[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/h/spr/isochp/978-3-031-27058-1_3.html
   My bibliography  Save this book chapter

In-Store Traffic Density Estimation

In: Retail Space Analytics

Author

Listed:
  • Jimmy Azar

    (American University of Beirut)

  • Hoda Daou

    (American University of Beirut)

Abstract
Estimating in-store traffic density is critical for a product allocation that improves the retailer profit and enhances the customer shopping experience. We propose a way to estimate traffic densities based on a regression analysis carried over e-receipts from a supermarket in Lebanon. Our approach simplifies alternative approaches in the specification of the regression variables. We obtain the dependent variable (i.e. the traffic) via a demand filtering approach without reconstructing shopping paths. We then estimate the independent variable reflecting the way product allocation drives traffic in neighboring shelves through a straightforward attraction approach. We propose and compare alternate regression models, such as support vector regression, regression trees, kernel regression, and Gaussian processes. Our results show that regression trees and kernel regression methods perform well.

Suggested Citation

  • Jimmy Azar & Hoda Daou, 2023. "In-Store Traffic Density Estimation," International Series in Operations Research & Management Science, in: Ahmed Ghoniem & Bacel Maddah (ed.), Retail Space Analytics, pages 35-50, Springer.
    • Handle: RePEc:spr:isochp:978-3-031-27058-1_3
    DOI: 10.1007/978-3-031-27058-1_3
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:isochp:978-3-031-27058-1_3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.