A Bayesian Model for Prelaunch Sales Forecasting of Recorded Music
In a situation where several hundred new music albums are released each month, producing sales forecasts in a reliable and consistent manner is a rather difficult and cumbersome task. The purpose of this study is to obtain sales forecasts for a new album before it is introduced. We develop a hierarchical Bayesian model based on a logistic diffusion process. It allows for the generalization of various adoption patterns out of discrete data and can be applied in a situation where the eventual number of adopters is unknown. Using sales of previous albums along with information known prior to the launch of a new album, the model constructs informed priors, yielding prelaunch sales forecasts, which are out-of-sample predictions. In the context of new product forecasting before introduction, the information we have is limited to the relevant background characteristics of a new album. Knowing only the general attributes of a new album, the meta-analytic approach proposed here provides an informed prior on the dynamics of duration, the effects of marketing variables, and the unknown market potential. As new data become available, weekly sales forecasts and market size (number of eventual adopters) are revised and updated. We illustrate our approach using weekly sales data of albums that appeared inBillboard'sTop 200 albums chart from January 1994 to December 1995.
Volume (Year): 49 (2003)
Issue (Month): 2 (February)
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- Mohanbir S. Sawhney & Jehoshua Eliashberg, 1996. "A Parsimonious Model for Forecasting Gross Box-Office Revenues of Motion Pictures," Marketing Science, INFORMS, vol. 15(2), pages 113-131.
- Ramya Neelamegham & Pradeep Chintagunta, 1999. "A Bayesian Model to Forecast New Product Performance in Domestic and International Markets," Marketing Science, INFORMS, vol. 18(2), pages 115-136.
- Frank M. Bass & Trichy V. Krishnan & Dipak C. Jain, 1994. "Why the Bass Model Fits without Decision Variables," Marketing Science, INFORMS, vol. 13(3), pages 203-223.
- John U. Farley & Donald R. Lehmann & Alan Sawyer, 1995. "Empirical Marketing Generalization Using Meta-Analysis," Marketing Science, INFORMS, vol. 14(3_supplem), pages 36-46.
- Sueyoshi, Glenn T, 1995. "A Class of Binary Response Models for Grouped Duration Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(4), pages 411-431, Oct.-Dec..
- Alan L. Montgomery, 1997. "Creating Micro-Marketing Pricing Strategies Using Supermarket Scanner Data," Marketing Science, INFORMS, vol. 16(4), pages 315-337.
- Peter E. Rossi & Robert E. McCulloch & Greg M. Allenby, 1996. "The Value of Purchase History Data in Target Marketing," Marketing Science, INFORMS, vol. 15(4), pages 321-340.
- Kiefer, Nicholas M, 1988. "Economic Duration Data and Hazard Functions," Journal of Economic Literature, American Economic Association, vol. 26(2), pages 646-679, June.
- Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
- Frank M. Bass, 1995. "Empirical Generalizations and Marketing Science: A Personal View," Marketing Science, INFORMS, vol. 14(3_supplem), pages 6-19.
- Vanhonacker, Wilfried R & Lehmann, Donald R & Sultan, Fareena, 1990. "Combining Related and Sparse Data in Linear Regression Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(3), pages 327-335, July.
- Christopher J. Flinn & James J. Heckman, 1982. "Models for the Analysis of Labor Force Dynamics," NBER Working Papers 0857, National Bureau of Economic Research, Inc.
- Barry L. Bayus, 1993. "High-Definition Television: Assessing Demand Forecasts for a Next Generation Consumer Durable," Management Science, INFORMS, vol. 39(11), pages 1319-1333, November.
- Peter J. Lenk & Ambar G. Rao, 1990. "New Models from Old: Forecasting Product Adoption by Hierarchical Bayes Procedures," Marketing Science, INFORMS, vol. 9(1), pages 42-53.
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