Marketing professor Joseph Pancras uses quantitative techniques to study targeted marketing and firm spatial competition in digital contexts such as mobile and online promotions as well as the interaction between digital and traditional marketing promotions. He also studies the effects of poaching and retail competition in the area of online paid search advertising, and firm and distribution channel competition in the context of targeted marketing and customer relationship management. His research has been published in Journal of Marketing Research, Management Science, Journal of Retailing and Journal of Interactive Marketing. His 2007 paper titled ‘Optimal Marketing Strategies for a Customer Data Intermediary’ won the 2008 Donald Lehmann award for best dissertation-based paper in the Journal of Marketing Research and his 2008 paper titled ‘Cross Buying in Retailing: Drivers and Consequences’ won the 2010 William Davidson award for best paper in the Journal of Retailing.
Dr. Pancras has several years of industry experience in custom marketing research in leading research groups such as Kantar and Taylor Nelson-Sofres, and brings these experiences to bear on his research and teaching.
Areas of Expertise (6)
Leonard N. Stern School of Business, New York University: Ph.D., Marketing 2005
Stern School of Business, New York University: M.A., Marketing 2003
Bharathidasan Institute of Management, Trichy, India: M.B.A., Marketing and Finance 1996
William R. Davidson Award (2010) (professional)
Best Article in the Journal of Retailing
Media Appearances (1)
Do deep promotional discounts work? New study sheds light on strategy
Science Daily online
Promotional discounts increase store traffic and lead to higher overall profits, especially if the advertised products are staples – items such as meat and produce that are purchased frequently and by many customers.
The received wisdom, reflected in popular marketing textbooks, is that featuring deeply discounted items will generate additional store traffic for retailers that in turn will lead to increased sales and profits. However, there is surprisingly little systematic evidence about the impact of these deep discounts on aggregate store traffic, sales, and profits. In this paper, we study the effects of promotional discounts and their characteristics on various store performance metrics employing a store level dataset pooled over 55 weeks and 24 stores. Many findings of our study lend credence to the continued popularity of such promotions by retailers. We find that feature promotions build store traffic, especially when the categories being featured are high penetration, high frequency. Also, promotions of branded items are found to be more effective than promotions of unbranded items. Discounting on more items in a category leads to lower store margins suggesting that the cost of discounting a large proportion of items in a category may not be justified by the profits generated by the sale. Using the coefficients from our model estimates, various counterfactuals provide insights into strategic change in level of discounts across categories. We discuss several implications of our findings for retailers.
Paid search advertising has been a widely used marketing tool in both Chinese and English countries. Matching strategy greatly influences the effectiveness of paid search advertising. Extant studies have examined the matching strategy between keywords and ad content in paid search advertising using the English language. However, the rapidly growing Chinese paid search advertising market has been largely ignored. Different from the English market, the Chinese paid search advertising market has a comparatively greater use of synonyms. Considering the high semantic dependence of words and characters in Chinese, we develop a method to classify Chinese keywords according to the information complexity of the keywords. Based on the keyword classification, we use synonym-based matching, defined as the semantic similarity of ad content and the keyword, to study the bidding behavior of Chinese paid search advertisers. Our results indicate that synonym-based matching increases click-through rate, especially for complex keywords that have multiple search attributes. Both the empirical analysis using secondary data from the Chinese paid search market and a subsequent controlled experiment show the robustness of the results. Our results point to the need for understanding the local characteristics (especially language) when studying online paid search advertising in the Chinese market.
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.
Advancements in mobile technologies mean that consumers can engage the digital world wherever they are and whenever they want. This intersection between the digital and the physical has important implications for consumer decision-making. We propose that mobile ecosystems vary in their capabilities and pervasivity (i.e., the degree to which a mobile ecosystem is accessible everywhere and at all times). Further, we propose that accounting for distinguishing aspects of mobile ecosystems, the context in which mobile ecosystems are used, and interactions between mobile ecosystems and mobile contexts are critical in advancing theoretical and substantive understanding of the role of mobile technologies in the marketplace. This perspective helps identify: 1) the types of data that empirical researchers may seek to gather and 2) the ways in which this data may be analyzed. Based on this perspective, we identify important research questions as well as opportunities and challenges for modeling mobile consumer decision-making.
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.