of Business, Sogang University, South Korea; [email protected]
2 Department of Public Management Information Systems, Korea National University of Transportation, South Korea
Understanding visitors’ invisible behaviors and responding with appropriate answers are important issues in continually increasing online market. To promote online transactions, customers’ behavior should be predicted correctly to keep low purchase conversion rate. In this study, we suggest an approach based on the idea that customers’ sessions in a web store can be transformed into the structure of a graph, which are represented as density of a session based on a graph theory. Online users visit lots of sites and their activities include information acquisition and browsing. The history of these activities can be used to construct a relationship network among web sites. This study analyzes this visit history made by website visitors with graph theory. The density of a network refers to the differentiated degrees of relationship among objects. In this study, we dichotomize into “purchase” and “no purchase group” since predicting whether a customer will buy or not buy our products is an important issue in web stores. We collect data on sessions which are a sequence of page views or a period of sustained web browsing. We model the sessions on the basis of density of a graph, which resulted in DOS (Density of a Session). The performance of other predictors including DOS is compared to that of suggested method in this study. Predictors are TVT (Total Visit Time during a period of a visit), AVT (The Average Time per Page Viewed), TNC (Total Number of Clicks), TPP (Total Number of Product-Related Pages Viewed), and DOS (Density of a Session Based on Graph Analysis). The study found that all predictors except total visit time are useful to differentiate between “purchase” and “no purchase” group. And we conducted Logit Analysis to examine the performance of each purchase prediction method. The results from Logit Analysis show that DOS predicts purchase behavior better in comparison with other predictors. It means understanding customers’ sessions with respect to a graph structure is useful to predict whether a customer will buy or not buy products in a web store.
Keywords: Click Stream Data, Predicting Customer Behavior, Web Usage Mining
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