Comparing with the traditional store, the online store can keep the track of customers’ purchasing records and personal information. By analyzing these customers’ records, online store can have a better understanding of their customers’ profile and purchasing behavior. In this paper, we define a standard product loyalty status, or SPLS, using customers’purchasing records to evaluate each customer’s loyalty to a certain product. SPLS is incorporated with loyal customers’ personal backgrounds as the input of cluster analysis that divides loyal customers into different groups. Loyal customers in the same groups have similar purchasing behavior and personal backgrounds. Similarity analysis measures the similarity of backgrounds between a non-loyal customer and groups of loyal customers in order to find this customer’s belonged group. Then, an expected SPLS value is assigned to this non-loyal customer to estimate his/her probability of purchasing a certain product. Customers who have expected SPLS value larger than a threshold are regarded as potential customers. Marketing specialists should recommend the product to potential customers. An experimental result shows that there are more than 50 percent of potential customers who actually purchase the product and become a“real” customer. Aprototype of our proposed model is used by a fast growing online retailer in Taiwan and is still in the experimental period.
淡江理工學刊=Tamkang Journal of Science and Engineering 13(2), pp.189-196