Eric Eisenstein

Eric Eisenstein
  • Full-Time Lecturer of Marketing
  • Senior Fellow of Analytics at Wharton (AAW)

Contact Information

  • office Address:

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

Overview

Eric Eisenstein is a Lecturer in Marketing and Senior Fellow in Analytics at the Wharton School.  Prior to Wharton, Eric served as the Director of the MS in Business Analytics and a professor in the Department of Statistics, Operations, and Data Science at the Fox School of Business, Temple University, and he was a professor at the Johnson School of Management at Cornell University.

Eric’s teaching is at the intersection of analytics, data science, strategy, and marketing.  In his research, he investigates the psychology of expertise, how to improve decision making, and strategic analytics.  Eric’s research has been published in outlets including the Journal of Marketing Research, the Journal of Business Research, Computers and Operations Research, and he helped to author an introductory statistics text.

Eric was an Associate at Oliver Wyman where he focused on the financial services and telecommunications industries.  He is currently Chair of the Board of Directors for the Visiting Nurses Association of Greater Philadelphia.  Eric helped to found a 1,000+ member social and community service organization, negotiated and obtained over $5 million/year in funding for a student community; he has tutored in numerous contexts, chaired an exploratory committee to found a charter school, and has consulted for charities ranging in size from $5 to $40 million and for numerous private firms.

Eric earned his Ph.D. in Applied Economics (Marketing) and an M.A. in Statistics at the Wharton School of Business, University of Pennsylvania.  He also graduated from the Jerome Fisher Management and Technology dual degree program at the University of Pennsylvania, where he concurrently earned a B.S. in Economics from Wharton and a B.S. in Computer Systems Engineering from the School of Engineering and Applied Science.

For more information please visit Eric’s personal webpage.

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Teaching

All Courses

  • MKTG1010 - Intro To Marketing

    The objective of this course is to introduce students to the concepts, analyses, and activities that comprise marketing management, and to provide practice in assessing and solving marketing problems. The course is also a foundation for advanced electives in Marketing as well as other business/social disciplines. Topics include marketing strategy, customer behavior, segmentation, customer lifetime value, branding, market research, product lifecycle strategies, pricing, go-to-market strategies, promotion, and marketing ethics.

  • MKTG2520 - 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. Converting data into increased business performance requires the ability to extract insights from data through analytics. This course covers the three pillars of analytics – descriptive, predictive and prescriptive – within the marketing context. Descriptive Analytics examines different types of data and how they can be visualized, ultimately helping you communicate your findings and strengthen your team’s or organization’s decision making. Predictive Analytics explores the use of data for forecasting. You will learn to utilize various tools, including regression analysis, to estimate relationships among variables and predict future behavior. Prescriptive Analytics takes you through the final step — formulating concrete recommendations. These recommendations can be directed toward a variety of marketing actions, including pricing and social-platform outreach. Students will be exposed to several methods such as linear regression, logistic regression, multinomial regression, machine learning methods (e.g., neural networks and random forests). We will learn how to employ these methods for such managerial decisions as demand forecasting, pricing, and valuing customers. Overall, you will develop a data analytics mindset, learn new tools, and understand how to convert numbers into actionable insights.

  • 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.

  • 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.

  • MKTG7520 - 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. Converting data into increased business performance requires the ability to extract insights from data through analytics. This course covers the three pillars of analytics – descriptive, predictive and prescriptive – within the marketing context. Descriptive Analytics examines different types of data and how they can be visualized, ultimately helping you communicate your findings and strengthen your team’s or organization’s decision making. Predictive Analytics explores the use of data for forecasting. You will learn to utilize various tools, including regression analysis, to estimate relationships among variables and predict future behavior. Prescriptive Analytics takes you through the final step — formulating concrete recommendations. These recommendations can be directed toward a variety of marketing actions, including pricing and social-platform outreach. Students will be exposed to several methods such as linear regression, logistic regression, multinomial regression, machine learning methods (e.g., neural networks and random forests). We will learn how to employ these methods for such managerial decisions as demand forecasting, pricing, and valuing customers. Overall, you will develop a data analytics mindset, learn new tools, and understand how to convert numbers into actionable insights.

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