The article presents an editorial focusing on marketing questions relating to the introduction of new products. The author believes that in the past, there was little product choice. In the 1960s however, variety has brought up new questions for marketing personnel, namely when to enter a new product into the marketplace. He thinks that an evaluation period of about two and a half years is needed to truly get a good idea of how well a product might do in the grocery marketplace.
Marketing planning, New product development, Product management, Industrial research, Rapid prototyping, Prototypes, Decision making, and Industrial management
Modeling efforts in the area of new product introductions have had a significant impact on marketing planning and strategy. One result of these efforts, BBDO's New Product Early Warning System (NEWS), has been used since the late 1960s to provide marketing managers with forecasts and diagnostic reformation regarding their new product strategies. This article presents the specification of the NEWS model, its parameter estimation methods, and its validation. A brief case history is also included which illustrates how the model is applied in a typical new product situation. NEWS is designed to use a variety of readily obtainable input data to generate forecasts of consumer awareness, trial, repeat purchase, usage, sales, and market share for a new brand. These outputs, combined with diagnostics from the model, can then be incorporated into the marketing plan in a way that will improve the new entry's chances of success in the marketplace. The model can be used to project early test market data (NEWS/Market); or it can be used to analyze pre-test market data (NEWS/Planner). [ABSTRACT FROM AUTHOR]
Diffusion of innovations, Marketing planning, Diffusion of innovations theory, New product development, Product management, Commercial products, Regression analysis, Least squares, Rapid prototyping, and Industrial research
A maximum likelihood approach is proposed for estimating an innovation diffusion model of new product acceptance originally considered by Bass (1969). The suggested approach allows: (1) computation of approximate standard errors for the diffusion model parameters, and (2) determination of the required sample size for forecasting the adoption level to any desired degree of accuracy. Using histograms from eight different product renovations, the maximum likelihood estimates are shown to outperform estimates front a model calibrated using ordinary least squares, m terms of both goodness of fit measures and one-step ahead forecasts. However, these advantages are not obtained without cost. The coefficients of innovation and imitation are easily interpreted in terms of the expected adoption pattern, but individual adoption times must be assumed to represent independent draws from this distribution In addition, instead of using standard linear regression, another (simple) program must be employed to estimate the model. Thus, tradeoffs between the maximum likelihood and least squares approaches are also discussed. [ABSTRACT FROM AUTHOR]
Consumer behavior, New product development, Product management, Rapid prototyping, Industrial research, and Resource allocation
Few published articles have dealt with the unique problem associated with the management of new, infrequently purchased products that exhibit Seasonal patterns of demand, Marvin Berkowitz demonstrates how seasonality influences the performance of a new consumer durable good, a new brand of battery-operated lights, during a 2-year period following its launch. The data presented support four hypotheses: (1) the newest brands in a product category, when compared to dominant brands, will be subject to higher seasonal variation in consumer awareness, advertising recall, product attribute positioning, and purchase intent; (2) the relationship between seasonal effects and brand share within a product category will not be linear: (3) differences between product attributes for competing brands will be most apparent to consumers during periods of peak seasonal activity: and (4) perceptions of product attributes that are most important in the buying decision are subject to the least seasonal variation. The article also demonstrates how seasonal variations may be charted and discusses how this analysis contributes to the overall management of the new product. INSET: BIOGRAPHICAL SKETCH. [ABSTRACT FROM AUTHOR]
Public Relations Quarterly. Winter86/87, Vol. 31 Issue 4, p25. 4p.
Mass media, Reporters & reporting, Public relations, Industrial management, Executives, Industrial publicity, Interviewing in journalism, and Rapid prototyping
The article presents information on how one can face staff reporters with special reference to business executives. Announcements of a new product, top executive change or corporate expansion generally are welcome assignments for public relations people and corporate spokespersons. Happily, there are techniques that can be useful not only when talking to reporters but also in discussions with customers, community leaders and other key publics when one do not want to release a lot of information. Unfortunately, a no comment response is like a lightning rod. It can result in TV stations carrying footage on the evening news showing their reporters standing in front of one's organization's closed front door or locked gates or conducting an empty chair interview. It causes the reporter to search out sources who will comment people like disgruntled employees, hostile competitors or other outside observers.
Marketing, Algorithms, New product development, Commercial products, Manufactures, Rapid prototyping, Industrial research, Sample size (Statistics), and Statistical sampling
Four algorithms lot locating an "optimal" new product in a multiattribute product space--Albers and Brockhoff's PROPOPP: Gavish, Horsky, and Srikanth's Method IV: May and Sudharshan's PRODSRCH; and GRID SEARCH--are compared in terms of the relative share of preferences the new product will capture under different simulated market environments. These environments were both ones for which the algorithms were designed as well as other "more realistic" environments. Results indicate that algorithm performance is sensitive to the number of customers or segments, and the presence of probabilistic choice, and less sensitive to the numbers of existing products. Gavish, Horsky, and Srikanth IV (GHS IV) and PROPOPP performed best under the market conditions for which they were designed and GHS IV proved quite robust under variation from these conditions PROPOPP's performance deteriorated. however, in large sample size problems (n ≥ 200). PRODSRCII (a general purpose optimizer) was inferior trader these special market conditions, but superior under other more general ones. [ABSTRACT FROM AUTHOR]