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Supporting customer-oriented marketing with artificial intelligence: automatically quantifying customer needs from social media

Author

Listed:
  • Niklas Kühl

    (Karlsruhe Service Research Institute (KSRI))

  • Marius Mühlthaler

    (Karlsruhe Service Research Institute (KSRI))

  • Marc Goutier

    (Karlsruhe Service Research Institute (KSRI))

Abstract

The elicitation and monitoring of customer needs is an important task for businesses, allowing them to design customer-centric products and services and control marketing activities. While there are different approaches available, most lack in automation, scalability and monitoring capabilities. In this work, we demonstrate the feasibility towards an automated prioritization and quantification of customer needs from social media data. To do so, we apply a supervised machine learning approach on the example of previously labeled Twitter data from the domain of e-mobility. We descriptively code over 1000 German tweets and build eight distinct classification models, so that a resulting artifact can independently determine the probabilities of a tweet containing each of the eight previously defined needs. To increase the scope of application, we deploy the machine learning models as part of a web service for public use. The resulting artifact can provide valuable insights for need elicitation and monitoring when analyzing user-generated content on a large scale.

Suggested Citation

  • Niklas Kühl & Marius Mühlthaler & Marc Goutier, 2020. "Supporting customer-oriented marketing with artificial intelligence: automatically quantifying customer needs from social media," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(2), pages 351-367, June.
    • Handle: RePEc:spr:elmark:v:30:y:2020:i:2:d:10.1007_s12525-019-00351-0
    DOI: 10.1007/s12525-019-00351-0
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    References listed on IDEAS

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    Cited by:

    1. Andreas J. Steur & Fabian Fritzsche & Mischa Seiter, 2022. "It’s all about the text: An experimental investigation of inconsistent reviews on restaurant booking platforms," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1187-1220, September.
    2. 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).
    3. Konstantinos Sikelis & George E. Tsekouras & Konstantinos Kotis, 2021. "Ontology-Based Feature Selection: A Survey," Future Internet, MDPI, vol. 13(6), pages 1-28, June.
    4. 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.
    5. Sanaz Ghorbanloo & Sajjad Shokouhyar, 2023. "Consumers' attitude footprint on sustainable development in developed and developing countries: a case study in the electronic industry," Operations Management Research, Springer, vol. 16(3), pages 1444-1475, September.
    6. Ahmadi, Sadra & Shokouhyar, Sajjad & Amerioun, Motahare & Salehi Tabrizi, Neda, 2024. "A social media analytics-based approach to customer-centric reverse logistics management of electronic devices: A case study on notebooks," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).
    7. Christian Janiesch & Patrick Zschech & Kai Heinrich, 2021. "Machine learning and deep learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 685-695, September.
    8. 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.
    9. Philipp Ebel & Matthias Söllner & Jan Marco Leimeister & Kevin Crowston & Gert-Jan Vreede, 2021. "Hybrid intelligence in business networks," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 313-318, June.
    10. Federico Barravecchia & Luca Mastrogiacomo & Martí Casadesús Fa & Fiorenzo Franceschini, 2024. "MOBI-Qual: a common framework to manage the product-service system quality of shared mobility," Flexible Services and Manufacturing Journal, Springer, vol. 36(4), pages 1359-1398, December.
    11. Kullak, Franziska S. & Baier, Daniel & Woratschek, Herbert, 2023. "How do customers meet their needs in in-store and online fashion shopping? A comparative study based on the jobs-to-be-done theory," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).

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    More about this item

    Keywords

    Customer needs; Supervised machine learning; Twitter; Web services; E-mobility; Social information Systems; Marketing;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

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