A framework for quantitative analysis and differentiated marketing of tourism destination image based on visual content of photos

Photos shared by tourists are being generated at an unprecedented speed, creating new opportunities to study tourism destination images. Nevertheless, little research has focused on the tourist's perception of images from multiple perspectives and how to construct differentiated marketing strategies that link tourists with destinations. With the support of deep-learning technology, we propose herein a quantitative analysis strategy and differentiated marketing framework driven by photo big data which contains images of tourism destinations. We draw on photos of tourism destinations shared by tourists and analyse three aspects of perceived images - composition scene, visual aesthetic quality and visual uniqueness. We further develop a set of objective image projection schemes that integrate multiple indicators of tourism destinations to improve destination marketing. Finally, we focus on an empirical case study of Wuyuan, China. The results of the study have methodological, theoretical and practical implications for the tourism industry.

Automated summary

Photos shared by tourists are being generated rapidly, presenting new opportunities for studying tourism destination images. However, little research has focused on the perception of images from multiple perspectives by tourists and how to develop differentiated marketing strategies that connect tourists with destinations. In this study, we propose a quantitative analysis strategy and differentiated marketing framework driven by deep-learning technology and photo big data of tourism destinations.