Jerry Wind

New Product Models: Practice, Shortcomings and Desired Improvements

Abstract Vijay Mahajan and Jerry Wind report the results of a study conducted to assess the role of new product models in supporting and improving the new product development process. The study focused on current industry practice in deciding which models and methods to use, their short-comings and desired improvements. The results have implications for developers, suppliers and users of new product forecasting models. The article concludes with a research and implementation agenda to further foster and benefit from advances in new product models.

Marketing Hype: A New Perspective for New Product Research and Introduction

Marketing research procedures typically used to support new product development activities often emphasize the collection of data from potential customers, even when the product success depends on the decisions of a number of key stakeholders such as distributors, media, etc. Consequently, most conventional product introduction efforts focus on a target customer segment and ignore the needs of other stakeholders. These narrowly concentrated research efforts can lead to unfounded expectations regarding the product performance. Similarly, the lopsided focus on consumers can lead to reduced marketing effectiveness. Jerry Wind and Vijay Mahajan argue for the recognition of the process of “marketing hype”, a set of prelaunch activities leading to the ereation of a supportive market environment. This can lead to the creation of broader strategies that focus on the key stakeholders as subjects for new product research, and targets for the introductory marketing programs. This could lead to a richer understanding of the intergroup influences on the adoption of the new product and increase the chances of a successful new product launch.

The dot.com retail failures of 2000: Were there any winners?

In the year 2000, several dot.com retailers filed for bankruptcy, shut down their operations, or faced the risk of their stock being delisted on the stock market. But did any dot.com retailer do it right? Were there any winners? If yes, who are these winners? What is the product and firm profile of these winners? What lesson, if any, can be learned from these winners and losers? This article addresses these questions based on a study of 48 dot.com retailers, conducted in December 2000. The study identified 1–800contacts.com as the sole winner, using two performance indicators: percentage change in stock price since the initial public offering and stock options underwater. Based on a proposed conceptual framework of product and firm characteristics, the profile of 1–800contacts.com is compared with the hypothesized winner, Amazon.com, and other dot.com retailers. Implications of the study and limitations and opportunities for future research are discussed.

Risk return approach to product portfolio strategy

Abstract This article describes how a risk-return portfolio analysis, as originally developed in economics and finance, can be applied to product-line decisions. This approach uses direct estimates of return, and explicitly considers risk, or variation in return; most of the product portfolio models in use today forecast return by correlation, and lack explicit treatment of risk. The approach provides guidance for new product development activities as well as for allocating resources among a corporations existing product lines. The article explains how organizations can apply this approach to their own product portfolio decisions, and includes a detailed example of how one company used this model.

A Customized Market Response Model: Development, Estimation, and Empirical Testing

A customized, stepwise, log-linear, distributed lag, restricted market response model is proposed to estimate the effects of various elements of promotion expenditures on sales in the presence of potentially significant effects due to trend and/or seasonality when using time-series data. As distinct from standardized software packages, the customization offers management several benefits: (a) an (optional) imposition of prior restrictions on the directions of the coefficient variables; (b) an empirical determination of the lag structure for selected variables; (c) the detrending of the data to allow for the assessment of incremental marketing mix effects above trend; and (d) a simplified sensitivity analysis. The model is empirically tested and validated using sales data for a brand where the impact of several marketing mix variables is estimated and investigated via policy simulations. A comparison of these results with those obtained from a corresponding unrestricted model illustrates the advantages of this approach. Finally, the limitations of this procedure and directions for future research are discussed.