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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
    DOI: 10.1287/mnsc.49.2.179.12744
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    3. 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.
    4. Lemmens, Aurélie & Croux, Christophe & Stremersch, Stefan, 2012. "Dynamics in the international market segmentation of new product growth," International Journal of Research in Marketing, Elsevier, vol. 29(1), pages 81-92.
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    6. 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.
    7. Ramírez-Hassan, Andrés & Montoya-Blandón, Santiago, 2020. "Forecasting from others’ experience: Bayesian estimation of the generalized Bass model," International Journal of Forecasting, Elsevier, vol. 36(2), pages 442-465.
    8. Dazhou Lei & Hao Hu & Dongyang Geng & Jianshen Zhang & Yongzhi Qi & Sheng Liu & Zuo‐Jun Max Shen, 2023. "New product life cycle curve modeling and forecasting with product attributes and promotion: A Bayesian functional approach," Production and Operations Management, Production and Operations Management Society, vol. 32(2), pages 655-673, February.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. Daniel Kaimann & Ilka Tanneberg & Joe Cox, 2021. "“I will survive”: Online streaming and the chart survival of music tracks," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(1), pages 3-20, January.
    14. Christian Hofmann, 2005. "Gestaltung von Erfolgsrechnungen zur Steuerung langfristiger Projekte," Schmalenbach Journal of Business Research, Springer, vol. 57(8), pages 689-716, December.
    15. Michel Clement & Anke Hille & Bernd Lucke & Christina Schmidt-Stölting & Frank Sambeth, 2008. "Der Einfluss von Rankings auf den Absatz — Eine empirische Analyse der Wirkung von Bestsellerlisten und Rangpositionen auf den Erfolg von Büchern," Schmalenbach Journal of Business Research, Springer, vol. 60(8), pages 746-777, December.
    16. Markus A. Fitza, 2017. "How much do CEOs really matter? Reaffirming that the CEO effect is mostly due to chance," Strategic Management Journal, Wiley Blackwell, vol. 38(3), pages 802-811, March.
    17. 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.
    18. Meade, Nigel & Islam, Towhidul, 2006. "Modelling and forecasting the diffusion of innovation - A 25-year review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 519-545.

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