Jehoshua Eliashberg

A Viral Branching Model for Predicting the Spread of Electronic Word of Mouth

In a viral marketing campaign, an organization develops a marketing message and encourages customers to forward this message to their contacts. Despite its increasing popularity, there are no models yet that help marketers to predict how many customers a viral marketing campaign will reach and how marketers can influence this process through marketing activities. This paper develops such a model using the theory of branching processes. The proposed viral branching model allows customers to participate in a viral marketing campaign by 1 opening a seeding e-mail from the organization, 2 opening a viral e-mail from a friend, and 3 responding to other marketing activities such as banners and offline advertising. The model parameters are estimated using individual-level data that become available in large quantities in the early stages of viral marketing campaigns. The viral branching model is applied to an actual viral marketing campaign in which over 200,000 customers participated during a six-week period. The results show that the model quickly predicts the actual reach of the campaign. In addition, the model proves to be a valuable tool to evaluate alternative what-if scenarios.

Structuring the New Product Development Pipeline

In many new product development NPD situations, the development process is characterized by uncertainty, and no single development approach will necessarily lead to a successful product. To increase the likelihood of having at least one successful product, multiple approaches may be simultaneously funded at the various NPD stages. The managerial challenge is to construct ex ante an appropriate NPD pipeline by choosing the right number of approaches to be funded at each stage. This so-called pipeline problem is also present in, among others, advertising copy selection and new products test markets problems. We describe here a normative model for structuring pipelines for such situations. The optimal structure of the pipeline is driven by the cost of the development approach, its probability of survival, and the expected profitability. We illustrate the workability and implications of the model by applying it to some real-world scenarios in the pharmaceutical industry, and by comparing its normative pipeline recommendations against actual pipelines. Our results suggest that, for the cases we studied, firms tend to use narrower pipelines for their new drug development than they should, and thereby they underspend on research and development. We also present general qualitative insights for one-and two-stage NPD optimal pipeline structures.

Customer Preferences, Supply-Chain Costs, and Product-Line Design

The modeling framework developed here to address the positioning and pricing of a product line assumes that products differ in price and in a single physical attribute. It incorporates a flexible representation of customer heterogeneity, allowing for price-sensitive demand, and a rich class of supply-chain cost models. A key characteristic of potentially optimal product lines, termed the cross-point property, is used to develop efficient dynamic programming algorithms to determine an optimal product line.

Chapter 18 Marketing-production joint decision-making

Publisher Summary This chapter describes some tangible benefits that emerge from coexistence and outlines some techniques in management science that have been developed to address this issue. At this point, it seems worthwhile to consider some additional perspectives in order to identify existing gaps that may offer further research opportunities. Few of the models incorporate competition, undoubtedly because the fact that the existence of both production and marketing decisions creates models which are already considerably complex. Despite the daunting nature of competitive formulations, these would be well worth investigating. Another dimension which should be looked into is the case of multiple products. Such models may be quite difficult to analyze, however, with the existing tools.

Technological innovation: Minimizing technological oversights: A marketing research perspective

Technological advances provide vast opportunities for new product development. Some technologies are transformed into successful new products; others are not. In this paper we investigate the role that marketing research methods as currently conceived can play in aligning marketplace needs with technological potential. We discuss the types of opportunities that new technologies present to the marketplace and why the existing set of market research methods are insufficient to assess the potential for all of these new technologies. We then discuss some emerging, non-traditional marketing research methods and assess their potential for addressing the technological oversights problem. We conclude with implications for academics and for practitioners. Prepared for Presentation at Technological Oversights and Foresights Conference Leonard N. Stern School of Business New York University April 10, 1994

An Experimental Study of Alternative Preparation Aids for International Negotiations

We test the relative effectiveness of alternative preparation aids in the context of an international negotiation. We consider three forms of training: reading material, a course on negotiation, and an expert system (NEGOTEX) expressly designed to train negotiators. We conducted a laboratory experiment involving 66 pairs of negotiators—one of each pair being American and the other Chinese. Results suggest that in this context, the course had the greatest effect on performance, followed by NEGOTEX, and then followed by reading material. In addition, we found that training effects were additive: multiple forms of training lead to better results than individual forms of training, suggesting that (1) training forms complement and do not substitute for one another, and (2) multiple forms of training should be considered, especially when stakes are high.

Optimal positioning of new product-concepts: some analytical implications and empirical results

Abstract This paper presents an analytical approach to determine optimal target positions (locations) for a discontinuous innovation in the perceived product-attributes space. The new product-concept positioning problem involves the determination of technologically and economically feasible (but believable) of either the specific combination(s) of the levels of the perceived products attributes, or the identification of subregion(s) in the space as targets that have the potential to optimize a certain objective set by the firm. Typically, researchers have approached this problem by applying mathematical programming techniques and various algorithms/heuristics have been proposed to solve the resultant problem. Their implications depend, of course, on the specific configurations and values of the parameters of the problem. This paper focuses on qualitative insights and generalizable location implications. We rely on the concepts of consumer preference, choice, market segmentation, and technological constraint to derive various analytical insights in the context of new product-concept positioning. Empirical studies addressing these issues in the consumer marketing area are presented, and directions for future research are also provided.

A New Stochastic Multidimensional Unfolding Model for the Investigation of Paired Comparison Consumer Preference/Choice Data

This paper presents the development of a new stochastic multidimensional (scaling) unfolding (Coombs 1964) methodology which operates on paired comparison consumer preference or choice data and renders a spatial representation of both consumers and the products or brands they choose. Consumers are represented as ideal points and products as points in a T-dimensional space, where the Euclidean distance between the product points and the consumer ideal points provide information as to the utility of such products to these consumers. The econometric and psychometric literature concerning related models which also operate on such paired comparisons data is reviewed, and a technical description of the new methodology is provided. To illustrate the versatility of the model, a small application measuring consumer preference for several actual brands of over-the-counter analgesics, utilizing one of the optional reparametrized models, is described. Finally, future areas of further research are identified.

Modeling DVD Preorder and Sales: An Optimal Stopping Approach

When a DVD title is announced prior to actual distribution, consumers can often preorder the title and receive it as soon as it is released. Alternatively, once a title becomes available i.e., formally released, consumers can obtain it upon purchase with minimal delay. We propose an individual-level behavioral model that captures the aggregate preorder/postrelease sales of motion picture DVDs. Our model is based on an optimal stopping framework. Starting with the utility function of a forward-looking consumer, and allowing for consumer heterogeneity, we derive the aggregate preorder/postrelease sales distribution. Even under a parsimonious specification for the heterogeneity distribution, our model recovers the typically observed temporal pattern of DVD preorder and sales, a pattern which exhibits an exponentially increasing number of preorder units before the release, peaks at release, and drops exponentially afterward. Using data provided by a major Internet DVD retailer, we demonstrate a number of important managerial implications stemming from our model. We investigate the role of preorder timing through a policy experiment, estimate residual sales, and forecast post-release sales based only on preorder information. We show that our model has substantially better predictive validity than benchmark models.

Assessing Box Office Performance Using Movie Scripts: A Kernel-Based Approach

We develop a methodology to predict box office performance of a movie at the point of green-lighting, when only its script and estimated production budget are available. We extract three levels of textual features (genre and content, semantics, and bag-of-words) from scripts using screenwriting domain knowledge, human input, and natural language processing techniques. These textual variables define a distance metric across scripts, which is then used as an input for a kernel-based approach to assess box office performance. We show that our proposed methodology predicts box office revenues more accurately (29 percent lower mean squared error (MSE)) compared to benchmark methods.