Blueprint for a Priming Study to Identify Customer Needs in Social Media Reviews

  • Kristof BrieleEmail author
  • Alexander Krause
  • Max Ellerich
  • Robert H. Schmitt
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1026)


Unbiased customer reviews in social networks may hold the key for innovations in the saturated market of consumer goods. Customer reviews do not only offer information directly about the product, they also provide insights into the user’s environment, customer habits and usage behaviour. These latent needs are stated objectively in reviews. This study aims to overcome the weakness of state-of-the-art machine-learning algorithms that can only extract explicitly stated needs. Key part of the study is the developed method to record and evaluate the reaction time of test subjects to analyse the association between a latent need category and a related word. As a result, we obtain word clusters that express an association with a latent need and a blueprint for upcoming studies that focus on the extraction and utilizing these needs. This knowledge can be used for further research in an automated need identification process and customer driven production.


Priming study Need identification Innovation engineering 



This paper results from the research project “Automated extraction of customer needs from reviews for the enhancement of innovation capability” (SCHM1856/82-1) of the Laboratory for Machine Tools and Product Engineering (WZL), RWTH Aachen University, Germany. The research project has been funded by the German National Science Foundation (DFG). The authors would like to express their gratitude to all parties involved.


  1. 1.
    Balazs, J.A., Velasquez, J.D.: Opinion mining and information fusion: a survey (2015)Google Scholar
  2. 2.
    Zogaj, S., Bretschneider, U.: Customer integration in new product development – a literatur review concerning the appropriateness of different customer integration methods to attain customer knowledge (2012)Google Scholar
  3. 3.
    Goffin, K., Lemke, F., Koners, U.: Identifying Hidden Needs: Creating Breakthrough Products, vol. 1. Palgrave Macmillan, Basingstoke (2010)CrossRefGoogle Scholar
  4. 4.
    Zhou, F., Linsey, J., Jiao, R.J.: Latent customer needs elicitation by use case analogical reasoning from sentiment analysis of online product reviews. J. Mech. Design 137(7), 071401 (2015)CrossRefGoogle Scholar
  5. 5.
    Roberts, D.L., Piller, F.T., Lüttgens, D.: Mapping the impact of social media for innovation: the role of social media in explaining innovation performance in the PDMA comparative performance assessment study. J. Prod. Innov. Manag. 33(3), 117–135 (2016). Scholar
  6. 6.
    Roberts, D.L., Piller, F.T.: Finding the right role for social media in innovation. MIT Sloan Manage. Rev. 57(3), 41 (2016)Google Scholar
  7. 7.
    Aggarwal, C.C., Zhai, C. (eds.): Mining Text Data, 2012th edn. Springer, US, Boston (2012)Google Scholar
  8. 8.
    Büschken, J., Allenby, G.M.: Sentence-based text analysis for customer reviews. Mark. Sci. 35(6), 953–975 (2016). Scholar
  9. 9.
    Giatsoglou, M., Vozalis, M.G., Diamantaras, K., et al.: Sentiment analysis leveraging emotions and word embeddings. Expert Syst. Appl. 69, 214–224 (2017). Scholar
  10. 10.
    Kang, M., Ahn, J., Lee, K.: Opinion mining using ensemble text hidden Markov models for text classification. Expert Syst. Appl. 94, 218–227 (2018). Scholar
  11. 11.
    Reichardt, T.: Bedürfnisorientierte Marktstrukturanalyse für technische Innovationen: Eine empirische Untersuchung am Beispiel Mobile Commerce. Gabler Edition Wissenschaft. Betriebswirtschaftlicher Verlag Dr. Th. Gabler/GWV Fachverlage GmbH Wiesbaden, Wiesbaden (2008)Google Scholar
  12. 12.
    Musch, J., Elze, A., Klauer, K.C.: Gibt es Wortlängeneffekte in der evaluativen Entscheidungsaufgabe. Zeitschrift für Exp. Psychol. 45, 109–119 (1998)Google Scholar
  13. 13.
    Eder, A.B., Erle, T.M.: Priming. In: Enzyklopädie der Psychologie (Bereich Sozialpsychologie) (2017)Google Scholar
  14. 14.
    Wentura, D., Degner, J.A.: Practical guide to sequential priming and related tasks. In: Gawronski, B., Payne, B.K. (eds.) Handbook of Implicit Social Cognition: Measurement, Theory, and Application. Guilford Press, New York (2010 )Google Scholar
  15. 15.
    Karnath, H.-O., Ackermann, H. (eds.): Kognitive Neurowissenschaften: Mit … 28 Tabellen, 3., aktualisierte und erw. Aufl. Springer, Berlin (2012)Google Scholar
  16. 16.
    Ahrens, V.: Priming und Ermüdung (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Kristof Briele
    • 1
    Email author
  • Alexander Krause
    • 1
  • Max Ellerich
    • 1
  • Robert H. Schmitt
    • 1
  1. 1.Laboratory for Machine Tools and Production EngineeringWZL of RWTH Aachen UniversityAachenGermany

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