Alex Leykin

Areas of Expertise

Computer Vision, Shopper Behavior, Software Engineering

Academic Degrees

• PhD, Indiana University
• MS, Indiana University
• BA, Kharkiv Polytechnical Institute, Ukraine

Professional Experience

• Kelley School of Business, Indiana University, 2008-present
• School of Informatics and Computing and Engineering, Indiana University, Adjunct Research Scientist, 2008-present

Selected Publications

• Chen, M., Burke, R. R., Hui, S. K., and Leykin, A. (2021). Understanding Lateral and Vertical Biases in Point-of-Purchase Product Considerations: An In-Store Ambulatory Eye-Tracking Study. Journal of Marketing Research, in press. View Full Text

Abstract

Abstract

Given the conventional wisdom that “unseen is unsold,” retail practitioners are keenly interested in understanding consumers’ attention to products in the store. Using in-store ambulatory eye-tracking, we investigate the extent to which lateral and vertical biases drive consumers'' attention in a grocery store environment. Our dataset offers a complete picture of not only where the shopper is located, but also the shopper’s field of view and visual fixations during the trip. Using our novel dataset, we address two research questions:  First, do shoppers have a higher propensity to pay attention to products on their left or right side as they traverse an aisle (i.e., is the right side the “right side”)?  Second, do shoppers tend to pay more attention to products at their eye level (i.e., is eye-level “buy-level”)? We utilize the exogenous variations in the direction by which shoppers traverse an aisle (northward vs southward), obtainable from their shopping paths, to identify lateral bias. The exogenous variation of shoppers'' eye-level positions, due to their differences in height, is used to identify vertical bias. We find that shoppers pay more attention to products on their right side when traversing an aisle, and this bias holds for both right- and left-handed shoppers. Contrary to many practitioners’ belief, we find that eye-level is not “buy-level”; rather, the product level that has the highest propensity to capture shoppers’ attention is about 14.7 inches below eye-level (which is around chest level). Further, this vertical bias becomes more prominent during the latter part of a shopping trip.

• Burke, R. R. and Leykin, A. (2014). Identifying the Drivers of Shopper Attention, Engagement, and Purchase. In Dhruv Grewal, Anne L. Roggeveen, and Jens Norfalt, (eds.), _Shopper Marketing and the Role of In-store Marketing, Review of Marketing Research, 11, _147-187. Bingley, UK: Emerald Group Publishing Limited.

Abstract

Abstract

To cope with the complexity of modern retail stores and personal time constraints, shoppers must be selective in processing information. During a typical shopping trip, they visit only a fraction of a store’s departments and categories, examine a small subset of the available products, and often make selections in just a few seconds. New research  techniques can help marketers understand how customers allocate their attention and assess the impact of in-store factors on shopper behavior. This chapter summarizes studies using observational research, virtual reality simulations, and eye tracking to identify the drivers of shopper attention, product engagement, and purchase conversion. These include shopper goals; product assortment, package appearance, price, and merchandising; shelf space allocation, organization, and adjacencies; and salesperson interaction. The research reveals that small changes in a product’s appearance and presentation can have a powerful impact on consideration and choice.

• Zhang, X., Li, S., Burke, R. R., and Leykin, A. (2014). An Examination of Social Influence on Shopper Behavior Using Video Tracking Data. Journal of Marketing, 78(5), 24-41.
• Ran Y., Leykin A., and Hammoud R. (2009). Thermal-Visible Video Fusion for Moving Target Tracking and Pedestrian Motion Analysis and Classification. In R.I. Hammoud (Ed.) Augmented Vision Perception in Infrared. Advances in Pattern Recognition. London, UK: Springer.
• Cutzu, F., Hammoud, R., and Leykin, A. (2005). Distinguishing paintings from photographs. _Computer Vision and Image Understanding (CVIU), 100(_3), 249-273.
• Leykin, A., Cutzu, F., and Tuceryan, M. (2004). Using multiple views to resolve human body tracking ambiguities. British Machine Vision Conference (BMVC). London, UK: Kingston University.

Edited on December 11, 2018