Eric Eisenstein

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.

• 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.

• 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.

• 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.