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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
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    References listed on IDEAS

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    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.
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    Citations

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    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.

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    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

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