Evaluating exposure of a retail rack layout in 3D

  • Bradley Guthrie
  • Pratik J. ParikhEmail author


Analyzing a retail layout accounting for how a shopper visually interacts with racks along their shopping path can help the retailer identify hot-warm-cold spots and better allocate products to racks in order to maximize revenue. We focus on estimating exposure (which locations are likely to be seen) and the intensity of exposure (how long are they seen) by accounting for the dynamic interaction between the human 3D field of regard with a physical rack layout. Our quantitative approach allows us to analyze the effect of varying layout design parameters (i.e., rack orientation, curvature, and height) on exposure considering both unidirectional and bidirectional travel. We also validate our approach through human participants in a virtual layout. We observed that curving racks in a layout with racks oriented 90° (i.e., perpendicular to the aisle) increase exposure by 3–121% over straight racks. Further, orientating curved racks at acute angles leads to substantially higher exposure (18–321%) over straight racks at 90°. Moreover, racks with a height just below shopper eye-height results in higher exposure over racks just above eye-height when top surfaces are accounted for as exposed. Considering the trade-off between exposure and layout floor space, when given an allowable 25% increase in floor space, we found feasible layouts that increased exposure ranging from 15–530% (over straight racks at 90°) depending on shopper scanning pattern, layout size, and traffic direction.


Retail layout Exposure Field of vision Quantitative model 



This research was supported in part by the US National Science Foundation under grant CMMI #1548394. We acknowledge the help of our collaborators in Computer Science (Thomas Wischgoll and Madison Glines) and Psychology (John Flach, Scott Watamaniuk, and Tyler Whitlock) at WSU during the human subjects study.

Supplementary material

10696_2019_9368_MOESM1_ESM.pdf (1 mb)
Supplementary material 1 (PDF 1039 kb)
10696_2019_9368_MOESM2_ESM.pdf (543 kb)
Supplementary material 2 (PDF 543 kb)


  1. Applebaum W (1951) Studying customer behavior in retail stores. J Mark 16(2):172–178CrossRefGoogle Scholar
  2. Botsali AR, Peters BA (2005) A network based layout design model for retail stores. In: Industrial engineering research conference, Atlanta, GAGoogle Scholar
  3. Cairns JP (1962) Suppliers, retailers, and shelf space. J Mark 26(3):34–36CrossRefGoogle Scholar
  4. Chandon P, Hutchinson J, Bradlow E, Young SH (2006) Measuring the value of point-of-purchase marketing with commercial eye-tracking data. In: INSEAD Business School Research Paper, (2007/22). Fontainebleau, FranceGoogle Scholar
  5. Daamen W (2004) Modelling passenger flows in public transport facilities (doctoral dissertation). Netherlands Research School for Transport, InfrastructureGoogle Scholar
  6. Dreze X, Hoch SJ, Purk ME (1994) Shelf management and space elasticity. J Retail 70(4):301–326CrossRefGoogle Scholar
  7. Dunne PM, Lusch RF, Gable M (1995) Retailing, 2nd edn. South-Western College Pub, CincinnatiGoogle Scholar
  8. Granbois DH (1968) Improving the study of customer in-store behavior. J Mark 32:28–33CrossRefGoogle Scholar
  9. Hendrickson K, Ailawadi KL (2014) Six lessons for in-store marketing from six years of mobile eye-tracking research. In: Grewal D, Roggeveen AL, NordfÄlt J (eds) Shopper marketing and the role of in-store marketing. Emerald Group Publishing Limited, Bingley, pp 57–74CrossRefGoogle Scholar
  10. Hui SK, Fader PS, Bradlow ET (2009) The traveling salesman goes shopping: the systematic deviations of grocery paths from TSP optimality. Mark Sci 28(3):566–572CrossRefGoogle Scholar
  11. Janiszewski C (1998) The influence of display characteristics on visual exploratory search behavior. J Consum Res 25(3):290–301CrossRefGoogle Scholar
  12. Knox G, Bell DR, Corsten D (2011) Situational determinants of unplanned buying in emerging and developed markets. In: Marketing science institute working paper series. Marketing Science Institute, Cambridge, MAGoogle Scholar
  13. Koltsova A, Tunçer B, Schmitt G (2013) Visibility analysis for 3D urban environments. In: eCAADe 2013: computation and performance—proceedings of the 31st international conference on education and research in computer aided architectural design in Europe, Delft, The NetherlandsGoogle Scholar
  14. Li C (2011) A facility layout design methodology for retail environments (Doctoral dissertation, University of Pittsburgh)Google Scholar
  15. Monty RA, Senders JW (1976) Eye movements and psychological processes. Lawrence Erlbaum Associates, Hillsdale.CrossRefGoogle Scholar
  16. Mowrey C, Parikh PJ, Gue KR (2018) A model for the retail rack layout problem. Eur J Oper Res 271(3):1100–1112CrossRefGoogle Scholar
  17. Mowrey C, Parikh PJ, Gue KR (2019) The impact of rack layout on visual experience in a retail store. Inf Syst Oper Res 57:75–98MathSciNetGoogle Scholar
  18. Parker JF Jr, West VR (1972) Bioastronautics Data Book. Biotechnology Inc., Falls Church VAGoogle Scholar
  19. Peters BA, Klutke G-A, Botsali AR (2004) Research issues in retail facility layout design. In Proceedings of eighth international material handling research, Colloquium, Graz, Austria, pp 399–414Google Scholar
  20. Phillips H (1993) How customers actually shop: customer interaction with the point of sale. J Res Mark Res Soc 35(1):51–63Google Scholar
  21. Pieters R, Warlop L (1999) Visual attention during brand choice: the impact of time pressure and task motivation. Int J Res Mark 16(1):1–16CrossRefGoogle Scholar
  22. Sorensen H (2009) Inside the mind of the shopper: the science of retailing. Wharton School Publications, Upper Saddle RiverGoogle Scholar
  23. Sorensen H, Bogomolova S, Anderson K, Trinh G, Sharp A, Kennedy R, Page B, Wright M (2017) Fundamental patterns of in-store shopper behavior. J Retail Consum Serv 37:182–194CrossRefGoogle Scholar
  24. US Census Bureau (2016) Latest annual retail trade report. Retrieved December 6, 2016, from
  25. Ware C (2004) Information visualization perception for design, 2nd edn. Morgan Kaufman Publishers, San FranciscoGoogle Scholar
  26. Wedel M, Pieters R (2008) A review of eye-tracking research in marketing. In: Malhotra NK (ed) Review of marketing research. Emerald Group Publishing Limited, Bingley, pp 123–147CrossRefGoogle Scholar
  27. Wickens CD, Hollands JG (2000) Engineering psychology and human performance, 3rd edn. Prentice Hall, Upper Saddle RiverGoogle Scholar
  28. Wischgoll T, Glines M, Whitlock T, Guthrie BR, Mowrey CM, Parikh PJ, Flach J (2017) Display infrastructure for virtual environments (DIVE). J Imaging Sci Technol 61(6):60406-1–60406-11Google Scholar
  29. Yapicioglu H, Smith AE (2012) Retail space design considering revenue and adjacencies using a racetrack aisle network. IIE Trans 44(6):446–458CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Biomedical, Industrial and Human Factors EngineeringWright State UniversityDaytonUSA

Personalised recommendations