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Data-Driven Mall Advertising

Data-Driven Mall Advertising
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Author(s): Jiaxing Shen (Hong Kong Polytechnic University, Hong Kong), Yi Lau (Hong Kong Polytechnic University, Hong Kong) and Jiannong Cao (Hong Kong Polytechnic University, Hong Kong)
Copyright: 2019
Pages: 16
Source title: Smart Marketing With the Internet of Things
Source Author(s)/Editor(s): Dora Simões (University of Aveiro, Portugal), Belem Barbosa (University of Porto, Portugal) and Sandra Filipe (University of Aveiro, Portugal)
DOI: 10.4018/978-1-5225-5763-0.ch007

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Abstract

Mall advertising is a critical factor for retailers to gain revenue. Traditional mall advertising strategies mainly rely on impression and empiricism which might be inefficient and result in a waste of resources. Recent research demonstrates that the effectiveness of advertisements can be affected by exposure time and relevance to customers. Authors in this chapter propose a data-driven approach to achieve these goals using fine-grained trajectories of customers. They first preprocess the trajectories and model the floorplan. Then detect stopping locations where customers stay for relatively long time and analyze the correlation between different locations. They also detect customers' facing directions at each stopping location. Lastly, according to the correlation of stopping locations and customers' facing direction, appropriate advertising locations and contents can be determined. According to evaluation analysis, the proposed approach can significant improve average advertisement exposure time and advertisement relevance by 75% and 58%, respectively.

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