http://repub.eur.nl/res/aut/4461/ List of Publications en http://repub.eur.nl/static-eur/img/logo.png http://repub.eur.nl http://repub.eur.nl/res/pub/19533/ 2010-02-01T00:00:00Z The authors propose a new model to capture unobserved consideration from discrete choice data. This approach allows for unobserved dependence in consideration among brands, easily copes with many brands, and accommodates different effects of the marketing mix on consideration and choice as well as unobserved consumer heterogeneity in both processes. An important goal of this study is to establish the validity of the existing practice to infer consideration sets from observed choices in panel data. The authors show with experimental data that underlying consideration sets can be reliably retrieved from choice data alone and that consideration is positively affected by display and shelf space. Next, the model is applied to Information Resources Inc. panel data. The findings suggest that promotion effects are larger when they are included in the consideration stage of the two-stage model than in a single-stage model. The authors also find that consideration covaries across brands and that this covariation is mainly driven by unobserved consumer heterogeneity. Finally, the authors show the implications of the model for promotion planning relative to a more standard model of choice. http://repub.eur.nl/res/pub/20948/ 2009-02-01T00:00:00Z New information technologies increasingly make it possible for service providers to adaptively personalize their service, fine-tuning the service over time for each individual customer, based on observation of that customer's behavior. We propose an "Adaptive Personalization System" and illustrate its implementation for digital audio players, a product category with rapidly expanding sales. The proposed system automatically downloads personalized playlists of MP3 songs into a consumer's mobile digital audio device and requires little proactive user effort (i.e., no explicit indication of preferences or ratings for songs). The system works in real time and is scalable to the massive data typically encountered in personalization applications. A simulation study shows the Adaptive Personalization System to outperform benchmark approaches. We implemented the Adaptive Personalization System on Palm PDAs and tested its performance with digital audio users. For actual users, the Adaptive Personalization System provides substantial improvements over benchmark approaches both in terms of the number of songs listened to and listening duration. http://repub.eur.nl/res/pub/15673/ 2008-09-01T00:00:00Z Brand salience - the extent to which a brand visually stands out from its competitors - is vital in competing on the shelf, yet is not easy to achieve in practice. This study proposes a methodology to determine the competitive salience of brands, based on a model of visual search and eye-movement recordings collected during a brand search experiment. We estimate brand 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, and is determined by two key components: The bottom-up component is due to in-store activity and package design. The top-down component is due to out-of-store marketing activities such as advertising. We show that about one-third of salience on the shelf is due to out-of-store and two-thirds due to in-store marketing. The proposed methodology for competitive salience analysis exposes the optimal visual differentiation level of a brand versus its competitors, and of each SKU versus the other SKUs of the same brand. The model of the visual search process and methodology for competitive salience analysis enable diagnostic analyses of the current levels of visual differentiation of brands and SKUs at the point of purchase, and provide directions for increasing these. http://repub.eur.nl/res/pub/15277/ 2008-06-01T00:00:00Z 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 scene's 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. http://repub.eur.nl/res/pub/7040/ 2005-11-09T00:00:00Z We propose a new model to describe consideration, consisting of a multivariate probit model component for consideration and a multinomial probit model component for choice, given consideration. The approach allows one to analyze stated consideration set data, revealed consideration set (choice) data or both, while at the same time it allows for unobserved dependence in consideration among brands. In addition, the model accommodates different effects of the marketing mix on consideration and choice, an error process that is correlated over time, and unobserved consumer heterogeneity in both processes. We attempt to establish the validity of existing practice to infer consideration sets from observed choices in panel data. To this end, we collect data in an on-line choice experiment involving interactive supermarket shelves and post-choice questionnaires to measure the choice protocol and stated consideration levels. We show with these experimental data that underlying consideration sets can be reliably retrieved from choice data alone. Next, we estimate the model on IRI panel data. We have two main results. First, compared with the single-stage multinomial probit model, promotion effects are larger when they are included in the consideration stage of the two-stage model. Second, we find that consideration of brands does not covary greatly across brands once we account for observed effects. http://repub.eur.nl/res/pub/13217/ 2005-01-01T00:00:00Z We present a statistical model for voter choice that incorporates a consideration set stage and final vote intention stage. The first stage involves a multivariate probit (MVP) model to describe the probabilities that a candidate or a party gets considered. The second stage of the model is a multinomial probit (MNP) model for the actual choice. In both stages, we use as explanatory variables data on voter choice at the previous election, as well as sociodemographic respondent characteristics. Importantly, our model explicitly accounts for the three types of “missing data” encountered in polling. First, we include a no-vote option in the final vote intention stage. Second, the “do not know” (DNK) response is assumed to arise from too little difference in the utility between the two most preferred options in the consideration set, or is considered to be a missing observation. Third, the “do not want to say” (DNWTS) response is modeled as a missing observation on the most preferred alternative in the consideration set. Thus, we consider the missing data generating mechanism to be nonignorable and build a model based on utility maximization to describe the vote intentions of these respondents. We illustrate the merits of the model as we have information on a sample of about 5000 individuals from the Netherlands for who we know how they voted last time (if at all), which parties they would consider for the upcoming election, and what their vote intention is. A unique feature of the data set is that information is available on actual individual voting behavior, measured at the day of election. We find that the inclusion of the consideration set stage in the model enables the user to make a more precise inference on the competitive structure in the political domain and to obtain better out-of-sample forecasts. http://repub.eur.nl/res/pub/320/ 2003-04-29T00:00:00Z Previous conjoint choice design construction procedures have produced a single design that is administered to all subjects. This paper proposes to construct a limited set of different designs. The designs are constructed in a Bayesian fashion, taking into account prior uncertainty about the parameter values. A computational procedure is developed that enables fast and easy implementation in practice. Even though the number of such different designs in the optimal set is small, it is demonstrated through a Monte Carlo study that substantial gains in efficiency are achieved over aggregate designs. http://repub.eur.nl/res/pub/1663/ 2000-12-15T00:00:00Z We present a statistical model for voter choice that incorporates a consideration set stage and final vote intention stage. The first stage involves a multivariate probit model for the vector of probabilities that a candidate or a party gets considered. The second stage of the model is a multinomial probit model for the actual choice. In both stages we use as explanatory variables data on voter choice at the previous election, as well as socio-demographic respondent characteristics. Importantly, our model explicitly accounts for the three types of "missing data" encountered in polling. First, we include a no-vote option in the final vote intention stage. Second, the "do not know" response is assumed to arise from too little difference in the utility between the two most preferred options in the consideration set. Third, the "do not want to say" response is modelled as a missing observation on the most preferred alternative in the consideration set. Thus, we consider the missing data generating mechanism to be non-ignorable and build a model based on utility maximization to describe the voting intentions of these respondents. We illustrate the merits of the model as we have information on a sample of about 5000 individuals from the Netherlands for who we know how they voted last time (if at all), which parties they would consider for the upcoming election, and what their voting intention is. A unique feature of the data set is that information is available on actual individual voting behavior, measured at the day of election. We find that the inclusion of the consideration set stage in the model enables the user to make more precise inferences on the competitive structure in the political domain and to get better out-of-sample forecasts. http://repub.eur.nl/res/pub/12822/ 1998-01-01T00:00:00Z The objective of this article is to assess the influence of variables over which suppliers have control (supply-side variables) on the adoption of innovations in addition to adopter-side variables. The empirical study focused on the adoption of electronic banking in the Dutch business market. A quantitative study was conducted to test hypotheses. The results show that the extent to which a supplier has pursued a strategy aimed at positioning the innovation in the marketplace or has focused on reducing the risk of adoption has a positive and significant effect on the probability of innovation adoption. The evidence corroborates that not only adopter-side variables significantly influence innovation, but supply-side variables as well. http://repub.eur.nl/res/pub/12575/ 1997-01-01T00:00:00Z