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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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    1. Colin F. Camerer & Teck-Hua Ho & Juin-Kuan Chong, 2004. "A Cognitive Hierarchy Model of Games," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(3), pages 861-898.
    2. Peter M. Guadagni & John D. C. Little, 1983. "A Logit Model of Brand Choice Calibrated on Scanner Data," Marketing Science, INFORMS, vol. 2(3), pages 203-238.
    3. Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
    4. Daniel Houser & Michael Keane & Kevin McCabe, 2004. "Behavior in a Dynamic Decision Problem: An Analysis of Experimental Evidence Using a Bayesian Type Classification Algorithm," Econometrica, Econometric Society, vol. 72(3), pages 781-822, May.
    5. Randolph E. Bucklin & James M. Lattin, 1991. "A Two-State Model of Purchase Incidence and Brand Choice," Marketing Science, INFORMS, vol. 10(1), pages 24-39.
    6. Robert J. Meyer & João Assunção, 1990. "The Optimality of Consumer Stockpiling Strategies," Marketing Science, INFORMS, vol. 9(1), pages 18-41.
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    4. 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.
    5. 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.
    6. Honka, Elisabeth & Seiler, Stephan & Ursu, Raluca, 2024. "Consumer search: What can we learn from pre-purchase data?," Journal of Retailing, Elsevier, vol. 100(1), pages 114-129.
    7. 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.
    8. 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.
    9. Ostermeier, Manuel & Düsterhöft, Tobias & Hübner, Alexander, 2021. "A model and solution approach for store-wide shelf space allocation," Omega, Elsevier, vol. 102(C).
    10. 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.
    11. 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.
    12. Stephan Seiler & Song Yao, 2017. "The impact of advertising along the conversion funnel," Quantitative Marketing and Economics (QME), Springer, vol. 15(3), pages 241-278, September.
    13. 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.
    14. Elisabeth Honka & Stephan Seiler & Raluca Ursu, 2023. "Consumer Search: What Can We Learn from Pre-Purchase Data?," CESifo Working Paper Series 10786, CESifo.
    15. 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).
    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.
    19. 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.

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