Analysis of customer satisfaction of HIMO Photo Studio based on Ordered Regresssion

Abstract

With the upgrading of consumption and the improvement of living standards, a group of new type photo studios represented by HIMO Photo Studio have arisen and developed dramatically and become the benchmark of the era in the portrait photography industry. In this paper, we apply LDA topic classification and TF-IDF feature extraction method to conduct text feature extraction and encoding vectorization on the comment data of Beijing HIMO Photo Studio. An ordered regression model with customer satisfaction as the responding variable is established, and the interaction effect is also introduced into model to study the influencing factors of customer satisfaction creatively and quantitatively so that we can give suggestions for improving service quality. The empirical results show that factors such as photo season, shop type, waiting time, photo works, service attitude, photo subject, photo category and so on have a significant impact on customer satisfaction and there are significant differences in customer satisfaction with the same type of photos in different shops. Combining the business goals of HIMO Photo Studio and the industry needs of today's new type photo studios, this paper puts forward targeted suggestions for HIMO Photo Studio to further improve its services according to the conclusions of the model.