Ralf van der Lans

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.

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 variable approach allows cluster probabilities to be country specific. Rather than a priori defined clusters, our procedure provides a posteriori cross-national logo clusters based on consumer response similarity. To compare the a posteriori cross-national logo clusters, our approach is integrated with Steenkamp and Baumgartner’s (1998) measurement invariance methodology. Our model reduces the ten countries to three cross-national clusters that respond differently to logo design dimensions: the West, Asia, and Russia. The dimensions underlying design are found to be similar across countries, suggesting that elaborateness, naturalness, and harmony are universal design dimensions. Responses (affect, shared meaning, subjective familiarity, and true and false recognition) to logo design dimensions (elaborateness, naturalness, and harmony) and elements (repetition, proportion, and parallelism) are also relatively consistent, although we find minor differences across clusters. Our results suggest that managers can implement a global logo strategy, but they also can optimize logos for specific countries if desired.

Eye-Movement Analysis of Search Effectiveness

Advances in eye-tracking technology have promoted its widespread use to understand and improve target searches in psychology, industrial engineering, human factors, medical diagnostics, and marketing. Eye movements are the realization of a complex, unobserved spatiotemporal attention process with many sources of variation. Eye-tracking data often have been aggregated and/or summarized descriptively, because few adequate statistical models are available for their analysis. This article proposes a model that may serve to uncover the latent attention processes of people searching for targets in complex scenes. It recognizes the spatial nature of eye movements and represents two latent attention states, a localization state and an identification state, between which people may switch over time according to a Markov process. A saliency map, based on low-level perceptual features and the scenes organization, guide target searches in the localization state. In the identification state, people verify whether a selected candidate object is the target. The model is applied to analyze commercial eye-tracking data from more than 100 people engaged in a target search task on a computer-simulated retail shelf display. Rapid switching between attention states over time is revealed. Estimates of the feature and saliency maps are provided and found to be related to search performance. The results facilitate the evaluation of the effectiveness of alternative visual search strategies.

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.

Partner Selection in Brand Alliances: An Empirical Investigation of the Drivers of Brand Fit

We investigate whether partners in a brand alliance should be similar or dissimilar in brand image to foster favorable perceptions of brand fit. Using a Bayesian nonlinear structural equation model and evaluations of 1,200 brand alliances, we find that the conceptual coherence in brand personality profiles predicts attitudes towards a brand alliance. More specifically, we find that similarity in Sophistication and Ruggedness and moderate dissimilarity in Sincerity and Competence result in more favorable brand alliance evaluations. Overall, we find that similarity effects are more pronounced than dissimilarity effects. Implications for brand alliance strategies and marketing managers are discussed.

Uncovering the Importance of Relationship Characteristics in Social Networks: Implications for Seeding Strategies

Seeding influential social network members is crucial for the success of a viral marketing campaign and product diffusion. In line with the assumption that connections between customers in social networks are binary (either present or absent), previous research has generally recommended seeding network members who are well-connected. However, the importance of connections between customers varies substantially depending on the relationships characteristics, such as its type (i.e., friend, colleague, or acquaintance), duration, and interaction intensity. This research introduces a new Bayesian methodology to identify influential network members and takes into account the relative influence of different relationship characteristics on product diffusion. Two applications of the proposed methodology—the launch of a microfinance program across 43 Indian villages and information propagation in a large online social network—demonstrate the importance of weighting connections in social networks. Compared with traditional seeding strategies, the proposed methodology recommends substantially different sets of seeds that increased the reach by up to 10% in the first empirical application and up to 92% in the second.

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.

Integrating Social Networks into Marketing Decision Models

The rise of online social networks has been the most significant development on the Internet in the last decade. It has not only transformed how consumers interact with each other, but also affected the way companies communicate with their customers. This development has also offered a wealth of data to better understand social interactions between consumers and to optimize marketing strategies in the presence of social influence. In this chapter, the authors provide a generalized framework to integrate social network data into traditional marketing decision models. The authors show how their framework nests several existing approaches to deal with social network data. They also discuss the empirical challenges researchers may encounter and possible solutions.

Eye Movements during Search and Choice

The last decade has seen an increasing interest in marketing in the use of modern eye-tracking equipment for developing and testing theories of search and choice. This chapter reviews this development and provides a framework that assists marketing researchers in collecting and processing eye tracking data and incorporating that data into marketing decision models. The authors demonstrate how eye movement data is collected using modern eye-tracking equipment, and how such data relates to underlying visual processes. The chapter discusses key eye-tracking measures and illustrates how such measures can be integrated into decision models of search and choice. This chapter provides useful insights for researchers interested in setting up eye-tracking experiments, as well as for researchers interested in understanding how such data can be summarized and incorporated into their models.

Modeling Gift Choice: The Effect of Uncertainty on Price Sensitivity

Gift giving generates high revenues for retailers. It is also marked with significant welfare, or deadweight, loss in that givers tend to pay more than the receivers’ valuation. Previous research has attributed this discrepancy to givers’ inaccurate predictions of the receivers’ preferences. This research demonstrates that reduced price sensitivity is another important source of the deadweight loss: givers use gift prices to signal the importance of their relationship with the receiver. In order to demonstrate this mechanism, the authors develop a new Bayesian gift-choice model that captures both preference predictions as well as the signaling value of price. The model is estimated on two choice-based conjoint studies for gift giving that allow for the manipulation of the givers uncertainty about the receivers preferences. Both studies show the strong signaling value of price, especially when givers are uncertain about receivers’ preferences. Decomposition of the deadweight loss shows that the signaling value of price is an important source of welfare loss, especially in markets with heterogeneous prices. These findings have key implications for the gift industry.