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Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Conditional Market Segmentation by Neural Networks: A Monte Carlo Study |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | Journal of Retailing and Consumer Services |
| Publisher | Elsevier |
| Geographical Reach | international |
| ISSN | 0969-6989 |
| Volume | 6 |
| Number | 4 |
| Page Range | 237 - 248 |
| Date | 1999 |
| Abstract Text | An artificial neural network (ANN) algorithm is proposed that incorporates both market segmentation and discriminant (regression) analysis of the segments. The method simultaneously estimates the models relating consumer characteristics to market segments, i.e., subjects are assigned to (unique) segments so that subjects within a class show similar purchase behavior and share the same characteristics (psychographics/sociodemographics). Parameters of all models are estimated by the backpropagation algorithm. The performance of the ANN methodology is assessed in a Monte-Carlo study. In contrast to the usual stepwise approach adopted in segmentation studies, our study found that simultaneous segmentation and discrimination are preferable for finding an overall optimum in that this way clusters are formed not only to create homogeneous submarkets but also to show a good dicriminatory behavior. |
| Digital Object Identifier | 10.1016/S0969-6989(98)00008-3 |
| Other Identification Number | merlin-id:14216 |
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