Viral marketing can be a safe bet

Today, from a business and marketing perspective, vast numbers of customers and potential customers interact with one another through electronic and online channels that range from emails to social media hubs such as Facebook, MySpace and Twitter

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 stimulates 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 email from the organization, 2) opening a viral email 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 already in the early stages of viral marketing campaigns. The Viral Branching Model is app

Competition for attention in online social networks: Implications for seeding strategies

Many firms try to leverage consumers’ interactions on social platforms as part of their communication strategies. However, information on online social networks only propagates if it receives consumers’ attention. This paper proposes a seeding strategy to maximize information propagation while accounting for competition for attention. The theory of exchange networks serves as the framework for identifying the optimal seeding strategy and recommends seeding people that have many friends, who, in turn, have only a few friends. There is little competition for the attention of those seeds’ friends, and these friends are therefore responsive to the messages they receive. Using a game-theoretic model, we show that it is optimal to seed people with the highest Bonacich centrality. Importantly, in contrast to previous seeding literature that assumed a fixed and non-negative connectivity parameter of the Bonacich measure, we demonstrate that this connectivity parameter is negative and needs to be estimated. Two independent empirical validations using a total of 34 social media campaigns on two different large online social networks show that the proposed seeding strategy can substantially increase a campaign’s reach. The second study uses the activity network of messages exchanged to confirm that the effects are driven by competition for attention

Defining eye-fixation sequences across individuals and tasks: the Binocular-Individual Threshold (BIT) algorithm

We propose a new fully automated velocity-based algorithm to identify fixations from eye-movement records of both eyes, with individual-specific thresholds. The algorithm is based on robust minimum determinant covariance estimators (MDC) and control chart procedures, and is conceptually simple and computationally attractive. To determine fixations, it uses velocity thresholds based on the natural within-fixation variability of both eyes. It improves over existing approaches by automatically identifying fixation thresholds that are specific to (a) both eyes, (b) x- and y- directions, (c) tasks, and (d) individuals. We applied the proposed Binocular-Individual Threshold (BIT) algorithm to two large datasets collected on eye-trackers with different sampling frequencies, and compute descriptive statistics of fixations for larger samples of individuals across a variety of tasks, including reading, scene viewing, and search on supermarket shelves. Our analysis shows that there are considerable differences in the characteristics of fixations not only between these tasks, but also between individuals

Cross-National Logo Evaluation Analysis: An Individual Level Approach

The universality of design perception and response is tested using data collected from ten countries: Argentina, Australia, China, Germany, Great Britain, India, the Netherlands, Russia, Singapore, and the United States. A Bayesian, finite-mixture, structural-equation model is developed that identifies latent logo clusters while accounting for heterogeneity in evaluations. The concomitant v

Bayesian Estimation of the Multinomial Logit Model: A Comment on Holmes and Held, "Bayesian Auxiliary Variable Models for Binary and Multinomial Regression"

This note provides two corrections to the pseudo-code of the algorithm for the Bayesian estimation of the multinomial logit model using auxiliary variables as developed by Holmes and Held (2006). After incorporating the two corrections, the algorithm works correctly for the multinomial as well as the binary logit model

Competitive Brand Salience

This study assesses brand salience using a model of eye-movement recordings, collected during a brand search experiment. We estimate brands' salience at the point-of-purchase, based on perceptual features (color, luminance, edges) and how these are influenced by consumers' search goals. We show that the salience of brands has a pervasive effect on search performance. We identify two key sources of brand salience. The bottom-up component is influenced by in-store activity and package design. The top-down component is influenced by out-of-store marketing activities such as advertising. Our study reveals that about one-third of salience on the shelf is due to out-of-store and two-thirds due to in-store marketing