IDEAS home Printed from https://ideas.repec.org/a/spr/jclass/v38y2021i1d10.1007_s00357-019-09360-2.html
   My bibliography  Save this article

Consumer Segmentation Based on Use Patterns

Author

Listed:
  • Juan José Fernández-Durán

    (ITAM)

  • María Mercedes Gregorio-Domínguez

    (ITAM)

Abstract

Recent technological advances have enabled the easy collection of consumer behavior data in real time. Typically, these data contain the time at which a consumer engages in a particular activity such as entering a store, buying a product, or making a call. The occurrence time of certain events must be analyzed as circular random variables, with 24:00 corresponding to 0:00. To effectively implement a marketing strategy (pricing, promotion, or product design), consumers should be segmented into homogeneous groups. This paper proposes a methodology based on circular statistical models from which we construct a clustering algorithm based on the use patterns of consumers. In particular, we model temporal patterns as circular distributions based on nonnegative trigonometric sums (NNTSs). Consumers are clustered into homogeneous groups based on their vectors of parameter estimates by using a spherical k-means clustering algorithm. For this purpose, we define the parameter space of NNTS models as a hypersphere. The methodology is applied to three real datasets comprising the times at which individuals send short-service messages and start voice calls and the check-in times of the users of a mobile application Foursquare.

Suggested Citation

  • Juan José Fernández-Durán & María Mercedes Gregorio-Domínguez, 2021. "Consumer Segmentation Based on Use Patterns," Journal of Classification, Springer;The Classification Society, vol. 38(1), pages 72-88, April.
    • Handle: RePEc:spr:jclass:v:38:y:2021:i:1:d:10.1007_s00357-019-09360-2
    DOI: 10.1007/s00357-019-09360-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00357-019-09360-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00357-019-09360-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hornik, Kurt, 2005. "A CLUE for CLUster Ensembles," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i12).
    2. Glenn Milligan, 1980. "An examination of the effect of six types of error perturbation on fifteen clustering algorithms," Psychometrika, Springer;The Psychometric Society, vol. 45(3), pages 325-342, September.
    3. Hornik, Jacob & Miniero, Giulia, 2009. "Synchrony effects on customers' responses and behaviors," International Journal of Research in Marketing, Elsevier, vol. 26(1), pages 34-40.
    4. Hornik, Kurt & Feinerer, Ingo & Kober, Martin & Buchta, Christian, 2012. "Spherical k-Means Clustering," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(i10).
    5. Douglas Steinley & Michael J. Brusco, 2007. "Initializing K-means Batch Clustering: A Critical Evaluation of Several Techniques," Journal of Classification, Springer;The Classification Society, vol. 24(1), pages 99-121, June.
    6. Montero, Pablo & Vilar, José A., 2014. "TSclust: An R Package for Time Series Clustering," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i01).
    7. Matthias Studer & Gilbert Ritschard & Alexis Gabadinho & Nicolas S. Müller, 2011. "Discrepancy Analysis of State Sequences," Sociological Methods & Research, , vol. 40(3), pages 471-510, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aurora Torrente & Juan Romo, 2021. "Initializing k-means Clustering by Bootstrap and Data Depth," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 232-256, July.
    2. Michael Brusco & Douglas Steinley, 2015. "Affinity Propagation and Uncapacitated Facility Location Problems," Journal of Classification, Springer;The Classification Society, vol. 32(3), pages 443-480, October.
    3. Michael Brusco & Douglas Steinley, 2007. "A Comparison of Heuristic Procedures for Minimum Within-Cluster Sums of Squares Partitioning," Psychometrika, Springer;The Psychometric Society, vol. 72(4), pages 583-600, December.
    4. Nicholas Longford & Ioana C. Salagean, 2013. "A study of the labour market trajectories in the Grand Duchy of Luxembourg," Economics Working Papers 1396, Department of Economics and Business, Universitat Pompeu Fabra.
    5. Kim, Junyung & Shah, Asad Ullah Amin & Kang, Hyun Gook, 2020. "Dynamic risk assessment with bayesian network and clustering analysis," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    6. Goethner, Maximilian & Hornuf, Lars & Regner, Tobias, 2021. "Protecting investors in equity crowdfunding: An empirical analysis of the small investor protection act," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    7. John C. McCabe-Dansted & Arkadii Slinko, 2006. "Exploratory Analysis of Similarities Between Social Choice Rules," Group Decision and Negotiation, Springer, vol. 15(1), pages 77-107, January.
    8. Curci, Ylenia & Mongeau Ospina, Christian A., 2016. "Investigating biofuels through network analysis," Energy Policy, Elsevier, vol. 97(C), pages 60-72.
    9. Ali Abdelzadeh, 2014. "The Impact of Political Conviction on the Relation Between Winning or Losing and Political Dissatisfaction," SAGE Open, , vol. 4(2), pages 21582440145, May.
    10. Gabriela Cecilia Stanciulescu & Gabriela Nicoleta Diaconescu & Dan Mihnea Diaconescu, 2015. "Health, Spa, Wellness Tourism. What is the Difference?," Knowledge Horizons - Economics, Faculty of Finance, Banking and Accountancy Bucharest,"Dimitrie Cantemir" Christian University Bucharest, vol. 7(3), pages 158-161, September.
    11. Bordt, Michael, 2018. "Discourses in Ecosystem Accounting: A Survey of the Expert Community," Ecological Economics, Elsevier, vol. 144(C), pages 82-99.
    12. Chang, Yuan-Chieh & Chen, Min-Nan, 2016. "Service regime and innovation clusters: An empirical study from service firms in Taiwan," Research Policy, Elsevier, vol. 45(9), pages 1845-1857.
    13. Jerzy Korzeniewski, 2016. "New Method Of Variable Selection For Binary Data Cluster Analysis," Statistics in Transition New Series, Polish Statistical Association, vol. 17(2), pages 295-304, June.
    14. Hossain, Ferdaus & Onyango, Benjamin M. & Adelaja, Adesoji O. & Schilling, Brian J. & Hallman, William K., 2002. "Uncovering Factors Influencing Public Perceptions Of Food Biotechnology," Research Reports 18178, Rutgers University, Food Policy Institute.
    15. Ricardo Fraiman & Badih Ghattas & Marcela Svarc, 2013. "Interpretable clustering using unsupervised binary trees," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(2), pages 125-145, June.
    16. Egu, Oscar & Bonnel, Patrick, 2021. "Medium-term public transit route ridership forecasting: What, how and why? A case study in Lyon," Transport Policy, Elsevier, vol. 105(C), pages 124-133.
    17. Faicel Chamroukhi, 2016. "Piecewise Regression Mixture for Simultaneous Functional Data Clustering and Optimal Segmentation," Journal of Classification, Springer;The Classification Society, vol. 33(3), pages 374-411, October.
    18. Balepur, Prashant Narayan, 1998. "Impacts of Computer-Mediated Communication on Travel and Communication Patterns: The Davis Community Network Study," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6cb1f85c, Institute of Transportation Studies, UC Berkeley.
    19. Matos, José M.A. & Ramos, Sandra & Costa, Vítor, 2019. "Stochastic simulated rents in Portuguese public-private partnerships," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 107-117.
    20. Glenn Milligan, 1981. "A monte carlo study of thirty internal criterion measures for cluster analysis," Psychometrika, Springer;The Psychometric Society, vol. 46(2), pages 187-199, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jclass:v:38:y:2021:i:1:d:10.1007_s00357-019-09360-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.