Development of a multifunctional sales response model with the diagnostic aid of artificial neural networks

Journal of Forecasting

Volumn 24, Issue 7, 2005, Pages 505-521

  Pantelidaki, Stefania ^a   Bunn, Derek W ^a  

^a LONDON BUSINESS SCHOOL   (United Kingdom)

Author keywords

Artificial neural networks; Complexity diagnostics; Response functional form; Sales response modelling

Indexed keywords

COMPUTATIONAL COMPLEXITY; FORECASTING; MATHEMATICAL MODELS;

COMPLEXITY DIAGNOSTICS; RESPONSE FUNCTIONAL FORM; SALES RESPONSE MODELING;

NEURAL NETWORKS;

EID: 28444474633     PISSN: 02776693     EISSN: None     Source Type: Journal    
DOI: 10.1002/for.945     Document Type: Review

References (36)

1. Allenby GM. 1988. The identification, estimation, and testing of demand structures. PhD thesis, University of Chicago.
2. Armstrong SJ, Collopy F. 1992. Error measures for generalising about forecasting methods: empirical comparisons. International Journal of Forecasting 8(1): 69-80.
3. Bass FM, Parsons LJ. 1969. A simultaneous equation regression analysis of sales and advertising. Applied Economics 1(May): 103-124.
4. Bemmaor AC. 1984. Testing alternative econometric models on the existence of advertising threshold effect. Journal of Marketing Research 21(August): 298-308.
5. Blattberg RC, Wisniewski KJ. 1987. How retail price promotions work: empirical results. University of Chicago, Center for Research in Marketing, Working Paper #43.
6. Blattberg RC, Wisniewski KJ. 1989. Price-induced patterns of competition. Marketing Science 8(4): 291-309.
7. Brodie RJ, Bonfrer A. 1994. Conditions when market share models are useful for forecasting: further empirical results. International Journal of Forecasting 10(2): 277-285.
8. Brodie R, De Kluyver CA. 1987. A comparison of the short term forecasting accuracy of econometric and naive extrapolation models of market share. International Journal of Forecasting 3: 423-437.
9. Carbone R, Armstrong JS. 1982. Evaluation of extrapolative forecasting methods: results of a survey of academicians and practitioners. Journal of Forecasting 1: 215-217.
10. Cardaliaguet P, Euvard G. 1992. Approximation of a function and its derivative with a neural network. Neural Networks 5(2): 207-220.
11. Church KB, Curram SP. 1996. Forecasting consumers' expenditure: a comparison between econometric and neural network models. International Journal of Forecasting 12: 255-267.
12. Dalrymple DJ, Haines GH. 1970. A study of the predictive ability of market period demand - supply relations for a firm selling fashion products. Applied Economics 1(January): 277-285.
13. De Kluyver C, Pessemier EA. 1986. Benefits of a marketing budgeting model: two case studies. Sloan Management Review 28(1): 27-38.
14. Eastlack JO, Rao AG. 1986. Modelling response to advertising and pricing changes for 'V-8' cocktail vegetable juice. Marketing Science 5(Summer): 245-259.
15. Farris PW, Olver J, de Kluyver C. 1989. The relationship between distribution and market share. Marketing Science 8(2): 107-128.
16. Godfrey LG. 1988. Misspecification Tests in Econometrics. Cambridge University Press: Cambridge.
17. Gopalakrishna S, Chatterjee R. 1992. A communication response model for a mature industrial product: application and implications. Journal of Marketing Research 29(2): 189-200.
18. Greene WH. 1993. Econometric Analysis. Prentice Hall: Englewood Cliffs, NJ.
19. Hanssens DM, Parsons LJ. 1993. Econometric and time-series market response models. In Handbooks in OR & MS, Vol. 5, Eliashberg J, Lilien GL. (eds, Kluwer); 409-464.
20. Hanssens DM, Parsons LJ, Schultz RL. 1990. Market Response Models: Econometric and Time-Series Analysis. Kluwer Academic Publishers: Dordrecht.
21. Hornik K, Stinchcombe M, White H. 1989. Multilayer feedforward networks are universal approximators. Neural Networks 2: 359-366.
22. Hornik K, Stinchcombe M, White H. 1990. Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Networks 3: 551-560.
23. Hruschka H. 1993. Determining market response functions by neural network modelling: a comparison to econometric techniques. European Journal of Operational Research 66: 27-35.
24. Ito Y. 1993. Extension of approximation capability of three layered neural networks to derivatives. ICNN'93 Proceedings, 377-381.
25. Johansson JK. 1979. Advertising and the S-curve: a new approach. Journal of Marketing Research 16(August): 346-354.
26. Kumar A, Heath TB. 1990. A comparative study of market share models using disaggregate data. International Journal of Forecasting 6: 163-174.
27. Kumar V. 1994. Forecasting performance of market share models: an assessment, additional insights, and guidelines. International Journal of Forecasting 10(2): 295-312.
28. Lambin JJ. 1976. Advertising, Competition and Market Conduct in Oligopoly Over Time. North-Holland: Amsterdam.
29. Makridakis S, Wheelwright SC. 1977. Forecasting: issues and challenges for marketing management. Journal of Marketing 41(October): 24-38.
30. Mercer A. 1991. Implementable Marketing Research. Prentice Hall: Englewood Cliffs, NJ.
31. Moore WL, Pessemier EA. 1992. Product Planning and Management: Designing and Delivering Value. McGraw-Hill: New York.
32. Naert PA, Weverbergh M. 1985. Market share specification, estimation, and validation: toward reconciling seemingly divergent views. Journal of Marketing Research 22: 453-461.
33. Reibstein DJ, Farris PW. 1995. Market share and distribution: a generalisation, a speculation, and some implications. Marketing Science 14(3): 190-202.
34. Reibstein DJ, Gatignon H. 1984. Optimal product line pricing: the influence of elasticities and cross-elasticities. Journal of Marketing Research 21(August): 259-267.
35. Verbeke W, Clement F, Farris PW. 1994. Product availability and market share in an oligopolistic market: the Dutch detergent market. The International Review of Retail, Distribution and Consumer Research 4(3): 277-296.
36. Wittink DR. 1987. Causal market share models in marketing: neither forecasting nor understanding? International Journal of Forecasting 3(3): 445-448.