In today's online environments, such as social media platforms and e-commerce websites, consumers are overloaded with information and firms are competing for their attention. Most of the data on these platforms comes in the form of text, images, or other unstructured data sources. It is important to understand which information on company websites and social media platforms are enticing and/or likeable by consumers. The impact of online visual content, in particular, remains largely unknown. Finding the drivers behind likes and clicks can help (1) understand how consumers interact with the information that is presented to them and (2) leverage this knowledge to improve marketing content. The main goal of this dissertation is to learn more about why consumers like and click on visual content online. To reach this goal visual analytics are used for automatic extraction of relevant information from visual content. This information can then be related, at scale, to consumer and their decisions.
The results of four empirical studies are presented. The first empirical chapter highlights the managerial importance of visual analytics and AI. In addition, it provides the reader with the definitions and problem understanding necessary to appreciate the methods and tools presented in the rest of this dissertation. The next chapter consists of a theory-driven investigation of the relationship between the visual complexity of firm-generated imagery and consumer liking on social media. The third chapter utilizes a data-driven exploration approach to study the impact of product images on consumers decisions on e-commerce websites. The final empirical chapter serves as a look into the future of visual analytics for marketing.