Advertisement

Forecasting in Marketing Planning. Forecasting Performance of the Logistic Model and Applications of S-4 Model

  • Sotiris Zontos
  • John Dimoticalis
  • Christos H. Skiadas
Part of the Applied Optimization book series (APOP, volume 19)

Abstract

Forecasting role in marketing is to provide current and future market data, all interrelated into meaningful interpretation for action. Forecasting is a part of the decision making process and has become an important component in all marketing activities. Forecasting as a tool, provides marketing managers with data and information regarding projected sales volumes, sales costs, market shares and other areas of marketing planning and control. This paper examines these issues and proposes the long-term forecasting while illustrating their use in planning and strategy. The most important lesson we have been taught in the field of forecasting before the late 1970s, was that there are models which best fit available data and which of these model gives the best results. But in the last 2 decades the scope of forecasting has been expanded well beyond technical aspects, encompassing a much broader set of planning, decisionmaking and managing issues. The S-shape models until now could describe successfully new product life cycle only for the three first stages, excluding the decline stage. But maturity is the most important stage in a product life cycle, and it’ s crucial to calculate in which time in the future, the product is going into the decline stage. This paper presents a new model which estimates market potential and forecasts market penetration for all stages in a new product life cycle. This model is compared 11 times against four different growth models included LOGISTIC model. Finally the Logistic Model applied in three different annual time series with main goal the forecasting performance.

Keywords

Marketing Forecasting Product Life Cycle LOGISTIC Model S-4 Model 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Adcock, D., and Bradfield, R. and Halborg, A. and Ross, C. (1995), ‘Marketing Principles and Practice’, Pitman Publishing -Second edition.Google Scholar
  2. Blackmail, A. W. Jr. (1970), ‘Normex Forecasting of Jet Engine Characteristics’. Technological Forecasting and Social Change 2. 61–76.CrossRefGoogle Scholar
  3. Blackman, A. W. Jr. (1972), ‘A mathematical model for trend forecasts’. Technological Forecasting and Social Change 3. 441–452.CrossRefGoogle Scholar
  4. Clifton, P., Nguyen, H., and Nutt, S. (1992), ‘Market research: using forecasting in business’, Butteworth- Heinemann Ltd.Google Scholar
  5. Cox, W.E.Jr. (1967), ‘Product life Cycles as Marketing Models’, The journal of business 4, 375–384.CrossRefGoogle Scholar
  6. Easingwood, C, Mahajan, V. and Muller, E. (1981), ‘Anon symmetric responding logistig model for forecasting techonological substitution’, Technological Forecasting and Social Change. 20, 199–213.CrossRefGoogle Scholar
  7. Floyd, A. (1981), ‘A methology for Trend Forecasting of Figure of Merit’, Technological Forecasting for Intustry and Goverment: Methods and applications.Google Scholar
  8. Giovanis, A. N. and Skiadas, C. H. (1995), ‘Forecasting the Electricity consumption by Applying Stochastic Modelling techniques’, Advances in Stochastic Modeling and Data AnalysisJansen, J., Skiadas, C. H., Zopounidis, C., Eds, p.p 85–100.Google Scholar
  9. Giovanis, A. N. and Skiadas, C. H. (1995), ‘Studying the Growth of the Electricity Consumption in Greece by Using a Stochastic Innovation Diffusion Model’, Proceeding of the 7th International Symposium on Applied Stochastic Models and Data Analysis, Dublin June 12–15p.p226–237.Google Scholar
  10. Harvey, A. C. (1990), ‘Forecasting, structural time series models and the Kaiman filter’, Cambridge University Press.Google Scholar
  11. Janssen, J. and Skiadas, C. (1995), H. ‘Dynamic Modelling of Life Table Data’, Applied Stochastic Models and Data Analysis, vol 11, 35–49.CrossRefGoogle Scholar
  12. Katsamaki, A. and Skiadas, C. (1995), H. ‘Analytic Solution and Estimation of Parameters on a Stochastic Exponential Model for a Technological Diffusion Process’, Applied Stochastic Models and Data Analysis, vol 11, 59–75.CrossRefGoogle Scholar
  13. Kotier, P. and Armstrong, G. (1993), ‘Marketing an introduction’, Prentice-Hall international edition, Third edition.Google Scholar
  14. Kotier, P. and Armstrong, G. (1996), ‘Principles of Marketing’, Prentice-Hall international edition, Seventh edition.Google Scholar
  15. Lilien, L.G., and Kotier, P. and Moorthy, K. S. (1992), ‘Marketing Models’, Prentice- Hall International Editions.Google Scholar
  16. Mahajan, V. and Peterson, R. A. (1985), ‘Models for Innovation Diffusion’, Beverly Hills. CA:sage, .Google Scholar
  17. Makridakis, S. (1990), ‘Sliding Simulation: A new approach to time series forecasting’, Management Science, Vol. 36, No 4.CrossRefGoogle Scholar
  18. Makridakis, S. (1996), ‘Forecasting: its role and value for planning and strategy’, International Journal of Forecasting 12 513–537.CrossRefGoogle Scholar
  19. Meade, N. (1985), ‘Forecasting using growth curves- An adaptive approach’, J. Oper. Res. Soc, Vol 36, No 12, 1103–1115.Google Scholar
  20. Meade, N. and Islam, T. (1995), ‘Forecasting with growth curves: An empirical comparison’, International Journal of Forecasting II199–215.Google Scholar
  21. Meade, N. and Islam, T. (1995), ‘Prediction Intervals for Growth Curve Forecasts’, Journal of Forecasting Voll 4, 413–430.CrossRefGoogle Scholar
  22. Oliver, F.R. (1981), ‘Tractors in Spain: a Further Logistic Analysis’, Journal Operational Research Society. Vol 32 pp499–502.Google Scholar
  23. Sahal, D. A(1975), ‘Generalized Logistic Model for Techological forecasting’, Technological Forecasting and Social Change 7, 81–97.CrossRefGoogle Scholar
  24. Sharif, M. N. and Kabir, C. (1976), A. ‘Generalized model for forecasting tegnological substitution’, Technological Forecasting and Social Change 8, 353–364.CrossRefGoogle Scholar
  25. Sharif, M. N. and Kabir, C. A (1976), ‘Generalized model for forecasting technological substitution’, Technological Forecasting and Social Change 8, 353–364.CrossRefGoogle Scholar
  26. Sharif, M. N. and Kabir, C. A. (1976), ‘System Dynamics Modelling for Forecasting Multilevel Technological Substitution’, Technological Forecasting and Social Change, 9, 89–112.CrossRefGoogle Scholar
  27. Skiadas, C. (1987), H. ‘Two Simple Models for the Early and Middle Stage Prediction of Innovation Diffusion’, IEEE Transaction on Engineering Management, 34, 79–84, .Google Scholar
  28. Skiadas, C. H. (1986), ‘Innovation Diffusion Models Expressing Asymmetry and/or Positively or Negatively Influencing Forces’, Technological Forecasting and Social Change 30. 313–330.CrossRefGoogle Scholar
  29. Skiadas, C. H. (1986), ‘Two Generalized Rational Models for Forecasting Innovation Diffusion’, Technological Forecasting and Social Change 27. 39–61.CrossRefGoogle Scholar
  30. Skiadas, C. H. (1988), ‘A New Model for Short and Medium Term Forecasting of the Greek Electric Energy Consumption’, 1st Balean Conference on Operational Research, Thessaloniki, Greece, 17–20.Google Scholar
  31. Skiadas, C. H. (1992), ‘A Product Growth Cycle Tending to Stationarity’, 21th Conference of the European Marketing Academy, 26–29 May, Aarhus, Denmark, p.p. 1081–1092.Google Scholar
  32. Skiadas, C. H. , (1989), ‘A Logistic Process with Varying Saturation Level’, Proceedings, Congress of the European Marketing Academy (EMAC), Athens, April 18–21, p.p. 801–815.Google Scholar
  33. Skiadas, C. H., Dimoticalis, J. and Zontos, S. (1997), ‘Chaotic Delay Models and Related Simulations’, VIII International Symposium on Applied Stochastic Models and Data Analysis, Napoli, June 11–14.Google Scholar
  34. Skiadas, C. H. and Zopounidis, C. and Dimoticalis, J. (1993), ‘Exploring the Fitting and Forecasting Performance of Sigmoid Forecasting Models: A Comparative Study in the Greek Economic Data Series’. Applied Stochastic Models and Data Analysis.Google Scholar
  35. Wasson, R. C. (1968), ‘How predictable are Fashion and Other Product Life Cycles?’, Journal of Marketing, Vol. 32 July, pp. 36–43.CrossRefGoogle Scholar
  36. Wheelwright, C. S. and Makridakis. S. (1985), Torecasting Methods for Management’, 4 th Ed. John Wiley & Sons, Canada.Google Scholar
  37. Wood, L. L. and Mouskopt, A. J. (1991), ‘Forecasting Market Potential and Market Penetration of Residential Water Heater Load Control Programs’. Journal of Forecasting, Vol. 10, pp399–413.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1998

Authors and Affiliations

  • Sotiris Zontos
    • 1
  • John Dimoticalis
    • 1
  • Christos H. Skiadas
    • 1
  1. 1.Department of Production Engineering and ManagementTechnical University of CreteGreece

Personalised recommendations