A systematic framework of predicting customer revisit with in-store sensors

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Recently, there is a growing number of off-line stores that are willing to conduct customer behavior analysis. In particular, predicting revisit intention is of prime importance, because converting first-time visitors to loyal customers is very profitable. Thanks to noninvasive monitoring, shopping behaviors and revisit statistics become available from a large proportion of customers who turn on their mobile devices. In this paper, we propose a systematic framework to predict the revisit intention of customers using Wi-Fi signals captured by in-store sensors. Using data collected from seven flagship stores in downtown Seoul, we achieved 67–80% prediction accuracy for all customers and 64–72% prediction accuracy for first-time visitors. The performance improvement by considering customer mobility was 4.7–24.3%. Furthermore, we provide an in-depth analysis regarding the effect of data collection period as well as visit frequency on the prediction performance and present the robustness of our model on missing customers. We released some tutorials and benchmark datasets for revisit prediction at https://github.com/kaist-dmlab/revisit.
Publisher
SPRINGER LONDON LTD
Issue Date
2020-03
Language
English
Article Type
Article
Citation

KNOWLEDGE AND INFORMATION SYSTEMS, v.62, no.3, pp.1005 - 1035

ISSN
0219-1377
DOI
10.1007/s10115-019-01373-y
URI
http://hdl.handle.net/10203/273859
Appears in Collection
IE-Journal Papers(저널논문)
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