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


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.

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  1. 1.

    e-tankstelle, eauto, elektroauto, elektrofahrzeug, elektromobilitaet, elektromobilität, ladesaeule, ladesäule

  2. 2.

    ecar, electric mobility, EV vehicle, e-mobility, emobility

  3. 3.

    bmw i3, egolf, eup, fortwo electric drive, miev, nissan leaf, opel ampera, peugeot ion, renault zoe, tesla model s

  4. 4.

    In case one tweet contains multiple needs that belong to the same need category, only one instance is used in the implementation. Therefore, the effective frequencies of tweets containing need categories is slightly different than the total amounts shown in Table 3.


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Correspondence to Niklas Kühl.

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Fig. 5

Example of a JSON response from the deployed REST API

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Kühl, N., Mühlthaler, M. & Goutier, M. Supporting customer-oriented marketing with artificial intelligence: automatically quantifying customer needs from social media. Electron Markets 30, 351–367 (2020).

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  • Customer needs
  • Supervised machine learning
  • Twitter
  • Web services
  • E-mobility
  • Social information Systems
  • Marketing

JEL classification

  • C38
  • C81
  • L94
  • O32