When AI meets store layout design: a review

Author:

Nguyen KienORCID,Le Minh,Martin Brett,Cil Ibrahim,Fookes Clinton

Abstract

AbstractAn efficient store layout presents merchandise to attract customer attention and encourages customers to walk down more aisles which exposes them to more merchandise, which has been shown to be positively correlated with the sales. It is one of the most effective in-store marketing tactics which can directly influence customer decisions to boost store sales and profitability. The recent development of Artificial Intelligence techniques, especially with its sub-fields in Computer Vision and Deep Learning, has enabled retail stores to take advantage of existing CCTV infrastructure to extract in-store customer and business insights. This research aims to conduct a comprehensive review on existing approaches in store layout design and modern AI techniques that can be utilized in the layout design task. Based on this review, we propose an AI-powered store layout design framework. This framework applies advanced AI and data analysis techniques on top of existing CCTV video surveillance infrastructure to understand, predict and suggest a better store layout.

Funder

Queensland University of Technology

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Reference119 articles.

1. Ahmed AH, Kpalma K, Guedi AO (2017) Human detection using hog-svm, mixture of gaussian and background contours subtraction. In: 2017 13th International conference on signal-image technology internet-based systems (SITIS), pp 334–338. https://doi.org/10.1109/SITIS.2017.62

2. Anic ID, Radas S, Lim LK (2010) Relative effects of store traffic and customer traffic flow on shopper spending. Int Rev Retail Distrib Consum Res 20(2):237–250

3. Artificial intelligence for retail in 2020: 12 real-world use cases. https://spd.group/artificial-intelligence/ai-for-retail/?fbclid=IwAR0HM8tP2vQ9MI6jE2lrkD7JnyBP1NMlEAgRWqWWKKlHoctFctHnPC60J9M#Route_Optimization. Accessed: 2020-10-15

4. Barghash MA, Al-Qatawneh L, Ramadan S, Dababneh A (2017) Analytical hierarchy process applied to supermarket layout selection. J Appl Res Ind Eng 4(4):215–226

5. Bill M, Dale M (2001) Superstore interactivity: a new self-service paradigm of retail service? Int J Retail Distrib Manag 29(8):379–389

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