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A Bayesian Model for Prelaunch Sales Forecasting of Recorded Music

Author

Listed:
  • Jonathan Lee

    () (Kelley School of Business, Indiana University, SPEA/BUS 4041, 801 W.Michigan Street, Indianapolis, Indiana 46202)

  • Peter Boatwright

    () (Graduate School of Industrial Administration, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213)

  • Wagner A. Kamakura

    () (Fuqua School of Business, Duke University, Box 90120, Durham, North Carolina 27708-0120)

Abstract

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.

Suggested Citation

  • Jonathan Lee & Peter Boatwright & Wagner A. Kamakura, 2003. "A Bayesian Model for Prelaunch Sales Forecasting of Recorded Music," Management Science, INFORMS, vol. 49(2), pages 179-196, February.
  • Handle: RePEc:inm:ormnsc:v:49:y:2003:i:2:p:179-196
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    File URL: http://dx.doi.org/10.1287/mnsc.49.2.179.12744
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    References listed on IDEAS

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    1. 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..
    2. Kiefer, Nicholas M, 1988. "Economic Duration Data and Hazard Functions," Journal of Economic Literature, American Economic Association, vol. 26(2), pages 646-679, June.
    3. 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.
    4. Ramya Neelamegham & Pradeep Chintagunta, 1999. "A Bayesian Model to Forecast New Product Performance in Domestic and International Markets," Marketing Science, INFORMS, pages 115-136.
    5. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Alan L. Montgomery, 1997. "Creating Micro-Marketing Pricing Strategies Using Supermarket Scanner Data," Marketing Science, INFORMS, pages 315-337.
    11. 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.
    12. 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.
    13. Mohanbir S. Sawhney & Jehoshua Eliashberg, 1996. "A Parsimonious Model for Forecasting Gross Box-Office Revenues of Motion Pictures," Marketing Science, INFORMS, pages 113-131.
    14. Frank M. Bass, 1995. "Empirical Generalizations and Marketing Science: A Personal View," Marketing Science, INFORMS, vol. 14(3_supplem), pages 6-19.
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    Cited by:

    1. Edlira Shehu & Tim Prostka & Christina Schmidt-Stölting & Michel Clement & Eva Blömeke, 2014. "The influence of book advertising on sales in the German fiction book market," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 38(2), pages 109-130, May.
    2. Goodwin, Paul & Meeran, Sheik & Dyussekeneva, Karima, 2014. "The challenges of pre-launch forecasting of adoption time series for new durable products," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1082-1097.
    3. Tomohiro Ando, 2008. "Measuring the baseline sales and the promotion effect for incense products: a Bayesian state-space modeling approach," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(4), pages 763-780, December.
    4. Hong Chen, 2010. "Using Financial and Macroeconomic Indicators to Forecast Sales of Large Development and Construction Firms," The Journal of Real Estate Finance and Economics, Springer, vol. 40(3), pages 310-331, April.
    5. Delis, Manthos & Iosifidi, Maria & Tsionas, Mike G, 2017. "Endogenous bank risk and efficiency," European Journal of Operational Research, Elsevier, vol. 260(1), pages 376-387.
    6. Lemmens, A. & Croux, C. & Stremersch, S., 2012. "Dynamics in international market segmentation of new product growth," Other publications TiSEM 306086bd-670f-48d2-97d1-3, Tilburg University, School of Economics and Management.
    7. repec:bla:stratm:v:38:y:2017:i:3:p:802-811 is not listed on IDEAS
    8. Lee, Hakyeon & Kim, Sang Gook & Park, Hyun-woo & Kang, Pilsung, 2014. "Pre-launch new product demand forecasting using the Bass model: A statistical and machine learning-based approach," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 49-64.
    9. Yi-Hui Chiang & Yiming Li & Chih-Young Hung, 2007. "A Dynamic Growth Model for Flows of Foreign Direct Investment," DEGIT Conference Papers c012_047, DEGIT, Dynamics, Economic Growth, and International Trade.

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