Joseph Pancras

Optimal marketing strategies for a customer data intermediary

Advances in data collection and storage technologies have given rise to the customer data intermediary (CDI), a firm that collects customer data to offer customer-specific marketing services to marketers. With widespread adoption of customer relationship management (CRM) and one-to-one (1:1) marketing, the demand for such services continues to grow. Extant empirical research using customer data for CRM and 1:1 marketing tends to have an engineering emphasis and focuses on developing analysis techniques to implement CRM and 1:1 marketing optimally (i.e., the technology for the CDI). In contrast, this article focuses on marketing strategy issues that the intermediary faces, given the availability of the technology to implement such services. Specifically, the authors develop an empirical framework to evaluate the optimal customer (exclusive/nonexclusive), product (quality or accuracy of the 1:1 customization), and pricing strategy for a CDI. They illustrate the framework for one type of CDI—a 1:1 coupon service firm that caters to grocery manufacturers—using household purchase history data from the ketchup market. The authors find that selling on a nonexclusive basis using the maximum available purchase history data is the most profitable strategy for the CDI in the particular market. They also evaluate the potential impact of retailers entering the 1:1 coupon service business. Because 1:1 marketing can increase the retailers profits from goods sold, it is optimal for the retailer to undercut the prices of a pure-play CDI that offers 1:1 coupon services.

Issues and Perspectives in Global Customer Relationship Management

Over the past few decades, cross-border business has experienced unparalleled growth. This growth is due to advances in communication and information technologies, privatization and deregulation in emerging economies, and emergence of the global consumer. As the era of globalization continues to manifest through the emergence of global companies, the importance of customer relationship management (CRM) in these companies has become increasingly significant. Global CRM (GCRM) is the strategic application of the processes and practices of CRM by firms operating in multiple countries or by firms serving customers who span multiple countries, which incorporates relevant differences in business practices, competition, regulatory characteristics, country characteristics, and consumer characteristics to CRM strategies to maximize customer value across the global customer portfolio of the firm. In this article, the authors present an overview of the GCRM environment and the challenges in formulation and implementation of CRM across national boundaries as a source of sustained advantage. The authors also provide a conceptual framework for GCRM and recommendations for future research in Global CRM.

The nested consideration model: Investigating dynamic store consideration sets and store competition☆

The nested logit model has been widely used to study nested choice. A typical example of such nested choice is store patronage and brand choice. An important limitation of the nested logit model is that it assumes that all alternatives at both levels of the nest are available in the choice set of the consumer. While there is a wide literature on the incorporation of restricted choice sets into the logit model, the logical extension of this analysis to nested restricted choice sets has not been pursued in the literature. In this study we develop a nested consideration model that integrates store choice and brand choice incorporating the formation of dynamic restricted choice sets of both stores and brands. We term the model the nested consideration model and derive the related probabilities in a manner analogous to the well-known nested logit model. In an empirical illustration, we find that the nested consideration model shows better prediction than nested logit models (with the same explanatory variables). We find that it is important to account for dynamic store consideration sets rather than static sets or store loyalty measures. We also conduct simulations to demonstrate the importance of the nested consideration set model for correct pricing and store location decisions of business managers.

The Impact of Gamification on Word-of-Mouth Effectiveness: Evidence from Foursquare

Companies are encouraging and incentivizing contributors of online word-of-mouth (WOM) through gamification elements such as badges, mayorships, points, and such. We study how gamification elements, which capture and signal contributors’ accumulated expertise, affect consumers’ perception of contributors’ knowledge, and therefore the perceived effectiveness of their contributed WOM. We focus on two specific gamification elements on Foursquare: badges, which signal breadth of knowledge, and mayorships, which signal depth of knowledge. Using experiments conducted on Amazon Mechanical Turk, we find: (1) badges and mayorships that appear alongside contributors’ online WOM, provide a unique way to signal WOM contributors’ knowledge and therefore have an impact on the perceived effectiveness of such WOM; (2) the impact of badges on perceived WOM effectiveness is higher than that of mayorships. Our findings have important implications for the ongoing research on the impact of gamification and also suggest ways for firms to benefit from gamification.

Investigating the impact of customer stochasticity on firm price discrimination strategies using a new Bayesian mixture scale heterogeneity model

In this paper, we study the impact of customer stochasticity on firm price discrimination strategies. We develop a new model termed the Bayesian Mixture Scale Heterogeneity (BMSH) model that incorporates both parameter heterogeneity and customer stochasticity using a mixture model approach, and demonstrate model identification using extensive simulations. We estimate the model on yogurt scanner data and find that compared to the benchmark mixed logit and multinomial probit models, our model shows that markets are less price elastic, and that a majority of customers exhibit stochasticity in purchases; our model also obtains better prediction and more profitable targeting strategies.