Open AccessThe use of neural networks and rule induction for customer segmentation and target market profiling
Author(s) -
Bloom Jz
Publication year - 2002
Publication title -
suid-afrikaanse tydskrif vir ekonomiese en bestuurswetenskappe/south african journal of economic and management sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 17
eISSN - 2222-3436
pISSN - 1015-8812
DOI - 10.4102/sajems.v5i1.2673
Subject(s) - market segmentation , profiling (computer programming) , segmentation , artificial neural network , cluster analysis , computer science , artificial intelligence , decision tree , tourism , self organizing map , machine learning , data mining , marketing , business , geography , operating system , archaeology
Inadequate market segmentation and clustering problems could cause an enterprise to either miss a strategic marketing opportunity or not cash in on a tactical campaign. The need for in-depth knowledge of customer segments and to overcome the limitations of non-linear problems require a different approach. The objectives of the research are (1) to consider the use of self-organising feature (SOM) neural networks for segmenting tourist markets and (2) to assess the use of inducing decision trees to obtain rules for profiling existing and classifying new respondents. The findings of the SOM neural network modelling indicate three definitive natural clusters. The induction of rules from decision trees were used to obtain a broad indication of a segment profile on the basis of a rule set and also enables the segment classification of customers from follow-up surveys.