Understanding the predictability of user demographics from cyber-physical-socialbehaviours in indoor retail spaces

Publication Year: 2018 Publication Type : JournalArticle


Understanding the association between customer demographics and behaviour is critical for operators of indoor retail spaces. This study explores such an association based on a combined understanding of customer Cyber (online), Physical, and (some aspects of ) Social (CPS) behaviour, at the conjunction of corresponding CPS spaces. We combine the results of a traditional questionnaire with large-scale WiFi access logs, which capture customer cyber and physical behaviour. We investigate the predictability of user demographics based on CPS behaviors captured from both sources. We find (1) strong correlations between users’ demographics and their CPS behaviors; (2) log-recorded cyber-physical behavior reflects well data captured in the corresponding questionnaire; (3) different CPS behaviors contribute differently to the predictability of demographic attributes; and (4) the predictability of user demographics from logs is comparable to questionnaire-based data. As such, our study provides strong support for demographic studies based on large-scale logs data capture.


    author = {Yongli Ren and Martin Tomko and Flora D. Salim and Jeffrey Chan and Mark Sanderson},
    bibsource = {dblp computer science bibliography, https://dblp.org},
    biburl = {https://dblp.org/rec/journals/epjds/RenTSCS18.bib},
    doi = {10.1140/epjds/s13688-017-0128-2},
    journal = {EPJ Data Sci.},
    number = {1},
    pages = {1},
    timestamp = {Wed, 25 Sep 2019 01:00:00 +0200},
    title = {Understanding the predictability of user demographics from cyber-physical-social behaviours in indoor retail spaces},
    url = {https://doi.org/10.1140/epjds/s13688-017-0128-2},
    volume = {7},
    year = {2018}


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