IDEAS home Printed from https://ideas.repec.org/a/spr/elmark/v29y2019i1d10.1007_s12525-019-00336-z.html
   My bibliography  Save this article

Cognitive computing for customer profiling: meta classification for gender prediction

Author

Listed:
  • Robin Hirt

    (Karlsruhe Institute of Technology, Karlsruhe Service Research Institute)

  • Niklas Kühl

    (Karlsruhe Institute of Technology, Karlsruhe Service Research Institute)

  • Gerhard Satzger

    (Karlsruhe Institute of Technology, Karlsruhe Service Research Institute)

Abstract

Analyzing data from micro blogs is an increasingly interesting option for enterprises to learn about customer sentiments, public opinion, or unsatisfied needs. A better understanding of the underlying customer profiles (considering e.g. gender or age) can substantially enhance the economic value of the customer intimacy provided by this type of analytics. In a design science approach, we draw on information processing theory and meta machine learning to propose an extendable, cognitive classifier that, for profiling purposes, integrates and combines various isolated base classifiers. We evaluate its feasibility and the performance via a technical experiment, its suitability in a real use case, and its utility via an expert workshop. Thus, we augment the body of knowledge by a cognitive method that enables the integration of existing, as well as emerging customer profiling classifiers for an improved overall prediction performance. Specifically, we contribute a concrete classifier to predict the gender of German-speaking Twitter users. We enable enterprises to reap information from micro blog data to develop customer intimacy and to tailor individual offerings for smarter services.

Suggested Citation

  • Robin Hirt & Niklas Kühl & Gerhard Satzger, 2019. "Cognitive computing for customer profiling: meta classification for gender prediction," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(1), pages 93-106, March.
    • Handle: RePEc:spr:elmark:v:29:y:2019:i:1:d:10.1007_s12525-019-00336-z
    DOI: 10.1007/s12525-019-00336-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12525-019-00336-z
    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/s12525-019-00336-z?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. Alexander Wieneke & Christiane Lehrer, 2016. "Generating and exploiting customer insights from social media data," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(3), pages 245-268, August.
    2. Heimbach, Irina & Gottschlich, Jörg & Hinz, Oliver, 2015. "The Value of User's Facebook Profile Data for Product Recommendation Generation," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 77135, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    3. H Andrew Schwartz & Johannes C Eichstaedt & Margaret L Kern & Lukasz Dziurzynski & Stephanie M Ramones & Megha Agrawal & Achal Shah & Michal Kosinski & David Stillwell & Martin E P Seligman & Lyle H U, 2013. "Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-16, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kühl, Niklas & Schemmer, Max & Goutier, Marc & Satzger, Gerhard, 2022. "Artificial intelligence and machine learning," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 135656, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. Ransome Epie Bawack & Samuel Fosso Wamba & Kevin Daniel André Carillo & Shahriar Akter, 2022. "Artificial intelligence in E-Commerce: a bibliometric study and literature review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 297-338, March.
    3. Rainer Alt & Haluk Demirkan & Jan Fabian Ehmke & Anne Moen & Alfred Winter, 2019. "Smart services: The move to customer orientation," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(1), pages 1-6, March.
    4. Niklas Kühl & Max Schemmer & Marc Goutier & Gerhard Satzger, 2022. "Artificial intelligence and machine learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2235-2244, December.
    5. Jong Hwan Suh, 2022. "Machine-Learning-Based Gender Distribution Prediction from Anonymous News Comments: The Case of Korean News Portal," Sustainability, MDPI, vol. 14(16), pages 1-17, August.

    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. Jean M. Twenge & Hannah VanLandingham & W. Keith Campbell, 2017. "The Seven Words You Can Never Say on Television: Increases in the Use of Swear Words in American Books, 1950-2008," SAGE Open, , vol. 7(3), pages 21582440177, August.
    2. Gallus, Jana & Bhatia, Sudeep, 2020. "Gender, power and emotions in the collaborative production of knowledge: A large-scale analysis of Wikipedia editor conversations," Organizational Behavior and Human Decision Processes, Elsevier, vol. 160(C), pages 115-130.
    3. Liang Xu & Min Xu & Zehua Jiang & Xin Wen & Yishan Liu & Zaoyi Sun & Hongting Li & Xiuying Qian, 2023. "How have music emotions been described in Google books? Historical trends and corpus differences," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-11, December.
    4. Karol Król & Dariusz Zdonek, 2021. "Most Often Motivated by Social Media: The Who, the What, and the How Much—Experience from Poland," Sustainability, MDPI, vol. 13(20), pages 1-20, October.
    5. Oghazi, Pejvak & Schultheiss, Rakel & Chirumalla, Koteshwar & Kalmer, Nicolas Philipp & Rad, Fakhreddin F., 2020. "User self-disclosure on social network sites: A cross-cultural study on Facebook’s privacy concepts," Journal of Business Research, Elsevier, vol. 112(C), pages 531-540.
    6. Jörn H. Block & Walter Diegel & Christian Fisch, 2024. "How venture capital funding changes an entrepreneur’s digital identity: more self-confidence and professionalism but less authenticity!," Review of Managerial Science, Springer, vol. 18(8), pages 2287-2319, August.
    7. Vivek Kulkarni & Margaret L Kern & David Stillwell & Michal Kosinski & Sandra Matz & Lyle Ungar & Steven Skiena & H Andrew Schwartz, 2018. "Latent human traits in the language of social media: An open-vocabulary approach," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-18, November.
    8. Jong Hwan Suh, 2022. "Machine-Learning-Based Gender Distribution Prediction from Anonymous News Comments: The Case of Korean News Portal," Sustainability, MDPI, vol. 14(16), pages 1-17, August.
    9. Karel Hrazdil & Jiri Novak & Rafael Rogo & Christine Wiedman & Ray Zhang, 2020. "Measuring executive personality using machine‐learning algorithms: A new approach and audit fee‐based validation tests," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 47(3-4), pages 519-544, March.
    10. Luo, Shuli & He, Sylvia Y., 2021. "Understanding gender difference in perceptions toward transit services across space and time: A social media mining approach," Transport Policy, Elsevier, vol. 111(C), pages 63-73.
    11. Gow, Ian D. & Kaplan, Steven N. & Larcker, David F. & Zakolyukina, Anastasia A., 2016. "CEO Personality and Firm Policies," Research Papers 3444, Stanford University, Graduate School of Business.
    12. Mikkel Wallentin, 2018. "Sex differences in post-stroke aphasia rates are caused by age. A meta-analysis and database query," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-18, December.
    13. Hannes Rosenbusch & Maya Aghaei & Anthony M. Evans & Marcel Zeelenberg, 2021. "Psychological trait inferences from women’s clothing: human and machine prediction," Journal of Computational Social Science, Springer, vol. 4(2), pages 479-501, November.
    14. Eszter Hargittai, 2015. "Is Bigger Always Better? Potential Biases of Big Data Derived from Social Network Sites," The ANNALS of the American Academy of Political and Social Science, , vol. 659(1), pages 63-76, May.
    15. Rachel Winter & Anna Lavis, 2021. "The Impact of COVID-19 on Young People’s Mental Health in the UK: Key Insights from Social Media Using Online Ethnography," IJERPH, MDPI, vol. 19(1), pages 1-13, December.
    16. Daniel Preoţiuc-Pietro & Svitlana Volkova & Vasileios Lampos & Yoram Bachrach & Nikolaos Aletras, 2015. "Studying User Income through Language, Behaviour and Affect in Social Media," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-17, September.
    17. Hassani, Abdeslam & Mosconi, Elaine, 2022. "Social media analytics, competitive intelligence, and dynamic capabilities in manufacturing SMEs," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    18. Jungmin Kim & Juyong Park & Wonjae Lee, 2018. "Why do people move? Enhancing human mobility prediction using local functions based on public records and SNS data," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-29, February.
    19. Othman Boujena & Isabelle Ulrich & Aikaterini Manthiou & Bruno Godey, 2021. "Customer engagement and performance in social media: a managerial perspective," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(4), pages 965-987, December.
    20. Ulrike Baumöl & Linda Hollebeek & Reinhard Jung, 2016. "Dynamics of customer interaction on social media platforms," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(3), pages 199-202, August.

    More about this item

    Keywords

    Cognitive computing; Micro blog data; Gender detection; Meta machine learning; Meta classifier;
    All these keywords.

    JEL classification:

    • C - Mathematical and Quantitative Methods
    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics
    • O - Economic Development, Innovation, Technological Change, and Growth

    Statistics

    Access and download statistics

    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:elmark:v:29:y:2019:i:1:d:10.1007_s12525-019-00336-z. 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.