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A Bayesian Model for Prelaunch Sales Forecasting of Recorded Music

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Author Info

  • 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)

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    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.

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    File URL: http://dx.doi.org/10.1287/mnsc.49.2.179.12744
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 49 (2003)
    Issue (Month): 2 (February)
    Pages: 179-196

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    Handle: RePEc:inm:ormnsc:v:49:y:2003:i:2:p:179-196

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    Related research

    Keywords: forecasting; empirical generalization; hierarchical bayes model;

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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, vol. 38(2), pages 109-130, May.
    2. 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, vol. 60(4), pages 763-780, December.
    3. 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.
    4. 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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