Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

An Adaptive Questionnaire generation using learning from fuzzy and post clustering of customers responses: an Experience with Communication Products


A central problem in marketing is the clear understanding of consumer’s choice or preferences. Designing questionnaires and then analyzing the answers of probable customers can achieve this. The traditional approach in the marketing analysis has been the designing of non-adaptive questionnaires, questionnaires that are predetermined and not at all influenced by respondent’s answers. The aim of this paper is to design a questionnaire that is influenced by respondent’s answer through implementation of soft computing and approximate reasoning methodologies. The learning of particular pattern on respondent’s fuzzy responses has also been envisaged in the post-survey (Post-conjoint) and further better clustering of choices and segregation is accomplished. The module of learning and finer clustering from respondent’s choice pattern could be a major pre-requisite for construction of adaptive questionnaires. Further extensions of the soft computing methods for product recommender system have also been mentioned for the design of adaptive questionnaire.

This is a preview of subscription content, log in to check access.


  1. 1.

    Abernethy, J., Evgeniou, T., Toubia, O., & Vert, J.-P. (2008). Eliciting consumer preferences using robust adaptive choice questionnaires. IEEE Transaction on Knowledge and Data Engineering, 20(2), 145–155.

  2. 2.

    Arora, N., & Huber, J. (2001). Improving parameter estimates and model prediction by aggregate customization in choice experiments. Journal of Consumer Research, 28, 15–23.

  3. 3.

    Banerjee, A., Krumpelman, C., Ghosh, J., Basu, S., & Andmooney, R. J. (2005). Model-based overlapping clustering. In Proceedings of the 11th ACMSIGKDD international conference on knowledge discovery and data Mining (KDD’05), Chicago, IL (pp. 532–537). New York: ACM.

  4. 4.

    Cui, D., & Curry, D. (2005). Predicting consumer choice using support vector machines with benchmark comparisons to multinomial logit. Marketing Science, 24(4), 595–615.

  5. 5.

    Green, P. E., & Rao, R. (1971). Conjoint measurement for quantifying judgmental data. Journal of Marketing Research, 8, 355–363.

  6. 6.

    Gupta, G., Liu, A., & Ghosh, J. (2008). Automated hierarchical density shaving: a robust, automated clustering and visualization framework for large biological datasets. IEEE/ACM Transactions on Computational Biology Bioinformatics 11 (Mar.). IEEE Computer Society Digital Library. IEEE Computer Society. http://doi.ieeecomputersociety.org/10.1109/TCBB.2008.32.

  7. 7.

    Hauser, J. R., Tellis, G., & Griffin, A. (2008). Research on innovation: a review and agenda for marketing science. Marketing Science (2), 23–31.

  8. 8.

    Urban, G.L., Hauser, J.R., Liberali, G., Braun, M., & Sultan, F. (2009). Morph the Web to build empathy, trust and sales. Sloan Management Review, 50(4), 53–61.

  9. 9.

    Kohli, R., & Jedidi, K. (2005). Representation and inference of lexicographic preference models and their variants. Marketing Science, 26(3), 380–399.

  10. 10.

    Korte, B., Lovász, L., & Schrader, R. (1991). Greedoids. In Algorithms and combinatorics series (Vol. 4). Berlin: Springer.

  11. 11.

    Kuhfeld Warren, F. (2005). Marketing research methods in SAS, SAS Institute Inc., Cary, NC (USA) 2005. Available at http://support.sas.com/techsup/technote/ts722.pdf.

  12. 12.

    Sonnevend, G. (1985). An ‘analytic’ center for polyhedrons and new classes of global algorithms for linear (Smooth, Convex) programming. Control and Information Sciences, 84, 866–876.

  13. 13.

    Theodoros, E., Boussios, C., & Zacharia, G. (2005). Generalized robust conjoint estimation. Marketing Science, 24(3), 1–14.

  14. 14.

    Tikhonov, A. N., & Arsenin, V. Y. (1977). In W. H. Winston (Ed.), Solutions of III-posed problems. New York: Wiley.

  15. 15.

    Tong, S., & Koller, D. (2000). Support vector machine active learning with applications to text classification. In Proc. 17th int’l conf. machine learning.

  16. 16.

    Toubia, H. (2005). The impact of utility balance and endogeneity in conjoint analysis. Marketing Science, 24(3), 498–507.

  17. 17.

    Toubia, O., Hauser, J. R., & Simester, D. I. (2004). Polyhedral methods for adaptive choice-based conjoint analysis. Journal of Marketing Research, 41, 116–131.

  18. 18.

    Wang, R. C., & Liang, T.F (2004). Application of fuzzy multi-objective linear programming to aggregate production planning. Computers & Industrial Engineering, 46, 17–41.

  19. 19.

    Yee, M., Dahan, E., Hauser, J. R., & Orlin, J. (2007). Greedoid-based non-compensatory inference. Marketing Science, 26(4), 532–549.

Download references

Author information

Correspondence to Soumya Banerjee.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Banerjee, S., Al-Qaheri, H., Chiş, M. et al. An Adaptive Questionnaire generation using learning from fuzzy and post clustering of customers responses: an Experience with Communication Products. Telecommun Syst 46, 273–284 (2011). https://doi.org/10.1007/s11235-010-9290-6

Download citation


  • Adaptive Questionnaire
  • Fuzzy Leaning and Clustering
  • Conjoint Analysis