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A Bayesian Model for Prelaunch Sales Forecasting of Recorded Music

Kelley School of Business, Indiana University, SPEA/BUS 4041, 801 W.Michigan Street, Indianapolis, Indiana 46202
Graduate School of Industrial Administration, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213
Fuqua School of Business, Duke University, Box 90120, Durham, North Carolina 27708–0120
jonalee{at}iupui.edu
pbhb{at}andrew.cmu.edu
kamakura{at}mail.duke.edu

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.

Key Words: forecasting; empirical generalization; hierarchical bayes model
History: Received: July 2, 1999;