With the advent of the Internet as the main channel for the supply of products and services in recent years, customers’ experience of online shopping has become vital. The purpose of this research was segmenting customer’s experience of online retail stores.
This study was an applied research in terms of purpose, and a descriptive-survey one in terms of method. The statistical population included all the people, who had an experience of buying from online retailers. The required sample size was calculated by using Cochran's formula for infinite communities. From among them, 384 people were selected through the available non-probability sampling method. The data collection tool was a standard questionnaire, the validity and reliability of which were confirmed by content validity and Cronbach's alpha coefficient, respectively. To analyze the data and segment the customers, the approach of Self-Organizing Maps (SOM) was taken based on Artificial Neural Networks (ANNs) by using ViscoverySOMine software.
According to the findings, the customers, who had an experience with online retailers, were divided into 3 segments with different demographic characteristics and components of online experience. The three categories of customers were apathetic, utilitarian, and visual customers. The first segment (apathetic customers) included those who were least affected by the components of the experience. The ability to communicate with the seller was most important to this group. The second group (utilitarian customers) consisted of the young and low-income women, who had the least online shopping in the short time. Benefit and trust were more important to the customers of this group. The young to middle-aged middle-income women constituted the third group (visual customers). These women often had online shopping for a long time. These people attached great importance to the product availability and paid more attention to store familiarity compared to other two clusters.
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