Raghuram Iyengar

Professor Raghu Iyengar's research interests fall in two domains: pricing and social influence. In the area of pricing, his work focuses on the impact of multipart pricing schemes on consumer response. The success of such pricing mechanisms to extract consumer surplus depends on how consumers respond to different components. Methodologically, Iyengar has developed novel consumer demand models that capture the effect of multipart pricing tariffs in a theoretically meaningful way and include contextual factors such as consumers’ uncertainty about usage. Substantively, he has shown that accounting for consumers’ uncertainty is important for firm profits especially when multipart prices are employed. In the area of social networks, Iyengar has done work that has investigated how and why such influence may be at work. Across several studies, Iyengar has identified the underlying mechanism(s) such as awareness, social learning or social normative pressure that may be at work in different contexts. Understanding the mechanism(s) is important not only theoretically but also managerially, because which customers to target and which ties to activate using what message depends on what mechanism is at work.

Professor Iyengar's other current research projects focus on the impact of referral coupons on consumer behavior and how changes in loyalty program requirements may change future customer behavior. His research has been published or forthcoming in Journal of Marketing Research, Marketing Science, Psychometrika, Quantitative and Marketing Economics and Experimental Economics. He serves on the Editorial Boards of Journal of Marketing Research, Marketing Science and the International Journal of Research in Marketing.

Professor Iyengar's teaching interests are in the area of Marketing Research and Analytics. He earned his PhD and MPhil from Columbia University and his B. Tech. from IIT Kanpur, India.

Yupeng Chen, Raghuram Iyengar, Garud Iyengar (2017), Modeling Multimodal Continuous Heterogeneity in Conjoint Analysis A Sparse Learning Approach , Marketing Science, 36 (1), pp. 140156.

Raghuram Iyengar and YoungHoon Park (Under Review), Shareable Coupons.

Jing Peng, Ashish Agarwal, Kartik Hosanagar, Raghuram Iyengar (Under Review), Network Overlap and Content Sharing on Social Media Platforms.

Abstract: Social media platforms allow users to connect and share content. The extent of information diffusion may depend on the characteristics of users’ connections, such as the overlap among users’ connections. We investigate the impact of network embeddedness (i.e., number of common followees, common followers, and common mutual followers between two users) on the information diffusion in directed networks. To accommodate the empirical observation that a user may receive the same information from several others, we propose a new hazard model that allows an event to have multiple causes. By analyzing the diffusion of sponsored ads on Digg and brandauthored tweets on Twitter, we find that the effect of embeddedness in directed networks varies across different types of “neighbors”. The number of common neighbors are not always conducive to information diffusion. Moreover, the effects of common followers and common mutual followers are negatively moderated by the novelty of information, which shows a boundary condition for previous finding on embeddedness in undirected networks. For marketing managers, these findings provide insights on how to target customers in a directed network at the micro level.

Florian Stahl, Raghuram Iyengar, Yuxin Chen (Under Review), Latent Change Point Model for Intertemporal Discounting with Reference Durations.

Yupeng Chen, Raghuram Iyengar, Iyengar Garud (2017), Modeling Multimodal Continuous Heterogeneity in Conjoint Analysis – A Sparse Learning Approach , Marketing Science.

Eva Ascarza, Raghuram Iyengar, Martin Schleicher (2016), The perils of proactive churn prevention using plan recommendations: Evidence from A Field Experiment , Journal of Marketing Research, 53 (1), pp. 4660.

Raghuram Iyengar, Christophe Van den Bulte, Jae Young Lee (2015), Social Contagion in New Product Trial and Repeat , Marketing Science, 34 (3), pp. 408429.

Arun Gopalakrishnan, Raghuram Iyengar, Robert Meyer (2015), Consumer Dynamic Usage Allocation and Learning Under Multipart Tariffs , Marketing Science.

Jonah Berger and Raghuram Iyengar (2013), Communication Channels and Word of Mouth: How the Medium Shapes the Message , Journal of Consumer Research.

Raghuram Iyengar and Kamel Jedidi (2012), A Conjoint Model for Quantity Discounts , Marketing Science, Forthcoming.

Abstract: Quantity discount pricing is a common practice used by businesstobusiness and businesstoconsumer companies. A key characteristic of quantity discount pricing is that the marginal price declines with higher purchase quantities. In this paper, we propose a choicebased conjoint model for estimating consumerlevel willingnesstopay (WTP) for varying quantities of a product and for designing optimal quantity discount pricing schemes. Our model can handle large quantity values and produces WTP estimates that are positive and increasing in quantity at a diminishing rate. In particular, we propose a tractable utility function which depends on both product attributes and product quantity and which captures diminishing marginal utility. We show how such a function embeds standard utility functions in the quantity discount literature as special cases and how to use it to estimate the WTP function and consumer value potential. We also propose an experimental design approach for implementation. We illustrate the model using data from a conjoint study concerning online movie rental services. The empirical results show that the proposed model has good fit and predictive validity. In addition, we find that marginal WTP in this category decays rapidly with quantity. We also find that the standard choicebased conjoint model results in anomalous WTP distributions with negative WTP values and nondiminishing marginal willingnesstopay curves. Finally, we identify four segments of consumers that differ in terms of magnitude of WTP and volume potential and derive optimal quantity discount schemes for a monopolist and a new entrant in a competitive market.

Past Courses

MKTG212 DATA & ANLZ FOR MKTG DEC

Firms have access to detailed data of customers and past marketing actions. Such data may include instore and online customer transactions, customer surveys as well as prices and advertising. Using realworld applications from various industries, the goal of the course is to familiarize students with several types of managerial problems as well as data sources and techniques, commonly employed in making effective marketing decisions. The course would involve formulating critical managerial problems, developing relevant hypotheses, analyzing data and, most importantly, drawing inferences and telling convincing narratives, with a view of yielding actionable results.

MKTG613 STRATGIC MKTG SIMULATION

Building upon Marketing 611, Marketing 613 is an intensive immersion course designed to develop skills in formulating and implementing marketing strategies for brands and businesses. The central activity will be participation in a realistic integrative product management simulation named SABRE. In SABRE, students will form management teams that oversee all critical aspects of modern product management: the design and marketing of new products, advertising budgeting and design, sales force sizing and allocation, and production planning. As in the real world, teams will compete for profitability, and the success that each team has in achieving this goal will be a major driver of the class assessment. ,The SABRE simulation is used to convey the two foci of learning in the course: the changing nature of strategic problems and their optimal solutions as industries progress through the product life cycle, and exposure to the latest analytic tools for solving these problems. Specifically, SABRE management teams will receive training in both how to make optimal use of marketing research information to reduce uncertainty in product design and positioning, as well as decision support models to guide resource allocation.

MKTG712 DATA & ANLZ FOR MKTG DEC

Firms have access to detailed data of customers and past marketing actions. Such data may include instore and online customer transactions, customer surveys as well as prices and advertising. Using realworld applications from various industries, the goal of the course is to familiarize students with several types of managerial problems as well as data sources and techniques, commonly employed in making effective marketing decisions. The course would involve formulating critical managerial problems, developing relevant hypotheses, analyzing data and, most importantly, drawing inferences and telling convincing narratives, with a view of yielding actionable results.

Finalist, ISMS Long Term Impact Award, 2017 Finalist, Paul E. Green Award, 2017 Finalist, John D. C. Little Award, 2016 MSI Robert D. Buzzell Best Paper Award, 2013 Finalist, John D. C. Little Award, 2012 Finalist, William O’Dell Award, 2012 MBA Excellence in Teaching: Elective Curriculum award, 2011 MSI Young Scholar Program, 2011 Dean’s Research Fund, 2010 Wharton Sports Business Initiative Grant, 2009 Finalist, Paul E. Green Award, 2008 Description

Finalist

WhartonSMU Research Grant, 2008 Editor’s Award – Best Paper of the Year, Experimental Economics, 2008 Finalist, Helen Kardon Moss Anvil Award, 2007 Description

Finalist

Alden G. Clayton Doctoral Dissertation Proposal Competition, 2004 Description

Honorable Mention

INFORMS Marketing Science Doctoral Consortium Fellow, 2003 AMASheth Foundation Doctoral Consortium Fellow, 2003 Rudolph Fellow, Columbia Business School, 2002 Description

20022003

Dean’s List, I.I.T. Kanpur, 1998

Knowledge @ Wharton

Everybody Likes Coupons … Except When They Make You Work, Knowledge @ Wharton 06/28/2016 Why Offering Rewards to Stay Can Drive Customers Away, Knowledge @ Wharton 06/11/2015 How Companies Can Boost Profits by Welcoming Social Reviews, Knowledge @ Wharton 09/08/2013 How ‘Scroogled’ Could Hurt Both Microsoft and Google, Knowledge @ Wharton 03/08/2013 Why Softbank’s Sprint Deal Is a Highwire Act, Knowledge @ Wharton 10/16/2012 Matching the Medium with the Message in Wordofmouth Marketing, Knowledge @ Wharton 04/11/2012 Research Roundup: The Upside of Bad Publicity, Skill vs. Luck for Hedge Fund Managers and Aligning Pricing with Value, Knowledge @ Wharton 03/16/2011 Will Online Streaming Work Out for Netflix?, Knowledge @ Wharton 12/08/2010 Catering to the Costco Mindset: Finding the ‘Sweet Spot’ in Quantity Discounts, Knowledge @ Wharton 10/27/2010 India’s 3G Wireless Play: An Economic Engine — or Out of Bandwidth?, Knowledge @ Wharton 05/20/2010 Higher Profits for the Major Record Labels? New Research Suggests a Counterintuitive Strategy, Knowledge @ Wharton 01/20/2010 How Casinos Can Find and Target Their Favorite Customers: The Biggest Losers, Knowledge @ Wharton 05/13/2009 The Buzz Starts Here: Finding the First Mouth for WordofMouth Marketing, Knowledge @ Wharton 03/04/2009 Taming Complexity in Services: Stay Close to Your Customer (But Not Too Close), Knowledge @ Wharton 03/01/2006