Sales Forecasting in High-Technology Markets: A Utility-Based Approach

Abstract

Accurate sales projections are of vital importance to the profitability and long-term survival of high-tech companies. This is especially true in the growth stage of product innovation, because major investments and marketing decisions are made in this phase. By examining recent empirical studies focusing on consumer behavior in high-technology markets, several factors are identified that can affect individual buying decisions and aggregate sales, namely, interpersonal communication, democratization of innovation, direct and indirect network effects, forward-looking behavior, and consumer heterogeneity. Against this background the diffusion-modeling and the utility-based approach are reviewed in terms of their basic conception and their applicability to the markets concerned. Based on this investigation a sales forecasting model for high-tech products, specifically in the growth stage, is developed. The model has a utility-theoretic background and a logistic structure. Since data are scarce in this early stage of the product life cycle two versions of the new approach are discussed, an extended version considering forward-looking behavior and a more parsimonious (''myopic'') one. The performance of the new model is demonstrated using real sales data on the CD player, the DVD player/recorder, and the digital camera market. The empirical comparisons include alternative specifications of the Bass diffusion model as well as a proportional hazard model and consist of two steps. First, the models are checked as to whether they are able to represent the sales data at all. It is shown that both versions of the proposed model are at least equivalent, if not superior, to the models used as benchmarks in terms of fit. In the next step, the models are applied to predict future sales in the three markets. The resulting forecasts show that the proposed model performs significantly better than its benchmarks. Its parsimony enables reliable predictions, even in cases, where only short time series are available for parameter estimation. The model is able to anticipate decreasing diffusion rates as they occur at the end of the growth stage and, thus, helps to avoid overoptimistic sales forecasts, which may cause severe economic damages. The new approach is easy to calibrate and can be applied without specialized econometric expertise.

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

“…Franses's () mathematically elegant approach to Gompertz curve fitting, as far as its application to the stock‐of‐cars example indicates, seems to be designed rather for long‐term forecasts for mature products (this issue is further investigated in the next section). At this point, the criticism by Goodwin and Meeran () regarding the shortness of the time series used in Decker and Gnibba‐Yukawa () is worth discussing: In high‐technology markets, product sales predictions are of particular interest in the early stage of a product life cycle. In later stages, the objects of interest typically are no longer high‐technology products, but rather commodities.…”

“…In Decker and Gnibba‐Yukawa (), the market potential for DVD players/recorders, as well as the one for digital cameras, was estimated based on the principle of analogy. The market potential regarding the former product category was estimated by multiplying the number of private households with the penetration rate of video recorders (and not by just using the number of households as indicated in Goodwin and Meeran [, p. 4]).…”

“…In order to further investigate the estimation of m , the Gompertz curve approach was applied to the CD player data published in Le Nagard‐Assayag and Manceau (), and also used by Decker and Gnibba‐Yukawa () . Based on interviews of marketing managers in the CE industry, Le Nagard‐Assayag and Manceau (, p. 210) concluded that the “ultimate market potential corresponds to the number of households in France M = 20,000,000.” As Table shows, the Gompertz curve approach is sensitive to the different levels of m .…”

The Problem of Goods Distribution Over Logistic Centers

“…Вопросы динамики покупа-тельского спроса рассмотрены в работе [4]. В ос-новном для исследований используются шесть категорий регрессии: ANOVA / MANOVA, моде-лирование структурных уравнений (SEM), анали-тическое моделирование, качественный анализ [4][5][6][7]. Шаблоны использования этих подходов и ме-тодов отслеживаются на протяжении многих лет в различных областях.…”

O Impacto Da Abordagem De Vendas Na Aceitação De Produtos Com Inovações Tecnológicas

“…Neste contexto, a postura do vendedor ao fornecer informações sobre novos produtos passa a ser um diferencial para a adoção de novas tecnologias (Davis, 1989;Rogers, 1995;Atuahene-Gima, 1997;Cooper, 2000;Decker & Gnibba-Yukawa, 2010;Bohlmann, et al 2009;Autahene-Gima & Michael, 1998).…”