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, que...

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Bibliographic Details
Published in:Telecommunication systems 2011-03, Vol.46 (3), p.273-284
Main Authors: Banerjee, Soumya, Al-Qaheri, Hameed, Chiş, Monica, Ali Hassan, M. B.
Format: Article
Language:English
Subjects:
Artificial Intelligence
Business and Management
Clustering
Communication
Communications networks
Computer Communication Networks
Conjoint analysis
Consumers
Customer feedback
Customers
Design engineering
Fuzzy
Fuzzy logic
Fuzzy set theory
Fuzzy sets
IT in Business
Learning
Machine learning
Marketing
Mathematical models
Probability
Probability Theory and Stochastic Processes
Questionnaires
Studies
Telecommunications systems
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Summary: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.
ISSN:1018-4864
1572-9451
DOI:10.1007/s11235-010-9290-6