Needmining: Towards Analytical Support for Service Design

  • Niklas Kuehl
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 247)


The identification of customer needs is an important task for Service Design. The paper proposes an approach for automatically detecting customer needs from micro blog data (e.g. Twitter). It shows first results on identification models and lays the foundation for future research in this field.


Service design Need elicitation Customer requirement analysis Machine learning Method evaluation 



The author would like to thank Lisa Schmittecker for her support in the field of need elicitation, Jan Scheurenbrand for his support in implementing the Needmining tool, Marc Goutier for his support with first clustering attempts as well as Gerhard Satzger for his general support.


  1. 1.
    Goldstein, S.M., Johnston, R., Duffy, J., Rao, J.: The service concept: the missing link in service design research? J. Oper. Manage. 20(2), 121–134 (2002)CrossRefGoogle Scholar
  2. 2.
    Herrmann, A., Huber, F., Braunstein, C.: Market-driven product and service design: bridging the gap between customer needs, quality management, and customer satisfaction. Int. J. Prod. Econ. 66(1), 77–96 (2000)CrossRefGoogle Scholar
  3. 3.
    Teare, R.E.: Interpreting and responding to customer needs. J. Workplace Learn. 10(2), 76–94 (1998)CrossRefGoogle Scholar
  4. 4.
    Brown, T., et al.: Design thinking. Harvard Bus. Rev. 86(6), 84 (2008)Google Scholar
  5. 5.
    Victorino, L., Verma, R., Plaschka, G., Dev, C.: Service innovation and customer choices in the hospitality industry. Managing Serv. Qual.: Int. J. 15(6), 555–576 (2005)CrossRefGoogle Scholar
  6. 6.
    Reynolds, T.J., Gutman, J.: Laddering theory, method, analysis, and interpretation. J. Advertising Res. 28(1), 11–31 (1988)Google Scholar
  7. 7.
    Driscoll, D.L.: Introduction to primary research: observations, surveys, and interviews. Writ. Spaces: Read. Writ. 2, 153–174 (2011)Google Scholar
  8. 8.
    Kärkkäinen, H., Elfvengren, K.: Role of careful customer need assessment in product innovation management? empirical analysis. Int. J. Prod. Econ. 80(1), 85–103 (2002)CrossRefGoogle Scholar
  9. 9.
    Line, M.B.: Draft definitions: information and library needs, wants, demands and uses. In: Aslib Proceedings, vol. 26, p. 87. MCB UP Ltd (1974)Google Scholar
  10. 10.
    Arndt, J.: How broad should the marketing concept be? J. Mark. 42(1), 101–103 (1978)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Armstrong, G., Adam, S., Denize, S., Kotler, P.: Principles of Marketing. Pearson Australia, Sydney (2014)Google Scholar
  12. 12.
    Bullinger, H.J., Scheer, A.W.: Service engineering - Entwicklung und Gestaltung innovativer Dienstleistungen. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Cowell, D.W.: New service development. J. Mark. Manage. 3(3), 296–312 (1988)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Dieste, O., Juristo, N., Shull, F.: Understanding the customer: what do we know about requirements elicitation? IEEE Softw. 25(2), 11–13 (2008)CrossRefGoogle Scholar
  15. 15.
    van Horn, D., Olewnik, A., Lewis, K.: Design analytics: capturing, understanding, and meeting customer needs using big data. In: Proceedings of the ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pp. 1–13 (2012)Google Scholar
  16. 16.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM (2004)Google Scholar
  17. 17.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2008)CrossRefGoogle Scholar
  18. 18.
    Zhou, F., Jiao, R.: Latent customer needs elicitation for big-data analysis of online product reviews. In: 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 1850–1854. IEEE (2015)Google Scholar
  19. 19.
    Jiao, J.R., Chen, C.H.: Customer requirement management in product development: a review of research issues. Concurrent Eng. 14(3), 173–185 (2006)CrossRefGoogle Scholar
  20. 20.
    Misopoulos, F., Mitic, M., Kapoulas, A., Karapiperis, C.: Uncovering customer service experiences with twitter: the case of airline industry. Manage. Decis. 52(4), 705–723 (2014)CrossRefGoogle Scholar
  21. 21. Leading social networks worldwide as of January 2016, ranked by number of active users (in millions). Last Accessed on 16 February 2016
  22. 22.
    Wilson, R.E., Gosling, S.D., Graham, L.T.: A review of facebook research in the social sciences. Perspect. Psychol. Sci. 7(3), 203–220 (2012)CrossRefGoogle Scholar
  23. 23.
    Twitter: About the company. Last Accessed on 15 December 2015
  24. 24.
    Bifet, A., Frank, E.: Sentiment knowledge discovery in twitter streaming data. In: Pfahringer, B., Holmes, G., Hoffmann, A. (eds.) DS 2010. LNCS, vol. 6332, pp. 1–15. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  25. 25.
    Miller, Z., Dickinson, B., Hu, W.: Gender prediction on twitter using stream algorithms with n-gram character features. Int. J. Intell. Sci. 2(4A), 143–148 (2012)CrossRefGoogle Scholar
  26. 26.
    Nguyen, D., Smith, N.A., Rosé, C.P.: Author age prediction from text using linear regression. In: Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities, pp. 115–123. LaTeCH 2011, Association for Computational Linguistics, Stroudsburg (2011)Google Scholar
  27. 27.
    Compton, R., Jurgens, D., Allen, D.: Geotagging one hundred million twitter accounts with total variation minimization. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 393–401. IEEE (2014)Google Scholar
  28. 28.
    Saldaña, J.: The Coding Manual for Qualitative Researchers. Sage, London (2012)Google Scholar
  29. 29.
    Egger, M., Lang, A., Schoder, D.: Who are we listening to? detecting user-generated content (ugc) on the web. In: ECIS 2015 Proceedings (2015)Google Scholar
  30. 30.
    Hastie, T., Tibshirani, R., Friedman, J., Franklin, J.: The elements of statistical learning: data mining, inference and prediction. Math. Intelligencer 27(2), 83–85 (2005)Google Scholar
  31. 31.
    van Rijsbergen, C.J.: Information Retrieval, 2nd edn. Butterworths, London (1979)zbMATHGoogle Scholar
  32. 32.
    Halkidi, M., Batistakis, Y., Vazirgiannis, M.: Clustering algorithms and validity measures. In: Thirteenth International Conference on Scientific and Statistical Database Management, SSDBM 2001. Proceedings, pp. 3–22. IEEE (2001)Google Scholar
  33. 33.
    Brocke, V., Simons, A., Niehaves, B., Riemer, K., Plattfaut, R., Cleven, A.: Reconstructing the giant: on the importance of rigour in documenting the literature search process. In: Proceedings of the 17th European Conference On Information Systems, Verona, pp. 2206–2217 (2009)Google Scholar
  34. 34.
    Griffin, A., Hauser, J.R.: The voice of the customer. Mark. Sci. 12(1), 1–27 (1993)CrossRefGoogle Scholar
  35. 35.
    Von Hippel, E.: Lead users: a source of novel product concepts. Manage. Sci. 32(7), 791–805 (1986)CrossRefGoogle Scholar
  36. 36.
    Töpfer, A., Silbermann, S.: Einsatz von kunden-fokusgruppen. In: Handbuch Kundenmanagement, pp. 267–279. Springer (2008)Google Scholar
  37. 37.
    Leonard, D., Rayport, J.F.: Spark innovation through empathic design. Harvard Bus. Rev. 75, 102–115 (1997)Google Scholar
  38. 38.
    Nagamachi, M.: Kansei engineering: a new ergonomic consumer-oriented technology for product development. Int. J. Ind. Ergonomics 15(1), 3–11 (1995)MathSciNetCrossRefGoogle Scholar
  39. 39.
    Herstatt, C., Verworn, B.: Management der frühen Innovationsphasen. Springer (2007)Google Scholar
  40. 40.
    Sauerwein, E., Bailom, F., Matzler, K., Hinterhuber, H.H.: The kano model: how to delight your customers. In: International Working Seminar on Production Economics, vol. 1, pp. 313–327. Innsbruck (1996)Google Scholar
  41. 41.
    Pirola, F., Pezzotta, G., Andreini, D., Galmozzi, C., Savoia, A., Pinto, R.: Understanding customer needs to engineer product-service systems. Advances in Production Management Systems. Innovative and Knowledge-Based Production Management in a Global-Local World. IFIP Advances in Information and Communication Technology, vol. 439, pp. 683–690. Springer, Heidelberg (2014)Google Scholar
  42. 42.
    Green, P.E., Krieger, A.M., Wind, Y.: Thirty years of conjoint analysis: reflections and prospects. Interfaces 31(3-supplement), S56–S73 (2001)CrossRefGoogle Scholar
  43. 43.
    Polaine, A., Løvlie, L., Reason, B.: Service Design. From Implementation to Practice. Reosenfeld Media, New York (2013)Google Scholar
  44. 44.
    Edvardsson, B., Kristensson, P., Magnusson, P., Sundström, E.: Customer integration within service development a review of methods and an analysis of insitu and exsitu contributions. Technovation 32(7), 419–429 (2012)CrossRefGoogle Scholar
  45. 45.
    Cooper, R.G., Kleinschmidt, E.J.: Success factors in product innovation. Ind. Mark. Manage. 16(3), 215–223 (1987)CrossRefGoogle Scholar
  46. 46.
    Hanski, J., Reunanen, M., Kunttu, S., Karppi, E., Lintala, M., Nieminen, H.: Customer observation as a source of latent customer needs and radical new ideas for product-service systems. In: Engineering Asset Management 2011, pp. 395–407. Springer (2014)Google Scholar
  47. 47.
    Bryman, A., Bell, E.: Business Research Methods. Oxford University Press, USA (2015)Google Scholar
  48. 48.
    Kuehl, N., Scheurenbrand, J., Satzger, G.: “needs from tweets”: towards deriving customer needs from micro blog data. Multikonferenz Wirtschaftsinformatik (MKWI) 2016 (2016)Google Scholar
  49. 49.
    Kuehl, N., Scheurenbrand, J., Satzger, G.: Needmining: identifying customer needs in micro blog data: karlsruhe Institute of Technology. Karlsruhe Service Research Institute, Discussion Paper (2015)Google Scholar
  50. 50.
    Scheurenbrand, J., Engel, C., Peters, F., Kuehl, N.: Holistically defining e-Mobility: a modern approach to systematic literature reviews. In: 5th Karlsruhe Service Summit, Karlsruhe, Germany, pp. 17–27 (2015)Google Scholar
  51. 51.
    Pfahl, S., Jochem, P., Fichtner, W.: When will electric vehicles capture the german market? and why?. In: Electric Vehicle Symposium and Exhibition (EVS27), 2013 World, pp. 1–12. IEEE (2013)Google Scholar
  52. 52.
    Sierzchula, W., Bakker, S., Maat, K., Van Wee, B.: The influence of financial incentives and other socio-economic factors on electric vehicle adoption. Energy Policy 68, 183–194 (2014)CrossRefGoogle Scholar
  53. 53.
    Stryja, C., Fromm, H., Ried, S., Jochem, P., Fichtner, W.: On the necessity and nature of e-mobility services? towards a service description framework. Exploring Services Science. Lecture Notes in Business Information Processing, vol. 201, pp. 109–122. Springer International Publishing, Switzerland (2015)Google Scholar
  54. 54.
    Hinz, O., Schlereth, C., Zhou, W.: Fostering the adoption of electric vehicles by providing complementary mobility services: a two-step approach using best-worst scaling and dual response. J. Bus. Econ. 85(8), 921–951 (2015)CrossRefGoogle Scholar
  55. 55.
  56. 56.
    Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)zbMATHGoogle Scholar
  57. 57.
    Su, J., Zhang, H., Ling, C.X., Matwin, S.: Discriminative parameter learning for bayesian networks. In: Proceedings of the 25th International Conference on Machine Learning, ICML 2008, NY, USA, pp. 1016–1023. ACM, New York (2008)Google Scholar
  58. 58.
    Cooper, G., Herskovits, E.: A Bayesian method for constructing Bayesian belief networks from databases. In: Proceedings of the Conference on Uncertainty in AI, pp. 86–94 (1990)Google Scholar
  59. 59.
    John, G.H., Langley, P.: Estimating continuous distributions in bayesian classifiers. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 338–345 (1995)Google Scholar
  60. 60.
    Breiman, L.: Random Forrest. Mach. Learn. 45(1), 5–32 (2001)CrossRefzbMATHGoogle Scholar
  61. 61.
    Platt, J.C.: Fast Training of Support Vector Machines Using Sequential Minimal Optimization. Advances in kernel methods, pp. 185–208. MIT Press, Cambridge (1998)Google Scholar
  62. 62.
    Shalev-Shwartz, S., Srebro, Y.S., N.: Pegasos: primal estimated sub-grAdient sOlver for SVM. In: 24th International Conference on Machine Learning, pp. 807–814 (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Karlsruhe Service Research Institute (KSRI)Karlsruhe Institute of Technology (KIT)KarlsruheGermany

Personalised recommendations