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.     

Accelerating the Durable Replacement Cycle with Marketing Mix Variables

In many established product categories, a large component of sales is due to the replacement of existing units. Because of the long expected lifetime of durables, replacement decisions can often be postponed by the consumer. In light of this, one marketing strategy for manufacturers of such products may be to accelerate the timing of product replacement decisions for buyers planning to replace. In this article, Barry Bayus presents the results of his investigation of the effect of marketing efforts on shortening the replacement cycle. Such information can be used by new product managers to forecast more accurately long-term sales and to help decide when to introduce new products (e.g., with enhanced features). Using data on color television purchases, he shows that the marketing variables of price, advertising, new features and styling are related to the timing of discretionary replacements. Further, these data suggest that price has the most impact in accelerating replacement purchases.     

Are really new product development projects harder to shut down?

Just as a good houseguest knows when it's time to say good-bye, effective managers must recognize when it's time to terminate a new product development (NPD) project. As a product progresses toward commercialization, a manager's reluctance to terminate a failing project becomes increasingly expensive. Despite this growing expense, however, many managers are reluctant to shut down failing NPD projects. Jeffrey Schmidt and Roger Calantone hypothesize that this reluctance may be even more pronounced for innovative new products than for incremental NPD efforts. They suggest that perhaps the excitement that really new products engender within a company makes managers more reluctant to shut down the NPD project, even in the face of clear-cut evidence that the project is not a winner. To test these assumptions, they conducted a decision-making experiment in which managers were asked to make go/no-go decisions at each stage in a hypothetical NPD project. One project involved an innovative new product; the other project involved an incremental development—that is, a line extension that offered only marginal size and cost reductions compared to current models. At the outset of the experiment, participants were given market share and profit objectives for assessing the new product's performance. At each stage in the hypothetical NPD project, the participants then received updated performance data. The performance data provided to participants was identical for the two hypothetical projects, and fell increasingly farther below the performance objectives as the project progressed. The results of the experiment support the hypothesized relationship between product innovativeness and managers' reluctance to terminate a failing NPD project. Given identical, poor, performance forecasts, the managers who participated in this experiment were more optimistic about the likelihood of success, were more committed to the project, and were more likely to opt for continuing the project when it involved the more innovative product. In fact, the participants were more likely to allow the highly innovative NPD project to proceed all the way through commercialization, notwithstanding the progressively ominous performance feedback.     

Managing salespeople strategically when promoting new products – incorporating market orientation into a sales management control framework

Salespeople play a pivotal role in promoting new products. Therefore, managers need to know what control mechanism (i.e., output-based control, behavior-based control, or knowledge-based control) can improve their salespeople’s new product sales performance. Furthermore, managers may be able to assist salespeople in performing better by having a strong market orientation. The literature has been inconsistent regarding the effects of sales management control mechanisms and has not yet incorporated market orientation into a sales management control framework. The current study surveyed 315 Taiwanese salespeople from publicly traded electronics companies with the aim of contributing to the sales management literature. The results show that sales management controls can directly affect salespeople’s innovativeness, which, in turn, affects new product sales performance. However, sales management controls cannot affect performance directly. Furthermore, market orientation can positively moderate the relationship between salespeople’s innovativeness and new product sales performance.     

Managing salespeople strategically when promoting new products—Incorporating market orientation into a sales management control framework

Salespeople play a pivotal role in promoting new products. Therefore, managers need to know what control mechanism (i.e., output-based control, behavior-based control, or knowledge-based control) can improve their salespeople's new product sales performance. Furthermore, managers may be able to assist salespeople in performing better by having a strong market orientation. The literature has been inconsistent regarding the effects of sales management control mechanisms and has not yet incorporated market orientation into a sales management control framework. The current study surveyed 315 Taiwanese salespeople from publicly traded electronics companies with the aim of contributing to the sales management literature. The results show that sales management controls can directly affect salespeople's innovativeness, which, in turn, affects new product sales performance. However, sales management controls cannot affect performance directly. Furthermore, market orientation can positively moderate the relationship between salespeople's innovativeness and new product sales performance.     

Effectiveness of sales forecasting methods

Effective sales forecasting has become a prerequisite for successful management. Unfortunately, recognition of the need for more effective forecasts has not produced better results. It appears that forecast error is increasing rather than decreasing. This paper reports the findings of a study of forecasting systems in manufacturing firms. The importance of forecasting to firms, users and preparers of forecasts, forecasting techniques employed, use of macroeconomic data, treatment of inflation, forecasting error, and management of forecasting systems are covered in the paper. Conclusions and recommendations for improvement of forecasting systems are developed from the findings of the study.     

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.     

Are Virtual Markets Efficient Predictors of New Product Success? The Case of the Hollywood Stock Exchange*

Defining effective methods for determining consumer preferences for products prior to their launch has been a mainstay of marketing and management literature for decades. Virtual Stock Markets (VSMs) is an emerging method in new product forecasting that has been shown to produce reliable new product sales estimates by combining individual preferences via market‐based aggregation mechanisms. Due to the emerging popularity of VSMs among practitioners, this cross‐disciplinary study (combining insights from finance, marketing, and new product development fields) uses the example of the Hollywood Stock Exchange (HSX) and examines its predictive validity and potential systematic biases in its predictions to help think about the general applicability of these forecasting methods to other product areas, or how forecasts in other product areas may need to be modified to be more precise. This study finds evidence of overestimating the sales potential associated with products on the low end of the revenue expectation spectrum, which could be linked to the fact that in artificial exchanges, where no money changes hands, people tend to gamble hoping to make excessive returns. However, this explanation is weakened by the introduction of additional variables linked to the negative influence of information search costs (harder to utilize information is not fully reflected in the stock prices) and over‐utilization of highly visible/conspicuous information. Practical implications for managers considering using VSMs for new product forecasting in creative gestalt‐like settings are discussed.     

Do Retailers Really Profit from Ambidextrous Managers? The Impact of Frontline Mechanisms on New and Existing Product Selling Performance

When manufacturers introduce a new product to the market, downstream retail partners are faced with inherent trade‐offs. Retail sales personnel have to support the new product's introduction with substantial sales efforts but also sell the existing products in stock, before storage and devaluation costs spin out of control. This study shows how retail sales managers can guide sales personnel's performance of new and existing product selling, respectively. The authors argue that a manager may prioritize selling new products, existing products, or both (i.e., have an ambidextrous selling orientation). Based on data gathered from sales representatives and company databases of a large European consumer electronics retailer, the authors perform a time‐lagged partial least squares analysis to test empirically their conceptual model. The authors find that ambidextrous sales managers outperform their singular‐oriented counterparts if they properly align their orientation with a frontline management mechanism consisting of task autonomy, performance feedback, and employee age. More specifically, ambidextrous managers promote net profit obtainment if they grant their sales employees task autonomy and give little performance feedback. In addition, a remarkable finding is that older sales agents tend to outperform their younger counterparts when working under an ambidextrous manager. The authors discuss the implications of these findings.     

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.     

When to outsource the sales force for new products

Executives and researchers continue to seek factors that lead to new product success. While prior research has suggested that outsourcing the selling function can help make the innovation process leaner and limit future liability, outsourcing can also pose risks in terms of safeguarding both customer relationships and confidential innovation capabilities. Moreover, examining the effects of outsourcing has been identified as a key research priority in recent marketing literature. Thus, using privileged access to managers in the biochemical industry, we employed a multi-group analysis of 229 new products to investigate the effect of outsourcing the sales force on new product success. Our empirical results demonstrate that outsourcing the sales force moderates the relationship between new product superiority and customer meaningfulness such that the relationship is stronger when outsourcing is employed; however, outsourcing the sales force moderates the relationship between new product good value and customer meaningfulness such that it is weaker when outsourcing is employed. These findings suggest that outsourcing may serve as a signal of added risk for customers. Thus, the decision to outsource the sales force should be made based upon customer needs and the characteristics of the new product.     

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.     

Breakeven Time on New Product Launches: An Investigation of the Drivers and Impact on Firm Performance

The ability to break even faster on new product projects is becoming increasingly critical for firms in fast‐moving industries where continually reinvesting in research and development efforts matters greatly for survival. However, most research to date has focused on studying the impact of two primary innovation outcomes: sales and profits. The exclusive emphasis on sales and profit may be warranted for certain types of goods such as durable goods, but when examining the effects of new products in fast‐moving consumer goods or in the entrepreneurial sphere, where cash to cash matters greatly for survival, it is critical for both researchers and practitioners to not only consider the profits and sales generated by the new product but also the time to breakeven. This paper develops a theoretical framework using the competency‐based literature to examine the effects of innovation drivers (customer idea source, speed to market, product quality, and product newness) on breakeven time (BET) and project profits, and their subsequent impact on firm performance. A three‐stage least square estimation method was employed using longitudinal data on 945 new product development projects and launches in the morning (breakfast) foods category. The results clearly pinpoint that for successful product innovation, managers need to consider the time taken to breakeven on new product development. Specifically, the results demonstrate that speed to market and product quality shorten BET, but customer idea source extends BET. Second, the analysis also empirically demonstrates that BET is an equally effective predictor of firm performance as project profits in the short run, but significantly a stronger predictor of firm performance in the long run (t + four years), suggesting that BET should be regarded as a superior leading indicator of firm performance versus product profitability for fast‐moving consumer goods segment. This is an important finding that suggests firms that recoup their cash investments more quickly experience greater short‐term and significantly more long‐term success.     

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.     

Examining salesperson versus sales manager evaluation of customer opportunities: A psychological momentum perspective on optimism,confidence, and overconfidence

Making correct decisions regarding the merit of sales opportunities, as well as deciding which opportunities to pursue and which to abandon, is one of the most critical tasks sales managers and salespeople grapple with. These frontline assessments typically become sales forecasts that are used to allocate resources within the firm. Assessments of future sales, however, are often inaccurate and little is known about how salespeople and sales managers make decisions about sales opportunities, or if they differ in their ability to (correctly) assess the merits of such opportunities. To address this research gap, we explore several decision-making biases – namely optimism, confidence, and overconfidence – through the lenses of both Psychological Momentum (PM) and Strategic Reference Point (SRP) theory. Analysis of rich CRM data from a Fortune 500 medical products company, across three studies, demonstrates notable differences between salespeople and sales managers concerning decision-making biases that influence their evaluations of sales opportunities.     

Using the Analytic Hierarchy Process in New Product Screening

The initial screening of a new product idea is critically important. Risky projects (i.e., those with high probabilities of failure) need to be eliminated early before significant investments are made and opportunity costs incurred. Unfortunately, previous research suggests that it is often difficult for managers to "kill" new product development projects once they have begun. Furthermore, recent studies (including some centering on PDMA members) suggest there is much room for improving new product screening, because this decision often is taken informally or unsystematically. Whereas tools such as Cooper's NewProd software are available to aid in the screening decision, management science decision support models for screening are not used frequently. In the present study, the authors illustrate the use of the Analytic Hierarchy Process (AHP) as a decision support model to aid managers in selecting new product ideas to pursue. The need for flexible models that are highly customized to each firm's challenges (such as AHP) to support the screening decision and to generate knowledge that will be used as input for a firm's expert support system is emphasized. The authors then present an in-depth example of an actual application of AHP in new product screening and discuss the usefulness of this process in gathering and processing knowledge for making new product screening decisions. Finally, the authors explain how a customized AHP process can be incorporated into a sophisticated information system or used as standalone support. © 1999 Elsevier Science Inc.     

Do Firms Need a Custom-Designed New Product Screening Model?

Organizations have traditionally taken a variety of approaches to screening new product ideas. The use of a formal evaluation process to support screening decisions can reduce the costs and risks associated with product development. Although making good new product choices is vital, many managers have not applied analytical screening models because these models have not been customized to reflect the particular character of their firm and industry. In a study of how managers from different types of companies screen new product proposals, Ulrike de Brentani finds that the essential criteria determining new product selection do not differ radically among firms. This indicates that managers can potentially benefit from applying an existing formal evaluation model that encompasses the critical factors generally applied when making new product evaluation decisions, even if that model has not been customized for their specific application.     

A Conjoint‐Hazard Model of the Timing of Buyers' Upgrading to Improved Versions of High‐Technology Products*

Understanding consumers' upgrading behavior is essential to product planning. Product managers would like to know what fraction of customers would upgrade to new and improved versions, and how fast. This paper presents a method to forecast the sales path of an improved version of a high‐technology product defined in terms of its price path and multiattribute product specification. The approach is potentially useful to managers to answer what‐if questions on the effects of alternative price paths and product specifications of the upgrade on when and what fraction of customers will upgrade. By doing such analysis for several product options under consideration, managers can choose the best feature specification and price path for the upgrade. The proposed approach integrates an individual‐level conjoint utility model with a hazard function specification. The first stage of estimation (i.e., conjoint analysis) measures individual‐level multiattribute utility functions, and the second stage (i.e., duration analysis) calibrates the coefficients of predictor variables of the time to upgrade via maximum likelihood. An illustrative application in the personal digital assistant (PDA) category confirms the predictive validity and potential usefulness of the proposed approach. Among the empirical findings are that higher upgrade costs and expectation of faster product improvement tend to delay buyers' upgrading decisions. The roles of other predictor variables such as product category characteristics, consumer characteristics, and peer pressure were also confirmed.     

A NEW APPROACH TO DEPRECIATION MEASUREMENT FOR PUBLIC UTILITIES: COMMENT

This paper examines the use of a simple heuristic for evaluating projects. We posit that ranking projects by IRR and rejecting marginal projects can be superior to a NPV rule if 1) project managers have incentives to overstate cash flow forecasts that occur late in a project's life, 2) project rankings determine project acceptance because not all positive NPV project's are accepted, and 3) a project's IRR is greater than the WACC. In these instances, the IRR heuristic undervalues distant cash flows and thus, reduces project managers' incentives to positively bias forecasts.     

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.