Quantitative Marketing and Economics

, Volume 2, Issue 3, pp 195–232 | Cite as

Modeling and Forecasting the Sales of Technology Products

  • Ramya Neelamegham
  • Pradeep K. Chintagunta


Managers in technology product markets require sales response models that provide substantive insights into the effects of marketing activities as well as reliable sales forecasts. Such markets are characterized by frequent introductions and withdrawals of multiple models by different companies. Thus, the data available on the performance of any individual model is scarce. A second characteristic is that the effects of product attributes and marketing activities could change over time as different types of consumers participate in the market at different points in time. Given sparse data, it becomes critical to specify a model that allows pooling of information across brand-models while at the same time providing brand-model specific parameters. We accomplish this via a hierarchical Bayesian model specification. Further, to capture the effects of changing consumer preferences over time, we specify a time varying parameter model. Our modeling framework therefore, integrates a hierarchical Bayesian model within a time varying parameter framework to develop a dynamic hierarchical Bayesian model. We employ data on digital cameras in the U.S. market to estimate the parameters of our proposed model. We use thirty-three months of national level data on the digital camera market with the data series beginning very close to the inception of this product category. We find that while there is little variation in reliance of benefits by early adopters, the second wave of adopters focus on Ease of Use followed by later adopters who rely on Storage and Image Quality. Looking at the elasticities of demand with respect to the various benefits, we find that at around the halfway point of our data series, the industry as a whole would have been better off investing in increasing image quality rather than storage if costs associated with the two are equal. However, at the end of the time horizon both benefits appear to have about equal impact. Further, the relative benefits of improving these attributes vary across brands and points in time. We then generate single period and multiple period ahead sales forecasts. We make different assumptions about information availability and find that the average (across brand-models and time) MAPE ranges from 7.5 to 14.5% for the model. We provide extensive comparisons of our model with 4 potential alternatives and find that our model outperforms these alternatives on the nature of substantive insights obtained as well as in forecasting out-of-sample especially when there is a very short time window of data.

technology products new product research Bayesian models time varying parameters 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Allenby, G.M. and P.E. Rossi. (1999). "Marketing Models of Consumer Heterogeneity." Journal of Econometrics 89(1/2), 57–78.Google Scholar
  2. Arino, M.A. and P.H. Franses. (2000). "Forecasting the Levels of Vector Autoregressive Log-Tranformed Time Series." International Journal of Forecasting 16, 111–116.Google Scholar
  3. Bayus, B.L. (1998). "An Analysis of Product Lifetimes in a Technologically Dynamic Industry." Management Science 44(6), 763–775.Google Scholar
  4. Bayus, B.L. and W.P. Putsis, Jr. (1999). "Product Proliferation: An Empirical Analysis of Product Line Determinants and Market Outcomes." Marketing Science 18(2), 137–153.Google Scholar
  5. Bass, F.M., T.V. Krishnan, and D.C. Jain. (1994). "Why the Bass Model Fits Without Decision Variables." Marketing Science 13(3), 203–223.Google Scholar
  6. Bridges, E., C.K. Yim, and R.A. Briesch. (1995). "A High-Tech Product Market Share Model with Customer Expectations." Marketing Science 14(1), 61–81Google Scholar
  7. Campbell, J.Y. and N.G. Mankiw. (1987). "Are Output Fluctuations Transitory?" The Quarterly Journal of Economics 102(4), 857–880.Google Scholar
  8. Carter, C.K. and R. Kohn. (1994). "On Gibbs Sampling for State Space Models." Biometrika 81(3), 541–553.Google Scholar
  9. Chaney, P.M., T.M. Devinney, and R.S. Winer. (1991). "The Impact of New Product Introductions on the Market Value of Firms." Journal of Business 64(4), 573–610.Google Scholar
  10. Danaher, P.J., B.G.S. Hardie, and W.P. Putsis. (2001). "Marketing-Mix Variables and the Diffusion of Successive Generations of a Technological Innovation." Journal of Marketing Research 38, 501–514.Google Scholar
  11. Dawid, A.P. (1981). "Some Matrix-Variate Distribution Theory: Notational Considerations and a Bayesian Application." Biometrika 68, 265–274.Google Scholar
  12. Fruhwirth-Schnatter, S. (1994). "Data Augmentation and Dynamic Linear Models." Journal of Time Series Analysis 15(2), 183–202.Google Scholar
  13. Gamerman, D. and H.S. Migon. (1993). "Dynamic Hierarchical Models." Journal of the Royal Statistical Society. Series B (Methodological) 55(3), 629–642.Google Scholar
  14. Gelfand, A.E. and A.F.M. Smith. (1990). "Sample-Based Approaches to Calculating Marginal Densities." Journal of the American Statistical Association 85, 398–409.Google Scholar
  15. Golder, P.N. and G.J. Tellis. (1998). "Beyond Diffusion: An Affordability Model of the Growth of New Consumer Durables." Journal of Forecasting 17, 259–280.Google Scholar
  16. Gupta, S., D. Jain, and M.S. Sawhney. (1999). "Modeling the Evolution of Markets with Indirect Network Externalities: An Application to Digital Television." Marketing Science 18(3), 396–416Google Scholar
  17. Hamilton, J.D. (1994). "State-Space Models." In R.F. Engel and D.L. McFadden (eds.), Handbook of Econometrics, Vol. IV, pp. 3041-3080.Google Scholar
  18. Hanssens, D.M. (1998). "Order Forecasts, Retail Sales, and the Marketing Mix for Consumer Durables." Journal of Forecasting 17(3), 327–346.Google Scholar
  19. Hardie, B.G.S., P.S. Fader, and M. Wisniewski. (1998). "An Empirical Comparison of New Product Trial Forecasting Models." Journal of Forecasting 17(3), 209–229.Google Scholar
  20. Landim, F.M.P.F. and D. Gamerman. (2000). "Dynamic Hierarchical Models-An Extension to Matrix-Variate Observations." Computational Statistics and Data Analysis 35, 11–47.Google Scholar
  21. Leeflang, P.S.H., D.R. Wittink, M. Wedel, and P.A. Naert. (2000). Building Models for Marketing Decisions, Kluwer Academic Press.Google Scholar
  22. Lenk, P.J. and A.G. Rao. (1990). "New Models from Old: Forecasting Product Adoption by Hierarchical Bayes Procedures." Marketing Science 9, 42–57.Google Scholar
  23. Neelamegham, R and P. Chintagunta. (1999). "A Bayesian Model to Forecast New Product Performance in Domestic and International Markets." Marketing Science 18(2), 115–136.Google Scholar
  24. Parker, P. and H. Gatignon. (1994). "Specifying Competitive Effects in Diffusion Models: An Empirical Analysis." International Journal of Research in Marketing 11, 17–39.Google Scholar
  25. Putsis, W.P. (1998). "Parameter Variation and New Product Diffusion." Journal of Forecasting 17, 231–257.Google Scholar
  26. Song, I. and P.K. Chintagunta. (2003). "A Micromodel of Newproduct Adoption with Heterogeneous and Forward-Looking Consumers: Application to the Digital Camera Category." Quantitative Marketing & Economics 1(4), 371–408.Google Scholar
  27. Tellis, G.J. (1998). "The Price Elasticity of Selective Demand: A Meta-Analysis of Econometric Models of Sales." Journal of Marketing Research XXV, 331–341.Google Scholar
  28. van Heerde, H.J., C.F.Mela, and P. Manchanda. (2004). "The Dynamic Effect of Innovation on Market Structure." Journal of Marketing Research 41(May), 166–183.Google Scholar
  29. West, M. and J. Harrison. (1997). Bayesian Forecasting and Dynamic Models. Springer-Verlang, NY.Google Scholar
  30. Xie, J, M.X. Song, M. Sirbu, and Q.Wang. (1997). "Kalman Filter Estimation of New Product Diffusion Models." Journal of Marketing Research 34(3), 378–393.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Ramya Neelamegham
    • 1
  • Pradeep K. Chintagunta
    • 1
  1. 1.Faculty of ManagementAmrita Vishwa VidyaPeethamIndia

Personalised recommendations