Raghuram Iyengar

Raghuram Iyengar
  • Associate Professor of Marketing
  • Faculty Co-Director - Wharton Customer Analytics Initiative

Contact Information

  • office Address:

    756 Jon M. Huntsman Hall
    3730 Walnut Street
    University of Pennsylvania
    Philadelphia, PA 19104

Research Interests: pricing, social influence

Links: CV

Overview

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 multi-part 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 multi-part 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 multi-part 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 Analytics. He earned his PhD and MPhil from Columbia University and his B. Tech. from IIT Kanpur, India.

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Research

  • Yupeng Chen, Raghuram Iyengar, Garud Iyengar (2017), Modeling Multimodal Continuous Heterogeneity in Conjoint Analysis - A Sparse Learning Approach, Marketing Science, 36 (1), pp. 140-156.

  • Raghuram Iyengar and Young-Hoon 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 brand-authored 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. 46-60.

  • Raghuram Iyengar, Christophe Van den Bulte, Jae Young Lee (2015), Social Contagion in New Product Trial and Repeat, Marketing Science, 34 (3), pp. 408-429.

  • 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 business-to-business and business-to-consumer companies. A key characteristic of quantity discount pricing is that the marginal price declines with higher purchase quantities. In this paper, we propose a choice-based conjoint model for estimating consumer-level willingness-to-pay (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 choice-based conjoint model results in anomalous WTP distributions with negative WTP values and non-diminishing marginal willingness-to-pay 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.

Teaching

Current Courses

  • MKTG712 - Data & Anlz For Mktg Dec

    Firms have access to detailed data of customers and past marketing actions. Such data may include in-store and online customer transactions, customer surveys as well as prices and advertising. Using real-world 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.

    MKTG712001 ( Syllabus )

    MKTG712002 ( Syllabus )

  • MKTG957 - Empirical Models Mktg B

    This course is designed to generate awareness and appreciation of the way several substantive topics in marketing have been studied empirically using quantitative models. This seminar reviews empirical models of marketing phenomena including consumer choice, adoption of new products, sales response to marketing mix elements, and competitive interaction. Applies methods and concepts developed in econometrics and statistics but focuses on substantive issues of model structure and interpretation, rather than on estimation techniques. Ultimately, the goals are a) to prepare students to read and understand the literature and b) to stimulate new research interests. By the end of the course, students should be familiar with the key issues and approaches in empirical marketing modeling.

    MKTG957302

Past Courses

  • MKTG212 - DATA & ANLZ FOR MKTG DEC

    Firms have access to detailed data of customers and past marketing actions. Such data may include in-store and online customer transactions, customer surveys as well as prices and advertising. Using real-world 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.

  • MKTG352 - SPECIAL TOPICS

    MARKETING ANALYTICS: Companies are currently spending millions of dollars on data-gathering initiatives - but few are successfully capitalizing on all this data to generate revenue and increase profit. Moving from collecting data to analysis to profitable results requires the ability to forecast and develop a business rationale based on identified data patterns. Marketing Analytics will cover the three pillars of analytics - descriptive, predictive and prescriptive. Descriptive Analytics examines different types of data and how they can be visualized, ultimately helping you leverage your findings and strengthen your decision making. Predictive Analytics explores the potential uses of data once collected and interpreted. You will learn to utilize different tools, such as regression analysis, and estimate relationships among variables to predict future behavior. Prescriptive Analytics takes you through the final step - formulating concrete recommendations. These recommendations can be directed toward a variety of efforts including pricing and social-platform outreach.

  • 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 in-store and online customer transactions, customer surveys as well as prices and advertising. Using real-world 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.

  • MKTG852 - SPECIAL TOPICS

    MARKETING ANALYTICS: Companies are currently spending millions of dollars on data-gathering initiatives - but few are sucessfully capitalizing on all this data to generate revenue and increase profit. Moving from collecting data to analysis to profitable results requires the ability to forecast and develop a business rationale based on identified data patterns. Marketing Analytics will cover the three pillars of analytics - descriptive, predictive and prescriptive. Descriptive Analytics examines different types of data and how they can be visualized, ultimately helping you leverage your findings and strengthen your decision making. Predictive Analytics explores the potential uses of data once collected and interpreted. You will learn to utilize different tools, such as regression analysis, and estimate relationships among variables to predict future behavior. Prescriptive Analytics takes you through the final step - formulating concrete recommendations. These recommendations can be directed toward a variety of efforts including pricing and social-platform outreach.

  • MKTG957 - EMPIRICAL MODELS MKTG B

    This course is designed to generate awareness and appreciation of the way several substantive topics in marketing have been studied empirically using quantitative models. This seminar reviews empirical models of marketing phenomena including consumer choice, adoption of new products, sales response to marketing mix elements, and competitive interaction. Applies methods and concepts developed in econometrics and statistics but focuses on substantive issues of model structure and interpretation, rather than on estimation techniques. Ultimately, the goals are a) to prepare students to read and understand the literature and b) to stimulate new research interests. By the end of the course, students should be familiar with the key issues and approaches in empirical marketing modeling.

  • MKTG995 - DISSERTATION

Awards and Honors

  • 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

  • Wharton-SMU Research Grant, 2008-2009
  • 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
  • AMA-Sheth Foundation Doctoral Consortium Fellow, 2003
  • Rudolph Fellow, Columbia Business School, 2003 Description

    2002-2003

  • Dean’s List, I.I.T. Kanpur, 1998

Activity

Latest Research

Yupeng Chen, Raghuram Iyengar, Garud Iyengar (2017), Modeling Multimodal Continuous Heterogeneity in Conjoint Analysis - A Sparse Learning Approach, Marketing Science, 36 (1), pp. 140-156.
All Research

In the News

Everybody Likes Coupons … Except When They Make You Work

Wharton’s Raghuram Iyengar explains what his research on referral coupons reveals about customers' behavior when you offer them the “opportunity” to be brand ambassadors.

Knowledge @ Wharton - 2016/06/28
All News

Awards and Honors

Finalist, ISMS Long Term Impact Award 2017
All Awards