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Modelling brand choice with the multinomial logit model and artificial neural networks: A hybrid approach
Internationalabstracts of research in marketing
4.2 Research Methodology Clustering-based Market Segmentation by Means of Artificial Neural Networks The authors study the use of artificial neural networks in clustering-based market segmentation. They determine segments on the basis of different criteria, and simultaneously form segments and discriminate between these segments via additional segment descriptors. Parameters of all models are estimated by an extended version of back-propagation. The findings support the expectation that non-linear networks may be superior to linear models. Hruschka, H and Natter, M, 1995, " Clusterorientierte Marktsegmentierung mit Hilfe kiinstlicher Neuraler Netzwerke", Marketing Zeitschrifi flit Forschung und PraMs (Germany), Vol. 17 (4), pp.249-254. 57 Modelling Brand Choice with the Multinomial Logit Model and Artificial Neural Networks: A Hybrid Approach A feedforward neural network with Softmax output units and shared weights can be viewed as a generalisation of the multinomial logit model. Neural networks can model non-linear preferences with few a priori assumptions, while the multinomial logit suffers from specification bias. In a complementary approach, the neural network is used as a diagnostic to specify the logit model. Bentz, Yves, and Merunka, Dwight, 1996, "La mod~lisation du choix des marques par le module multinomial logit et les rdseaux de neurones artificiels: proposition d'une approche hybricle", Recherche et Applications en Marketing (France), Vol. 11(2), pp.43-61. 58 Data Mining and the Choice between Classical Models and Neural Networks Describes the data mining process, a new approach to extract information from large databases. Proposes a methodology for combining data mining techniques with traditional regression analysis to better understand the information in a database. Examples from the car and television industries are used as illustrations. Ainslie, Andrew, and Drbze, Xavier, 1996, "Le data-mining et l'alternative modbles classiques/
205
r£seaux neuronaux", Ddcisions Marketing (France), N~7, pp. 77-86. 59
Predictive Ability for Scoring in Direct Marketing of Neural Networks with Retropropagation vs. MCO, Logistic Regression and AID Compared with MCO, logistic regression, discriminant analysis or AID, neural networks with retropropagation have well-known advantages: non-linear effects, freely distributed variables, and robustness to outliers or missing variables. However, implementation and efficiency have not been studied often enough. The current study reviews previous studies and presents new results on a fund-raising example. Desmet, Pierre, 1996, "Comparaison de la pr~dictivitC d'un rdseau de neurones ~ r~tropropagation avec celles des m~thodes de r~gression lindaire, logistique et AID, pour le calcul des scores en marketing direct", Recherche et Applications en Marketing (France), Vol. 11(2), pp. 17-27. 60 See also 3.2
Neural Networks versus Statistical Methods: A Comparison The authors compare the use of neural networks with 'classical' statistical methods in marketing research. They provide an overview of the application areas of neural networks and describe the basic concepts of the methodology. Neural networks and statistical techniques are compared with respect to development procedures, models and architecture, estimation algorithms and performance. It is concluded that both approaches have many similarities. Based on previous literature, the authors conclude that the predictions of neural networks outperform classical statistical methods like regression and discriminant analysis, especially when the underlying relationships are non-linear. However, the performance of more sophisticated statistical methods is comparable to that of neural networks. Huizingh, K R E, de Boer, T W, and Wedel, M, 1995, "Neural Netwerken versus Statistische Methoden: Een Vergelijking", Recente Ontwikkelingen in het Marktonderzoek, Jaarboek van de Nederlandse Vereniging van Marktonderzoekers (The Netherlands), Issue 9 5 / 9 6 , pp.55-66. 61
4.2 Research Methodology Clustering-based Market Segmentation by Means of Artificial Neural Networks The authors study the use of artificial neural networks in clustering-based market segmentation. They determine segments on the basis of different criteria, and simultaneously form segments and discriminate between these segments via additional segment descriptors. Parameters of all models are estimated by an extended version of back-propagation. The findings support the expectation that non-linear networks may be superior to linear models. Hruschka, H and Natter, M, 1995, " Clusterorientierte Marktsegmentierung mit Hilfe kiinstlicher Neuraler Netzwerke", Marketing Zeitschrifi flit Forschung und PraMs (Germany), Vol. 17 (4), pp.249-254. 57 Modelling Brand Choice with the Multinomial Logit Model and Artificial Neural Networks: A Hybrid Approach A feedforward neural network with Softmax output units and shared weights can be viewed as a generalisation of the multinomial logit model. Neural networks can model non-linear preferences with few a priori assumptions, while the multinomial logit suffers from specification bias. In a complementary approach, the neural network is used as a diagnostic to specify the logit model. Bentz, Yves, and Merunka, Dwight, 1996, "La mod~lisation du choix des marques par le module multinomial logit et les rdseaux de neurones artificiels: proposition d'une approche hybricle", Recherche et Applications en Marketing (France), Vol. 11(2), pp.43-61. 58 Data Mining and the Choice between Classical Models and Neural Networks Describes the data mining process, a new approach to extract information from large databases. Proposes a methodology for combining data mining techniques with traditional regression analysis to better understand the information in a database. Examples from the car and television industries are used as illustrations. Ainslie, Andrew, and Drbze, Xavier, 1996, "Le data-mining et l'alternative modbles classiques/
205
r£seaux neuronaux", Ddcisions Marketing (France), N~7, pp. 77-86. 59
Predictive Ability for Scoring in Direct Marketing of Neural Networks with Retropropagation vs. MCO, Logistic Regression and AID Compared with MCO, logistic regression, discriminant analysis or AID, neural networks with retropropagation have well-known advantages: non-linear effects, freely distributed variables, and robustness to outliers or missing variables. However, implementation and efficiency have not been studied often enough. The current study reviews previous studies and presents new results on a fund-raising example. Desmet, Pierre, 1996, "Comparaison de la pr~dictivitC d'un rdseau de neurones ~ r~tropropagation avec celles des m~thodes de r~gression lindaire, logistique et AID, pour le calcul des scores en marketing direct", Recherche et Applications en Marketing (France), Vol. 11(2), pp. 17-27. 60 See also 3.2
Neural Networks versus Statistical Methods: A Comparison The authors compare the use of neural networks with 'classical' statistical methods in marketing research. They provide an overview of the application areas of neural networks and describe the basic concepts of the methodology. Neural networks and statistical techniques are compared with respect to development procedures, models and architecture, estimation algorithms and performance. It is concluded that both approaches have many similarities. Based on previous literature, the authors conclude that the predictions of neural networks outperform classical statistical methods like regression and discriminant analysis, especially when the underlying relationships are non-linear. However, the performance of more sophisticated statistical methods is comparable to that of neural networks. Huizingh, K R E, de Boer, T W, and Wedel, M, 1995, "Neural Netwerken versus Statistische Methoden: Een Vergelijking", Recente Ontwikkelingen in het Marktonderzoek, Jaarboek van de Nederlandse Vereniging van Marktonderzoekers (The Netherlands), Issue 9 5 / 9 6 , pp.55-66. 61