IS-DT: A New Feature Selection Method for Determining the Important Features in Programmatic Buying

Abstract

Traditional data-driven feature selection techniques for extracting important attributes are often based on the assumption of maximizing the overall classification accuracy. However, the selected attributes are not always meaningful for practical problems. So, we need additional confirmation from the experts in the domain knowledge to determine whether these extracted features are meaningful knowledge. Moreover, due to advances in mobile devices and wireless environments, programmatic buying (PB) has become one of the critical consumer behaviors in e-commerce. However, it is extremely difficult for PB service providers to build customers’ loyalty, since PB customers require a high level of service quality and can quickly shift the purchases from one website to another. Previous studies developed various dimensions/models to measure the service quality of PB; nevertheless, they did not identify the key factors for increasing customers’ loyalty and satisfaction. Consequently, this study used an importance–satisfaction (IS) model as domain knowledge and proposed a new IS-DT feature selection method. This new IS-DT method combined the IS model and the decision tree (DT) algorithm to extract useful service quality factors for enhancing customer satisfaction and loyalty in PB. An actual case was also provided to illustrate the effectiveness of our proposed method. The results showed that for increasing customer satisfaction, the highest impact factors included “problem solving”, “punctuality”, “valence”, and “ease of use”; for building customer loyalty, the most important factors were “expertise”, “problem solving”, “information”, “single column”, “voice guidance”, “QR code”, “situation”, “tangibles”, “assurance”, “entertainment”, and “safety”. Our IS-DT method can effectively determine important service quality factors in programmatic buying.