Visual Listening In: Extracting Brand Image Portrayed on Social Media

Liu, Liu; Dzyabura, Daria; Mizik, Natalie

doi:10.1287/mksc.2020.1226

Cited by 153 publications

(54 citation statements)

References 28 publications

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“…Until recently, methods for automated unstructured data analysis have been limited. While we have seen increased interest in automated analyses of textual data (Berger et al 2020), the development of methods for visual (e.g., Liu, Dzyabura, and Mizik 2020), audio, and video data (e.g., Liu et al 2018) lags. Moreover, the analysis of unstructured data must be conducted over time and linked to customer behaviors to assess the relationship between customer–firm interactions and retention.…”

Section: Customer Retentionmentioning

confidence: 99%

Capturing Marketing Information to Fuel Growth

Du¹,

Netzer²,

Schweidel³

et al. 2020

Journal of Marketing

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Marketing is the functional area primarily responsible for driving the organic growth of a firm. In the age of digital marketing and big data, marketers are inundated with increasingly rich data from an ever-expanding array of sources. Such data may help marketers generate insights about customers and competitors. One fundamental question remains: How can marketers wrestle massive flows of existing and nascent data resources into coherent, effective growth strategies? Against such a backdrop, the Marketing Science Institute has made “capturing information to fuel growth” a top research priority. We begin by discussing the streetlight effect—an overreliance on readily available data due to ease of measurement and application—as contributing to the disconnect between marketing data growth and firm growth. We then use the customer equity framework to structure our discussion of six areas where we see substantial under-tapped opportunities—incorporating social network and biometric data in customer acquisition, trend and competitive interaction data in customer development, and unstructured and causal data in customer retention. We highlight challenges that obstruct firms from realizing such data-driven growth opportunities and how future research may help overcome those challenges.

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Section: Customer Retentionmentioning

confidence: 99%

Capturing Marketing Information to Fuel Growth

Du¹,

Netzer²,

Schweidel³

et al. 2020

Journal of Marketing

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show abstract

“…In the field of visual brand-related UGC some first steps have been taken with the use of computer vision, resulting in practical implications for marketing. Liu, Dzyabura, and Mizik (2017) trained a computer vision model to analyze brand image portrayal on Instagram. The model analyzes Instagram pictures that are associated with a particular brand (by means of a hashtag) and derives overall impressions that are conveyed about a brand, such as "rugged" or "romantic."…”

Section: Computer Vision To Analyze Visual Brand-related Ugcmentioning

confidence: 99%

The Use of Computer Vision to Analyze Brand-Related User Generated Image Content

Nanne

Antheunis

Lee

et al. 2020

Journal of Interactive Marketing

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With the increasing popularity of visual-oriented social media platforms, the prevalence of visual brand-related User Generated Content (UGC) have increased. Monitoring such content is important as this visual brand-related UGC can have a large influence on a brand's image and hence provides useful opportunities to observe brand performance (e.g., monitoring trends and consumer segments). The current research discusses the application of computer vision for marketing practitioners and researchers and examines the usability of three different pre-trained ready-to-use computer vision models (i.e., YOLOV2, Google Cloud Vision, and Clarifai) to analyze visual brand-related UGC automatically. A 3-step approach was adopted in which 1) a database of 21,738 Instagram pictures related to 24 different brands was constructed, 2) the images were processed by the three different computer vision models, and 3) a label evaluation procedure was conducted with a sample of the labels (object names) outputted by the models. The results of the label evaluation procedure are quantitatively assessed and complemented with four concrete examples of how the output of computer vision can be used to analyze visual brand-related UGC. Results show that computer vision can yield various marketing insights. Moreover, we found that the three tested computer vision models differ in applicability. Google Cloud Vision is more accurate in object detection, whereas Clarifai provides more useful labels to interpret the portrayal of a brand. YOLOV2 did not prove to be useful to analyze visual brand-related UGC. Results and implications of the findings for marketers and marketing scholars will be discussed.

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“…In advertising and marketing, more studies have begun to apply computer vision to analyze visual social media (Liu, Dzyabura, and Mizik 2020;Nanne et al 2020;Tous et al 2018;Vassey et al 2020). Such studies use computer vision to detect or predict brand attributes from images or suggest better ways to curate social media content.…”

Section: Using Computer Vision To Detect Visual Information Mismatchmentioning

confidence: 99%

Automatically Detecting Image–Text Mismatch on Instagram with Deep Learning

Park

Kim

et al. 2021

Journal of Advertising

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Visual social media have emerged as an essential brand communication channel for advertisers and brands. The active use of hashtags has enabled advertisers to identify customers interested in their brands and better understand their consumers. However, some users post brand-incongruent content-for example, posts composed of brand-irrelevant images with brand-relevant hashtags. Such visual information mismatch can be problematic because it hinders other consumers' information search processes and advertisers' insight generation from consumer-initiated social media data. This study aims to characterize visually mismatched content in brand-related posts on Instagram and builds a visual information mismatch detection model using computer vision. We propose a machine-learning model based on three cues: image, text, and metadata. Our analysis shows the effectiveness of deep learning and the importance of combining text and image features for mismatch detection. We discuss the advantages of machine-learning methods as a novel research tool for advertising research and conclude with implications of our findings.Instagram is one of the fastest growing photo-and video-sharing social media platforms and has attracted more than 1 billion monthly users worldwide. In the United States, there were approximately 107.2 million Instagram users by 2018, and this number is expected to grow to 120.3 million by 2023 (Nuñez 2020). With its increasing popularity, advertisers and brands have paid attention to Instagram's potential as a brand communication channel in social media. For the term brand communication in social media, we follow the definition of Alhabash, Mundel, and Hussain (2017) and refer to it as brand-related communication distributed via social media that enables Internet users to access, share, engage with, add to, and co-create. This definition includes both brand-generated posts (e.g., advertisements) and user-generated content (UGC) (Voorveld 2019), but we particularly focus on consumer-generated brand communication messages in this study because we are interested in examining how consumers use images and texts when they create brand-related posts in Instagram. Consumers create brand-related posts by using an image of a product with a hashtag indicating the brand (e.g., #apple, #chanel) as a form of brand engagement and loyalty (Phua, Jin, and Kim 2017). For consumers, this combined use of brand-related images and hashtags is a way to find brand-related information and connect with other consumers who have similar tastes (Sung, Kim, and Choi 2018). For advertisers and brands, consumer-generated social media data help to better

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Visual Listening In: Extracting Brand Image Portrayed on Social Media

Abstract: A new approach for measuring consumer brand perceptions from consumer-created brand imagery via deep learning.

Cited by 153 publications

References 28 publications

Capturing Marketing Information to Fuel Growth

Capturing Marketing Information to Fuel Growth

The Use of Computer Vision to Analyze Brand-Related User Generated Image Content

Automatically Detecting Image–Text Mismatch on Instagram with Deep Learning

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