Asim Ansari’s research while affiliated with Columbia University and other places

Internet recommender systems are popular in contexts that include heterogeneous consumers and numerous products. In such contexts, product features that adequately describe all the products are often not readily available. Content-based systems therefore rely on user-generated content such as product reviews or textual product tags to make recommendations. In this paper, we develop a novel covariate-guided, heterogeneous supervised topic model that uses product covariates, user ratings, and product tags to succinctly characterize products in terms of latent topics and specifies consumer preferences via these topics. Recommendation contexts also generate big-data problems stemming from data volume, variety, and veracity, as in our setting, which includes massive textual and numerical data. We therefore develop a novel stochastic variational Bayesian framework to achieve fast, scalable, and accurate estimation in such big-data settings and apply it to a MovieLens data set of movie ratings and semantic tags. We show that our model yields interesting insights about movie preferences and makes much better predictions than a benchmark model that uses only product covariates. We show how our model can be used to target recommendations to particular users and illustrate its use in generating personalized search rankings of relevant products.

This article proposes a framework for studying how a brand, firm, or individual can use networking activities to manage a social network and drive its success. Using data from ego networks of music artists, the article models how artists can enhance their social networking presence and stimulate relationships between fans to achieve long-term benefits in terms of music plays. The authors use a Bayesian modeling framework to model the heterogeneous and dynamic impact of networking activities on network structure and on music popularity, while relying on instrumental variables from another independent online social network to handle potential endogeneity. The results imply that artists can shape network structure via marketing activities and thereby achieve a long-term impact on success that far exceeds the direct and short-term impact in magnitude. Specifically, improving the density of ego networks enables long-term effects beyond those that stem from growth in network size.

We model the multifaceted impact of pricing decisions in business-to-business (B2B) relationships that are governed by trust. We show how a seller can develop optimal intertemporal targeted pricing strategies to maximize profits over time while taking into consideration the impact of pricing decisions on short-term profit margin, reference price formation, and long-term relationships. Our modeling framework uses a hierarchical Bayesian approach to weave together a multivariate nonhomogeneous hidden Markov model, buyer heterogeneity, and control functions to facilitate targeting, capture the evolution of trust, and control for price endogeneity. We estimate our model on longitudinal transactions data from a retailer in the industrial consumables domain. We find that buyers in our data set can be best represented by two latent states of trust toward the seller-a "vigilant" state that is characterized by heightened price sensitivity and a cautious approach to ordering and a "relaxed" state with purchase behaviors that are consistent with high relational trust. The seller's pricing decisions can transition buyers between these two states. An optimal dynamic and targeted pricing strategy based on our model suggests a 52% improvement in profitability compared with the status quo. Furthermore, a counterfactual analysis examines the seller's optimal pricing policy under fluctuating commodity prices.

Marketing variables that are included in consumer discrete choice models are often endogenous. Extant treatments using likelihood-based estimators impose parametric distributional assumptions, such as normality, on the source of endogeneity. These assumptions are restrictive because misspecified distributions have an impact on parameter estimates and associated elasticities. The normality assumption for endogeneity can be inconsistent with some marginal cost specifications given a price-setting process, although they are consistent with other specifications. In this paper, we propose a heterogeneous Bayesian semiparametric approach for modeling choice endogeneity that offers a flexible and robust alternative to parametric methods. Specifically, we construct centered Dirichlet process mixtures (CDPM) to allow uncertainty over the distribution of endogeneity errors. In a similar vein, we also model consumer preference heterogeneity nonparametrically via a CDPM. Results on simulated data show that incorrect distributional assumptions can lead to poor recovery of model parameters and price elasticities, whereas the proposed semiparametric model is able to robustly recover the true parameters in an efficient fashion. In addition, the CDPM offers the benefits of automatically inferring the number of mixture components that are appropriate for a given data set and is able to reconstruct the shape of the underlying distributions for endogeneity and heterogeneity errors. We apply our approach to two scanner panel data sets. Model comparison statistics indicate the superiority of the semiparametric specification and the results show that parameter and elasticity estimates are sensitive to the choice of distributional forms. Moreover, the CDPM specification yields evidence of multimodality, skewness, and outlying observations in these real data sets. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1811 . This paper was accepted by J. Miguel Villas-Boas, marketing.

Over the course of a repeated game, players often exhibit learning in selecting their best response. Research in economics and marketing has identified two key types of learning rules: belief and reinforcement. It has been shown that players use either one of these learning rules or a combination of them, as in the Experience-Weighted Attraction (EWA) model. Accounting for such learning may help in understanding and predicting the outcomes of games. In this research, we demonstrate that players not only employ learning rules to determine what actions to choose based on past choices and outcomes, but also change their learning rules over the course of the game. We investigate the degree of state dependence in learning and uncover the latent learning rules and learning paths used by the players. We build a non-homogeneous hidden Markov mixture of experts model which captures shifts between different learning rules over the course of a repeated game. The transition between the learning rule states can be affected by the players’ experiences in the previous round of the game. We empirically validate our model using data from six games that have been previously used in the literature. We demonstrate that one can obtain a richer understanding of how different learning rules impact the observed strategy choices of players by accounting for the latent dynamics in the learning rules. In addition, we show that such an approach can improve our ability to predict observed choices in games.

In recent years, newer kinds of platforms have emerged on the Internet by expanding the use of social media in the healthcare arena. By facilitating patient-to-patient interactions, these healthcare social media platforms host online communities interested in discussing health-related topics. The successful operation of social media in the healthcare realm requires patients’ participation in the form of open sharing of their personal medical information online. This study investigates the antecedents and consequences of medical disclosure on healthcare social media sites. Data on the disclosure behavior of patients on a large healthcare social media site over 28 weeks was analyzed using a hierarchical Bayesian approach. After accounting for patient-level unobserved heterogeneity, our analyses revealed that social identity enhancing factors such as peer disclosure and membership length encourage disclosure behavior. Interestingly, personal identity revealing factors such as location of the user does not exert a significant impact on the disclosure behavior. To identify the effect of medical disclosure on health outcomes, we formulated an instrumental variable strategy within the Bayesian framework, by exploiting an exogenous shock that only affected physiological outcomes through disclosure extent. Results suggest that increased frequency of symptom disclosure brings about a decrease in health outcomes. Implications for businesses and policy makers are discussed. More specifically, we discuss the operating guidelines for fostering participation while preventing unintended health consequences on healthcare social media platforms.

We model the multifaceted impact of pricing decisions in B2B contexts and show how a seller can develop optimal inter-temporal targeted pricing strategies to maximize long-term customer value. We empirically model the B2B customer’s purchase decisions in an integrated fashion. In order to facilitate targeting and to capture the short and long-term dynamics of B2B customer purchasing, our modeling framework weaves together in a hierarchical Bayesian manner, multivariate copulas, a non-homogeneous hidden Markov model, and control functions for price endogeneity. We estimate our model on longitudinal transactions data from an aluminum retailer. We find that customers in our dataset can be best represented by two latent states - a “vigilant” state characterized by heightened price sensitivity and a cautious approach to ordering, and a more “relaxed” state. The seller’s pricing decisions can transition customers between these two states. An optimal dynamic and targeted pricing strategy based on our model suggests a 52% improvement in profitability compared to the status quo. Furthermore, a counterfactual analysis which examines the optimal policy under fluctuating commodity prices reveals that the seller should pass much of the costs to customers when commodity prices increase, but hoard most of the profit when commodity prices (seller’s costs) decrease.

Firms are increasingly seeking to harness the potential of social networks for marketing purposes. Therefore, marketers are interested in understanding the antecedents and consequences of relationship formation within networks and in predicting interactivity among users. The authors develop an integrated statistical framework for simultaneously modeling the connectivity structure of multiple relationships of different types on a common set of actors. Their modeling approach incorporates several distinct facets to capture both the determinants of relationships and the structural characteristics of multiplex and sequential networks. They develop hierarchical Bayesian methods for estimation and illustrate their model with two applications: the first application uses a sequential network of communications among managers involved in new product development activities, and the second uses an online collaborative social network of musicians. The authors' applications demonstrate the benefits of modeling multiple relations jointly for both substantive and predictive purposes. They also illustrate how information in one relationship can be leveraged to predict connectivity in another relation.