DOI QR코드

DOI QR Code

Application of a Hybrid System of Probabilistic Neural Networks and Artificial Bee Colony Algorithm for Prediction of Brand Share in the Market

  • Shahrabi, Jamal (Department of Industrial Engineering and Management Systems, Amirkabir University of Technology) ;
  • Khameneh, Sara Mottaghi (Department of Industrial Engineering and Management Systems, Amirkabir University of Technology)
  • Received : 2016.11.28
  • Accepted : 2016.12.12
  • Published : 2016.12.30

Abstract

Manufacturers and retailers are interested in how prices, promotions, discounts and other marketing variables can influence the sales and shares of the products that they produce or sell. Therefore, many models have been developed to predict the brand share. Since the customer choice models are usually used to predict the market share, here we use hybrid model of Probabilistic Neural Network and Artificial Bee colony Algorithm (PNN-ABC) that we have introduced to model consumer choice to predict brand share. The evaluation process is carried out using the same data set that we have used for modeling individual consumer choices in a retail coffee market. Then, to show good performance of this model we compare it with Artificial Neural Network with one hidden layer, Artificial Neural Network with two hidden layer, Artificial Neural Network trained with genetic algorithms (ANN-GA), and Probabilistic Neural Network. The evaluated results show that the offered model is outperforms better than other previous models, so it can be use as an effective tool for modeling consumer choice and predicting market share.

Keywords

References

  1. Agrawal, D. and Schorling, C. (1996), Market share forecasting: an empirical comparison of artificial neural networks and multinomial logit model, J Retail, 72, 383-407. https://doi.org/10.1016/S0022-4359(96)90020-2
  2. Armano, G., Marchesi, M., and Murru, A. (2005), A hybrid genetic-neural architecture for stock indexes forecasting, Information Sciences, 170(1), 3-33. https://doi.org/10.1016/j.ins.2003.03.023
  3. Baghchesaraei, A., Kaptan, M. V., and Baghchesaraei, O. R. (2015), Using Prefabrication Systems in Building Construction, International Journal of Applied Engineering Research, 10(24), 44258-44262.
  4. Baghchesaraei, O. R. and Baghchesaraei, A. (2014), Analytical survey of structural engineering and long-term resistive environmental elements in an Iranian magnificent palace, International Journal of Civil and Structural Engineering, 4(3), 372-380.
  5. Baghchesaraei, O. R., Lavasani, H. H., and Baghchesaraei, A. (2016), Behavior of Prefabricated Structures in Developed and Developing Countries, Bulletin de la Societe des Sciences de Liege, 85, 1229-1234.
  6. Banerjee, A., Awasthy, D., and Gupta, V. (2005), A choice modelling approach to evaluate effective-ness of brand development initiatives, International Journal of Management and Decision Making, 6(2), 180-198. https://doi.org/10.1504/IJMDM.2005.006031
  7. Barthwal, R. R. (2007), Industrial Economics: an introductory text book, New Age International.
  8. Bates, J. M. and Granger, C. W. J. (1969), The combination of forecasts, Journal of the Operational Research Society, 20(4), 451-468. https://doi.org/10.1057/jors.1969.103
  9. Bentz, Y. and Merunka, D. (2000), Neural networks and the multinomial logit for brand choice modelling: a hybrid approach, Journal of Forecasting, 19(3), 177-200. https://doi.org/10.1002/(SICI)1099-131X(200004)19:3<177::AID-FOR738>3.0.CO;2-6
  10. Borenstein, S. (1990), Airline mergers, airport dominance, and market power, The American Economic Review, 80(2), 400-404.
  11. Buzzell, R. D., Gale, B. T., and Sultan, R. G. M. (1975), Market share-a key to profitability, Harvard business review, 53(1), 97-106.
  12. Chen, K.-Y. and Wang, C.-H. (2007), A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan, Expert Systems with Applications, 32(1), 254-264. https://doi.org/10.1016/j.eswa.2005.11.027
  13. Cheng, J.-H., Chen, H.-P., and Lin, Y.-M. (2010), A hybrid forecast marketing timing model based on probabilistic neural network, rough set and C4.5, Expert systems with Applications, 37(3), 1814-1820. https://doi.org/10.1016/j.eswa.2009.07.019
  14. Clemen, R. T. (1989), Combining forecasts: A review and annotated bibliography, International journal of forecasting, 5(4), 559-583. https://doi.org/10.1016/0169-2070(89)90012-5
  15. Fish, K. E., Johnson, J. D., Dorsey, R. E., and Blodgett, J. G. (2004), Using an artificial neural network trained with a genetic algorithm to model brand share, Journal of Business research, 57(1), 79-85. https://doi.org/10.1016/S0148-2963(02)00287-4
  16. Gan, C., Limsombunchai, V., Clemes, M., and Weng, A. (2005), Consumer Choice Prediction: Artificial Neural Networks versus Logistic Models, Journal of Social Sciences, 1(4), 211-219. https://doi.org/10.3844/jssp.2005.211.219
  17. Gorunescu, F. (2006), Benchmarking probabilistic neural network algorithms, In Proceedings of International Conference on Artificial Intelligence and Digital Communications, 1-7.
  18. Guadagni, J. D. C. and Little (1983), A Logit Model of Brand Choice Calibrated on Scanner Data, Mark Sci., 2, 203-238. https://doi.org/10.1287/mksc.2.3.203
  19. Hansen, G. S. and Wernerfelt, B. (1989), Determinants of firm performance: The relative importance of economic and organizational factors, Strategic management journal, 10(5), 399-411. https://doi.org/10.1002/smj.4250100502
  20. Hruschka, H. (2007), Using a heterogeneous multinomial probit model with a neural net extension to model brand choice, Journal of Forecasting, 26(2), 113-127. https://doi.org/10.1002/for.1013
  21. Hruschka, H. Fettes, W., Probst, M., and Mies, C. (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(2), 127-43. https://doi.org/10.1007/s00291-002-0095-1
  22. Hu, M. Y. (2003), Tsoukalas C. Explaining consumer choice through neural networks: The stacked generalization approach, European Journal of Operational Research, 146(3), 650-660. https://doi.org/10.1016/S0377-2217(02)00368-5
  23. Hu, M. Y., Shanker, M., Zhang, G. P., and Hung, M. S. (2008), Modeling consumer situational choice of long distance communication with neural networks, Decision Support Systems, 44(4), 899-908. https://doi.org/10.1016/j.dss.2007.10.009
  24. Kalwani, M. U., Yim, C. K., Rinne, H. J., and Sugita, Y. (1990), A price expectations model of customer brand choice, Journal of Marketing Research, 251-262.
  25. Karaboga, D. (2005), An idea based on honey bee swarm for numerical optimization, Technical report-tr06, Erciyesuniversity, engineering faculty, computer engineering department, 200.
  26. Karaboga, D. and Akay, B. (2007), Artificial bee colony (ABC) algorithm on training artificial neural networks, In IEEE 15th Signal Processing and Communications Applications.
  27. Karaboga, D. and Akay, B. (2009), A comparative study of artificial bee colony algorithm, Applied Mathematics and Computation, 214(1), 108-132. https://doi.org/10.1016/j.amc.2009.03.090
  28. Karaboga, D. and Basturk, B. (2007), A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, Journal of global optimization, 39(3), 459-471. https://doi.org/10.1007/s10898-007-9149-x
  29. Karaboga, D. and Basturk, B. (2007), A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, Journal of Global Optimization, 39(3), 459-471. https://doi.org/10.1007/s10898-007-9149-x
  30. Karaboga, D., Gorkemli, B., Ozturk, C., and Karaboga, N. (2014), A comprehensive survey: artificial bee colony (ABC) algorithm and applications, Artificial Intelligence Review, 42(1), 21-57. https://doi.org/10.1007/s10462-012-9328-0
  31. Kaya, T., Aktas, E., Topcu, I., and Ulengin, B. (2010), Modeling toothpaste brand choice: an empirical comparison of artificial neural networks and multinomial probit model, International Journal of Computational Intelligence Systems, 3(5), 674-687. https://doi.org/10.1080/18756891.2010.9727732
  32. Kazemi, S. M. R., Hadavandi, E., Mehmanpazir, F., and Nakhostin, M. M. (2013), A hybrid intelligent approach for modeling brand choice and constructing a market response simulator, Knowledge-Based Systems, 40, 101-110. https://doi.org/10.1016/j.knosys.2012.11.016
  33. Khashei, M., Hejazi, S. R., and Bijari, M. (2008), A new hybrid artificial neural networks and fuzzy regression model for time series forecasting, Fuzzy Sets and Systems, 159(7), 769-786. https://doi.org/10.1016/j.fss.2007.10.011
  34. Kiran, M. S. and Findik, O. (2015), A directed artificial bee colony algorithm, Applied Soft Computing, 26, 454-462. https://doi.org/10.1016/j.asoc.2014.10.020
  35. Kumar, A., Rao, V., and Soni, H. (1995), An empirical comparison of neural network and logistic regression models, Marketing Letters, 6(4), 251-263. https://doi.org/10.1007/BF00996189
  36. Lee, W.-I., Shih, B.-Y., and Chung, Y.-S. (2008), The exploration of consumers' behavior in choosing hospital by the application of neural network, Expert systems with applications, 34(2), 806-816. https://doi.org/10.1016/j.eswa.2006.10.020
  37. Reid, M. J. (1968), Combining three estimates of gross domestic product, Economica, 35, 431-444. https://doi.org/10.2307/2552350
  38. Specht, D. F. (1990), Probabilistic neural networks and the polynomial adaline as complementary techniques for classification. Neural Networks, IEEE Transactions on.
  39. Specht, D. F. (1990), Probabilistic neural networks, Neural networks, 3(1), 109-118. https://doi.org/10.1016/0893-6080(90)90049-Q
  40. van Wezel, M. and Potharst, R. (2007), Improved customer choice predictions using ensemble methods, European Journal of Operational Research, 181(1), 436-452. https://doi.org/10.1016/j.ejor.2006.05.029
  41. Vroomen, B., Franses, P. H., and van Nierop, E. (2004), Modeling consideration sets and brand choice using artificial neural networks, European Journal of Operational Research, 154(1), 206-217. https://doi.org/10.1016/S0377-2217(02)00673-2
  42. Wasserman, P. D. (1993), Advanced methods in neural networks, Chapter, 3, 35-55.
  43. West, P. M., Brockett, P. L., and Golden, L. L. (1997), A comparative analysis of neural networks and statistical methods for predicting consumer choice, Marketing Science, 16(4), 370-391. https://doi.org/10.1287/mksc.16.4.370
  44. Wierenga, B., van Bruggen, G. H., and Althuizen, N. A. P. (2008), Advances in marketing management support systems. Springer US.
  45. Xiang, W.-L. and An, M.-Q. (2013), An efficient and robust artificial bee colony algorithm for numerical optimization, Computers and Operations Research, 40(5), 1256-1265. https://doi.org/10.1016/j.cor.2012.12.006