Analyzing Customer Behavior In-Store: A Review of Available Technologies

Olaf Saßnick (), Robert Zniva (), Christina Schlager (), Matthäus Horn (), Reuf Kozlica (), Tina Neureiter (), Simon Kranzer (), Viktoria Müllner () and Julian Nöbauer ()
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Olaf Saßnick: Salzburg University of Applied Sciences
Robert Zniva: Salzburg University of Applied Sciences
Christina Schlager: Salzburg University of Applied Sciences
Matthäus Horn: Salzburg University of Applied Sciences
Reuf Kozlica: Salzburg University of Applied Sciences
Tina Neureiter: Salzburg University of Applied Sciences
Simon Kranzer: Salzburg University of Applied Sciences
Viktoria Müllner: Salzburg University of Applied Sciences
Julian Nöbauer: Salzburg University of Applied Sciences

A chapter in Advances in Digital Marketing and eCommerce, 2023, pp 243-252 from Springer

Abstract: Abstract Online channels collect almost effortlessly a lot of behavioral customer data in a fully automated manner. Providing a comparable number of metrics for traditional brick-and-mortar environments is challenging and requires the introduction of additional sensor technology. In this work a systematic computer science literature review is carried out to provide an overview of measurement objects, associated characteristics, and sensor technologies for brick-and-mortar retail environments. The measurement objects can be divided into product and person detection, with the latter focusing on determining the characteristics of persons, namely frequency, path, and features. From the identified sensor technologies, image and depth sensors are the most versatile, but also require the highest computational effort and infrastructure cost. For the detection of some characteristics, other technologies, like wireless beacons, provide a viable alternative. Results are presented in a suitability matrix. Based on the results we propose a stronger interdisciplinary collaboration between marketing and computer science scholars.

Keywords: In-store technology; Brick-and-mortar; Retailing; Stationary; Analytics; Sensor technology; Customer behavior (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-31836-8_25

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DOI: 10.1007/978-3-031-31836-8_25

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