Neural nets and genetic algorithms in marketing

International Series in Operations Research and Management Science

Volumn 121, Issue , 2008, Pages 399-433

  Hruschka, Harald ^a  

^a UNIVERSITY OF REGENSBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84955064618     PISSN: 08848289     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-0-387-78213-3_12     Document Type: Chapter

References (111)

1. Abe, M. 1999. A Generalized Additive Model for Discrete Choice Data. Journal of Business and Economics. 17 271-284.
2. Agrawal, D., C. Schorling. 1996. Market Share Forecasting: An Empirical Comparison of Artificial Neural Networks and Multinomial Logit Model. Journal of Retailing 72 383-407.
3. Akaike, H. 1974. A New Look at Statistical Model Identification. IEEE Transactions on Automatic Contribution 19 716-727.
4. Alexouda, G., K. Paparrizos. 2001. A Genetic Algorithm Approach to the Product Line Design Problem Using the Seller’s Return Criterion: An Extensive Comparative Computational Study. European Journal of Operational Research 134 165-178.
5. Amemiya, T. 1980. Selection of Regressors. International Economic Review 21 331-354.
6. Baesens, B., S. Viaene, D. van den Poel, J. Vanthienen, G. Dedene. 2002. Bayesian Neural Network Learning for Repeat Purchase Modelling in Direct Marketing. European Journal of Operational Research 134 191-211.
7. Balakrishnan, P.V., R. Gupta, V.S. Jacob. 2004. Development of Hybrid Genetic Algorithms for Product Line Designs. IEEE Transactions on Systems, Man and Cybernetics - Part B 33 468-483.
8. Balakrishnan, P.V., R. Gupta, V.S. Jacob. 2006. An Investigation of Mating and Population Maintenance Strategies in Hybrid Genetic Heuristics for Product Line Design. Computers and Operational Research 33 639-659.
9. Balakrishnan, P.V., V.S. Jacob. 1996. Genetic Algorithm for Product Design. Management Science 42 1105-1117.
10. Barron, A.R. 1993. Universal Approximation Bounds for Superpositions of a Sigmoidal Function. IEEE Transactions on Information Theory 39 930-945.
11. Bentz, Y., D. Merunka. 1996. La modelisation du choix des marques par le modele multinomial logit et les reseaux de neurones artificiels: proposition d’une approche hybride. Recherche et Applications en Marketing 11 43-61.
12. Bentz, Y., D. Merunka. 2000. Neural Networks and the Multinomial Logit for Brand Choice Modelling: A Hybrid Approach. Journal of Forecasting 19 177-200.
13. Bishop, C.M. 1995. Neural Networks for Pattern Recognition, Clarendon Press, Oxford, UK.
14. Breiman, L. 2001. Random Forests. Machine Learning 45(1) 5-32.
15. Bronnenberg, B.J., W.R. Vanhonacker. 1996. Limited Choice Sets, Local Price Response and Implied Measures of Price Competition. Journal of Marketing Research 33 163-173.
16. Brusco, M.J, J.D. Cradit, S. Stahl. 2002. A Simulated Annealing Heuristic for a Bicriterion Partitioning Problem in Market Segmentation. Journal of Marketing Research 39 99-109.
17. Buckinx, W., D. van den Poel. 2005. Customer Base Analysis: Partial Defection of Behaviourally Loyal Clients in a Non-contractual FMCG Retail Setting. European Journal of Operational Research 164 252-268.
18. Carlin, B.P., T.A. Louis. 1996. Bayes and Empirical Bayes Methods for Data Analysis. Chapman and Hall, London.
19. Cheng, B., D.M. Titterington. 1994. Neural Networks. A review from a Statistical Perspective. Statistics Science 9 2-54.
20. Cooper, L.G., M. Nakanishi. 1988. Market-Share Analysis. Kluwer, Boston.
21. Cybenko, G. 1989. Continuous Value Neural Networks with Two Hidden Layers are Sufficient. Mathematics of Control, Signal and Systems 2 303-314.
22. Dasgupta, C.G., G.S. Dispensa, S. Ghose. 1994. Comparing the Predictive Performance of a Neural Network Model with Some Traditional Market Response Models. International Journal of Forecasting 10 235-244.
23. DeSarbo, W.S., R.L. Oliver, A. Rangaswamy. 1989. A Simulated Annealing Methodology for Clusterwise Linear Regression. Psychometrika 4 707-736.
24. Desmet, P. 1996. Comparaison de la prédictivité d’un réseau de neurones a retropropagation avec celle des methodes de regression lineaire, logistique et AID, pour le calcul des scores en marketing direct. Recherche et Applications en Marketing 11 17-27.
25. Dobson, G., S. Kalish. 1988. Positioning and Pricing of a Product Line: Formulation and Heuristics. Marketing Science 7 107-125.
26. Elman, J.L. 1991. Distributed Representations, Simple Recurrent Networks, and Grammatical Structure. Machine Learning 7 195-225.
27. Fish, K.E., J.D. Johnson, R.E. Dorsey, J.G. Blodgett. 2004. Using an Artificial Neural Network Trained with a Genetic Algorithm to Model Brand Share. Journal of Business Research 57 79-85.
28. Gelfand, A.E., D.K. Dey. 1994. Bayesian model choice: asymptotics and exact calculations. Journal of the Royal Statistical Society Series B 56 501-514.
29. Ghosh, M., T. Maiti, D. Kim, S. Chakraborty, A. Tewari. 2004. Hierarchical Bayesian Neural Networks: An Application to a Prostate Cancer Study. Journal of the American Statistical Association 99 601-608.
30. Goldberg, D.E. 1989. Genetic Algorithm in Search, Optimization, and Machine Learning Addison Wesley, Reading, MA.
31. Goldberg, D.E., J. Richardson. 1987. Genetic Algorithm with Sharing for Multimodal Function Optimization. Second Conference on Genetic Algorithms, 41-49.
32. Greene, W.H. 2003. Econometric Analysis, 5th edition, Prentice Hall, Upper Saddle River, NJ.
33. Green, P.E., A.M. Krieger. 1985. Models and Heuristics for Product Line Selection. Marketing Science 4 1-19.
34. Gruca, T.S., B.R. Klemz. 2003. Optimal New Product Positioning: A Genetic Algorithm Approach. European Journal of Operational Research 146 621-633.
35. Gruca, T.S., B.R. Klemz, E.A. Petersen. 1998. Mining Sales Data Using a Neural Network Model of Market Response. SIGKDD Explorations 1(1) 39-43.
36. Ha, K., S. Cho, D. MacLachlan. 2005. Response Models Based on Bagging Neural Networks. Journal of Interactive Marketing 19(1) 17-30.
37. Hastie, T.J., R. Tibshirani. 1994. Nonparametric Regression and Classification. Part I -Nonparametric Regression. Cherkassky, V., J.H. Friedman, H. Wechsler, Eds. From Statistics to Neural Networks. Springer, Berlin, 62-69.
38. Hastie, T., R. Tibshirani, J. Friedman. 2002. The Elements of Statistical Learning. Springer, New York.
39. Heilman, C.M., F. Kaefer, S.D. Ramenofsky. 2003. Determining the Appropriate Amount of Data for Classifying Consumer for Direct Marketing Purposes. Journal of Interactive Marketing 17(3) 5-28.
40. Heimel, J.P., H. Hruschka, M. Natter, A. Taudes. 1998. Konnexionistische Kaufakt- und Markenwahlmodelle. Zeitschrift fur betriebswirtschaftliche Forschung 50 596-613.
41. Hornik, K., M. Stinchcombe, H. White. 1989. Multilayer Feedforward Networks are Universal Approximators. Neural Networks 3 359-366.
42. Hruschka, H. 1993. Determining Market Response Functions by Neural Network Modeling. A Comparison to Econometric Techniques. European Journal of Operational Research 66 27-35.
43. Hruschka, H. 2001. An Artificial Neural Net Attraction Model (ANNAM) to Analyze Market Share Effects of Marketing Instruments. Schmalenbach Business Review-zfbf 53 27-40.
44. Hruschka, H. 2006. Relevance of Functional Flexibility for Heterogeneous Sales Response Models: A Comparison of Parametric and Seminonparametric Models. European Journal of Operational Research 174 1009-1020.
45. Hruschka, H., W. Fettes, M. Probst. 2004. An Empirical Comparison of the Validity of a Neural Net Based Multinomial Logit Choice Model to Alternative Model Specifications. European Journal of Operational Research 159 166-180.
46. Hruschka, H., W. Fettes, M. Probst, C. Mies. 2002. A Flexible Brand Choice Model Based on Neural Net Methodology. A Comparison to the Linear Utility Multinomial Logit Model and its Latent Class Extension. OR Spectrum 24 127-143.
47. Hruschka, H., J.P. Heimel, M. Natter, A. Taudes. 1998. Connectionist and Logit Models of Brand Choice. Arnott, D. et al., Eds. Marketing: Progress, Prospects, Perspectives, Proceedings of the Annual Conference of the European Marketing Academy (EMAC), Warwick Business School, UK, 1772-1778.
48. Hruschka, H., M. Natter.1993a. Analyse von Marktsegmenten mit Hilfe konnexionistischer Modelle. Zeitschrift fur Betriebswirtschaft 63 425-442.
49. Hruschka, H., M. Natter. 1993b. A-Posteriori Segmentation in Marketing by Neural Network Models. Janssens, J., C.H. Skiadas, Eds. Applied Stochastic Models and Data Analysis, Vol. I, World Scientific Singapore, 375-387.
50. Hruschka, H., M. Natter 1999. Comparing Performance of Feedforward Neural Nets and K-Means for Cluster-Based Market Segmentation. European Journal of Operational Research 114 346-353.
51. Hu, M.Y., M. Shanker, M.S. Hung. 1999. Estimation of Posterior Probabilities of Consumer Situational Choices with Neural Network Classifiers. International Journal of Research in Marketing 16 307-317.
52. Hu, M.Y., C. Tsoukalas. 2003. Explaining Consumer Choice Through Neural Networks: The Stacked Generalization Approach. European Journal of Operational Research 146 650-660.
53. Hurley, S., L. Moutinho, S.F. Witt. 1997. Genetic Algorithms for Tourism Marketing. Annals of Tourism Research 25 498-514.
54. Ingrassia, S., I. Morlini. 2005. Neural Network Modeling for Small Datasets. Technometrics 47 297-311.
55. Jonker, J.-J., N. Piersma, D. van den Poel. 2004. Joint Optimization of Customer Segmentation and Marketing Policy to Maximize Long-Term Profitability. Expert Systems with Applications 27 159-168.
56. Kaefer, F., C.M. Heilman, S.D. Ramenofsky. 2005. A Neural Network Application to Consumer Classification to Improve the Timing of Direkt Marketing Activities. Computers and Operational Research 32 2505-2615.
57. Kamakura, W.A., G.J. Russel. 1989. A Probabilistic Choice Model for Market Segmentation and Elasticity Structure. Journal of Marketing Research 26 379-390.
58. Kim, Y., W.N. Street, G.J. Russell, F. Menczer. 2005. Customer Targeting: A Neural Network Approach by Genetic Algorithms. Management Science 51 264-276.
59. Klemz, B. 1999. Using Genetic Algorithms to Assess the Impact of Pricing Activity Timing. Omega 27 363-372.
60. Knott, A., A. Hayes, S.A. Neslin. 2002. Next-Product-to-Buy Models for Cross-selling Applications. Journal of Interactive Marketing 16(3) 59-75.
61. Kohli, R., R. Krishnamurthi 1987. A Heuristic Approach to Product Design. Management Science 33 1523-1533.
62. Kumar, A., V.R. Rao, H. Soni. 1995. An Empirical Comparison of Neural Networks and Logistic Regression Models. Marketing Letters 6 251-263.
63. Lampinen, J., A. Vehtari. 2001. Bayesian Approach for Neural Networks - Review and Case Studies. Neural Networks 14 257-274.
64. Lee, H.K.H. 2000. A Noninformative Prior for Neural Networks. Working Paper, ISDS, Duke University, Durham NC.
65. Li, C., Y. Xu, H. Li. 2005. An Empirical Study of Dynamic Customer Relationship Management. Journal of Retailing and Consumer Services 12 431-441.
66. Lim, C.W., T. Kirikoshi. 2005. Predicting the Effects of Physician-Directed Promotion on Prescription Yield and Sales Uptake Using Neural Networks. Journal of Targeting, Measurement and Analysis for Marketing 13 156-167.
67. Lindley, D.V., A.F.M. Smith. 1972. Bayes Estimates for the Linear Model. Journal of the Royal Statistical Society B 34 1-14.
68. MacKay, D.J.C. 1992a. A Practical Bayesian Framework for Backpropagation Networks. Neural Computation 4 448-472.
69. MacKay, D.J.C. 1992b. Bayesian Interpolation. Neural Computation 4 415-447.
70. Mazanec, J.A. 2001. Neural Market Structure Analysis: Novel Topology-Sensitive Methodology. European Journal of Marketing 35 894-914.
71. McGuire, T.W., D.L. Weiss, F.S. Houston. 1977. Consistent Multiplicative Market Share Models. Greenwood, B.A., D.N. Bellinger, Eds. Contemporary Marketing Thought, AMA, Chicago, 129-134.
72. Michalewicz, Z. 1996. Genetic Algorithms + Data Structures = Evolutionary Programs, 3rd edition, Springer, Berlin.
73. Midgley, D.F., R.E. Marks, L.G. Cooper. 1997. Breeding Competitive Strategies. Management Science 43 257-275.
74. Mild, A., T. Reutterer. 2003. An Improved Collaborative Filtering Approach for Predicting Cross-Category Purchases Based on Binary Market Basket Data. Journal of Retailing and Consumer Services 10 123-133.
75. Mitchell, M. 1995. An Introduction to Genetic Algorithms MIT Press, Cambridge, MA.
76. Möller, M. 1993. A Scaled Conjugated Gradient Algorithm for Fast Supervised Learning. Neural Networks 6 525-533.
77. Muller, P., D.R. Insua. 1998. Issues in Bayesian Analysis of Neural Network Models. Neural Computation 10 571-592.
78. Naik, P.A., M.K. Mantrala, A.G. Sawyer. 1998. Planing Media Schedules in the Presence of Dynamic Advertising Quality. Marketing Science 17 214-235.
79. Nair, S.K., P. Tarasewich. 2003. A Model and a Solution Method for Multi-Period Sales Promotion Design. European Journal of Operational Research 150 672-687.
80. Nair, S.K., L. Thakur, K.-W. Wen. 1995. Near Optimal Solutions for Product Line Design and Selection: Beam Search Heuristics. Management Science 41 767-785.
81. Natter, M., H. Hruschka. 1997. Ankerpreise als Erwartungen oder dynamische latente Variablen in Marktreaktionsmodellen. Zeitschrift fur betriebswirtschaftliche Forschung 49 747-764.
82. Natter, M., H. Hruschka. 1998a. Evaluation of Aggressive Competitive Pricing Strategies. Marketing Letters 9 337-347.
83. Natter, M., H. Hruschka. 1998b. Using Artificial Neural Nets to Specify and Estimate Aggregate Reference Price Models. Aurifeille, J.-M., C. Deissenberg, Eds. Bio-Mimetic Approaches in Management Science. Kluwer, Dordrecht, Netherlands, 101-118.
84. Neal, R.M. 1996. Bayesian Learning for Neural Networks. Springer, New York.
85. Nichols, K.B., M.A. Venkataramanan, K.W. Ernstberger. 2005. Product Line Selection and Pricing Analysis: Impact of Genetic Relaxations. Mathematical and Computer Modelling 42, 1397-1410.
86. Pantelidaki, S., D. Bunn. 2005. Development of a Multifunctional Sales Response Model with the Diagnostic Aid of Artifical Neural Networks. Journal of Forecasting 24 505-521.
87. Potharst, R., M. van Rijthoven, M. van Wezel. 2005. Modeling Brand Choice Using Boosted and Stacked Neural Networks. Voges K. E. et.al., Eds. Business Applications and Computational Intelligence. Idea Group Inc.
88. Racine, J.S. 2002. ‘New and Improved’ Direct Marketing: A Non-parametric Approach. Fraanses, P.H., A.L. Montgomery, Eds. Econometric Models in Marketing. JAI Amsterdam, 141-164.
89. Rangaswamy, A. 1993. Marketing Decision Models: From Linear Programs to Knowledge-Based Systems. Eliashberg, J., G.L. Lilien, Eds. Marketing, North-Holland. Amsterdam, 733-771.
90. Reutterer, T., M. Natter. 2000. Segmentation Based Competitive Analysis with MULTICLUS and Topology Representing Networks. Computers and Operational Research 27 1227-1247.
91. Rhin, H., L.G. Cooper. 2005. Assessing Potential Threats to Incumbent Brands: New Product Positioning Under Price Competition in a Multisegmented Market. International Journal of Research in Marketing 22 159-182.
92. Ripley, B.D. 1993. Statistical Aspects of Neural Networks. Barndorff-Nielsen, O.E., J.L. Jensen, W.S. Kendall, Eds. Networks and Chaos - Statistical and Probabilistic Aspects. Chapman & Hall, London, 40-123.
93. Ripley, B. 1996. Pattern Recognition and Neural Networks, Cambridge Univeristy Press, Cambridge, UK.
94. Romeijn, H.E., R. Smith. 1994. Simulated Annealing for Constrained Global Optimization. Journal of Global Optimization 5, 101-126.
95. Rossi, P.E., G.M. Allenby, R. McCulloch 2005. Bayesian Statistics and Marketing. Wiley John, Chichester.
96. Saito, K., R. Nakano. 1997. Partial BFGS Update and Efficient Step-Length Calculation for Three-Layer Neural Networks. Neural Computation 9 123-141.
97. Schwarz, G. 1979. Estimating the Dimension of a Model. Annals of Statistics 6 461-464.
98. Silva-Russo, J.M., R.E. Bucklin, D.G. Morrison. 1999. A Decision Support System for Planning Manufacturers’ Sales Promotion Calendars. Marketing Science 18 274-300.
99. Steiner, W., H. Hruschka. 2000. A Probabilistic One-Step Approach to the Optimal Product Line Design Problem Using Conjoint and Cost Data. Review of Marketing Science 441.
100. Steiner, W., H. Hruschka. 2002. Produktliniengestaltung mit Genetischen Algorithmen. Zeitschrift fur betriebswirtschaftliche Forschung 54 575-601.
101. Sudharsan, D., J.H. May, A.D. Shocker. 1987. A Simulation Comparison of Methods for New Product Location. Marketing Science 6 182-201.
102. Venkatesan, R., V. Kumar. 2002. A Genetic Algorithms Approach to Forecasting of Wireless Subscribers. International Journal of Forecasting 18(4) 625-646.
103. Venkatesan, R., V. Kumar. 2004. A Customer Lifetime Value Framework for Customer Selection and Resource Allocation Strategy. Journal ofMarketing 68(October) 106-125.
104. Venkatesan, R., T.V. Krishnan, V. Kumar. 2004. Evolutionary Estimation of Macro-Level Diffusion Models Using Genetic Algorithms: An Alternative to Non Linear Least Squares. Marketing Science 23 451-464.
105. Vroomen, B., P.H. Franses, E. van Nierop. 2004. Modeling Considerations Sets and Brand Choice Using Artificial Neural Networks. European Journal of Operational Research 154 206-217.
106. West, P.M., P.L. Brocket, L. Golden. 1997. A Comparative Analysis of Neural Networks and Statistical Methods for Predicting Consumer Choice. Marketing Science 16 370-391.
107. van Wezel, M.C., W.R.J. Baets. 1995. Predicting Market Responses with a Neural Network: The Case of fast moving consumer goods. Marketing Intelligence & Planning 13(7) 23-30.
108. Wierenga, B., J. Kluytmans. (1996). Prediction with Neural Nets in Marketing Time Series Data. Management Report Series no. 258. Erasmus Universiteit Rotterdam.
109. Winer, R.S. 1988. Behavioral Perspective on Pricing: Buyers’ Subjective Perceptions of Price Revisited. Devinney T.M., Ed. Issues in Pricing. Lexington Books, Lexington, MA, 35-57.
110. Xie, J., X.M. Song, M. Sirbu, Q. Wang. 1997. Kalman Filter Estimation of New Product Diffusion Models. Journal of Marketing Research 34 378-393.
111. Zahavi, J., N. Levin. 1997. Applying Neural Computing to Target Marketing. Journal of Direct Marketing 11(1) 5-22.