A Bayesian Model for Prelaunch Sales Forecasting of Recorded Music

Management Science, Vol. 49, No. 2, February 2003 pp. 179-196

18 Pages Posted: 14 Feb 2014

See all articles by Jonathon Lee

Jonathon Lee

Independent

Peter Boatwright

Carnegie Mellon University - David A. Tepper School of Business

Wagner A. Kamakura

Rice University

Date Written: 2003

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 in Billboard’s Top 200 albums chart from January 1994 to December 1995.

Keywords: Forecasting, Empirical Generalization, Hierarchical Bayes Model

Suggested Citation

Lee, Jonathon and Boatwright, Peter and Kamakura, Wagner A., A Bayesian Model for Prelaunch Sales Forecasting of Recorded Music (2003). Management Science, Vol. 49, No. 2, February 2003 pp. 179-196. Available at SSRN: https://ssrn.com/abstract=2394891

Jonathon Lee

Independent ( email )

No Address Available
United States

Peter Boatwright (Contact Author)

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States

Wagner A. Kamakura

Rice University ( email )

6100 South Main Street
P.O. Box 1892
Houston, TX 77005-1892
United States
(713) 348-6307 (Phone)

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