9.4 Images

Potential Variables:

  • Structural Complexity: complexity due to visual features (Pieters, Wedel, and Batra 2010). Measured by the Canny Edge Detection technique (Forsythe, Sheehy, and Sawey 2003), because edge information is highly correlated with observed complexity

  • Color Complexity: complexity due to variety of colors within an image (Reinecke et al. 2013). Measured by the color variety algorithm (Bing Zhou, Shuang Xu, and Xin-xin Yang 2015)

    • \(C = \sum_{i = 0}^m n_i \log(\frac{n_i}{N})\) where

      • \(m\) is the number of distinct colors

      • \(n_i\) the number of pixels of the i-th color

      • \(N\) is the total number of pixels

  • Image Permanence: how adept images are at remaining in users’ memory (after users are no longer looking at them) (Khosla et al. 2012, 2015). Measured based on AMNet memorability index (Fajtl et al. 2018), which has been verified by (Leyva and Sanchez 2021) (achieved near human consistency in predicting image memorability).

  • Number of faces: based on Google Vision API

  • Aspect ratio: height divided by width

  • Average HSV value: Average HSV values across all pixels within an image

  • Themes: LDA based on keywords (labels) from Google Vision API

Pieters, Wedel, and Zhang (2007) found that optimal feature design of ads (e.g., brand, text, pictorial, price, and promotion) can be achieved without increased costs. Moreover, the authors also propose two entropy-based measures of clutter effects, where they characterize the salience of feature ads based on Attention Engagement Theory.

Wedel and Kannan (2016) is a review on marketing analytics for data-rich environments. And competition for attention in these environments are more fierce.

Y. Li and Xie (2019) found a significant and robust positive mere presence effect of image content on user engagement in both product categories on Twitter, and high-quality and professionally shot pictures consistently lead to higher engagement on both platforms (e.g., Twitter and Instagram) for both product categories. However, the effect of colorfulness varies by product category, while the presence of human face and image–text fit can induce higher user engagement on Twitter but not on Instagram. And the fit between the image and the accompanying message matters

Jalali and Papatla (2016) study visual UGC. Where color compositions were operationalized as combinations level of hue, chroma, and brightness. Consumer engagement (i.e., click rate) is higher for photos that have higher proportions of green and lower proportions of red and cyan, as well as higher chroma of red and blue.

References

Bing Zhou, Shuang Xu, and Xin-xin Yang. 2015. “Computing the Color Complexity of Images.” 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), August. https://doi.org/10.1109/fskd.2015.7382237.
Fajtl, Jiri, Vasileios Argyriou, Dorothy Monekosso, and Paolo Remagnino. 2018. “AMNet: Memorability Estimation with Attention.” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June. https://doi.org/10.1109/cvpr.2018.00666.
Forsythe, Alex, Noel Sheehy, and Martin Sawey. 2003. “Measuring Icon Complexity: An Automated Analysis.” Behavior Research Methods, Instruments and Computers 35 (2): 334–42. https://doi.org/10.3758/bf03202562.
Jalali, Nima Y., and Purushottam Papatla. 2016. “The Palette That Stands Out: Color Compositions of Online Curated Visual UGC That Attracts Higher Consumer Interaction.” Quantitative Marketing and Economics 14 (4): 353–84. https://doi.org/10.1007/s11129-016-9178-1.
Khosla, Aditya, Akhil S. Raju, Antonio Torralba, and Aude Oliva. 2015. “Understanding and Predicting Image Memorability at a Large Scale.” 2015 IEEE International Conference on Computer Vision (ICCV), December. https://doi.org/10.1109/iccv.2015.275.
Khosla, Aditya, Jianxiong Xiao, Phillip Isola, Antonio Torralba, and Aude Oliva. 2012. “Image Memorability and Visual Inception.” SIGGRAPH Asia 2012 Technical Briefs on - SA ’12. https://doi.org/10.1145/2407746.2407781.
Leyva, Roberto, and Victor Sanchez. 2021. “Video Memorability Prediction via Late Fusion of Deep Multi-Modal Features.” In 2021 IEEE International Conference on Image Processing (ICIP), 2488–92. IEEE.
Li, Yiyi, and Ying Xie. 2019. “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. https://doi.org/10.1177/0022243719881113.
Pieters, Rik, Michel Wedel, and Rajeev Batra. 2010. “The Stopping Power of Advertising: Measures and Effects of Visual Complexity.” Journal of Marketing 74 (5): 48–60. https://doi.org/10.1509/jmkg.74.5.48.
Pieters, Rik, Michel Wedel, and Jie Zhang. 2007. “Optimal Feature Advertising Design Under Competitive Clutter.” Management Science 53 (11): 1815–28. https://doi.org/10.1287/mnsc.1070.0732.
Reinecke, Katharina, Tom Yeh, Luke Miratrix, Rahmatri Mardiko, Yuechen Zhao, Jenny Liu, and Krzysztof Z. Gajos. 2013. “Predicting Users’ First Impressions of Website Aesthetics with a Quantification of Perceived Visual Complexity and Colorfulness.” Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, April. https://doi.org/10.1145/2470654.2481281.
Wedel, Michel, and P.K. Kannan. 2016. “Marketing Analytics for Data-Rich Environments.” Journal of Marketing 80 (6): 97–121. https://doi.org/10.1509/jm.15.0413.