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

  • Sundong Kim
  • Jae-Gil LeeEmail author
Regular Paper
  • 72 Downloads

Abstract

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.

Keywords

Revisit prediction Retail analytics Predictive analytics Feature engineering Marketing Mobility data 

Notes

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) (No. 2017R1E1A1A01075927). We appreciate Minseok Kim for helping surveys on off-line stores and drawing floor plans. We also thank ZOYI for providing active discussion in regard to the datasets.

Supplementary material

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Graduate School of Knowledge Service EngineeringKAISTDaejeonRepublic of Korea
  2. 2.Department of Industrial and Systems EngineeringKAISTDaejeonRepublic of Korea

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