Raghuram (Raghu) Iyengar

• Khaled Boughanmi, Raghuram Iyengar, Young-Hoon Park (Under Review), The Impact of Experiential Store on Customer Purchases. Abstract

  Despite the rising popularity of non-traditional retail spaces providing immersive experiences, empirical evidence on their impact on customer behavior remains limited. We study the causal impact of customers visiting an experiential store on their purchase behavior. Analyzing individual-level transactions from a personal care business over a year before and after the store’s launch, we find a positive and economically significant average treatment effect on customer spending. However, substantial heterogeneity exists, with only around 20\% of customers exhibiting a significant positive effect, while the majority show no significant change. The most substantial treatment effects are observed among high-value customers who, despite a long lapse since their last interaction, actively engaged with the firm. We decompose the treatment effect across the differing needs using a model that links product purchases in a customer basket to underlying customer needs. We find that needs linked to sophisticated skincare routines, connecting to high-priced items that customers can assess through hands-on testing and workshops provided in the store, exhibit positive significant effects. In contrast, treatment effects associated with basic skincare routines show no significant impact. The results align with experiential learning and haptics, offering insights into the implications for experiential retailing.

• Zijun Tian, Ryan Dew, Raghuram Iyengar (2024), Mega or Micro? Optimal Influencer Selection by Follower Elasticity, Journal of Marketing Research.
• Mingyung Kim, Eric Bradlow, Raghuram Iyengar (Under Review), A Bayesian Dual-Network Clustering Approach for Selecting Data and Parameter Granularities. Abstract

  While there are well-established methods for model selection (e.g., BIC, marginal likelihood), they generally condition on an a priori selected data (e.g., SKU-level data) and parameter granularity (e.g., brand-level parameters). That is, researchers think they are doing model selection, but what they are really doing is model selection conditional on their choices of data and parameter granularities. In this research, we propose a Bayesian dual-network clustering method as a novel way to make these two decisions simultaneously. To accomplish this, the method represents data and parameters as two separate networks with nodes being the unit of analysis (e.g., SKUs). The method then (a) clusters the two networks using a covariate-driven distance function which allows for a high degree of interpretability and (b) infers the data and parameter granularities that offer the best in-sample fit, akin to standard model selection methods. We apply our method to SKU-level demand analysis. The results show that the choices of data and parameter granularities based on our method as compared to those from extant approaches (e.g., latent class analysis) impact the demand elasticities and the optimal pricing of SKUs. We conclude by highlighting the generalizability of our framework to a broad array of marketing problems.

• David Reibstein and Raghuram Iyengar (2023), Metaverse—will it change the world or be a whole new world in and of itself?, Academy of Marketing Science Review, 13 (), pp. 144-150.
• Brian Gregg, Raghuram Iyengar, Mukul Pandya, David Reibstein, Eli Stein, Resilient Marketing: What’s Next in Growth (:, 2023)
• Ravi Gupta, Raghuram Iyengar, Meghana Sharma, Carolyn C Cannuscio, Raina M. Merchant, David A. Asch, Nandita Mitra, David Grande (2023), Consumer Views on Privacy Protections and Sharing of Personal Digital Health Information, JAMA Network Open, 6 ().
• Raghuram Iyengar, Qi Yu, Young-Hoon Park (2022), The Impact of Subscription Programs on Customer Purchases, Journal of Marketing Research, 59 (), pp. 1101-1119.
• Mingyung Kim, Eric Bradlow, Raghuram Iyengar (2022), Selecting Data Granularity and Model Specification Using the Scaled Power Likelihood with Multiple Weights, Marketing Science, 41 (), pp. 848-866.
• Yupeng Chen, Qi Yu, Raghuram Iyengar (Work In Progress), An Affine-Subspace Shrinkage Approach to Choice-Based Conjoint Estimation.
• Zachary Frosch, Esin Namoglu, Nandita Mitra, Daniel Landsburg, Sunita Nasta, Justin Bekelman, Raghuram Iyengar, Carmen Guerra, Marilyn Schapira (2022), Willingness to Travel for Cellular Therapy: The Influence of Follow-Up Care Location, Oncologist Continuity, and Race, JCO Oncology Practice, 18 (), pp. 193-203. Abstract

  Patients weigh competing priorities when deciding whether to travel to a cellular therapy center for treatment. We conducted a choice-based conjoint analysis to determine the relative value they place on clinical factors, oncologist continuity, and travel time under different post-treatment follow-up arrangements. We also evaluated for differences in preferences by sociodemographic factors.

• All Research from Raghuram (Raghu) Iyengar »

Current Courses

• MKTG6110 - 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.

  MKTG6110017 ( Syllabus )

  MKTG6110019 ( Syllabus )

  MKTG6110021 ( Syllabus )

  MKTG6110023 ( Syllabus )

• MKTG9560 - Empirical Models In Marketing - Part A

  This course is designed to generate knowledge of the use of quantitative statistical, econometric, and Machine Learning methods and their application to Marketing problems. A strong emphasis is also placed on the applied nature of applying these methods in terms of data requirements, exogenous versus endogenous variation, and computational challenges when using complex models. Students outside of Marketing are welcome, and we discuss how these models can be applied to other disciplines. By the end of the course, students should be familiar with the key issues and approaches in empirical marketing modeling.

  MKTG9560302 ( Syllabus )

Past Courses

• MKTG2120 - 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.

• MKTG3520 - 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.

• MKTG4010 - 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.

• MKTG6110 - 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.

• MKTG7120 - 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.

• MKTG8520 - 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.

• MKTG8900 - Advanced Study Project

  ADVANCED STUDY PROJECT (GENERAL): The principal objectives of this course are to provide opportunities for undertaking an in-depth study of a marketing problem and to develop the students' skills in evaluating research and designing marketing strategies for a variety of management situations. Selected projects can touch on any aspect of marketing as long as this entails the elements of problem structuring, data collection, data analysis, and report preparation. The course entails a considerable amount of independent work. (Strict library-type research is not appropriate) Class sessions are used to monitor progress on the project and provide suggestions for the research design and data analysis. The last portion of the course often includes an oral presentation by each group to the rest of the class and project sponsors. Along with marketing, the projects integrate other elements of management such as finance, production, research and development, and human resources.

• MKTG8990 - 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.

• MKTG9560 - Empirical Models Mktg A

  This course is designed to generate knowledge of the use of quantitative statistical, econometric, and Machine Learning methods and their application to Marketing problems. A strong emphasis is also placed on the applied nature of applying these methods in terms of data requirements, exogenous versus endogenous variation, and computational challenges when using complex models. Students outside of Marketing are welcome, and we discuss how these models can be applied to other disciplines. By the end of the course, students should be familiar with the key issues and approaches in empirical marketing modeling.

• MKTG9570 - 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.

• MKTG9740 - 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.

• MKTG9950 - Dissertation

  Dissertation

• MKTG9990 - Independent Study

  Requires written permission of instructor and the department graduate adviser.

• MKTG9999 - Independent Study

  Requires written permission of instructor and the department graduate adviser.

• 2022 MBA Teaching Excellence Award, 2022
• Finalist, 2021 William O’Dell Award (AMA), 2022
• 2021 MBA Teaching Excellence Award, 2021
• 2020 MBA Teaching Excellence Award, 2020
• 2019 MBA Teaching Excellence Award, 2019
• Marketing Science Institute Scholar, 2018
• Finalist, ISMS Long Term Impact Award, 2017
• Finalist, Paul E. Green Award, 2017
• Finalist, John D. C. Little Award, 2016
• Finalist, William O’Dell Award, 2012
• Finalist, John D. C. Little Award, 2012
• MBA Excellence in Teaching: Elective Curriculum award, 2011
• MSI Young Scholar Program, 2011
• Finalist, Paul E. Green Award, 2008 Description

  Finalist

• Finalist, Helen Kardon Moss Anvil Award, 2007 Description

  Finalist

Knowledge @ Wharton

• How Dynamic Electricity Pricing Can Improve Market Efficiency, Knowledge @ Wharton - 11/12/2024
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• The Hidden Cost of AI Energy Consumption, Knowledge @ Wharton - 11/12/2024
• Charitable and Political Giving | Pinar Yildirim, Knowledge @ Wharton - 11/12/2024
• The Future of Banking, Knowledge @ Wharton - 11/11/2024
• Election Statistics, Knowledge @ Wharton - 11/06/2024
• Robots Are Taking Over Low-skilled Jobs — and Changing Votes, Knowledge @ Wharton - 11/05/2024
• Crypto Politics: Liberals’ and Conservatives’ Confidence in Cryptocurrencies, Knowledge @ Wharton - 11/05/2024
• Leadership and Elections | Mike Useem, Knowledge @ Wharton - 11/05/2024
• Automation Is Harming Low-skilled Workers and Possibly Their Voting Preferences, Knowledge @ Wharton - 11/01/2024