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

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dc.contributor.authorKim, Sundongko
dc.contributor.authorLee, Jae-Gilko
dc.date.accessioned2020-04-09T08:20:20Z-
dc.date.available2020-04-09T08:20:20Z-
dc.date.created2019-06-23-
dc.date.created2019-06-23-
dc.date.issued2020-03-
dc.identifier.citationKNOWLEDGE AND INFORMATION SYSTEMS, v.62, no.3, pp.1005 - 1035-
dc.identifier.issn0219-1377-
dc.identifier.urihttp://hdl.handle.net/10203/273859-
dc.description.abstractRecently, 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.-
dc.languageEnglish-
dc.publisherSPRINGER LONDON LTD-
dc.titleA systematic framework of predicting customer revisit with in-store sensors-
dc.typeArticle-
dc.identifier.wosid000519573800007-
dc.identifier.scopusid2-s2.0-85068335901-
dc.type.rimsART-
dc.citation.volume62-
dc.citation.issue3-
dc.citation.beginningpage1005-
dc.citation.endingpage1035-
dc.citation.publicationnameKNOWLEDGE AND INFORMATION SYSTEMS-
dc.identifier.doi10.1007/s10115-019-01373-y-
dc.contributor.localauthorLee, Jae-Gil-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorRevisit prediction-
dc.subject.keywordAuthorRetail analytics-
dc.subject.keywordAuthorPredictive analytics-
dc.subject.keywordAuthorFeature engineering-
dc.subject.keywordAuthorMarketing-
dc.subject.keywordAuthorMobility data-
dc.subject.keywordPlusSHOPPING PATH-
dc.subject.keywordPlusPREDICTABILITY-
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