Raghuram Iyengar

Raghuram Iyengar
  • Miers-Busch, W’1885 Professor
  • Professor of Marketing
  • Faculty Director - Wharton Customer Analytics

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

  • Daniela Schmitt, Rom Y. Schrift, Raghuram Iyengar, Florian Stahl, The Effect of Price Promotions on New Customer Acquisition for Information Goods.

    Abstract: Can price promotions help firms acquire profitable customers? Extant research from consumer packaged goods suggests that promotionally-acquired customers have little long-term value. We examine whether the same answer holds for price promotions for information goods (e.g., digital newspapers). One notable feature of companies selling such goods is that they employ a dual revenue model - customers pay to access the service and advertisers pay based on customers' consumption. In this context, a promotion may still attract customers with low willingness to pay. These customers, however, can become profitable based on their consumption. We empirically assess the tradeoff between lower subscription and higher advertising revenue using individual-level data from a digital newspaper that implemented its first ever price promotion. We find that promotionally acquired customers can be more valuable than those who join at the regular price. The main driver of our result is self-selection - promotional customers appear to have a high product valuation, but face budget and time constraints. Furthermore, customers who missed the price promotion reduce their consumption. This effect is, however, short-lived. We discuss the implications for pricing of information goods and customer management.

  • Yupeng Chen and Raghuram Iyengar, A Low-Dimension Shrinkage Approach to Choice-Based Conjoint Estimation.

  • Mingyung Kim, Eric Bradlow, Raghuram Iyengar, Selecting Data Granularity and Model Specification Using the Scaled Power Likelihood with Multiple Weights.

  • Raghuram Iyengar, Qi Yu, Young-Hoon Park, The Impact of Subscription Programs on Customer Purchases.

  • ludovic stourm, Raghuram Iyengar, Eric Bradlow (2020), A Flexible Demand Model for Complements Using Household Production Theory, Marketing Science, 39, pp. 763-787.

  • Jing Peng, Ashish Agarwal, Kartik Hosanagar, Raghuram Iyengar (2018), 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.

  • 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.

  • 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.

Teaching

Current Courses

  • MKTG611 - Marketing Management

    This course addresses how to design and implement the best combination of marketing efforts to carry out a firm's strategy in its target markets. Specifically, this course seeks to develop the student's (1) understanding of how the firm can benefit by creating and delivering value to its customers, and stakeholders, and (2) skills in applying the analytical concepts and tools of marketing to such decisions as segmentation and targeting, branding, pricing, distribution, and promotion. The course uses lectures and case discussions, case write-ups, student presentations, and a comprehensive final examination to achieve these objectives.

    MKTG611017 ( Syllabus )

    MKTG611019 ( Syllabus )

    MKTG611021 ( Syllabus )

    MKTG611023 ( Syllabus )

  • MKTG712 - Data & Anlz For Mktg Dec

    This course introduces students to the fundamentals of data-driven marketing, including topics from marketing research and analytics. It examines the many different sources of data available to marketers, including data from customer transactions, surveys, pricing, advertising, and A/B testing, and how to use those data to guide decision-making. Through real-world applications from various industries, including hands-on analyses using modern data analysis tools, students will learn how to formulate marketing problems as testable hypotheses, systematically gather data, and apply statistical tools to yield actionable marketing insights.

    MKTG712751

Past Courses

  • MKTG212 - DATA & ANLZ FOR MKTG DEC

    This course introduces students to the fundamentals of data-driven marketing, including topics from marketing research and analytics. It examines the many different sources of data available to marketers, including data from customer transactions, surveys, pricing, advertising, and A/B testing, and how to use those data to guide decision-making. Through real-world applications from various industries, including hands-on analyses using modern data analysis tools, students will learn how to formulate marketing problems as testable hypotheses, systematically gather data, and apply statistical tools to yield actionable marketing insights.

  • 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.

  • MKTG401 - MARKETING ANALYTICS

    In this class students will (1) Apply knowledge to practice for an actual client, with a focus on the synthesis of knowledge acquired across curriculum (2) Practice analytical thinking skills (analyzing and framing business problems and problem-solving techniques), including consideration of ethical issues. (3) Practice written and oral communication skills, as well as working in an (assigned) team environment, by leveraging the experience developed in earlier years of the leadership Journey. (4) Reflect on their own social and intellectual development over their time at Wharton and Penn.

  • MKTG611 - MARKETING MANAGEMENT

    This course addresses how to design and implement the best combination of marketing efforts to carry out a firm's strategy in its target markets. Specifically, this course seeks to develop the student's (1) understanding of how the firm can benefit by creating and delivering value to its customers, and stakeholders, and (2) skills in applying the analytical concepts and tools of marketing to such decisions as segmentation and targeting, branding, pricing, distribution, and promotion. The course uses lectures and case discussions, case write-ups, student presentations, and a comprehensive final examination to achieve these objectives.

  • 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

    This course introduces students to the fundamentals of data-driven marketing, including topics from marketing research and analytics. It examines the many different sources of data available to marketers, including data from customer transactions, surveys, pricing, advertising, and A/B testing, and how to use those data to guide decision-making. Through real-world applications from various industries, including hands-on analyses using modern data analysis tools, students will learn how to formulate marketing problems as testable hypotheses, systematically gather data, and apply statistical tools to yield actionable marketing insights.

  • 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.

  • MKTG899 - INDEPENDENT STUDY

    A student contemplating an independent study project must first find a faculty member who agrees to supervise and approve the student's written proposal as an independent study (MKTG 899). If a student wishes the proposed work to be used to meet the ASP requirement, he/she should then submit the approved proposal to the MBA adviser who will determine if it is an appropriate substitute. Such substitutions will only be approved prior to the beginning of the semester.

  • 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.

  • MKTG974 - RESEARCH SEM MKTG PART B

    This course is taught collectively by the faculty members from the Marketing Department. It is designed to expose Doctoral students to the cutting-edge research in marketing models in order to help them to define and advance their research interests. This course will offer: in-depth discussions on some important topics in marketing by experts in respective areas; tools, and methodologies required for conducting research in those areas; broad exposure to our faculty members and their proven research styles.

  • MKTG995 - DISSERTATION

  • MKTG999 - INDEPENDENT STUDY

    Requires written permission of instructor and the department graduate adviser.

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
  • Marketing Science Institute Scholar, 1970
  • Marketing Science Institute Scholar, 1970
  • 2019 MBA Teaching Excellence Award, 1970

In the News

Knowledge @ Wharton

Activity

Latest Research

Daniela Schmitt, Rom Y. Schrift, Raghuram Iyengar, Florian Stahl, The Effect of Price Promotions on New Customer Acquisition for Information Goods.
All Research

In the News

Creating Inclusive Public Policies: Guidelines for Compassionate Regulators

“A policy’s success largely rests on how well inclusion is embedded in its blueprints,” write Santosh K. Misra and Wharton’s Raghuram Iyengar in this opinion piece.

Knowledge @ Wharton - 2020/09/1
All News

Awards and Honors

Finalist, ISMS Long Term Impact Award 2017
All Awards