Article ID Journal Published Year Pages File Type
896671 Technological Forecasting and Social Change 2012 12 Pages PDF
Abstract

Forecasting demand during the early stages of a product's life cycle is a difficult but essential task for the purposes of marketing and policymaking. This paper introduces a procedure to derive accurate forecasts for newly introduced products for which limited data are available. We begin with the assumption that the consumer reservation price is related to the timing with which the consumer adopts the product. The model is estimated using reservation price data derived through a consumer survey, and the forecast is updated with sales data as they become available using Bayes's rule. The proposed model's forecasting performance is compared with that of benchmark models (i.e., Bass model, logistic growth model, and a Bayesian model based on analogy) using 23 quarters' worth of data on South Korea's broadband Internet services market. The proposed model outperforms all benchmark models in both prelaunch and postlaunch forecasting tests, supporting the thesis that consumer reservation price can be used to forecast demand for a new product before or shortly after product launch.

► We suggest an alternative forecasting model for new product with a short history. ► The model is based on individual reservation price and a Bayesian approach. ► The proposed model outperforms existing Bayesian and traditional diffusion models. ► The model produces good forecasts even in the absence of historical sales data. ► Those forecasts are improved as actual sales data accumulate by Bayesian updating.

Related Topics
Social Sciences and Humanities Business, Management and Accounting Business and International Management
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