An exploratory Investigation of new product forecasting practices

To guide new product forecasting efforts, the following study offers preliminary data on new product forecasting practices during the commercialization stage (prelaunch and launch stage). Data on department responsibility for and involvement in the new product forecasting process, technique usage, forecast accuracy, and forecast time horizon across different types of new products are reported. Comparisons of new product forecasting practices for consumer firms versus industrial firms are also reported.
Overall, study results show that the marketing department is predominantly responsible for the new product forecasting effort, there is a preference to employ judgmental forecasting techniques, forecast accuracy is 58% on average across the different types of new products, and two to four forecasting techniques are typically employed during the new product forecasting effort. Compared to consumer firms, industrial firms appear to have longer forecast time horizons and rely more on the sales force for new product forecasting. Additional analyses show that there does not appear to be a general relationship between a particular department's involvement and higher forecast accuracy or greater satisfaction, nor does it appear that use of a particular technique relates to higher forecast accuracy and greater satisfaction. Countering previous research findings, the number of forecasting techniques employed also does not appear to correlate to higher forecasting accuracy or greater satisfaction. Managerial and research implications are discussed.     

Estimating Market Growth for New Products: An Analogical Diffusion Model Approach

There has been a lot of interest in diffusion models as a basis for prelaunch estimates of the sales of new products, and indeed there have been several models developed that have achieved fairly good acceptance by new product managers. One of the limitations of such models, however, has been the requirement that a sales history for the new product, even a short one from a test market, for example, be available to derive the parameters of the model. For some types of products—consumer durables, services, industrial products, for example—a sales history isn't available. In this article, Professor Robert Thomas suggests some steps toward the development of models that incorporate the attractive features of diffusion models. His approach is to use, in a systematic way, the sales histories of products that can be considered to have analogous features from a buyer's point of view. He illustrates the approach by forecasting the sales of a new service.     

A comparison of opinion and regression forecasting for an industrial product

Opinion-based forecasting techniques are widely used by industrial marketers. Rarely, however, are the results of these forecasts compared with alternative forecasting techniques and/or evaluated against actual operating results. In this study, opinion-based forecasting results for an industrial equipment manufacturer are evaluated against actual sales data. Further, the opinion-based predictions are compared with the predictions of a naive regression model. The results of these analyses suggest that the current opinion-based forecasting system is deficient.     

A descriptive model of the trade-show budgeting decision process

Spending decisions for industrial trade shows are studied here. A discriminant analysis procedure identifies those factors that separate products that use trade shows from those that do not. The product category, fraction of sales made to order, industry sales level, importance of the product to the customer, and purchase frequency were found to be most instrumental. Another model evaluates the level of spending for products that use trade shows. The sales of the product and stage in the life cycle are shown to be most important in the budget-setting process. The potential use of these results for industrial communications and promotional planning and evaluation decisions is discussed.     

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

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.     

A decision-support system for evaluating sales prospects and launch strategies for new products

New products are important elements in the success of most industrial enterprises. But they are risky and costly. In this article, we briefly review methods that are used to evaluate the likely sales level for new industrial products prior to launch. Then, we report the results of a study of industrial product diffusion, focusing on those factors associated with successful market penetration. These results led to the development of a microcomputer based marketing decision support system (MDSS) that can be used to help plan the entry strategy for a new industrial product.     

Financial Incentives and Salesperson Time Orientation in New Product Launch: A Longitudinal Study

The success of a new product launch critically depends on an engaged and dedicated sales force. Salespeople who are involved in a new product launch must overcome significant uncertainty associated with the new product's performance, which can affect success expectations and, in turn, sales effort for the new product. Moreover, success expectations may drop in the first few months of the launch period, due to initial negative market feedback or general decline in sales force enthusiasm. Diminished expectations may start a vicious circle effect where lower success expectations for the new product lead to lower sales effort that, in turn, leads to lower performance, which further lowers expectations, and so on. Based on insights from attribution‐expectancy theory, this study investigates two distinct mechanisms to counteract the potential downward spiral in success expectations and sales effort devoted to a new product. Specifically, this research examines the role of financial incentives and salespersons' long‐term orientation in creating and maintaining high new product success expectations and sales effort during a new product launch. To investigate how the effect of these factors changes over time, success expectations and sales effort are examined across two critical points in time: the start of a new product launch and at completion of the first sales cycle. To test the model empirically, the North American sales force (n = 129) of a business unit of a global firm is surveyed longitudinally during the launch of a new line of industrial products. The data are analyzed using a partial least squares model. The results show that initial success expectations have a significant effect on sales effort later in the launch, and that this relationship is mediated by success expectations later in the launch. Success expectations and sales effort early in the launch are also shown to impact the perceived attractiveness of the financial incentives offered, but this does not translate into higher success expectations or sales effort at the end of the launch. In contrast, the long‐term orientation of salespersons is key to maintaining higher success expectations and sales effort at the end of the launch.     

Robust Testing of the Utility‐based High‐technology Product Sales Forecasting Models Proposed by Decker and Gnibba‐Yukawa (2010)

Decker and Gnibba‐Yukawa (2010) propose an elegant utility‐based model for forecasting the sales of high‐technology products and suggest that the model yields forecasts that are highly accurate. However, this finding is based on forecasts for a total of only six holdout observations shared across three products. This number of observations is insufficient for reliable inferences to be drawn about the accuracy of a method and the use of such a small data set runs counter to an accepted principle of forecast evaluation. The authors’ proposed model was tested on more extensive data and sensitivity analysis applied to the results. No evidence was found that the utility‐based model could outperform a relatively simple extrapolative model despite the much greater effort involved in applying the proposed model. In addition, the utility‐based model is only applicable for forecasting sales during a narrow interval in a product's life cycle and requires several periods of historic sales data before it can be implemented. It also depends heavily on the accurate estimates of parameters that are determined outside the model (and which may depend on difficult judgments by managers) and assumes that consumers or households will only purchase the product once between the launch date and the forecast horizon. In light of this, it is argued that the utility‐based model is likely to have limited usefulness as a sales forecasting tool.     

建筑业产业成长发展轨迹的回归分析

采用横向研究方法,利用44个国家的截面数据为样本,建立了关于建筑业产业成长发展轨迹的回归模型。回归结果表明,国内生产总值中建筑业增加值的比重与人均国民生产总值之间呈现倒U型,符合库兹涅茨倒U形曲线假说。基于该回归模型,对我国十一五期间建筑业产业发展现状进行了评价,分析了我国建筑业发展水平高于回归预测值的原因,并提出为实现我国建筑业理性、可持续的发展,应从人才、企业、投资、科技创新几个方面实现产业升级。     

Fashion retail forecasting by evolutionary neural networks

Recent literature on nonlinear models has shown that neural networks are versatile tools for forecasting. However, the search for an ideal network structure is a complex task. Evolutionary computation is a promising global search approach for feature and model selection. In this paper, an evolutionary computation approach is proposed in searching for the ideal network structure for a forecasting system. Two years’ apparel sales data are used in the analysis. The optimized neural networks structure for the forecasting of apparel sales is developed. The performances of the models are compared with the basic fully connected neural networks and the traditional forecasting models. We find that the proposed algorithms are useful for fashion retail forecasting, and the performance of it is better than the traditional SARIMA model for products with features of low demand uncertainty and weak seasonal trends. It is applicable for fashion retailers to produce short-term retail forecasting for apparels, which share these features.     

Optimizing the number of industrial salespersons

The objective of this article is to examine the major alternative methods currently used for optimizing the industrial sales force size and to assess their applicability in industrial firms. The article examines in detail the main difficulties facing an industrial firm trying to optimize its sales force, discussing some of the factors that affect industrial selling strategy and policy. The five alternative major techniques for optimizing the number of industrial salespersons, are then discussed in detail, presenting the basis of each approach/method, its advantages and disadvantages for an industrial firm, and when the method is applicable in an industrial marketing situation.     

A big data driven framework for demand-driven forecasting with effects of marketing-mix variables

This study aims to investigate the contributions of promotional marketing activities, historical demand and other factors to predict, and develop a big data-driven fuzzy classifier-based framework, also called “demand-driven forecasting,” that can shape, sense and respond to real customer demands. The availability of timely information about future customer needs is a key success factor for any business. For profit maximization, manufacturers want to sense demand signals and shape future demands using price, sales, promotion and others economic factors so that they can fulfil customer's orders immediately. However, most demand forecasting systems offer limited insight to manufacturers as they fail to capture contemporary market trends, product seasonality and the impact of forecasting on the magnitude of the bullwhip effect. This paper aims to improve the accuracy of demand forecasts. In order to achieve this, a back-propagation neural network-based model is trained by fuzzy inputs and compared with benchmark forecasting methods on a time series data, by using historical demand and sales data in combination with advertising effectiveness, expenditure, promotions, and marketing events data. A statistical analysis is conducted, and the experiments show that the method used in the proposed framework outperforms in optimality, efficiency and other statistical metrics. Finally, some invaluable insights for managers are presented to improve the forecast accuracy of fuzzy neural networks, develop marketing plans for products and discuss their implications in several fields.     

New replacement part buying

This article reports on buying industrial equipment in the paper manufacturing industry. An analysis of written call reports by sales representatives at 26 paper mills, and interviews with sales representatives indicated that several generalizations on industrial buying behavior for this industry are possible. Buying behavior for the equipment part describes five decision stages and five overlapping buying centers.     

Developing and Using Forecasting Models of Consumer Durables The Case of Color Television

Predicting the future sales of new and established products is a critical activity for companies to be able to plan and control their operations. Forecasting consumer durable sales is an especially difficult and challenging task since the marketplace is always changing. Barry Bayus, Saman Hong and Russell Labe present the concept of a market-driven forecasting model and discuss an application involving the forecasting of color television industry sales for RCA's Consumer Electronics Division. An important aspect of this application is that a single approach or model was inadequate to accurately forecast sales over the entire period since the introduction of color TV. Econometric and simulation models which were developed are described, along with their forecasting performance and management acceptance and use.     

Promotional Competition Between Supermarket Chains

This article measures promotional competition between two major supermarket chains in the United States. The approach does not rely upon quantity sales data and can be applied to any industry that features advertised sales. The results indicate that retailers do compete using promotions. A supermarket promotion in one week significantly increases the probability of a promotion for the same product at rival stores in the following week. Additionally, competitors seek to match or exceed the price cuts of their rivals when responding to promotions. Oligopolistic competition plays a significant role in determining the timing of sales and retail price variation.     

Factors Affecting New Product Forecasting Accuracy in New Firms

This study identifies factors that seem to influence a new firm's ability to accurately forecast new product sales. William Gartner and Robert Thomas present a conceptual model and develop hypotheses that specify antecedent factors prior to new product launch, such as the founder's expertise and the marketing research methods used, as well as environmental factors occurring after product launch, such as competitive factors and market volatility, that influence new product forecasting accuracy. The hypotheses were tested with data collected from a survey of 113 new U.S. software firms. Some tentative guidelines for improving sales forecast accuracy among new firms are offered. Directions for future research are discussed.     

Forecasting organizational adoption of high-technology product innovations separated by impact: Are traditional macro-level diffusion models appropriate?

This study examines forecasting accuracy when applying macro-level diffusion models to high-tech product innovations among organizational adopters. In addition, it explores whether the accuracy of macro-level diffusion models differs according to the impact of the new product. As a benchmark for comparison, three types of basic diffusion models are compared to three simple trend extrapolation models. The role of innovation impact in explaining forecasting accuracy is also considered. These issues are addressed by empirically testing organizational adoption data for 39 new high-tech products. Results indicate that for radical innovations the Bass model is best while for incremental innovations an external influence model is best. However, simple trend extrapolation models produced the most accurate overall forecasts. The purpose of the study is to reintroduce an important topic and give practitioners better insight into forecasting the organizational adoption of high-tech products once initial sales data becomes available.     

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, Decker and Gnibba‐Yukawa 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 Gompertz curve as a benchmark, Goodwin and Meeran 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 Goodwin and Meeran, especially regarding the use of short time series and the consideration of replacement purchases. Then, the quasi‐endogenous estimation method for m favored by Goodwin and Meeran for the Gompertz 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 Decker and Gnibba‐Yukawa is investigated. In addition, the results presented in Goodwin and Meeran 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 Gompertz curve approach in 64% of the cases considered. Furthermore, if product 14 (portable MP3 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 Decker and Gnibba‐Yukawa 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 Goodwin and Meeran. It turns out that the Gompertz 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.     

How integrating industrial design in the product development process impacts on company performance

There is a growing belief that investing in industrial design is beneficial to company performance. This article sheds more light on how and when integrating industrial design in the product development process can enhance a company's competitive position. The basic premise is that the impact of industrial design on company performance is not unconditional, but dependent on industry evolution and design strategy. We opted to define industrial design in a general way, namely as the activity that transforms a set of product requirements into a configuration of materials, elements and components. This activity can have an impact on a product's appearance, user friendliness, ease of manufacture, efficient use of materials, functional performance, and so on. The empirical data incorporated in this study stems from two Dutch manufacturing industries, namely home furniture and precision instruments. Home furniture and precision instruments were selected because the strategy of integrating industrial design in the product development process is rather mature in the first‐named industry and emerging in the second. We collected data from firms investing considerably in industrial design (n = 23) and firms investing little to nothing in industrial design (n = 24), using a semistructured questionnaire that was administrated during face‐to‐face sessions with senior managers. Two out of the three research hypotheses were supported. It was found that the extent to which firms integrate industrial design in new product development projects has a significant and positive influence on company performance (Hla), in particular when the strategy of investing in industrial design is relatively new for the industry involved (Hlb). There was no systematic pattern indicating that design innovation is more important in industries where the use of design is mature than in industries where the use of design is emerging (H2). Instead, we found that design innovation has significant positive performance effects in both types of industries. One important managerial inference from our study is that new product development managers should consider the changing nature of competition during industry evolution while developing strategies that encompass the use of industrial design in new product development. Another important managerial inference is that, besides being innovative in the field of products, being innovative with respect to design and design strategy can help to enhance competitiveness regardless of industry evolution.