Pre-launch new product demand forecasting using the Bass model: A statistical and machine learning-based approach

Technological Forecasting and Social Change

Volumn 86, Issue , 2014, Pages 49-64

  Lee, Hakyeon ^a   Kim, Sang Gook ^b   Park, Hyun woo ^b   Kang, Pilsung ^a  

^a Seoul National University of Science and Technology   (South Korea)

^b Korea Institute of Science and Technology Information (KISTI)   (South Korea)

Author keywords

Bass model; Ensemble; Machine learning; Multivariate linear regression; Pre launch forecasting

Indexed keywords

ARTIFICIAL INTELLIGENCE; FORECASTING; LEARNING SYSTEMS; MATHEMATICAL MODELS;

BASS MODEL; ENSEMBLE; ENSEMBLE PREDICTION; EXPERIMENTAL VALIDATIONS; LEARNING-BASED APPROACH; MULTIVARIATE LINEAR REGRESSIONS; PREDICTION ACCURACY; REGRESSION ALGORITHMS;

LEARNING ALGORITHMS;

ACCURACY ASSESSMENT; ALGORITHM; FORECASTING METHOD; MULTIVARIATE ANALYSIS; NUMERICAL MODEL; REGRESSION ANALYSIS;

EID: 84902077620     PISSN: 00401625     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.techfore.2013.08.020     Document Type: Article

References (52)

1. Bass F.M., Gordon K., Ferguson T.L., Githens M.L. DIRECTV: forecasting diffusion of a new technology prior to product launch. Interfaces 2001, 31:S82-S93.
2. Ozkaya E. Demand management in global supply chains 2008, (Doctoal thesis), Georgia Institute of Technology, Atlanta.
3. Rogers E.M. Diffusion of Innovations 1995, The Free Press, New York.
4. Meade N., Islam T. Modelling and forecasting the diffusion of innovation-a 25-year review. Int. J. Forecast. 2006, 22:519-545.
5. Bass F.M. A new product growth model for consumer durables. Manag. Sci. 1969, 15:215-227.
6. Turk T., Trkman P. Bass model estimates for broadband diffusion in European countries. Technol. Forecast. Soc. Change 2012, 79:85-96.
7. Lilien G.L., Rao A.G., Kalish S. Bayesian estimation and control of detailing effort in a repeat purchase diffusion environment. Manag. Sci. 1981, 27:493-506.
8. Rao A.G., Yamada M. Forecasting with a repeat purchase diffusion model. Manag. Sci. 1988, 34:734-752.
9. Lenk P.J., Rao A.G. New models from old: forecasting product adoption by hierarchical Bayes procedures. Mark. Sci. 1990, 9:42-53.
10. Xie J., Song X.M., Sirbu M., Wang Q. Kalman filter estimation of new product diffusion models. J. Mark. Res. 1997, 378-393.
11. Mahajan V., Sharma S. A simple algebraic estimation procedure for innovation diffusion models of new product acceptance. Technol. Forecast. Soc. Change 1986, 30:331-345.
12. Lawrence K.D., Lawton W.H. Applications of diffusion models: some empirical results. New Product Forecasting 1981, 529-541. Lexington Books, Lexington. V. Mahajan, R.C. Cardozo (Eds.).
13. Kim T., Hong J., Koo H. Forecasting diffusion of innovative technology at pre-launch. Ind. Manage. Data Syst. 2013, 113:800-816.
14. Thomas R.J. Estimating market growth for new products: an analogical diffusion model approach. J. Prod. Innov. Manag. 1985, 2:45-55.
15. Bayus B.L. High-definition television: assessing demand forecasts for a next generation consumer durable. Manag. Sci. 1993, 39:1319-1333.
16. Choffray J.M., Lilien G.L. A decision-support system for evaluating sales prospects and launch strategies for new products. Ind. Market. Manag. 1986, 15:75-85.
17. Lee J., Boatwright P., Kamakura W.A. A Bayesian model for prelaunch sales forecasting of recorded music. Manag. Sci. 2003, 49:179-196.
18. Seol H., Park G., Lee H., Yoon B. Demand forecasting for new media services with consideration of competitive relationships using the competitive Bass model and the theory of the niche. Technol. Forecast. Soc. Change 2012, 79:1217-1228.
19. Goodwin P., Dyussekeneva K., Meeran S. The use of analogies in forecasting the annual sales of new electronics products. IMA J. Manag. Math. 2012, 24:407-422.
20. Mahajan V., Muller E., Bass F.M. New product diffusion models in marketing: a review and directions for research, J. Marketing 1990, 1-26.
21. Bishop C.M. Pattern Recognition and Machine Learning 2006, Springer-Verlag, New York.
22. Mitchell T.M. Machine Learning 1997, McGraw-Hill, Inc., Singapore.
23. Schmittlein D.C., Mahajan V. Maximum likelihood estimation for an innovation diffusion model of new product acceptance. Mark. Sci. 1982, 1:57-78.
24. Srinivasan V., Mason C.H. Technical Note-Nonlinear least squares estimation of new product diffusion models. Mark. Sci. 1986, 5:169-178.
25. Heeler R.M., Hustad T.P. Problems in predicting new product growth for consumer durables. Manag. Sci. 1980, 26:1007-1020.
26. Putsis W.P., Srinfvasan V. Estimation techniques for macro diffusion models. New-product Diffusion Models 2000, 263-291. Springer, New York. V. Mahajan, E. Muller, Y. Wind (Eds.).
27. Sultan F., Farley J.U., Lehmann D.R. A meta-analysis of applications of diffusion models. J. Mark. Res. 1990, 70-77.
28. Lee J., Lee C.-Y., Lee K.S. Forecasting demand for a newly introduced product using reservation price data and Bayesian updating. Technol. Forecast. Soc. Change 2012, 79:1280-1291.
29. Bass F.M. The adoption of a marketing model: comments and observations. Innovation Diffusion Models of New Product Acceptance Ballinger, Cambridge 1986, V. Mahajan, Y. Wind (Eds.).
30. Lee W.Y., Goodwin P., Fildes R., Nikolopoulos K., Lawrence M. Providing support for the use of analogies in demand forecasting tasks. Int. J. Forecast. 2007, 23:377-390.
31. Ilonen J., Kamarainen J.-K., Puumalainen K., Sundqvist S., Kälviäinen H. Toward automatic forecasts for diffusion of innovations. Technol. Forecast. Soc. Change 2006, 73:182-198.
32. Gatignon H., Eliashberg J., Robertson T.S. Modeling multinational diffusion patterns: an efficient methodology. Mark. Sci. 1989, 8:231-247.
33. Srivastava R.K., Mahajan V., Ramaswami S.N., Cherian J. A multi-attribute diffusion model for forecasting the adoption of investment alternatives for consumers. Technol. Forecast. Soc. Change 1985, 28:325-333.
34. Mahajan V., Peterson R.A. Innovation diffusion in a dynamic potential adopter population. Manag. Sci. 1978, 24:1589-1597.
35. Ross S.M. Introduction to Probability and Statistics for Engineers and Scientists 2009, Elsevier Science, London.
36. Hastie T., Tibshirani R.J., Friedman J.J.H. The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2009, Springer, New York.
37. Iwerks G.S., Samet H., Smith K. Continuous k-nearest neighbor queries for continuously moving points with updates. Proceedings of the 29th International Conference on Very Large Data Bases (VLDB'03) 2003, 512-523. VLDB Endowment, Berlin, Germany.
38. Xiaohui Y., Pu K.Q., Koudas N. Monitoring k-nearest neighbor queries over moving objects. Proceedings of the 21st International Conference on Data Engineering (ICDE 2005), Tokyo, Japan 2005, 631-642.
39. Mouratidis K., Yiu M.L., Papadias D., Mamoulis N. Continuous nearest neighbor monitoring in road networks. Proceedings of the 32nd International Conference on Very Large Data Bases 2006, 43-54. VLDB Endowment, Seoul, Korea.
40. Baxt W.G. Use of an artificial neural network for the diagnosis of myocardial infarction. Ann. Intern. Med. 1991, 115:843-848.
41. Park D.C., El-Sharkawi M.A., Marks R.J., Atlas L.E., Damborg M.J. Electric load forecasting using an artificial neural network. IEEE Trans. Power Syst. 1991, 6:442-449.
42. Heiat A. Comparison of artificial neural network and regression models for estimating software development effort. Inform. Software Tech. 2002, 44:911-922.
43. Snyman J. Practical mathematical optimization: an introduction to basic optimization theory and classical and new gradient-based algorithms 2005, Springer, New York, NY.
44. Ypma T.J. Historical development of the Newton-Raphson method. SIAM Rev. 1995, 37:531-551.
45. Marquardt D.W. An algorithm for least-squares estimation of nonlinear parameters. SIAM J. Appl. Math. 1963, 11:431-441.
46. Smola A.J., Schölkopf B. A tutorial on support vector regression. Stat. Comput. 2004, 14:199-222.
47. Breiman L., Friedman J.H., Olshen R.A., Stone C.J. Classification and Regression Trees 1984, Wadsworth and Brooks, Monterey.
48. Rasmussen C.E., Williams C.K.I. Gaussian Processes for Machine Learning 2005, The MIT Press, London.
49. Avnimelech R., Intrator N. Boosted mixture of experts: an ensemble learning scheme. Neural. Comput. 1999, 11:483-497.
50. Saaty T.L. How to make a decision: the analytic hierarchy process. Eur. J. Oper. Res. 1990, 48:9-26.
51. Displaybank 3D TV Industry Trend and Market Forecast, Speical Report May 2010, Gyeonggi-do, Korea 2010.
52. Mahajan V., Muller E., Srivastava R.K. Determination of adopter categories by using innovation diffusion models. J. Mark. Res. 1990, 27:37-50.