Conditional market segmentation by neural networks: a Monte-Carlo study
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) seg...
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| Vydané v: | Journal of retailing and consumer services Ročník 6; číslo 4; s. 237 - 248 |
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| Hlavný autor: | |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Oxford
Elsevier Ltd
01.10.1999
Oxford, UK :Butterworth-Heinemann, 1994 |
| ISSN: | 0969-6989, 1873-1384 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Natter, Martin |
| Author_xml | – sequence: 1 givenname: Martin surname: Natter fullname: Natter, Martin email: martin.natter@wu-wien.ac.at organization: Vienna University of Economics & BA, Pappenheimgasse 35/3/5, A-1200 Vienna, Austria |
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| CitedBy_id | crossref_primary_10_1016_j_eswa_2013_05_052 crossref_primary_10_1016_j_eswa_2004_02_005 crossref_primary_10_1023_A_1020321132568 crossref_primary_10_1016_S0167_8116_02_00080_0 crossref_primary_10_1057_s41270_021_00145_4 crossref_primary_10_1080_09544820600752781 crossref_primary_10_1016_S0969_6989_00_00011_4 |
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| Copyright | 1999 Elsevier Science Ltd |
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| DOI | 10.1016/S0969-6989(98)00008-3 |
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| Title | Conditional market segmentation by neural networks: a Monte-Carlo study |
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