Shibo Li

Areas of Expertise

• Consumer Dynamics, Analytical Customer Relationship Management, Internet and Interactive Marketing, Analytical and Empirical Analysis of Signaling Models

Education

• Ph.D. Carnegie Mellon University (1998 — 2003)
• Master of Industrial Administration Carnegie Mellon University (1998 — 2000)
• Master's degree Peking University (1995 — 1998)
• Bachelor's degree Peking University (1990 — 1995)

Companies

• John R. Gibbs Professor and Professor of Marketing Indiana University Bloomington (2017)
• Professor of Marketing Indiana University Bloomington (2016)
• Associate Professor of Marketing Indiana University Bloomington (2011 — 2016)
• Assistant Professor of Marketing Indiana University Bloomington (2005 — 2011)
• Assistant Professor of Marketing Rutgers Business School (2003 — 2005)

Awards, Honors & Certificates

• Kelley School of Business Faculty Research Award, 2012
• Weimer Faculty Fellow, Kelley School of Business, 2011-present
• Dean''s Citation for Teaching Excellence, 2010, 2011
• Nominee for Indiana University Trustees Teaching Award, 2010
• Marketing Science Institute Young Scholar, 2009
• Nominee for Doctoral Student Association Exceptional Inspiration and Guidance Award, Kelley School of Business, Indiana University, 2008
• AMA-Sheth Doctoral Consortium Faculty Fellow, University of Missouri-Columbia, 2008
• 3M Junior Faculty Grant Award, Kelley School of Business, Indiana University, 2008, 2009, 2010
• Editorial Review Board, Marketing Science, 2007 - 2008
• CART Research Frontier Award for Innovative Research, Carnegie Mellon University, 2006
• Finalist, John D. C. Little Award, Marketing Science and Management Science, 2005
• Faculty Service Award, Alpha Kappa Psi, Rutgers University, 2005
• John A. Howard AMA Doctoral Dissertation Award, 2004
• William Cooper Dissertation Competition Award, Carnegie Mellon University, 2003
• AMA-Sheth Doctoral Consortium Fellow, University of Miami, 2001
• Best Student Teacher Award, Carnegie Mellon University, 2001
• William Larimer Mellon Fellowship, Carnegie Mellon University, 1998-2001
• Lincoln Scholarship, Peking University, 1995-1996
• Outstanding Student Scholarship, Peking University, 1991-1995, 1997-1998

Selected Publications

• Xiaoling Zhang, Shibo Li, and Raymond R. Burke (2018), “Modeling the Effects of Dynamic Group Influence on Shopper Zone Choice, Purchase Conversion and Spending,” forthcoming, Journal of the Academy of Marketing Science.
• Li, Shibo, Eugene Sivadas, and Mark Johnson (2015), “Explaining Article Influence: Capturing Article Citability and Its Dynamic Effects,” Journal of the Academy of Marketing Science, Vol. 43, No. 1, pp. 52-72.
• Sun, Yacheng, Shibo Li, and Baohong Sun (2015), “An Empirical Analysis of Consumer Purchase Decisions under Bucket-Based Price Discrimination,” Marketing Science, Vol. 34, No. 5, pp. 646-668.
• Ding, Amy Wenxuan, Shibo Li, and Patrali Chatterjee (2015), “Learning User Real-Time Intent for Optimal Dynamic Webpage Transformation,” Information Systems Research, Vol. 26, No. 2, pp. 339-359.
• Zhang, Xiaoling, Shibo Li, Raymond R. Burke, and Alex Leykin (2014), “An Examination of Social Influence on Shopper Behavior Using Video Tracking Data,” Journal of Marketing, September 2014, Vol. 78, No. 5, pp. 24-41.
• Sun, Baohong and Shibo Li (2011), "Learning and Acting on Customer Information: A Simulation-Based Demonstration on Service Allocations with Offshore Centers," Journal of Marketing Research, Vol. 48, No. 1, February, pp. 72-86.

Abstract As service centers become crucial corporate assets for increasing customer relationships and profits, it is imperative to understand customer reactions to service allocations. Using customer call history from a DSL service, the authors empirically investigate how customers’ onshore and offshore experiences affect service duration and customer retention. They formulate service channel allocation decisions as solutions to a dynamic programming problem in which the firm learns about heterogeneous customer preferences, balances short-term service costs with long-term customer retention, and optimally matches customers with their preferred centers to maximize long-term profit. They demonstrate through simulations that learning enables a firm to make more customized allocations and that acting on long-term customer responses prompts the firm to make proactive decisions that prevent customers from leaving. As a result, the firm can improve customer retention and profit. The proposed framework also mirrors the recent trend of companies seeking solutions that transform customer information into customized and dynamic marketing decisions to improve long-term profit.

• Li, Shibo, Baohong Sun, and Alan L. Montgomery (2011), “Cross-Selling the Right Product to the Right Customer at the Right Time,” Journal of Marketing Research, Vol. 48, No. 4, 683-700. 

Abstract Firm are challenged to improve the effectiveness of cross-selling campaigns.  In this research, we propose a customer-response model that recognizes the evolvement of customer demand for various products, the possible multi-faceted roles of cross-selling solicitations for promotion, advertising, and education, and customer heterogeneous preference for communication channels. We 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 multi-stage role of cross-selling promotion. The model yields optimal cross-selling strategies about 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, we demonstrate that households have different preferences and responsiveness to cross-selling solicitations. Other than generating immediate sales, cross-selling solicitations also help households move faster along the financial continuum (educational role) and build up good will (advertising role). We show that the suggested cross-selling solicitations are more customized and dynamic and significantly improve over the currently adopted campaign-centric solicitations.

• Kalra, Ajay, Shibo Li, and Wei Zhang (2011), “Understanding Responses to Contradictory Information about Products,” Marketing Science, Vol. 30, No. 6, 1098-1114.

Abstract While prior literature has examined reactions to drastic negative news, we examine the situation how decision makers receive contradictory information about products where they have to decide whether to persist or abandon product usage.  We investigate physician reactions to conflicting information concerning cardiovascular risk of Avandia, a diabetes drug.  We examine how beliefs both about drug effectiveness and drug safety are updated speculating that experience, expertise and self-efficacy impacts how such information is integrated with current quality beliefs.  Unlike previous Bayesian learning models, we consider that some signals like 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 PCPs, heavy and light prescribers) update their beliefs and information sources they use to do so.  We find evidence that safety issue 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 due to receiving conflicting messages.

• Li, Shibo, Kannan Srinivasan, and Baohong Sun (2009), "Internet Auction Features as Quality Signals," Journal of Marketing, Vol. 73, No. 1, pp. 75-92.

Abstract Internet auction companies have developed innovative tools that enable sellers to reveal more information about their credibility and product quality to avoid the “lemons” problem. On the basis of signaling and auction theories, the authors propose a typology of Internet auction quality and credibility indicators, adopt and modify Park and Bradlow''s (2005) model, and use eBay as an example to examine empirically how different types of indicators help alleviate uncertainty. This empirical evidence demonstrates how Internet auction features affect consumer participation and bidding decisions, what modifies the credibility of quality indicators, and how different buyers react to indicators. The signaling-based hypotheses provide coherent explanations of consumers'' bidding behavior. As the first empirical study to evaluate the signaling role of comprehensive Internet auction institutional features in mitigating the adverse selection problem, this research provides evidence to clarify the economic foundation behind innovative Internet auction designs.

• Kalra, Ajay and Shibo Li (2008), “Signaling Quality through Specialization,” Marketing Science, Vol. 27, No. 2, pp. 168-184.

Abstract Firms frequently position themselves as specialists. An implication of specialization is that the firm has forgone alternative opportunities. In the context of effort-intensive categories, we show that a firm can signal quality to consumers by specializing. In the model, a firm must decide to provide one service offering or to market two services. By entering a single category, the firm incurs an opportunity cost of not entering the secondary profitable category, but may attain reduced costs. The net cost is the signaling cost that a high-quality type firm incurs to signal quality over a low-quality type firm. We show that in homogenous markets, a high-quality type firm signals its high-quality type by specializing in one category. When consumers are heterogeneous, the firm can signal its high-quality type by using prices alone in both the primary and secondary categories. However, specialization can be used as a secondary signal of quality in heterogeneous markets because of lower signaling costs. We also find that signaling using specialization is more likely in the presence of competition.

• Sun, Baohong, Shibo Li, and Catherine Zhou (2006), "''Adaptive'' Learning and ''Proactive'' Customer Relationship Management," Journal of Interactive Marketing, Vol. 20, No. 3-4, pp. 82-96.

Abstract Customer Relationship Management (CRM) is about introducing the right product to the right customer at the right time through the right channel to satisfy the customer''s evolving demands; however, most existing CRM practice and academic research focuses on methods to select the most profitable customers for a scheduled CRM intervention. In this article, we discuss a two-step procedure comprising "adaptive learning" and "proactive" CRM decisions. We also discuss three key components for customer-centric CRM: adaptive learning, forward-looking, and optimization. We then formulate CRM interventions as solutions to a stochastic dynamic programming problem under demand uncertainty in which the company learns about the evolution of customer demand as well as the dynamic effect of its marketing interventions, and make optimal CRM decisions to balance the cost of interventions and the long-term payoff. Finally, we choose two examples to demonstrate the input, output, and benefit of "adaptive" learning and "proactive" CRM.

• Li, Shibo, Baohong Sun, and Ronald T. Wilcox (2005), "Cross-Selling Sequentially Ordered Products: An Application to Consumer Banking Services," Journal of Marketing Research, Vol. 42, No. 2, pp. 233-239.

Abstract Customers have predictable life cycles. As a result of these life cycles, firms that sell multiple products or services frequently observe that, in general, certain items are purchased before others. This predictable phenomenon provides opportunities for firms to cross-sell additional products and services to existing customers. This article presents a structural multivariate probit model to investigate how customer demand for multiple products evolves over time and its implications for the sequential acquisition patterns of naturally ordered products. The authors investigate customer purchase patterns for products that are marketed by a large midwestern bank. Among the substantive findings are that women and older customers are more sensitive to their overall satisfaction with the bank than are men and younger customers when determining whether to purchase additional financial services, and households whose head has a greater level of education or is male move more quickly along the financial maturity continuum than do households whose head has less education or is female.

• Montgomery, Alan L., Shibo Li, Kannan Srinivasan, and John C. Liechty (2004), “Modeling Online Browsing and Path Analysis Using Clickstream Data,” Marketing Science, Vol. 23, No. 4, pp. 579-595.

Abstract Clickstream data provide information about the sequence of pages or the path viewed by users as they navigate a website. We show how path information can be categorized and modeled using a dynamic multinomial probit model of Web browsing. We estimate this model using data from a major online bookseller. Our results show that the memory component of the model is crucial in accurately predicting a path. In comparison, traditional multinomial probit and first-order Markov models predict paths poorly. These results suggest that paths may reflect a user''s goals, which could be helpful in predicting future movements at a website. One potential application of our model is to predict purchase conversion. We find that after only six viewings purchases can be predicted with more than 40% accuracy, which is much better than the benchmark 7% purchase conversion prediction rate made without path information. This technique could be used to personalize Web designs and product offerings based upon a user''s path.