/ Journals / Foreign Economics & Management
Foreign Economics & Management
LiZengquan, Editor-in-Chief
ZhengChunrong, Vice Executive Editor-in-Chief
YinHuifang HeXiaogang LiuJianguo, Vice Editor-in-Chief
Editorial Board  |  Read more about this journal »
AboutAll IssuesAuthors GuidePeer ReviewSubscribeContact Us
Home  >  All Issues  >  2022  >  No.09  >  Article Details
A Picture Is Worth a Thousand Words: The Application of Image Data in Marketing
Xu Jie1, Xiao Li2
Full Article  |  PDF Article(688.7 KB)
Foreign Economics & Management Vol. 44, Issue 09, pp. 51 - 69 (2022)  DOI:10.16538/j.cnki.fem.20220621.301
First published at:Sep 20, 2022
Summary
References
Summary

With the vigorous development of image generation, storage technology and social media, image data has become an increasingly important information carrier and an important data source for enterprises to obtain consumer insight. However, image data is difficult to analyze and process because of its unstructured data characteristics, and there are few marketing studies on image analytics at present. Therefore, there is a certain gap between research and practice, and the existing theoretical results are difficult to give full guidance to the booming marketing practice.

This paper first combs the relevant concepts of image analytics and the relevant marketing practice carried out by image analytics at home and abroad. Then, it reviews the relevant marketing literature based on image analytics, extracts important topics and cutting-edge directions, analyzes the theoretical gaps in the current research, and looks forward to future research.

In the relevant marketing research field, we select 20 representative core papers from 19 international top journals in the field of marketing, management science, and information systems. Some well-cited working papers are also included to represent the cutting-edge marketing research utilizing image analytics. Generally speaking, the marketing research literature utilizing image analytics is somewhat sparse and mostly published after 2017, which mainly focuses on the fields of brand management, communication management and customer relationship management, and deep learning methods are ubiquitously used.

Based on the review of relevant marketing research and practice, this paper further proposes and discusses six potential directions for future research: Consumer behavioral research that utilizes image analytics to investigate theoretical mechanisms; product design with artificial intelligence; real-time responses to customer needs; how to increase model interpretability; multi-type image data and multi-modal data; and how to improve the fairness of products or services.

The main contributions of this paper are as follows: First, it reviews the fundamental concepts and methods of image analytics, providing guidance for marketing scholars to carry out relevant research. Second, it identifies and discusses some major research streams in marketing that image analytics could potentially be utilized. Third, it proposes several potential directions for future research, hoping to provide inspirations for domestic marketing scholars and call on more scholars to participate in this type of research.

Keywords
image data; image analytics; machine learning; deep learning
[1] Fan Jun, Shen Dongqiang, Lin Fan. The impact of product image on consumer’s purchasing intentions ――Based on moderating effects of product type[J]. Journal of Marketing Science, 2014, 10(4): 97-108.
[2] Fei Xianzheng, Xiao Dengyang. How does the haptic mental imagery of applications icon influence consumer preference?[J]. Journal of Management World, 2020, 36(7): 153-171.
[3] Huang Jing, Guo Yulang, Xiong Xiaoming, Wang Yili. Impact of online picture presentation order on consumers’ purchase intention：Based on the perspective of information processing mode[J]. Journal of Marketing Science, 2016, 12(1): 51-69.
[4] Lei Lei, Huang Minxue. The exploration of product evaluation in big data time--An empirical study of text mini in online customer reviews[J]. Luojia Management Review, 2014, (1): 129-141.
[5] Li Jiang, Yan Qing, Yang Qiang, JiangYushi. Research on the impact of digital presentation on consumer purchase intention in slogan: Eye movement based evidence[J]. Journal of Marketing Science, 2019, 15(4): 52-70.
[6] Li Wei, Yan Qing, Miao Miao, He Fan, Jiang Yushi. Image congruence and visual object structure of anthropomorphic advertisement--Eye movement research based on self-construct[J]. Journal of Marketing Science, 2019, 15(2): 116-131.
[7] Lin Zijun, Wu Qionglin, Cai Fengyan. A research review on artificial intelligence in marketing[J]. Foreign Economics & Management, 2021, 43(3): 89-106.
[8] Liu Yezheng, Sun Jianshan, Jiang Yuanchun, Chen Xiayu, Liu Chunli. 4C model: Value discovery in big data[J]. Journal of Management World, 2020, 36(2): 129-138.
[9] Meng Lu, Yang Qiang, Du Jiangang, Dong Zerui. Impact of innovative product categories and ordering on consumer purchase intention[J]. Journal of Marketing Science, 2017, (4): 83-103.
[10] Qian Minghui, Xu Zhixuan. A study of dynamic recognition of consumer brand decision-making preference based on machine learning method[J]. Nankai Business Review, 2019, 22(3): 66-76.
[11] Tian Wenhui, Li Yanjun, Li Linzhu. Research on the impact of online picture content of agricultural products on consumers’ willingness to click [J]. Journal of Marketing Science, 2018, 14(Z1): 222-239.
[12] Zhu Yimin. The influences of type of fit between company and cause, and information framing on consumers’ responses to cause-related marketing[J]. Nankai Business Review, 2014, 17(4): 128-139.
[13] Aouf R S. Algorithm designs seven million different jars of Nutella[EB/OL]. https://www.dezeen.com/2017/06/01/algorithm- seven-million-different-jars-nutella-packaging-design, 2017-06-01.
[14] Balducci B, Marinova D. Unstructured data in marketing[J]. Journal of the Academy of Marketing Science，2018, 46（4）: 557-590.
[15] Berry D S, McArthur L Z. Some components and consequences of a babyface[J]. Journal of Personality and Social Psychology，1985, 48（2）: 312-323.
[16] Burnap A, Hauser J R, Timoshenko A. Design and evaluation of product aesthetics: A human-machine hybrid approach[EB/OL]. http://dx.doi.org/10.2139/ssrn.3421771, 2019-07-19.
[17] Chintagunta P, Hanssens D M, Hauser J R. Editorial—marketing science and big data[J]. Marketing Science，2016, 35（3）: 341-342.
[18] Davenport T, Guha A, Grewal D, et al. How artificial intelligence will change the future of marketing[J]. Journal of the Academy of Marketing Science，2020, 48（1）: 24-42.
[19] Dew R, Ansari A, Toubia O. Letting logos speak: Leveraging multiview representation learning for data-driven branding and logo design[J]. Marketing Science，2022, 41（2）: 401-425.
[20] Diehl K, Zauberman G, Barasch A. How taking photos increases enjoyment of experiences[J]. Journal of Personality and Social Psychology，2016, 111（2）: 119-140.
[21] Dzyabura D, El Kihal S, Hauser J R, et al. Leveraging the power of images in managing product return rates[EB/OL]. http://dx.doi.org/10.2139/ssrn.3209307, 2019-09-04.
[22] Dzyabura D, Peres R. Visual elicitation of brand perception[J]. Journal of Marketing，2021, 85（4）: 44-66.
[23] Gabel S, Timoshenko A. Product choice with large assortments: A scalable deep-learning model[J]. Management Science， 2022, 68（3）: 1808-1827.
[24] Gaudin S. At Stitch Fix, data scientists and A. I. become personal stylist[EB/OL]. https://www.cmo.com.au/article/ 599435/stitch-fix-data-scientists-become-personal-stylists, 2016-05-07.
[25] Goodfellow I, Bengio Y, Courville A. Deep learning[M]. Cambridge, MA: MIT Press, 2016.
[26] Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative adversarial networks[J]. Communications of the ACM，2020, 63（11）: 139-144.
[27] Guan Y, Tan Y, Wei Q, et al. Information or distortion? The effect of customer generated images on product rating dynamics[EB/OL]. http://dx.doi.org/10.2139/ssrn.3633590, 2020-06-23.
[28] Hall P, Gill N. An introduction to machine learning interpretability[M]. 2nd ed. Newton, MA: O’Reilly Media, Inc. , 2019.
[29] Hartmann J, Heitmann M, Schamp C, et al. The power of brand selfies[J]. Journal of Marketing Research，2021, 58（6）: 1159-1177.
[30] Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: Data mining, inference, and prediction[M]. 2nd ed. New York: Springer, 2009.
[31] Hauser J R. Phenomena, theory, application, data, and methods all have impact[J]. Journal of the Academy of Marketing Science，2017, 45（1）: 7-9.
[32] Heater B. Amazon’s new Echo Look has a built-in camera for style selfies[EB/OL]. https://techcrunch.com/2017/04/26/amazons-new-echo-look-has-a-built-in-camera-for-style-selfies, 2017-04-26.
[33] Huetter J. Tractable photo-analyzing AI to assess vehicle damage for Mitchell[EB/OL]. https://www.repairerdrivennews. com/2016/10/11/tractable-ceo-says-ai-will-assess-vehicle-damage-for-mitchell-company-specializes-in-ai-photo-analysis, 2016-10-11.
[34] Jindal R P, Sarangee K R, Echambadi R, et al. Designed to succeed: Dimensions of product design and their impact on market share[J]. Journal of Marketing，2016, 80（4）: 72-89.
[35] Jones A. The full picture: Using image analysis for consumer research[EB/OL]. https://www.brandwatch.com/blog/image- analysis-consumer-research, 2017-06-07.
[36] Jordan M I, Mitchell T M. Machine learning: Trends, perspectives, and prospects[J]. Science，2015, 349（6245）: 255-260.
[37] Karras T, Laine S, Aila T. A style-based generator architecture for generative adversarial networks[A]. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition[C]. Long Beach: IEEE, 2019.
[38] Karras T, Laine S, Aittala M, et al. Analyzing and improving the image quality of StyleGAN[A]. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition[C]. Seattle: IEEE, 2020.
[39] Kazemi V, Sullivan J. One millisecond face alignment with an ensemble of regression trees[A]. Proceedings of the IEEE conference on computer vision and pattern recognition[C]. Columbus: IEEE, 2014.
[40] Klostermann J, Plumeyer A, Böger D, et al. Extracting brand information from social networks: Integrating image, text, and social tagging data[J]. International Journal of Research in Marketing，2018, 35（4）: 538-556.
[41] Kreuzbauer R, Malter A J. Embodied cognition and new product design: Changing product form to influence brand categorization[J]. Journal of Product Innovation Management，2005, 22（2）: 165-176.
[42] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[A]. Proceedings of the 25th international conference on neural information processing systems[C]. Lake Tahoe: Curran Associates Inc. , 2012.
[43] Landwehr J R, Labroo A A, Herrmann A. Gut liking for the ordinary: Incorporating design fluency improves automobile sales forecasts[J]. Marketing Science，2011, 30（3）: 416-429.
[44] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature，2015, 521（7553）: 436-444.
[45] LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE， 1998, 86（11）: 2278-2324.
[46] Li H W, Simchi-Levi D, Wu M X, et al. Estimating and exploiting the impact of photo layout: A structural approach[EB/OL]. http://dx.doi.org/10.2139/ssrn.3470877, 2019-10-16.
[47] Li Y Y, Xie Y. Is a picture worth a thousand words? An empirical study of image content and social media engagement[J]. Journal of Marketing Research，2020, 57（1）: 1-19.
[48] Liesa. Introducing image recognition: The future of social listening[EB/OL]. https://www.talkwalker.com/blog/introducing- image-recognition-the-future-of-social-listening, 2016-03-09.
[49] Liu L, Dzyabura D, Mizik N. Visual listening in: Extracting brand image portrayed on social media[J]. Marketing Science，2020, 39（4）: 669-686.
[50] Liu Y, Li K J, Chen H P, et al. The effects of products’ aesthetic design on demand and marketing-mix effectiveness: The role of segment prototypicality and brand consistency[J]. Journal of Marketing，2017, 81（1）: 83-102.
[51] Minaee S, Boykov Y, Porikli F, et al. Image segmentation using deep learning: A survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence，2022, 44（7）: 3523-3542.
[52] Mitchell T M. Machine learning[M]. Maidenhead: McGraw-Hill, 1997.
[53] Mnih V, Kavukcuoglu K, Silver D, et al. Human-level control through deep reinforcement learning[J]. Nature，2015, 518（7540）: 529-533.
[54] Murphy K P. Machine learning: A probabilistic perspective[M]. Cambridge: MIT Press, 2012.
[55] Nissan. AI technology brings excitement to Nissan Japan[EB/OL]. https://global.nissanstories.com/en/releases/nissan-ai- technology, 2022-01-21.
[56] Peng L, Cui G, Chung Y, et al. The faces of success: Beauty and ugliness premiums in e-commerce platforms[J]. Journal of Marketing，2020, 84（4）: 67-85.
[57] Pieters R, Wedel M, Batra R. The stopping power of advertising: Measures and effects of visual complexity[J]. Journal of
[58] Marketing，2010, 74（5）: 48-60.
[59] Raghubir P, Greenleaf E A. Ratios in proportion: What should the shape of the package be?[J]. Journal of Marketing，2006, 70（2）: 95-107.
[60] Reppel A E, Szmigin I, Gruber T. The iPod phenomenon: Identifying a market leaders’ secrets through qualitative marketing research[J]. Journal of Product & Brand Management，2006, 15（4）: 239-249.
[61] Rizkallah, J. The big (unstructured) data problem[EB/OL]. https://www.forbes.com/sites/forbestechcouncil/2017/06/05/the- big-unstructured-data-problem/?sh=da78e05493a3, 2017-06-05.
[62] Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagating errors[J]. Nature，1986, 323（6088）: 533-536.
[63] Shin D, He S, Lee G M, et al. Enhancing social media analysis with visual data analytics: A deep learning approach[J]. MIS Quarterly，2020, 44（4）: 1459-1492.
[64] Shorten C, Khoshgoftaar T M. A survey on image data augmentation for deep learning[J]. Journal of Big Data，2019, 6（1）: 60.
[65] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[A]. 3rd international conference on learning representations[C]. San Diego, 2015.
[66] Sutton R S, Barto A G. Reinforcement learning: An introduction[M]. Cambridge, Massachusetts: MIT Press, 1998. [66]Tkachenko Y, Jedidi K. What personal information can a consumer facial image reveal? Implications for marketing ROI and consumer privacy[EB/OL]. http://dx.doi.org/10.2139/ssrn.3616470, 2020-06-01.
[67] Trafton A. In the blink of an eye[EB/OL]. https://news.mit.edu/2014/in-the-blink-of-an-eye-0116, 2014-01-16.
[68] Troncoso I, Luo L. Look the part? The role of profile pictures in online labor markets[EB/OL]. http://dx.doi.org/10.2139/ ssrn.3709554, 2020-11-25.
[69] Urban G, Timoshenko A, Dhillon P, et al. Is deep learning a game changer for marketing analytics?[J]. MIT Sloan Management Review，2020, 61（2）: 71-76.
[70] Verganti R, Vendraminelli L, Iansiti M. Innovation and design in the age of artificial intelligence[J]. Journal of Product Innovation Management，2020, 37（3）: 212-227.
[71] Vogel D R, Dickson G W, Lehman J A. Persuasion and the role of visual presentation support: The UM/3M study[R]. Working Paper No. MISRC-WP-86-11, 1986.
[72] Wedel M, Kannan P K. Marketing analytics for data-rich environments[J]. Journal of Marketing，2016, 80（6）: 97-121. [73]Wedel M, Pieters R. The buffer effect: The role of color when advertising exposures are brief and blurred[J]. Marketing Science，2015, 34（1）: 134-143.
[73] Xia F H, Chatterjee R, May J H. Using conditional restricted Boltzmann machines to model complex consumer shopping patterns[J]. Marketing Science，2019, 38（4）: 711-727.
[74] Xiao L, Ding M. Just the faces: Exploring the effects of facial features in print advertising[J]. Marketing Science，2014, 33（3）: 338-352.
[75] Xiao L, Kim H J, Ding M. An introduction to audio and visual research and applications in marketing[J]. Review of Marketing Research，2013, 10: 213-253.
[76] Xu J, Ding M. Transparent model of unabridged data (TMUD)[EB/OL]. https://doi.org/10.48550/arXiv.2106.07558, 2021-05- 23.
[77] Zhang J, Wedel M, Pieters R. Sales effects of attention to feature advertisements: A Bayesian mediation analysis[J]. Journal of Marketing Research，2009, 46（5）: 669-681.
[78] Zhang M X, Luo L. Can consumer-posted photos serve as a leading indicator of restaurant survival? Evidence from Yelp[J]. Management Science, 2022, doi: 10.1287/MNSC.2022.4359.
[79] Zhang S Y, Lee D, Singh P V, et al. What makes a good image? Airbnb demand analytics leveraging interpretable image features[J]. Management Science, 2021a, doi: 10.1287/mnsc.2021.4175.
[80] Zhang S Y, Mehta N, Singh P V, et al. Frontiers: Can an artificial intelligence algorithm mitigate racial economic inequality?
[81] An analysis in the context of Airbnb[J]. Marketing Science，2021b, 40（5）: 813-820.
Cite this article
Xu Jie, Xiao Li. A Picture Is Worth a Thousand Words: The Application of Image Data in Marketing[J]. Foreign Economics & Management, 2022, 44(9): 51-69. 
Export Citations as: For   
Previous: A Research on Digital Transformation: Integration Framework and Prospects
Next: Building the Microfoundation of Management: Insights from James G. March
ISSUE COVER
2022, Issue 09
RELATED ARTICLES
Forward-looking Information Disclosure and Analysts’ Earnings Forecasts: Evidence from Text Analysis and Machine Learning
Liu Yaoyao, Lu Junwei
Computational Advertising Research from the Perspective of Psychological Synergy: Review and Outlook
Liu Tong, Huang Minxue, Yu Zhengdong
Green Economy，Green Growth and Green Development：Concept Connotation and Literature Review
Shang Di, Li Huajing, Yao Jun
Journals  |   Articles  |   Submission  |   Subscribe  |   Contact us
Copyright 2024 © Department of Academic Journals, SUFE    No. 321 Guoding Rd, Shanghai, China    P.O: 200433