Understanding Visual Attention in Mobile Ads

Wen Xie, PhD, Mi Hyun Lee, PhD, Ming Chen, PhD, and Zhu Han, PhD

A survey by the Pew Research Center reveals that 85% of Americans own smartphones, with usage consistent across age groups—95% of users aged 18 to 49 and 83% of those aged 50 to 64.¹ This widespread adoption has driven significant growth in mobile advertising. According to Statista, as of Q2 2022, 60% of retail website orders in the United States were placed using smartphones, significantly higher than the 38% via desktops and 2% via tablets.² Thus, understanding how consumers engage with mobile advertisements is crucial for developing effective digital advertising strategies. 

Our study examines how online advertising elements, such as product descriptions and images, and shopping devices (mobile vs. PC) impact consumers’ visual attention in online shopping environments. Using a wearable eye-tracking device, we conducted a lab experiment with 132 participants, comparing their visual attention when shopping online on a PC versus a mobile device. Our findings demonstrate how different ad elements and shopping device types, as well as their interactions, influence consumer attention.

Our Study 

In our research, we focus on determinants and drivers of visual attention, a concept traced back to the 1890s. The attention process is described as “withdrawal from some things in order to deal effectively with others.” Drivers of attention can be categorized into either passive (bottom-up) or active (top-down). Passive or bottom-up factors pertain to advertisement features that determine perceptual salience (e.g., size and shape), while active or top-down factors reside in an individual’s attentional processes (e.g., product involvement).³

Research has shown that larger textual elements in print advertisements receive more attention. Accordingly, online shoppers tend to allocate attention to more salient ad elements—particularly those with large sizes or square layouts—within a rapid, transient viewing window.⁴

In this study, we focus on four ad elements—text, image, rating, and price—that capture consumers’ visual attention in an online shopping environment. We focus on these elements because each ad element represents a specific attribute that carries distinct information, and most online ads contain a combination of pictorial and textual presentations of a brand or product.⁵ We hypothesize that 1) textual ad elements (text, price, and rating) receive more attention than pictorial ad elements; 2) mobile ads receive less attention than PC ads; and 3) the effects of ad element types and shopping device types on attention interact such that shopping on mobile devices strengthens the positive effect of textual ad elements on attention but attenuates it for those shopping on a PC. To test our hypotheses, we conducted two lab experiments in two settings (PC and mobile devices) using a portable eye-tracking device, Tobii Pro Glass 2, which is commonly used in eye-tracking studies. The device had four sensors underneath the participants’ eyes to capture their eye movements. 

Setting One: PC-Based Eye-Tracking Experiment 

We first collected a data set of consumers’ visual attention to PC web ads. Fifty-three subjects (35 males and 18 females) participated. All had prior experience in booking hotels using PCs. We selected hotel shopping as the experimental context due to the availability of numerous hotel options, which facilitated training our models that are based on computer vision techniques to detect different ad elements. We tested two destinations: (1) Austin, Texas, as a representative business-trip destination and (2) Hawaii, as a representative leisure-trip destination. 

Setting Two: Smartphone-Based Portable Eye-Tracking Experiment

Next, we gathered data on consumers’ visual attention to smartphone ads. Among the 79 participants (62 males and 17 females), 83% had prior experience using smartphones to book hotels. To study consumers’ visual attention to mobile ads, we focused on the same four ad components—hotel image, rating, price, and text—as those in the PC web ads. 

Our Findings

The results indicate that hotel text elements received more eye-fixation count and duration than hotel images, a pattern consistent across both devices. These findings support our hypotheses that textual ad elements receive more attention than pictorial elements. We also found that mobile ads receive less attention than PC ads, and that the positive effect of textual ad elements on attention is more pronounced on mobile devices. We also observed that ads received increased attention when consumers were nearing the end of their hotel search and preparing to make a decision, compared to earlier in the shopping process. Consumers typically formed an initial set of hotel options through a quick glance, then gathered more detailed information before making a final decision.⁶ This deliberation phase involved heightened attention to ads, further supporting our study’s conclusions.

Real Estate Implications

Our findings offer multiple implications for real estate managers seeking to optimize online advertising strategies. First, given that textual ad elements garnered more attention than images, listing agents should prioritize clear and informative text in online listings to convey key property details effectively to potential buyers or renters. Additionally, because mobile ads receive less attention than those viewed on PCs, real estate professionals should optimize mobile ad formats by increasing the prominence of key information and ensuring that mobile-responsive ad layouts are utilized. By aligning advertising strategies with these insights, real estate firms can improve user engagement and increase the likelihood of converting online views into inquiries and sales.  

. . . . . . . . . . . . . . . . . . .

Recommended Reading 

Xie, Wen, Mi Hyun Lee, Ming Chen, and Zhu Han (2024), “Understanding Consumers’ Visual Attention in Mobile Advertisements: An Ambulatory Eye-Tracking Study with Machine Learning Techniques,” Journal of Advertising, 53(3), 397-415. https://doi.org/10.1080/00913367.2023.2258388

. . . . . . . . . . . . . . . . . . .

References

1. Pew Research Center (2021), “Mobile Fact Sheet,” April 07, 2021, from https://www.pewresearch.org/internet/fact-sheet/mobile/.
2. Statista (2024), “U.S. retail site device Visit & Order Share,” Retrieved March 24, 2025, from https://www.statista.com/statistics/201680/retail-site-device-visit-order-share-usa/
3. Pieters, Rik and Michel Wedel (2004), “Attention Capture and Transfer in Advertising: Brand, Pictorial, and Text-Size Effects,” Journal of Marketing, 68(2), 36–50. https://doi.org/10.1509/jmkg.68.2.36.27794
4. Chun, Marvin and Jeremy Wolfe (2005), “Visual Attention,” In Blackwell Handbook of Sensation and Perception, edited by E. Bruce Goldstein, 272-310. Malden, MA: Blackwell Publishing.
5. Wedel, Michel and Rik Pieters (2008), “Eye Tracking for Visual Marketing,” Foundations and Trends® in Marketing, 1(4), 231-320. https://doi.org/10.1561/1700000011
6. Noone, Breffni M. and Stephani K. A. Robson (2016), “Understanding Consumers’ Inferences from Price and Nonprice Information in the Online Lodging Purchase Decision,” Service Science, 8(2), 108-123. https://doi.org/10.1287/serv.2016.0141

. . . . . . . . . . . . . . . . . . .

About the Authors

Wen Xie, PhD 
Faculty Postdoctoral Fellow, Northeastern University 
Dr. Wen Xie (PhD – University of Houston) leverages computer vision and big data analytics to conduct marketing research for actionable business implications. In his first research stream, he analyzes large observational datasets using empirical modeling to address important problems in visual marketing, TV and online advertising, and social media. In the second research stream, he conducts eye-tracking and behavioral experiments to understand consumer behavior.

Mi Hyun Lee, PhD 
Assistant Professor, Northwestern University 
Dr. Mi Hyun Lee (PhD – Virginia Tech & Arizona State University) research interests lie in studying consumer response to digital and mobile platforms, advertising, and media using econometric and statistical models. Her research has been published in premier journals, such as Information Systems Research, Journal of Advertising, International Journal of Advertising, Journal of Advertising Research, and New Media & Society. Dr. Lee also worked as a senior research fellow at the Samsung Research Institute of Finance in Seoul, South Korea. 

Ming Chen, PhD 
Assistant Professor, University of North Carolina, Charlotte  
Dr. Ming Chen’s (PhD – University of Houston) research interests focus on quantitative marketing and retailing. She is interested in applying statistical models and machine learning methods to examine the influences of consumers’ visual attention on their subsequent behaviors and to uncover the underlying factors that affect consumers’ eye movement during shopping journeys. Her research has been published in top-tier marketing academic journals, including the Journal of Marketing Research and the Journal of Retailing, and others. Dr. Chen teaches Digital Marketing Analytics to both undergraduate and graduate-level (MBA) students.  

Zhu Han, PhD 
Moores Professor, University of Houston 
Dr. Zhu Han’s (PhD – University of Maryland) research interests are in game theory, wireless networking, security, data analysis, and the smart grid. He has served in various academic roles over the years at several institutions, including the University of Maryland, Boise State University, and now the University of Houston. Dr. Han has received several awards and honors, including the ACM/IEEE/AAAS Fellow and ACM/IEEE Distinguished Speaker. 

. . . . . . . . . . . . . . . . . . .

Download this Article