The Visual Narrative: Conveying Meaning Through Visual Content

Abstract

The human brain is naturally wired for dealing with visual information, processing it up to 600,000 times faster—often within a mere 100 milliseconds timeframe (Pieters and Wedel 2012)—and retaining it up to four times better than text (Vogel et al. 1986). Recently, images have become an integral component of online conversations, with projections indicating that up to 82% of Internet traffic will be coming from visual data by 2025 (Cisco 2019). The sheer volume and diversity of online visual content overwhelm digital consumers, leading to decreasing engagement rates for companies despite increasing digital marketing budgets (Statista 2023a). Nevertheless, brands predominantly design their visual content intuitively, relying on anecdotal evidence from previous marketing campaigns or drawing inspiration from their competitors’ social media profiles (Institute 2023). Learning how consumers process and respond to unstructured visual data could help organizations tailor their communications to the needs and wants of their target group. For consumers, understanding the impact of visual information can help make more informed decisions. Existing research on online content has primarily adopted a text-based perspective, focusing on unstructured textual data from sources like product reviews (e.g., Ludwig et al. 2013; Villarroel Ordenes et al. 2017; Ye et al. 2009), tweets (e.g., Liu et al. 2017b), social media posts (e.g., Wu et al. 2014), and online forums (e.g., Hennig-Thurau et al. 2004; Ludwig et al. 2014). Despite visual information being considered the richest, most engaging, and most pervasive among all content types (De Vries et al. 2012), it has received relatively little scholarly attention thus far, with a few notable exceptions (see Villarroel Ordenes and Zhang 2019 for a review). The existing studies on the topic demonstrate that certain design elements of digital images can effectively predict brand perceptions (Liu et al. 2020) and associations (Klostermann et al. 2018), as well as engagement levels (Hartmann et al. 2021; Overgoor et al. 2022), purchase intention (Hartmann et al. 2021), and product attitudes (Farace et al. 2020). The present dissertation extends the stream of marketing literature connecting visual perception and image analytics by addressing the following overarching research question, with the goal of better understanding how images shape brand–consumer interactions: RQ: Which elements within an image influence its perception and elaboration, and how?