Comments on the Model Testing by Goodwin and Meeran (2012): Comparison of the Utility‐based and the Gompertz Curve Approach for High‐technology Product Sales Forecasting

Starting from a comprehensive examination of recent empirical studies focusing on consumer behavior in high‐technology markets and the resulting identification of factors probably affecting individual buying decisions as well as aggregate product sales, D ecker and G nibba‐ Y ukawa developed and empirically verified a utility‐based sales forecasting approach in their earlier work. Based on data for 14 consumer electronic products and using the G ompertz curve as a benchmark, G oodwin and M eeran carried out a “more extensive testing” of this proposal. However, at least from a practical point of view, the plausibility of their testing framework regarding the market potential m is not unquestionable. This paper, therefore, first discusses some theoretical aspects of both approaches by addressing issues challenged by G oodwin and M eeran, especially regarding the use of short time series and the consideration of replacement purchases. Then, the quasi‐endogenous estimation method for m favored by G oodwin and M eeran for the G ompertz curve is examined in terms of sensitivity to better understand its influence on sales forecasts, and the adequacy of the suggested range for m in the case of the approach by D ecker and G nibba‐ Y ukawa is investigated. In addition, the results presented in G oodwin and M eeran are considered from a more distant perspective, and possible causes of the variations in forecasting accuracy are discussed, which finally reveals that the forecasting performance of the utility‐based approach is not that “disappointing” as claimed. It provides more accurate (or at least equivalent) forecasts than the G ompertz curve approach in 64% of the cases considered. Furthermore, if product 14 (portable MP 3 players) is excluded from the analysis because of the nonconsideration of probably existing product improvement effects, then the utility‐based approach, on average, outperforms the benchmark in all forecasting years. Altogether, this suggests that the approach by D ecker and G nibba‐ Y ukawa could achieve more accurate forecasts when applying a more reasonable range for m , rather than varying it between 2 and 15 times the cumulative sales by the end of year 7 as proposed by G oodwin and M eeran. It turns out that the G ompertz curve approach can perform on a par with the utility‐based approach in high‐technology product sales forecasting based on short time series if the market potential m is estimated exogenously. A combination of the outcomes of both approaches can even lead to more accurate forecasts as when being used individually insofar as composite forecasting seems to be a practicable approach to the problem of shorter time series compelled by the accelerated diffusion speed in high‐technology markets, rather than relying on one presumably “best” model.