Inferring gender and age of customers in shopping malls via indoor positioning data

doi:10.1177/2399808319841910

	Inferring gender and age of customers in shopping malls via indoor positioning data
	Liu, Yaxi 1,2; Cheng, Dayu 2,3; Pei, Tao 1,2,4; Shu, Hua 1,2; Ge, Xianhui 5; Ma, Ting 2; Du, Yunyan 2; Ou, Yang 6; Wang, Meng 6; Xu, Lianming 6
刊名	ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE
	2020-11-01
卷号	47 期号:9 页码:1672-1689
关键词	Customer profiles indoor positioning data spatial– temporal mobility interest preferences profile inference model
ISSN号	2399-8083
DOI	10.1177/2399808319841910
通讯作者	Pei, Tao(peit@lreis.ac.cn)
英文摘要	Customer profiles that include gender and age information are important to businesses and can be used to promote sales and provide personalized services. This information is gathered in e-commerce by analyzing customer visit records in virtual web space. However, such practice is difficult in brick-and-mortar businesses because the data that can be utilized to infer customer profiles are limited in physical spaces. In this paper, we attempt to infer the gender and age of customers using indoor positioning data generated by the Wi-Fi engine. To achieve this, we first construct a synthesized features vector to distinguish different profiles. This vector contains both customer spatial-temporal mobility characteristics and interest preferences. A hidden Markov model group detection method is then applied to detect customers who shop together because they usually show the same shopping behavior and it is difficult to distinguish their profiles. Finally, a random forest inference model is proposed to infer profiles of customers who shop alone. The indoor positioning data collected in the Longhu Tianjie Plaza in Chongqing were used as a case study. The result shows that customer profiles are indeed inferable from indoor positioning data. The accuracy of the gender inference model reaches 73.9%, while that of the age inference model is 67.9%. This demonstrates the potential value of new "big data" for promoting precision marketing and customer management in brick-and-mortar businesses.
资助项目	National Natural Science Foundation of China (NSFC)[41525004] ; National Natural Science Foundation of China (NSFC)[41421001]
WOS关键词	DEMOGRAPHICS ; ATTRIBUTES ; BEHAVIOR
WOS研究方向	Environmental Sciences & Ecology ; Geography ; Public Administration ; Urban Studies
语种	英语
出版者	SAGE PUBLICATIONS LTD
WOS记录号	WOS:000590190100011
资助机构	National Natural Science Foundation of China (NSFC)
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/156699]
专题	中国科学院地理科学与资源研究所
通讯作者	Pei, Tao
作者单位	1.Univ Chinese Acad Sci, Beijing, Peoples R China 2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, 11A,Datun Rd Anwai, Beijing 100101, Peoples R China 3.Hebei Univ Engn, Handan, Peoples R China 4.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing, Peoples R China 5.Longhu Grp Business Informat Ctr, Shanghai, Peoples R China 6.RTMAP Sci & Technol Ltd, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Liu, Yaxi,Cheng, Dayu,Pei, Tao,et al. Inferring gender and age of customers in shopping malls via indoor positioning data[J]. ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE,2020,47(9):1672-1689.
APA	Liu, Yaxi.,Cheng, Dayu.,Pei, Tao.,Shu, Hua.,Ge, Xianhui.,...&Xu, Lianming.(2020).Inferring gender and age of customers in shopping malls via indoor positioning data.ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE,47(9),1672-1689.
MLA	Liu, Yaxi,et al."Inferring gender and age of customers in shopping malls via indoor positioning data".ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE 47.9(2020):1672-1689.