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International Conference on Smart Objects and Technologies for Social Good

GOODTECHS 2017: Smart Objects and Technologies for Social Good pp 353–363Cite as

Recognizing Residents and Tourists with Retail Data Using Shopping Profiles

Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 233)

Abstract

The huge quantity of personal data stored by service providers registering customers daily life enables the analysis of individual fingerprints characterizing the customers’ behavioral profiles. We propose a methodological framework for recognizing residents, tourists and occasional shoppers among the customers of a retail market chain. We employ our recognition framework on a real massive dataset containing the shopping transactions of more than one million of customers, and we identify representative temporal shopping profiles for residents, tourists and occasional customers. Our experiments show that even though residents are about 33% of the customers they are responsible for more than 90% of the expenditure. We statistically validate the number of residents and tourists with national official statistics enabling in this way the adoption of our recognition framework for the development of novel services and analysis.

Keywords

  • Residents tourists classification
  • Customer shopping profile
  • Retail data
  • Spatio-temporal analytics
  • Data mining

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Notes

  1. 1.

    https://www.unicooptirreno.it/.

  2. 2.

    http://dati.istat.it/, http://demo.istat.it/.

  3. 3.

    Missing years are not available on the ISTAT website.

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Acknowledgement

This work is partially supported by the European Project SoBigData, 654024, http://www.sobigdata.eu. We thank UniCoop Tirreno for allowing us to analyze the data and to publish the results.

Author information

Authors and Affiliations

  1. KDDLab, ISTI-CNR, Pisa, Italy

    Riccardo Guidotti & Lorenzo Gabrielli

Authors
  1. Riccardo Guidotti
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  2. Lorenzo Gabrielli
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Corresponding author

Correspondence to Riccardo Guidotti .

Editor information

Editors and Affiliations

  1. University of Pisa, Pisa, Italy

    Barbara Guidi

  2. University of Pisa, Pisa, Italy

    Laura Ricci

  3. Polytechnic University of Valencia, Valencia, Spain

    Carlos Calafate

  4. University of Padua, Padua, Italy

    Ombretta Gaggi

  5. University of Antwerp, Antwerp, Belgium

    Johann Marquez-Barja

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Guidotti, R., Gabrielli, L. (2018). Recognizing Residents and Tourists with Retail Data Using Shopping Profiles. In: Guidi, B., Ricci, L., Calafate, C., Gaggi, O., Marquez-Barja, J. (eds) Smart Objects and Technologies for Social Good. GOODTECHS 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 233. Springer, Cham. https://doi.org/10.1007/978-3-319-76111-4_35

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  • DOI: https://doi.org/10.1007/978-3-319-76111-4_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76110-7

  • Online ISBN: 978-3-319-76111-4

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