4.7 Article

Dynamic Pricing for New Products Using a Utility-Based Generalization of the Bass Diffusion Model

期刊

MANAGEMENT SCIENCE
卷 68, 期 3, 页码 1904-1922

出版社

INFORMS
DOI: 10.1287/mnsc.2021.4257

关键词

dynamic pricing; optimal pricing; prescriptive analytics; new products; diffusion model; Bass Model (BM); Generalized Bass Model (GBM); new product sales forecasting

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This paper investigates the application of a utility-based extension of the Bass Model in forecasting new product sales. It compares the performance of this model with the Generalized Bass Model and demonstrates its superiority in terms of accuracy. Furthermore, the utility-based extension allows for the prediction of both the introductory price and the price path after launch, and provides a computationally convenient dynamic pricing approach for managers to derive optimal marketing policies.
The Bass Model (BM) has an excellent track record in the realm of new product sales forecasting. However, its use for optimal dynamic pricing or advertising is relatively limited because the Generalized Bass Model (GBM), which extends the BM to handle marketing variables, uses only percentage changes in marketing variables, rather than their actual values. This restricts the GBM's prescriptive use, for example, to derive the optimal price path for a new product, conditional on an assumed launch price, but not the launch price itself. In this paper, we employ a utility-based extension of the BM, which can yield normative prescriptions regarding both the introductory price and the price path after launch, for the new product. We offer two versions of this utility-based diffusion model, namely, the Bass-Gumbel Diffusion Model (BGDM) and the Bass-Logit Diffusion Model (BLDM), the latter of which has been previously used. We show that both the BGDM and BLDM handily outperform the GBM in forecasting new product sales using empirical data from four product categories. We discuss how to estimate the BGDM and BLDM in the absence of past sales data. We compare the optimal pricing policy of the BLDM with the GBM and derive optimal pricing policies that are implied by the BLDM under various ranges of model parameters. We illustrate a dynamic pricing approach that allows managers to derive optimal marketing policies in a computationally convenient manner and extend this approach to a competitive, multiproduct case.

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