Identify customer profiles: case study of Thai supermarkets

Today, the retail store industry faces intense competition as a result of an increase in the number of firms. This means that customers have more choices and are price-conscious. As a result, understanding customer behavior and how they make decisions is a critical strategy for business success. Therefore, the purposes of this study is to identify customers' profiles that lead to customer satisfaction through their behaviors.The researchers adjusted the 4 main factors, including demographics, brand awareness, logistics performance, and the 7 key elements of the service marketing mix, as the research’s predictors. Although 380 survey respondents provided data for the study, only 366 questionnaires were used for analysis. By knowing the customer's profile, K-mean Clustering was processed to segment the customer into groups that have similar characteristics. Then a decision tree is used to learn the customer profile. All the methods were applied RapidMiner software. Additionally, there are two clusters in Thai supermarkets in this data set: elderly with a high income and younger with a low income.