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2022 | OriginalPaper | Chapter

Image Analytics in Marketing

Authors : Daria Dzyabura, Siham El Kihal, Renana Peres

Published in: Handbook of Market Research

Publisher: Springer International Publishing

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Abstract

Recent technical advances and the rise of digital platforms enhanced consumers’ abilities to take and share images and led to a tremendous increase in the importance of visual communication. The abundance of visual data, together with the development of image processing tools and advanced modeling techniques, provides unique opportunities for marketing researchers, in both academia and practice, to study the relationship between consumers and firms in depth and to generate insights which can be generalized across a variety of people and contexts.
However, with the opportunity come challenges. Specifically, researchers interested in using image analytics for marketing are faced with a triple challenge: (1) To which type of research questions can image analytics add insights that cannot be obtained otherwise? (2) Which visual data should be used to answer the research questions, and (3) which method is the right one?
In this chapter, the authors provide a guidance on how to formulate a worthy research question, select the appropriate data source, and apply the right method of analysis. They first identify five relevant areas in marketing that would benefit greatly from image analytics. They then discuss different types of visual data and explain their merits and drawbacks. Finally, they describe methodological approaches to analyzing visual data and discuss issues such as feature extraction, model training, evaluation, and validation as well as application to a marketing problem.

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Literature
go back to reference Amit, E., Algom, D., & Trope, Y. (2009). Distance-dependent processing of pictures and words. Journal of Experimental Psychology: General, 138(3), 400.CrossRef Amit, E., Algom, D., & Trope, Y. (2009). Distance-dependent processing of pictures and words. Journal of Experimental Psychology: General, 138(3), 400.CrossRef
go back to reference Ang, S. H., Lee, Y. H., & Leong, S. M. (2007). The ad creativity cube: Conceptualization and initial validation. Journal of the Academy of Marketing Science, 35(2), 220–232.CrossRef Ang, S. H., Lee, Y. H., & Leong, S. M. (2007). The ad creativity cube: Conceptualization and initial validation. Journal of the Academy of Marketing Science, 35(2), 220–232.CrossRef
go back to reference Bengio, Y. (2012). Deep learning of representations for unsupervised and transfer learning. In I. Guyon, G. Dror, V. Lemaire, G. Taylor, & D. Silver (Eds.), Proceedings o the ICML workshop unsupervised transfer learn (pp. 17–36). Bellevue. Bengio, Y. (2012). Deep learning of representations for unsupervised and transfer learning. In I. Guyon, G. Dror, V. Lemaire, G. Taylor, & D. Silver (Eds.), Proceedings o the ICML workshop unsupervised transfer learn (pp. 17–36). Bellevue.
go back to reference Bengio, Y., Bergeron, A., Boulanger-Lewandowski, N., Breuel, T., Chherawala, Y., Cisse, M., & Erhan, D. (2011). Deep learners benefit more from out-of-distribution examples. In G. Gordon, D. Dunson, & D. Miroslav (Eds.), Proceedings of the 14th international conference artificial intelligence statist (pp. 164–172). Fort Lauderdale, FL. Bengio, Y., Bergeron, A., Boulanger-Lewandowski, N., Breuel, T., Chherawala, Y., Cisse, M., & Erhan, D. (2011). Deep learners benefit more from out-of-distribution examples. In G. Gordon, D. Dunson, & D. Miroslav (Eds.), Proceedings of the 14th international conference artificial intelligence statist (pp. 164–172). Fort Lauderdale, FL.
go back to reference Bloch, P. H. (1995). Seeking the ideal form: Product design and consumer response. Journal of Marketing, 59(3), 16–29.CrossRef Bloch, P. H. (1995). Seeking the ideal form: Product design and consumer response. Journal of Marketing, 59(3), 16–29.CrossRef
go back to reference Burnap, A., & Hauser, J. (2018). Predicting “design gaps” in the market: Deep consumer choice models under probabilistic design constraints. arXiv preprint arXiv, 1812.11067. Burnap, A., & Hauser, J. (2018). Predicting “design gaps” in the market: Deep consumer choice models under probabilistic design constraints. arXiv preprint arXiv, 1812.11067.
go back to reference Burnap A., Hauser, J., & Timoshenko A. (2019). Design and evaluation of product aesthetics: A human-machine hybrid approach. Available at SSRN 3421771. Burnap A., Hauser, J., & Timoshenko A. (2019). Design and evaluation of product aesthetics: A human-machine hybrid approach. Available at SSRN 3421771.
go back to reference Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020). NuScenes: A multimodal dataset for autonomous driving. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 11621–11631). Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020). NuScenes: A multimodal dataset for autonomous driving. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 11621–11631).
go back to reference Chan, T. H., Mihm, J., & Sosa, M. E. (2018). On styles in product design: An analysis of US design patents. Management Science, 64(3), 1230–1249.CrossRef Chan, T. H., Mihm, J., & Sosa, M. E. (2018). On styles in product design: An analysis of US design patents. Management Science, 64(3), 1230–1249.CrossRef
go back to reference Cho, H., Schwarz, N., & Song, H. (2008). Images and preferences: A feelings-as-information analysis. In M. Wedel & R. Pieters (Eds.), Visual marketing: From attention to action (pp. 259–276). New York: Lawrence Erlbaum Associates. Cho, H., Schwarz, N., & Song, H. (2008). Images and preferences: A feelings-as-information analysis. In M. Wedel & R. Pieters (Eds.), Visual marketing: From attention to action (pp. 259–276). New York: Lawrence Erlbaum Associates.
go back to reference Crilly, N., Moultrie, J., & Clarkson, P. J. (2004). Seeing things: Consumer response to the visual domain in product design. Design Studies, 25(6), 547–577.CrossRef Crilly, N., Moultrie, J., & Clarkson, P. J. (2004). Seeing things: Consumer response to the visual domain in product design. Design Studies, 25(6), 547–577.CrossRef
go back to reference Dew, R., Ansari, A., & Toubia, O. (2019). Letting logos speak: Leveraging multiview representation learning for data-driven logo design. Available at SSRN 3406857. Dew, R., Ansari, A., & Toubia, O. (2019). Letting logos speak: Leveraging multiview representation learning for data-driven logo design. Available at SSRN 3406857.
go back to reference Dhar, S., Ordonez, V., & Berg, T. L. (2011, June). High level describable attributes for predicting aesthetics and interestingness. In CVPR 2011 (pp. 1657–1664). IEEE.CrossRef Dhar, S., Ordonez, V., & Berg, T. L. (2011, June). High level describable attributes for predicting aesthetics and interestingness. In CVPR 2011 (pp. 1657–1664). IEEE.CrossRef
go back to reference Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2014, January). Decaf: A deep convolutional activation feature for generic visual recognition. In International conference on machine learning (pp. 647–655). Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2014, January). Decaf: A deep convolutional activation feature for generic visual recognition. In International conference on machine learning (pp. 647–655).
go back to reference Dzyabura, D., & Peres, R. (2021). Visual elicitation of brand perception. Journal of Marketing, forthcoming. Dzyabura, D., & Peres, R. (2021). Visual elicitation of brand perception. Journal of Marketing, forthcoming.
go back to reference Dzyabura, D., El Kihal, S., Ibragimov, M., & Hauser J. (2020). Leveraging the power of images in managing product return rates. Available at SSRN 3209307. Dzyabura, D., El Kihal, S., Ibragimov, M., & Hauser J. (2020). Leveraging the power of images in managing product return rates. Available at SSRN 3209307.
go back to reference Eisenman, M., Frenkel, M., & Wasserman, V. (2016). Toward a theory of effective aesthetic communication. In Academy of management proceedings (Vol. 2016, p. 12822). Briarcliff Manor: Academy of Management. Eisenman, M., Frenkel, M., & Wasserman, V. (2016). Toward a theory of effective aesthetic communication. In Academy of management proceedings (Vol. 2016, p. 12822). Briarcliff Manor: Academy of Management.
go back to reference Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580–587). Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580–587).
go back to reference Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1, No. 2). Cambridge: MIT Press. Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1, No. 2). Cambridge: MIT Press.
go back to reference Gorn, G. J., Chattopadhyay, A., Yi, T., & Dahl, D. W. (1997). Effects of color as an executional cue in advertising: They’re in the shade. Management Science, 43(10), 1387–1400.CrossRef Gorn, G. J., Chattopadhyay, A., Yi, T., & Dahl, D. W. (1997). Effects of color as an executional cue in advertising: They’re in the shade. Management Science, 43(10), 1387–1400.CrossRef
go back to reference Greenleaf, E., & Raghubir, P. (2008). Geometry in the marketplace. In M. Wedel & R. Pieters (Eds.), Visual marketing: From attention to action (pp. 113–143). New York: Lawrence Erlbaum Associates. Greenleaf, E., & Raghubir, P. (2008). Geometry in the marketplace. In M. Wedel & R. Pieters (Eds.), Visual marketing: From attention to action (pp. 113–143). New York: Lawrence Erlbaum Associates.
go back to reference Han, Y. J., Nunes, J. C., & Drèze, X. (2010). Signaling status with luxury goods: The role of brand prominence. Journal of Marketing, 74(4), 15–30.CrossRef Han, Y. J., Nunes, J. C., & Drèze, X. (2010). Signaling status with luxury goods: The role of brand prominence. Journal of Marketing, 74(4), 15–30.CrossRef
go back to reference Hauser, J. R., Urban, G. L., Liberali, G., & Braun, M. (2009). Website morphing. Marketing Science, 28(2), 202–223.CrossRef Hauser, J. R., Urban, G. L., Liberali, G., & Braun, M. (2009). Website morphing. Marketing Science, 28(2), 202–223.CrossRef
go back to reference Hauser, J. R., Liberali, G., & Urban, G. L. (2014). Website morphing 2.0: Switching costs, partial exposure, random exit, and when to morph. Management Science, 60(6), 1594–1616.CrossRef Hauser, J. R., Liberali, G., & Urban, G. L. (2014). Website morphing 2.0: Switching costs, partial exposure, random exit, and when to morph. Management Science, 60(6), 1594–1616.CrossRef
go back to reference Heinonen, R., Luoto, R., Lindfors, P., & Nygård, C. H. (2012). Usability and feasibility of mobile phone diaries in an experimental physical exercise study. Telemedicine and e-Health, 18(2), 115–119.CrossRef Heinonen, R., Luoto, R., Lindfors, P., & Nygård, C. H. (2012). Usability and feasibility of mobile phone diaries in an experimental physical exercise study. Telemedicine and e-Health, 18(2), 115–119.CrossRef
go back to reference Hektner, J. M., Schmidt, J. A., & Csikszentmihalyi, M. (2007). Experience sampling method: Measuring the quality of everyday life. Thousand Oaks: Sage Publications, Inc.CrossRef Hektner, J. M., Schmidt, J. A., & Csikszentmihalyi, M. (2007). Experience sampling method: Measuring the quality of everyday life. Thousand Oaks: Sage Publications, Inc.CrossRef
go back to reference Hensel, D. J., Fortenberry, J. D., Harezlak, J., & Craig, D. (2012). The feasibility of cell phone based electronic diaries for STI/HIV research. BMC Medical Research Methodology, 12(75), 1–12. Hensel, D. J., Fortenberry, J. D., Harezlak, J., & Craig, D. (2012). The feasibility of cell phone based electronic diaries for STI/HIV research. BMC Medical Research Methodology, 12(75), 1–12.
go back to reference Hofmann, W., & Patel, P. V. (2015). Survey signal a convenient solution for experience sampling research using participants’ own smartphones. Social Science Computer Review, 33(2), 235–253.CrossRef Hofmann, W., & Patel, P. V. (2015). Survey signal a convenient solution for experience sampling research using participants’ own smartphones. Social Science Computer Review, 33(2), 235–253.CrossRef
go back to reference Jalali, N. Y., & Papatla, P. (2016). The palette that stands out: Color compositions of online curated visual UGC that attracts higher consumer interaction. Quantitative Marketing and Economics, 14(4), 353–384.CrossRef Jalali, N. Y., & Papatla, P. (2016). The palette that stands out: Color compositions of online curated visual UGC that attracts higher consumer interaction. Quantitative Marketing and Economics, 14(4), 353–384.CrossRef
go back to reference Janiszewski, C. (1998). The influence of display characteristics on visual exploratory search behavior. Journal of Consumer Research, 25(3), 290–301.CrossRef Janiszewski, C. (1998). The influence of display characteristics on visual exploratory search behavior. Journal of Consumer Research, 25(3), 290–301.CrossRef
go back to reference John, D. R., Loken, B., Kim, K., & Monga, A. B. (2006). Brand concept maps: A methodology for identifying brand association networks. Journal of Marketing Research, 43(4), 549–563.CrossRef John, D. R., Loken, B., Kim, K., & Monga, A. B. (2006). Brand concept maps: A methodology for identifying brand association networks. Journal of Marketing Research, 43(4), 549–563.CrossRef
go back to reference Keller, K. L. (2003). Brand synthesis: The multidimensionality of brand knowledge. Journal of Consumer Research, 29(4), 595–600.CrossRef Keller, K. L. (2003). Brand synthesis: The multidimensionality of brand knowledge. Journal of Consumer Research, 29(4), 595–600.CrossRef
go back to reference Klostermann, J., Plumeyer, A., Böger, D., & Decker, R. (2018). Extracting brand information from social networks: Integrating image, text, and social tagging data. International Journal of Research in Marketing, 35(4), 538–556.CrossRef Klostermann, J., Plumeyer, A., Böger, D., & Decker, R. (2018). Extracting brand information from social networks: Integrating image, text, and social tagging data. International Journal of Research in Marketing, 35(4), 538–556.CrossRef
go back to reference Koll, O., Von Wallpach, S., & Kreuzer, M. (2010). Multi-method research on consumer–brand associations: Comparing free associations, storytelling, and collages. Psychology & Marketing, 27(6), 584–602.CrossRef Koll, O., Von Wallpach, S., & Kreuzer, M. (2010). Multi-method research on consumer–brand associations: Comparing free associations, storytelling, and collages. Psychology & Marketing, 27(6), 584–602.CrossRef
go back to reference Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90.CrossRef Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90.CrossRef
go back to reference Labrecque, L. I. (2014). Fostering consumer–brand relationships in social media environments: The role of Parasocial interaction. Journal of Interactive Marketing, 28(2), 134–148.CrossRef Labrecque, L. I. (2014). Fostering consumer–brand relationships in social media environments: The role of Parasocial interaction. Journal of Interactive Marketing, 28(2), 134–148.CrossRef
go back to reference Lehnert, K., Till, B. D., & Ospina, J. M. (2014). Advertising creativity: The role of divergence versus meaningfulness. Journal of Advertising, 43(3), 274–285.CrossRef Lehnert, K., Till, B. D., & Ospina, J. M. (2014). Advertising creativity: The role of divergence versus meaningfulness. Journal of Advertising, 43(3), 274–285.CrossRef
go back to reference Li, Y., & Xie, Y. (2020). Is a picture worth a thousand words? An empirical study of image content and social media engagement. Journal of Marketing Research, 57(1), 1–19.CrossRef Li, Y., & Xie, Y. (2020). Is a picture worth a thousand words? An empirical study of image content and social media engagement. Journal of Marketing Research, 57(1), 1–19.CrossRef
go back to reference Li, H., Simchi-Levi, D., Wu, M. X., & Zhu, W. (2019a). Estimating and exploiting the impact of photo layout in sharing economy. Available at SSRN. Li, H., Simchi-Levi, D., Wu, M. X., & Zhu, W. (2019a). Estimating and exploiting the impact of photo layout in sharing economy. Available at SSRN.
go back to reference Li, X., Shi, M., & Wang, X. S. (2019b). Video mining: Measuring visual information using automatic methods. International Journal of Research in Marketing, 36(2), 216–231.CrossRef Li, X., Shi, M., & Wang, X. S. (2019b). Video mining: Measuring visual information using automatic methods. International Journal of Research in Marketing, 36(2), 216–231.CrossRef
go back to reference Liu, X., Burns, A. C., & Hou, Y. (2017). An investigation of brand-related user-generated content on Twitter. Journal of Advertising, 46(2), 236–247.CrossRef Liu, X., Burns, A. C., & Hou, Y. (2017). An investigation of brand-related user-generated content on Twitter. Journal of Advertising, 46(2), 236–247.CrossRef
go back to reference Liu, L., Dzyabura, D., & Mizik, N. (2020). Visual listening in: Extracting brand image portrayed on social media. Marketing Science, 39(4), 669–686.CrossRef Liu, L., Dzyabura, D., & Mizik, N. (2020). Visual listening in: Extracting brand image portrayed on social media. Marketing Science, 39(4), 669–686.CrossRef
go back to reference Lovett, M. J., & Peres, R. (2018). Mobile diaries – Benchmark against metered measurements: An empirical investigation. International Journal of Research in Marketing, 35(2), 224–241.CrossRef Lovett, M. J., & Peres, R. (2018). Mobile diaries – Benchmark against metered measurements: An empirical investigation. International Journal of Research in Marketing, 35(2), 224–241.CrossRef
go back to reference Lovett, M. J., Peres, R., & Shachar, R. (2013). On brands and word of mouth. Journal of Marketing Research, 50(4), 427–444.CrossRef Lovett, M. J., Peres, R., & Shachar, R. (2013). On brands and word of mouth. Journal of Marketing Research, 50(4), 427–444.CrossRef
go back to reference MacInnis, D. J., & Price, L. L. (1987). The role of imagery in information processing: Review and extensions. Journal of Consumer Research, 13(4), 473–491.CrossRef MacInnis, D. J., & Price, L. L. (1987). The role of imagery in information processing: Review and extensions. Journal of Consumer Research, 13(4), 473–491.CrossRef
go back to reference McAuley, J., & Leskovec, J. (2012, October). Image labeling on a network: Using social-network metadata for image classification. In European conference on computer vision (pp. 828–841). Berlin/Heidelberg: Springer. McAuley, J., & Leskovec, J. (2012, October). Image labeling on a network: Using social-network metadata for image classification. In European conference on computer vision (pp. 828–841). Berlin/Heidelberg: Springer.
go back to reference McQuarrie, E. F. (2008). Differentiating the pictorial element in advertising – A rhetorical perspective. In M. Wedel & R. Pieters (Eds.), Visual marketing: From attention to action (pp. 91–112). New York: Psychology Press. McQuarrie, E. F. (2008). Differentiating the pictorial element in advertising – A rhetorical perspective. In M. Wedel & R. Pieters (Eds.), Visual marketing: From attention to action (pp. 91–112). New York: Psychology Press.
go back to reference Meyers-Levy, J., & Zhu, R. (2008). Perhaps the store made you purchase it: Toward an understanding of structural aspects of indoor shopping environment. In M. Wedel & R. Pieters (Eds.), Visual marketing: From attention to action (pp. 193–224). New York: Psychology Press. Meyers-Levy, J., & Zhu, R. (2008). Perhaps the store made you purchase it: Toward an understanding of structural aspects of indoor shopping environment. In M. Wedel & R. Pieters (Eds.), Visual marketing: From attention to action (pp. 193–224). New York: Psychology Press.
go back to reference Nanne, A. J., Antheunis, M. L., van der Lee, C. G., Postma, E. O., Wubben, S., & van Noort, G. (2020). The use of computer vision to analyze brand-related user generated image content. Journal of Interactive Marketing, 50, 156–167.CrossRef Nanne, A. J., Antheunis, M. L., van der Lee, C. G., Postma, E. O., Wubben, S., & van Noort, G. (2020). The use of computer vision to analyze brand-related user generated image content. Journal of Interactive Marketing, 50, 156–167.CrossRef
go back to reference Orsborn, S., Cagan, J., & Boatwright, P. (2009). Quantifying aesthetic form preference in a utility function. Journal of Mechanical Design, 131(6), 061001.CrossRef Orsborn, S., Cagan, J., & Boatwright, P. (2009). Quantifying aesthetic form preference in a utility function. Journal of Mechanical Design, 131(6), 061001.CrossRef
go back to reference Pavlov, E., & Mizik, N. (2019). Increasing consumer engagement with firm-generated social media content: The role of images and words. Working Paper, University of Washington. Pavlov, E., & Mizik, N. (2019). Increasing consumer engagement with firm-generated social media content: The role of images and words. Working Paper, University of Washington.
go back to reference Peng, L., Cui, G., Chung, Y., & Zheng, W. (2020). The faces of success: Beauty and ugliness premiums in e-commerce platforms. Journal of Marketing, 84(4), 67–85.CrossRef Peng, L., Cui, G., Chung, Y., & Zheng, W. (2020). The faces of success: Beauty and ugliness premiums in e-commerce platforms. Journal of Marketing, 84(4), 67–85.CrossRef
go back to reference Peracchio, L. A., & Meyers-Levy, J. (1994). How ambiguous cropped objects in ad photos can affect product evaluations. Journal of Consumer Research, 21(1), 190–204.CrossRef Peracchio, L. A., & Meyers-Levy, J. (1994). How ambiguous cropped objects in ad photos can affect product evaluations. Journal of Consumer Research, 21(1), 190–204.CrossRef
go back to reference Peracchio, L. A., & Meyers-Levy, J. (2005). Using stylistic properties of ad pictures to communicate with consumers. Journal of Consumer Research, 32(1), 29–40.CrossRef Peracchio, L. A., & Meyers-Levy, J. (2005). Using stylistic properties of ad pictures to communicate with consumers. Journal of Consumer Research, 32(1), 29–40.CrossRef
go back to reference Pieters, R., & Wedel, M. (2004). Attention capture and transfer in advertising: Brand, pictorial, and text-size effects. Journal of Marketing, 68(2), 36–50.CrossRef Pieters, R., & Wedel, M. (2004). Attention capture and transfer in advertising: Brand, pictorial, and text-size effects. Journal of Marketing, 68(2), 36–50.CrossRef
go back to reference Pieters, R., Wedel, M., & Zhang, J. (2007). Optimal feature advertising design under competitive clutter. Management Science, 53(11), 1815–1828.CrossRef Pieters, R., Wedel, M., & Zhang, J. (2007). Optimal feature advertising design under competitive clutter. Management Science, 53(11), 1815–1828.CrossRef
go back to reference Radach, R., Lemmer, S., Vorstius, C., Heller, D., & Radach, K. (2003). Eye movements in the processing of print advertisements. In R. Radach & H. Deubel (Eds.), The mind’s eye (pp. 609–632). Amsterdam: Elsevier Science Publishers.CrossRef Radach, R., Lemmer, S., Vorstius, C., Heller, D., & Radach, K. (2003). Eye movements in the processing of print advertisements. In R. Radach & H. Deubel (Eds.), The mind’s eye (pp. 609–632). Amsterdam: Elsevier Science Publishers.CrossRef
go back to reference Raghubir, P., & Greenleaf, E. A. (2006). Ratios in proportion: What should the shape of the package be? Journal of Marketing, 70(2), 95–107.CrossRef Raghubir, P., & Greenleaf, E. A. (2006). Ratios in proportion: What should the shape of the package be? Journal of Marketing, 70(2), 95–107.CrossRef
go back to reference Reavey, P. (Ed.). (2012). Visual methods in psychology: Using and interpreting images in qualitative research. Routledge. London. Reavey, P. (Ed.). (2012). Visual methods in psychology: Using and interpreting images in qualitative research. Routledge. London.
go back to reference Rietveld, R., van Dolen, W., Mazloom, M., & Worring, M. (2020). What you feel, is what you like influence of message appeals on customer engagement on Instagram. Journal of Interactive Marketing, 49, 20–53.CrossRef Rietveld, R., van Dolen, W., Mazloom, M., & Worring, M. (2020). What you feel, is what you like influence of message appeals on customer engagement on Instagram. Journal of Interactive Marketing, 49, 20–53.CrossRef
go back to reference Rosbergen, E., Pieters, R., & Wedel, M. (1997). Visual attention to advertising: A segment-level analysis. Journal of Consumer Research, 24(3), 305–314.CrossRef Rosbergen, E., Pieters, R., & Wedel, M. (1997). Visual attention to advertising: A segment-level analysis. Journal of Consumer Research, 24(3), 305–314.CrossRef
go back to reference Rubera, G. (2015). Design innovativeness and product sales’ evolution. Marketing Science, 34(1), 98–115.CrossRef Rubera, G. (2015). Design innovativeness and product sales’ evolution. Marketing Science, 34(1), 98–115.CrossRef
go back to reference Sheinin, D. A., Varki, S., & Ashley, C. (2011). The differential effect of ad novelty and message usefulness on brand judgments. Journal of Advertising, 40(3), 5–18.CrossRef Sheinin, D. A., Varki, S., & Ashley, C. (2011). The differential effect of ad novelty and message usefulness on brand judgments. Journal of Advertising, 40(3), 5–18.CrossRef
go back to reference Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In Proceedings of International Conference on Learning Representations (ICLR). Available at https://arxiv.org/abs/1409.1556 Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In Proceedings of International Conference on Learning Representations (ICLR). Available at https://​arxiv.​org/​abs/​1409.​1556
go back to reference Smith, R. E., MacKenzie, S. B., Yang, X., Buchholz, L. M., & Darley, W. K. (2007). Modeling the determinants and effects of creativity in advertising. Marketing Science, 26(6), 819–833.CrossRef Smith, R. E., MacKenzie, S. B., Yang, X., Buchholz, L. M., & Darley, W. K. (2007). Modeling the determinants and effects of creativity in advertising. Marketing Science, 26(6), 819–833.CrossRef
go back to reference Toubia, O., & Netzer, O. (2017). Idea generation, creativity, and prototypicality. Marketing Science, 36(1), 1–20.CrossRef Toubia, O., & Netzer, O. (2017). Idea generation, creativity, and prototypicality. Marketing Science, 36(1), 1–20.CrossRef
go back to reference Van House, N., Davis, M., Ames, M., Finn, M., & Viswanathan, V. (2005). The uses of personal networked digital imaging: An empirical study of cameraphone photos and sharing. In CHI’05 extended abstracts on human factors in computing systems (pp. 1853–1856). ACM.CrossRef Van House, N., Davis, M., Ames, M., Finn, M., & Viswanathan, V. (2005). The uses of personal networked digital imaging: An empirical study of cameraphone photos and sharing. In CHI’05 extended abstracts on human factors in computing systems (pp. 1853–1856). ACM.CrossRef
go back to reference Vilnai-Yavetz, I., & Tifferet, S. (2015). A picture is worth a thousand words: Segmenting consumers by Facebook profile images. Journal of Interactive Marketing, 32, 53–69.CrossRef Vilnai-Yavetz, I., & Tifferet, S. (2015). A picture is worth a thousand words: Segmenting consumers by Facebook profile images. Journal of Interactive Marketing, 32, 53–69.CrossRef
go back to reference Wedel, M., & Pieters, R. (2000). Eye fixations on advertisements and memory for brands: A model and findings. Marketing Science, 19(4), 297–312.CrossRef Wedel, M., & Pieters, R. (2000). Eye fixations on advertisements and memory for brands: A model and findings. Marketing Science, 19(4), 297–312.CrossRef
go back to reference Wedel, M., & Pieters, R. (2008). A review of eye-tracking research in marketing. Review of Marketing Research, 4(2008), 123–147.CrossRef Wedel, M., & Pieters, R. (2008). A review of eye-tracking research in marketing. Review of Marketing Research, 4(2008), 123–147.CrossRef
go back to reference Wedel, M., & Pieters, R. (2014). Looking at vision (p. 2014). Abingdon: Routledge. Wedel, M., & Pieters, R. (2014). Looking at vision (p. 2014). Abingdon: Routledge.
go back to reference Xiao, H., Rasul, K., & Vollgraf, R. (2017). Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv, 3. Xiao, H., Rasul, K., & Vollgraf, R. (2017). Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv, 3.
go back to reference Yang, X., & Smith, R. E. (2009). Beyond attention effects: Modeling the persuasive and emotional effects of advertising creativity. Marketing Science, 28(5), 935–949.CrossRef Yang, X., & Smith, R. E. (2009). Beyond attention effects: Modeling the persuasive and emotional effects of advertising creativity. Marketing Science, 28(5), 935–949.CrossRef
go back to reference Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? In Advances in neural information processing systems (pp. 3320–3328). Available at https://arxiv.org/abs/1411.1792 Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? In Advances in neural information processing systems (pp. 3320–3328). Available at https://​arxiv.​org/​abs/​1411.​1792
go back to reference Zaltman, G., & Coulter, R. H. (1995). Seeing the voice of the customer: Metaphor-based advertising research. Journal of Advertising Research, 35(4), 35–51. Zaltman, G., & Coulter, R. H. (1995). Seeing the voice of the customer: Metaphor-based advertising research. Journal of Advertising Research, 35(4), 35–51.
go back to reference Zaltman, G., & Zaltman, L. H. (2008). Marketing metaphoria: What deep metaphors reveal about the minds of consumers. Boston: Harvard Business Press. Zaltman, G., & Zaltman, L. H. (2008). Marketing metaphoria: What deep metaphors reveal about the minds of consumers. Boston: Harvard Business Press.
go back to reference Zhang, M., & Luo, L. (2019). Can User-posted photos serve as a leading indicator of restaurant survival? Evidence from Yelp. Available at SSRN 3108288. Zhang, M., & Luo, L. (2019). Can User-posted photos serve as a leading indicator of restaurant survival? Evidence from Yelp. Available at SSRN 3108288.
go back to reference Zhang, H., Korayem, M., You, E., & Crandall, D. J. (2012). Beyond co-occurrence: Discovering and visualizing tag relationships from geo-spatial and temporal similarities. In Proceedings of the fifth ACM international conference on web search and data mining (pp. 33–42). Available at https://doi.org/10.1145/2124295.2124302 Zhang, H., Korayem, M., You, E., & Crandall, D. J. (2012). Beyond co-occurrence: Discovering and visualizing tag relationships from geo-spatial and temporal similarities. In Proceedings of the fifth ACM international conference on web search and data mining (pp. 33–42). Available at https://​doi.​org/​10.​1145/​2124295.​2124302
go back to reference Zhang, S., Mehta, N., Singh, P. V., & Srinivasan, K. (2019). Can lower-quality images lead to greater demand on AirBnB? Technical report, working paper, Carnegie Mellon University. Zhang, S., Mehta, N., Singh, P. V., & Srinivasan, K. (2019). Can lower-quality images lead to greater demand on AirBnB? Technical report, working paper, Carnegie Mellon University.
go back to reference Zhang, S., Lee, D., Singh, P. V., & Srinivasan, K. (2021) “What makes a good image? Airbnb demand analytics leveraging interpretable image features”. Management Science. Forthcomming. Zhang, S., Lee, D., Singh, P. V., & Srinivasan, K. (2021) “What makes a good image? Airbnb demand analytics leveraging interpretable image features”. Management Science. Forthcomming.
Metadata
Title
Image Analytics in Marketing
Authors
Daria Dzyabura
Siham El Kihal
Renana Peres
Copyright Year
2022
DOI
https://doi.org/10.1007/978-3-319-57413-4_38