An anticipation model of potential customers’ purchasing behavior based on clustering analysis and association rules analysis

This paper proposes an anticipation model of potential customers’ purchasing behavior. This model is inferred from past purchasing behavior of loyal customers and the web server log files of loyal and potential customers by means of clustering analysis and association rules analysis. Clustering analysis collects key characteristics of loyal customers’ personal information; these are used to locate other potential customers. Association rules analysis extracts knowledge of loyal customers’ purchasing behavior, which is used to detect potential customers’ near-future interest in a star product. Despite using offline analysis to filter out potential customers based on loyal customers’ personal information and generate rules of loyal customers’ click streams based on loyal customers’ web log data, an online analysis which observes potential customers’ web logs and compares it with loyal customers’ click stream rules can more readily target potential customers who may be interested in the star products in the near future.