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—The Traveling Salesman Goes Shopping: The Systematic Deviations of Grocery Paths from TSP Optimality

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  • Sam K. Hui

    (Stern School of Business, New York University, New York 10012)

  • Peter S. Fader

    (The Wharton School of the University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Eric T. Bradlow

    (The Wharton School of the University of Pennsylvania, Philadelphia, Pennsylvania 19104)

Abstract

We examine grocery shopping paths using the traveling salesman problem (TSP) as a normative frame of reference. We define the TSP-path for each shopper as the shortest path that connects all of his purchases. We then decompose the length of each observed path into three components: the length of the TSP-path, the additional distance because of (i.e., not following the TSP-order of category purchases), and the additional distance because of (i.e., not following the shortest point-to-point route). We explore the relationship between these deviations and different aspects of in-store shopping/purchase behavior. Among other things, our results suggest that (1) a large proportion of trip length is because of travel deviation; (2) paths that deviate substantially from the TSP solution are associated with larger shopping baskets; (3) order deviation is strongly associated with purchase behavior, while travel deviation is not; and (4) shoppers with paths closer to the TSP solution tend to buy more from frequently purchased product categories.

Suggested Citation

  • Sam K. Hui & Peter S. Fader & Eric T. Bradlow, 2009. "—The Traveling Salesman Goes Shopping: The Systematic Deviations of Grocery Paths from TSP Optimality," Marketing Science, INFORMS, vol. 28(3), pages 566-572, 05-06.
    • Handle: RePEc:inm:ormksc:v:28:y:2009:i:3:p:566-572
    DOI: 10.1287/mksc.1080.0402
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    Cited by:

    1. Bradlow, Eric T. & Gangwar, Manish & Kopalle, Praveen & Voleti, Sudhir, 2017. "The Role of Big Data and Predictive Analytics in Retailing," Journal of Retailing, Elsevier, vol. 93(1), pages 79-95.
    2. Kakatkar, Chinmay & Spann, Martin, 2019. "Marketing analytics using anonymized and fragmented tracking data," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 117-136.
    3. Yonezawa, Koichi & Richards, Timothy J., 2016. "Risk Aversion and Preference for Store Price Format," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 41(3), pages 1-23.
    4. Xiaoling Zhang & Shibo Li & Raymond R. Burke, 2018. "Modeling the effects of dynamic group influence on shopper zone choice, purchase conversion, and spending," Journal of the Academy of Marketing Science, Springer, vol. 46(6), pages 1089-1107, November.
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    6. Pascucci, Federica & Nardi, Lorenzo & Marinelli, Luca & Paolanti, Marina & Frontoni, Emanuele & Gregori, Gian Luca, 2022. "Combining sell-out data with shopper behaviour data for category performance measurement: The role of category conversion power," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    7. Peter C. Reiss, 2011. "Structural Workshop Paper --Descriptive, Structural, and Experimental Empirical Methods in Marketing Research," Marketing Science, INFORMS, vol. 30(6), pages 950-964, November.
    8. Bradley Guthrie & Pratik J. Parikh, 2021. "Evaluating exposure of a retail rack layout in 3D," Flexible Services and Manufacturing Journal, Springer, vol. 33(1), pages 107-135, March.
    9. Péter Boros & Orsolya Fehér & Zoltán Lakner & Sadegh Niroomand & Béla Vizvári, 2016. "Modeling supermarket re-layout from the owner’s perspective," Annals of Operations Research, Springer, vol. 238(1), pages 27-40, March.
    10. Péter Boros & Orsolya Fehér & Zoltán Lakner & Sadegh Niroomand & Béla Vizvári, 2016. "Modeling supermarket re-layout from the owner’s perspective," Annals of Operations Research, Springer, vol. 238(1), pages 27-40, March.
    11. Larsen, Nils Magne & Sigurdsson, Valdimar & Breivik, Jørgen & Orquin, Jacob Lund, 2020. "The heterogeneity of shoppers’ supermarket behaviors based on the use of carrying equipment," Journal of Business Research, Elsevier, vol. 108(C), pages 390-400.
    12. Ostermeier, Manuel & Düsterhöft, Tobias & Hübner, Alexander, 2021. "A model and solution approach for store-wide shelf space allocation," Omega, Elsevier, vol. 102(C).
    13. Yina Lu & Andrés Musalem & Marcelo Olivares & Ariel Schilkrut, 2013. "Measuring the Effect of Queues on Customer Purchases," Management Science, INFORMS, vol. 59(8), pages 1743-1763, August.
    14. Ferracuti, N. & Norscini, C. & Frontoni, E. & Gabellini, P. & Paolanti, M. & Placidi, V., 2019. "A business application of RTLS technology in Intelligent Retail Environment: Defining the shopper's preferred path and its segmentation," Journal of Retailing and Consumer Services, Elsevier, vol. 47(C), pages 184-194.
    15. Elisabeth Honka & Stephan Seiler & Raluca Ursu, 2023. "Consumer Search: What Can We Learn from Pre-Purchase Data?," CESifo Working Paper Series 10786, CESifo.
    16. Wangungu J. & Robert Gichira, 2014. "Influence of Supply Chain Management Practices to Branding In Fast Moving Consumer Goods Industry in Kenya A Case Study of Thika Small and Medium Enterprises," International Journal of Academic Research in Business and Social Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Business and Social Sciences, vol. 4(9), pages 251-264, September.
    17. Sorensen, Herb & Bogomolova, Svetlana & Anderson, Katherine & Trinh, Giang & Sharp, Anne & Kennedy, Rachel & Page, Bill & Wright, Malcolm, 2017. "Fundamental patterns of in-store shopper behavior," Journal of Retailing and Consumer Services, Elsevier, vol. 37(C), pages 182-194.
    18. Boone, Tonya & Ganeshan, Ram & Jain, Aditya & Sanders, Nada R., 2019. "Forecasting sales in the supply chain: Consumer analytics in the big data era," International Journal of Forecasting, Elsevier, vol. 35(1), pages 170-180.

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