Jacob Goldenberg

Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth

Though word-of-mouth (w-o-m) communications is a pervasive and intriguing phenomenon, little is known on its underlying process of personal communications. Moreover as marketers are getting more interested in harnessing the power of w-o-m, for e-business and other net related activities, the effects of the different communications types on macro level marketing is becoming critical. In particular we are interested in the breakdown of the personal communication between closer and stronger communications that are within an individuals own personal group (strong ties) and weaker and less personal communications that an individual makes with a wide set of other acquaintances and colleagues (weak ties).We use a technique borrowed from Complex Systems Analysis called stochastic cellular automata in order to generate data and analyze the results so that answers to our main research issues could be ascertained. The following summarizes the impact of strong and weak ties on the speed of acceptance of a new product:••The influence of weak ties is at least as strong as the influence of strong ties. Despite the relative inferiority of the weak tie parameter in the models assumptions, their effect approximates or exceeds that of strong ties, in all stages of the product life cycle.••External marketing efforts (e.g., advertising) are effective. However, beyond a relatively early stage of the growth cycle of the new product, their efficacy quickly diminishes and strong and weak ties become the main forces propelling growth. The results clearly indicate that information dissemination is dominated by both weak and strong w-o-m, rather than by advertising.••The effect of strong ties diminishes as personal network size decreases. Market attributes were also found to mediate the effects of weak and strong ties. When personal networks are small, weak ties were found to have a stronger impact on information dissemination than strong ties.

The Idea Itself and the Circumstances of Its Emergence as Predictors of New Product Success

In view of the distressingly low rate of success in new product introduction, it is important to identify predictive guidelines early in the new product development process so that better choices can be made and unnecessary costs avoided. In this paper, we propose a framework for early analysis based on the success potential embodied in the product-idea itself and the circumstances of its emergence. Based on two studies reporting actual introductions, we identified several determinants such as how the ideas originated, their specific configurations, and the level of technology required for their implementation that significantly distinguish successful from unsuccessful new products in the marketplace. We suggest that these factors, together with already known factors of success/failure, may aid in the estimation of the potential of a concept early in its development.

Riding the Saddle: How Cross-Market Communications Can Create a Major Slump in Sales

Using data on a large number of innovative products in the consumer electronics industry, the authors find that between one-third and one-half of the sales cases involved the following pattern: an initial peak, then a trough of sufficient depth and duration to exclude random fluctuations, and eventually sales levels that exceeded the initial peak. This newly identified pattern, which the authors call a “saddle,” is explained by the dual-market phenomenon that differentiates between early market adopters and main market adopters as two separate markets. If these two segments—the early market and the main market—adopt at different rates, and if this difference is pronounced, then the overall sales to the two markets will exhibit a temporary decline at the intermediate stage. The authors employ both empirical analysis and cellular automata, an individual-level, complex system modeling technique for generating and analyzing data, to investigate the conditions under which a saddle occurs. The model highlights the importance of cross-market communication in determining the existence of a saddle. At low levels of this parameter, more than 50% of the cases of new product growth involved a saddle. This percentage gradually decreased as the parameter increased, and at values close to the within-market parameters, the proportion of saddle occurrences dropped below 5%.

Using cellular automata modeling of the emergence of innovations

Introducing innovations and new products to the market is an essential activity for leading firms. Firms wishing to exploit the advantages of pioneer status strive to attain exclusivity in their discovery of a new market need. Our work introduces some insights relating to the clarification and representation of the dynamics of market awareness of an emerging need. The implications of eliciting knowledge from consumers are discussed, and the probability of competitors attaining “pioneer” status in the market is examined. A low probability of achieving the objective of an exclusive and original discovery of an emerging need via marketing research is indicated. We use Stochastic Cellular Automata to model the collective dynamics grounded in the study of local interactions between agents. Using this paradigm, we show that due to extreme volatility of discovery probabilities concentrated in a short time span, there is a high probability that at least one other competitor will discover the same need before, or concurrently with, its discovery by the firm in question, if traditional exploration is applied. Consequently, a firm is unable to ensure that its discovery of a new need is a singular, pioneering event. A model to assess the odds that the emergent need discovery is exclusive (based on parameters that can be collected during the survey itself) is proposed and evaluated.

Extracting Product Comparisons from Discussion Boards

In recent years, product discussion forums have become a rich environment in which consumers and potential adopters exchange views and information. Researchers and practitioners are starting to extract user sentiment about products from user product reviews. Users often compare different products, stating which they like better and why. Extracting information about product comparisons offers a number of challenges; recognizing and normalizing entities (products) in the informal language of blogs and discussion groups require different techniques than those used for entity extraction in the more formal text of newspapers and scientific articles. We present a case study in extracting information about comparisons between running shoes and between cars, describe an effective methodology, and show how it produces insight into how consumers view the running shoe and car markets.

Network Traces on Penetration: Uncovering Degree Distribution from Adoption Data

We show how networks modify the diffusion curve by affecting its symmetry. We demonstrate that a networks degree distribution has a significant impact on the contagion properties of the subsequent adoption process, and we propose a method for uncovering the degree distribution of the adopter network underlying the dissemination process, based exclusively on limited early-stage penetration data. In this paper we propose and empirically validate a unified network-based growth model that links network structure and penetration patterns. Specifically, using external sources of information, we confirm that each network degree distribution identified by the model matches the actual social network that is underlying the dissemination process. We also show empirically that the same method can be used to forecast adoption using an estimation of the degree distribution and the diffusion parameters at an early stage (15%) of the penetration process. We confirm that these forecasts are significantly superior to those of three benchmark models of diffusion. Our empirical analysis indicates that under heavily right-skewed degree distribution conditions (such as scale-free networks), the majority of adopters (in some cases, up to 75%) join the process after the sales peak. This strong asymmetry is a result of the unique interaction between the dissemination process and the degree distribution of its underlying network.

Database Submission---The Evolving Social Network of Marketing Scholars

The interest in social networks among marketing scholars and practitioners has sharply increased in the last decade. One social network of which network scholars increasingly recognize the unique value is the academic collaboration (coauthor) network. We offer a comprehensive database of the collaboration network among marketing scholars over the last 40 years (available at http://mktsci.pubs.informs.org ). Based on the ProQuest database, it documents the social collaboration among researchers in dozens of the leading marketing journals, enabling us to create networks of active marketing researchers. Unlike most of the published academic collaboration research, our database is dynamic and follows the evolution of the field over many years. In this paper, we describe the database and point to some basic network descriptives that lead to interesting research questions. We believe this database can be of much value to researchers interested in the evolution of social networks over time, as well as the specific evolution of the marketing discipline. The data set described in this paper is maintained by the authors and available through http://mktsci.pubs.informs.org

Templates of Original Innovation: Projecting Original Incremental Innovations from Intrinsic Information

A systematic framework for the enhancement of inventiveness is introduced. According to the proposed approach, the starting point is an existing system rather than external pressures. By a sequence of formal operations (defined as templates) on the initial structure of a system, an innovative structure involving a new system is obtained. The sequence of operations is prescribed by well-defined procedures. The replacement template is illustrated in this work by two field cases and its potential value is tested empirically. Given the abundance of innovations in which the templates are manifested, the replacement template can be considered an exemplar for utilizing intrinsic information about a system in the development of innovations.

World-size global markets lead to economic instability

Economic and cultural globalization is one of the most important processes humankind has been undergoing lately. This process is assumed to be leading the world into a wealthy society with a better life. However, the current trend of globalization is not unprecedented in human history, and has had some severe consequences in the past. By applying a quantitative analysis through a microscopic representation we show that globalization, besides being unfair (with respect to wealth distribution), is also unstable and potentially dangerous as one event may lead to a collapse of the system. It is proposed that the optimal solution in controlling the unwanted aspects and enhancing the advantageous ones lies in limiting competition to large subregions, rather than making it worldwide.

Zooming In: Self-Emergence of Movements in New Product Growth

In this paper, we propose an individual-level approach to diffusion and growth models. By zooming in, we refer to the unit of analysis, which is a single consumer instead of segments or markets and the use of granular sales data daily instead of smoothed e.g., annual data as is more commonly used in the literature. By analyzing the high volatility of daily data, we show how changes in sales patterns can self-emerge as a direct consequence of the stochastic nature of the process. Our contention is that the fluctuations observed in more granular data are not noise, but rather consist of accurate measurement and contain valuable information. By stepping into the noise-like data and treating it as information, we generated better short-term predictions even at very early stages of the penetration process. Using a Kalman-Filter-based tracker, we demonstrate how movements can be traced and how predictions can be significantly improved. We propose that for such tasks, daily data with high volatility offer more insights than do smoothed annual data.