Segmenting customers in online stores based on factors that affect the customer’s intention to purchase

This study has proposed an approach that enables online stores to offer customized marketing by segmenting their customers based on customers’ psychographic data. Online stores can concentrate on more profitable activities by identifying customers’ value as they segment their customers into a few groups of customers with similar intentions to purchase. To segment online customers, based on previous research that explains the behavior of online customers regarding purchasing, the approach has employed the factors that affect the customers’ intention to purchase on the Web. We integrated the clustering results of SOM (self-organized map) and the k-means algorithm into a single model. Online stores can develop promotional marketing and offer personalized service for e-customers, who are more valuable and more promising, according to the market segments presented by our approach.

► We segment online customers with customers’ psychographic data using clustering techniques. ► We use the factors that affect the customers’ intention to purchase on the online store. ► We predict the customer’s segment by k-nearest neighbor method. ► Our proposed method help online stores give personalizing service to customers.