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A business application of RTLS technology in Intelligent Retail Environment: Defining the shopper's preferred path and its segmentation

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  • Ferracuti, N.
  • Norscini, C.
  • Frontoni, E.
  • Gabellini, P.
  • Paolanti, M.
  • Placidi, V.

Abstract

Over the last few years, shopper behaviour analysis in the retail environment has become an interesting topic both for managers who want to see the tangible impact of their trade marketing activities and researchers who are trying to identify new patterns or confirm known trends in this field. In such a context, technologies today play a central role, because of the possibility of implicitly observing how shoppers move inside the store, and collecting a wide data-set, through an unbiased approach, free from distortion. In this paper, we will describe the major outcomes from a study based on data collected through an innovative technology, Real Time Locating System (RTLS). We base our conclusions on a data-set, collected over three months of observations, composed of more than 18 million records transmitted by RTLS tags, monitoring the entire path of each shopper throughout the entire store area. The outcomes of our study are 1) the identification of the store's best performing areas based on traffic and dwell time metrics, 2) the development of a novel method to estimate the probability of in-store shopper paths and 3) a preliminary shopping trip segmentation.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:joreco:v:47:y:2019:i:c:p:184-194
    DOI: 10.1016/j.jretconser.2018.11.005
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    References listed on IDEAS

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    Cited by:

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    2. 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).
    3. Hossain, Md Afnan & Akter, Shahriar & Yanamandram, Venkata, 2020. "Revisiting customer analytics capability for data-driven retailing," Journal of Retailing and Consumer Services, Elsevier, vol. 56(C).
    4. Ladeira, Wagner Junior & de Oliveira Santini, Fernando & Pinto, Diego Costa, 2022. "Clockwise versus counterclockwise turning bias: Moderation effects of foot traffic and cognitive experience on visual attention," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
    5. Aversa, Joseph & Hernandez, Tony & Doherty, Sean, 2021. "Incorporating big data within retail organizations: A case study approach," Journal of Retailing and Consumer Services, Elsevier, vol. 60(C).
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    7. Aiello, Gaetano & Donvito, Raffaele & Acuti, Diletta & Grazzini, Laura & Mazzoli, Valentina & Vannucci, Virginia & Viglia, Giampaolo, 2020. "Customers’ Willingness to Disclose Personal Information throughout the Customer Purchase Journey in Retailing: The Role of Perceived Warmth," Journal of Retailing, Elsevier, vol. 96(4), pages 490-506.

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