MYRA School of Business Mysore, India. 3. Case: Rodeo Jeans: Promotional and Pricing Strategy (2012)

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1 MYRA School of Business Mysore, India MyE217 Marketing Models Professor Sachin Gupta Table of Contents 1. Course Syllabus 2. Case: Newfood 3. Case: Rodeo Jeans: Promotional and Pricing Strategy (2012) 4. Article by Huber The Importance of Multinomial Logit Analysis of Individual Consumer Choices 5. Case: ABB Electric 6. Chapter 7 From Clicks to Value with Internet Marketing Metrics from Mark Jeffery, Data Driven Marketing, Wiley, Case: Air France Internet Marketing (2009) 8. Case: Cardagin: Local Mobile Rewards (2013) 9. Article by Elie Ofek: Customer Profitability and Lifetime Value, Harvard Business School Note Number Case: O Neal Labs: Using Frequent Shopper Data for Customer Acquisition (2014) 1

2 MYRA School of Business Mysore, India MyE217 Marketing Models Professor Sachin Gupta Objectives of the Course This course deals with the use of data to make marketing decisions. It introduces concepts, methods, and applications of analytics to products, markets, and marketing actions. Unlike marketing courses that focus on conceptual material, this course will provide skills to translate conceptual understanding into specific operational plans a skill in increasing demand in organizations today. The pedagogic philosophy in this course embraces the principle of learning by doing. Each concept that we cover has a software implementation (I will use Excel only) and a problem or case whose resolution can be enhanced through use of the analysis. In the process of learning-by-doing, students will find out what the tools and software can do as well as what they cannot do, because they will be using them and adapting them to marketing problems. Relevance of This Course The course will be particularly valuable to students planning careers in management consulting, marketing, and market research. The course requires students to have some background in quantitative methods and have a willingness to deal with mathematical concepts. The following topics will be essential prerequisites for this course: logarithms, exponential functions (e x ), and regression. However, the course is not a statistics or mathematics course. Another pre-requisite for this course is complete ease with Microsoft Excel. Course Material Course packet Additional material will be made available by the instructor via Dropbox. Class Format Class activity will be divided into lectures, hands-on data analysis, and case discussions. The lectures will go beyond materials included in the readings for that session. Therefore, it is imperative that students be prepared with the readings before they come to class. Class Participation I expect students to contribute to the discussion in class. Since the material is sometimes technical, students should feel comfortable interrupting the lecture with questions about the tools and techniques and how they are applied. Remember that your questions may benefit others as well. 2

3 Evaluation of Student Work There will be two take-home team assignments and one individual, in-class final exam. Assignments and the exam involve hands-on work with data and models. The due dates for the assignments are noted on the daily class schedule. Your overall grade will be determined based on the following components: Team assignment 1 25 Team assignment 2 25 Final Exam 40 Class Participation 10 Teams: Students will be asked to work in teams both inside and outside class. Please form a team with five members. Laptop Requirement: I will demonstrate applications in Excel frequently. Further, you will often be asked to do data analysis in class. So you must bring a laptop with you in each class session. 3

4 Daily Class Schedule (tentative, some changes are possible) Session Topic Material for preparation (available either in Course Packet or to be made available by the instructor) To be turned in 1. Mon, March Mon, March Tue, March Tue, March Wed, March Wed, March Thurs, March Thurs, March Fri, March Fri, March 20 A. Introduction to the Download class slides in advance of class and look through. course. B. Demand Analytics Download class slides in advance of class and look through. Review Regression Analysis from previous Probability and Statistics course. A. Demand Analytics Download class slides in advance of class and look through B. Case: Newfood Read the case in advance of class and download case data. A. Demand Analytics Download class slides in advance of class and look through B. Case: Rodeo Jeans Read the case in advance of class and download data. A. Case: Rodeo Jeans In-class presentation and discussion of the case B. Discrete Choice Models Read the article by Huber The Importance of Multinomial Logit Analysis of Individual Consumer Choices Download class slides in advance of class and look through A. Discrete Choice Models Download class slides in advance of class and look through (contd) B. Case: ABB Electric Read the case in advance of class and download data. Team Assignment #1 4

5 Session Topic Material for preparation (available either in Course Packet or to be made available by the instructor) To be turned in 6. Mon, March Mon, March Tue, March Tue, March Wed, March Wed, March Thurs, March Thurs, March Fri, March Fri, March 27 A. Digital Analytics Read Chapter 7 from Data Driven Marketing by Mark Jeffery B. Case: Air France Internet Read the case in advance of class and download data. Marketing A. PROSAD Materials will be available before class for PROSAD: A Bidding Decision Support System for Profit Optimizing Search Engine Advertising B. PROSAD (continued) A. Case: Cardagin Mobile Read the case in advance of class and download data. B. Customer Analytics Read the note Customer Profitability and Lifetime Value Download class slides in advance of class and look through. A. Customer Analytics (Continued) B. Case: O Neal Labs Read the case in advance of class and download data. A. Wrap up and Review Download class slides in advance of class and look through. B. Final Exam Team Assignment #2 5