Secondary Titles (1)
- Arthur M. Weimer Faculty Fellow
Shibo Li is Professor of Marketing and Arthur M. Weimer Faculty Fellow at the Kelley School of Business, Indiana University at Bloomington, Indiana. His research interests are consumer dynamics, analytical customer relationship management, digital marketing, shopper marketing, signaling models, and quantitative models in marketing. His research has appeared in Marketing Science, Journal of Marketing, Journal of Marketing Research, Information Systems Research, Journal of the Academy of Marketing Science, and Journal of Interactive Marketing. Shibo was selected as a MSI Young Scholar by Marketing Science Institute in 2009, and won the Faculty Research Award in 2012 and the 3M Junior Faculty Grant Award at Kelley School of Business at Indiana University in 2008, 2009 and 2010. He was the winner of the John A. Howard Doctoral Dissertation Award of the American Marketing Association in 2004 and received the CART Research Frontier Award from Carnegie Mellon University in 2006. His dissertation has also won the William Cooper Dissertation Competition Award at Carnegie Mellon University in 2003. He also won the Best Student Teacher Award at Carnegie Mellon University in 2001. Shibo was on the editorial review board of Marketing Science from 2007-2008 and has served as an ad hoc reviewer for various top-tiered journals including Marketing Science, Journal of Marketing Research, Journal of Marketing, Information Systems Research, and Management Science. Shibo teaches undergraduate-level marketing management, MBA dynamic analysis of customer data course and doctoral marketing models seminar at Kelley. He taught marketing principles and marketing strategy at Carnegie Mellon University and Rutgers University prior to joining Indiana University.
Industry Expertise (6)
Writing and Editing
Public Relations and Communications
Areas of Expertise (5)
Customer Relationship Management
Analytical and Empirical Analysis of Signaling Models
Excellent Reviewer Award (professional)
Awarded by Journal of Interactive Marketing
Kelley School of Business Research Award (professional)
Awarded by Indiana University
Dean’s Citation for Teaching Excellence (professional)
Awarded by Indiana University
Carnegie Mellon University: Ph.D., Industrial Administration 2003
Carnegie Mellon University: M.S.I.A., Marketing 2000
Peking University: M.A., Economics 1998
Peking University: B.A., Economics 1995
Many e-commerce websites struggle to turn visitors into real buyers. Understanding online users' real-time intent and dynamic shopping cart choices may have important implications in this realm. This study presents an individual-level, dynamic model with concurrent optimal page adaptation that learns users' real-time, unobserved intent from their online cart choices, then immediately performs optimal Web page adaptation to enhance the conversion of users into buyers. To suggest optimal strategies for concurrent page adaptation, the model analyzes each individual user's browsing behavior, tests the effectiveness of different marketing and Web stimuli, as well as comparison shopping activities at other sites, and performs optimal Web page transformation. Data from an online retailer and a laboratory experiment reveal that concurrent learning of the user's unobserved purchase intent and real-time, intent-based optimal interventions greatly reduce shopping cart abandonment and increase purchase conversions. If the concurrent, intent-based optimal page transformation for the focal site starts after the first page view, shopping cart abandonment declines by 32.4% and purchase conversion improves by 6.9%. The optimal timing for the site to intervene is after three page views, to achieve efficient learning of users' intent and early intervention simultaneously.
Researchers from diverse disciplines have exam-ined the many factors that contribute to the influence of published research papers. Such influence dynamics are in essence a marketing of science issue. In this paper, we propose that in addition to known established, overt drivers of influ-ence such as journal, article, author, and Matthew effects, a latent factor "citability" influences the eventual impact of a paper. Citability is a mid-range latent variable that captures the changing relationship of an article to a field. Our analysis using a discretized Tobit model with hidden Markov processes suggests that there are two states of citability, and these dynamic states determine eventual influence of a paper. Prior research in marketing has relied on models where the various effects such as author and journal effects are deemed static. Unlike ours, these models fail to capture the continuously evolving impact dynamics of a paper and the differential effect of the various drivers that depend on the latent state a paper is in at any given point of time. Our model also captures the impact of uncitedness, which other models fail to do. Our model is estimated using articles published in seven leading marketing journals during the years 1996–2003. Findings and implications are discussed.
This research investigates how the social elements of a retail store visit affect shoppers' product interaction and purchase likelihood. The research uses a bivariate model of the shopping process, implemented in a hierarchical Bayes framework, which models the customer and contextual factors driving product touch and purchase simultaneously. A unique video tracking database captures each shopper's path and activities during the store visit. The findings reveal that interactive social influences (e.g., salesperson contact, shopper conversations) tend to slow the shopper down, encourage a longer store visit, and increase product interaction and purchase. When shoppers are part of a larger group, they are influenced more by discussions with companions and less by third parties. Stores with customers present encourage product interaction up to a point, beyond which the density of shoppers interferes with the shopping process. The effects of social influence vary by the salesperson's demographic similarity to the shopper and the type of product category being shopped. Several behavioral cues signal when shoppers are in a potentially high need state and may be good sales prospects.
Although prior literature has examined reactions to drastic negative news, we examine the situation in which decision makers receive contradictory information about products and they have to decide whether to persist with or abandon product usage. We investigate physician reactions to conflicting information concerning the cardiovascular risk of Avandia, a diabetes drug. We examine how beliefs about both drug effectiveness and drug safety are updated and speculate that experience, expertise, and self-efficacy impact how such information is integrated with current quality beliefs. Unlike previous Bayesian learning models, we consider that some signals, such as positive and negative news releases and the firm's marketing effort, may be biased in that they provide an opinionated point of view. The results show interesting differences in how physician types (specialists, hospital-based primary care physicians, heavy and light prescribers) update their beliefs and the information sources they use to do so. We find evidence that safety issues about Avandia resulted in spillover concern to close competitor Actos. The results have implication for determining who should be targeted and what vehicles should be used if a firm is faced with a situation where consumers are in a quandary because of receiving conflicting messages.
Firms are challenged to improve the effectiveness of cross-selling campaigns. The authors propose a customer-response model that recognizes the evolvement of customer demand for various products; the possible multifaceted roles of cross-selling solicitations for promotion, advertising, and education; and customer heterogeneous preference for communication channels. They formulate cross-selling campaigns as solutions to a stochastic dynamic programming problem in which the firm's goal is to maximize the long-term profit of its existing customers while taking into account the development of customer demand over time and the multistage role of cross-selling promotion. The model yields optimal cross-selling strategies for how to introduce the right product to the right customer at the right time using the right communication channel. Applying the model to panel data with cross-selling solicitations provided by a national bank, the authors demonstrate that households have different preferences and responsiveness to cross-selling solicitations. In addition to generating immediate sales, cross-selling solicitations also help households move faster along the financial continuum (educational role) and build up goodwill (advertising role). A decomposition analysis shows that the educational effect (83%) largely dominates the advertising effect (15%) and instantaneous promotional effect (2%). The cross-selling solicitations resulting from the proposed framework are more customized and dynamic and improve immediate response rate by 56%, long-term response rate by 149%, and long-term profit by 177%.