MKT 543: MARKET DEMAND AND SALES FORECASTING COURSE SYLLABUS SPRING 2015

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1 MKT 543: MARKET DEMAND AND SALES FORECASTING COURSE SYLLABUS SPRING 2015 Instructor: Schedule: Office Hours: Required Text: Dr. S. Siddarth Hoffman Hall (HOH) 613 Phone: (213) Fax: (213) MW, 2-3:20 pm, HOH 415 Monday and Wednesday: 11:00 12:00 pm, 3:30 pm 4:30 pm, or by appointment None Reasons to Take This Course This course is designed to introduce you to a number of quantitative techniques and analytical tools that provide insight into the nature of consumer demand and its response to changes in the marketing mix. The use of these techniques can improve a manager s forecasting ability, provide a better understanding of market behavior and, ultimately, form the basis for making more effective and efficient marketing decisions. Course Goals The specific objectives of the course are: 1. To familiarize you with several advanced, quantitatively oriented marketing tools that enhance demand forecasting ability and marketing decision-making. 2. Provide extensive experience in using these tools through computer exercises (i.e., dirtying your hands with the data). 3. Develop an ability to critically assess the strengths and weaknesses of these modeling approaches when applied to specific marketing problems (via case and discussion). 1

2 CLASS MATERIALS 1. Readings All required readings are included in the course packet available for purchase from the Campus Bookstore. 2. Lecture Notes Copies of the PowerPoint slides used in class will be made available on the course Blackboard page. 3. Computer Exercises The course requires statistical of a variety of datasets and hands on use of the computer in every topic area. These datasets will also be posted on Blackboard. While most of the will require you to use spreadsheet based statistical tools, some sessions may require you to use specialized statistical software such as SAS or JMP. ASSESSMENT Your final grade in the course will be based on the following criteria. A. Class participation 10% B. Three group assignments (9% each) 27% C. One individual assignment 9% D. project 14% E. Final exam 40% Please note that no late assignments will be accepted. All written work is due at the beginning of class on the due date. Also note that final exams are scheduled by the Registrar, not the instructor, and cannot be changed under any circumstances. A. CLASS PARTICIPATION (10%) Every session of the course will involve interaction in the form of class discussion. I expect each class member to be prepared at all times to contribute to any class session. To reinforce this expectation, I will randomly select (i.e., cold call) students at the beginning of the session to open the class and throughout the ensuing discussion whether or not the student s hand is raised. However, self-motivated participation is preferred and encouraged: don t wait to be called upon. A necessary, but not sufficient, condition for class participation is that you come to class. In order to obtain a grade for class participation you must attend the class sessions (please let me know in advance if you cannot attend a session). 1 1 Missing more than 10% of the sessions will seriously affect your participation grade. 2

3 B. GROUP ASSIGNMENTS (27%) I ask that you form groups of four or five people to work on the group assignments and the forecasting project. Please submit the names of the teams by the second week of classes. The group assignments will either be related to a case that will be discussed in the class or will be an independent exercise. All exercises are designed to provide you with a better understanding of the research techniques discussed in class and to dirty your hands with the data. C. INDIVIDUAL ASSIGNMENTS (9%) These assignments will be similar in scope to the group assignments discussed above but must be completed on your own. Collaboration of any kind is unacceptable and will be penalized. D. GROUP PROJECT (14%) Each team will take on a real-world marketing situation that requires you to perform some forecasting/ market response. The purpose of this project is to apply one or more of the concepts and methods of the course to the marketing decisions problems faced by a company that you are familiar with. The output of the research will include an in-class presentation and a summary report. Each group will submit a proposal for approval in the week before Spring Break. Project presentations are scheduled for the last week of the term and completed project reports are due on the last day of class. Further guidelines for this project will be provided in class. E. FINAL EXAM (40%) The final exam will test students on their understanding of the concepts and theory covered in the class as well as their mastery of data skills. POLICY ON TECHNOLOGY USE Please note that communication devices such as cell phones, smartphones and tablets, capable of sending and or receiving electronic communication, and all entertainment devices such as ipods, or other Mp3 players must be turned off and kept off throughout the class session. Receiving or sending communication or entertainment during class disrupts the learning environment and is rude to those around you. IMPORTANT INFORMATION FOR STUDENTS WITH DISABILITIES Any student requesting academic accommodations based on a disability is required to register with Disability Services and Programs (DSP) each semester. A letter of verification for approved accommodations can be obtained from DSP. Please be sure that the letter is delivered to me as early in the semester as possible. DSP is located in STU 301 and is open 8:30 am 5:00 pm, Monday through Friday. The phone number for DSP is (213)

4 CLASS NOTES POLICY Notes or recordings made by students based on a university class or lecture may only be made for purposes of individual or group study, or for other non-commercial purposes that reasonably arise from the student s membership in the class or attendance at the university. This restriction also applies to any information distributed, disseminated or in any way displayed for use in relationship to the class, whether obtained in class, via or otherwise on the Internet, or via any other medium. Actions in violation of this policy constitute a violation of the Student Conduct Code, and may subject an individual or entity to university discipline and/or legal proceedings. 4

5 CLASS SCHEDULE Session Topic Reading Case Discussion 1/12 Course introduction 1/14 Overview of forecasting tools and techniques 1/21 Sales forecasting for new products 1/26 Sales forecasting for new products 1/28 Sales forecasting for established products 2/2 Sales forecasting approaches for new products 2/4 Measuring consumer preferences: Introduction to conjoint 2/9 Conjoint Analysis Estimation 1. Note on Marketing Mix Models: Evaluating Bang for the Buck 2. Lehmann, Gupta & Steckel, chapter 13, Regression Analysis 3. Davenport & Harris, The Nature of Analytical Competition 1. Ofek, Forecasting the Adoption of a New Product 1. Lilien, Kotler, and Moorthy, Repeat Purchase Models for New Products 1. Wilcox, A Practical Guide to Conjoint Analysis Illinois Superconducting Corporation In Class Exercise / Example Regression Analysis of MBA Rankings with Excel Estimation of the Bass diffusion model for different datasets In-class conjoint exercise Due Form groups and turn in group member names 1 Bass Model Forecasting Demand of Superconduct ing Filters 5

6 Session Topic Reading Case Discussion 2/11 Further applications of conjoint In Class Exercise / Example Due 2/18 Analysis of aggregate scanner data 2/23 Analysis of aggregate scanner data (contd.) 2/25 Promotion profitability 3/2 Promotion profitability (contd.) 3/4 Analysis of disaggregate data 3/9 Analysis of disaggregate data 3/11 Analysis of disaggregate data 1. Blattberg & Neslin, Regression Analysis Applied to Sales Promotion 1. Blattberg & Neslin, Promoter Methodology 1. Material from Humby, Hunt and Phillips, Scoring Points, How Tesco is Winning Customer Loyalty Demand in the laundry detergent market Calculating own- and crossprice elasticities. Clout vulnerability Application of exception and regression approaches to evaluation 2 Conjoint Analysis project description due 3 Price Response and Promotion Profitability 6

7 Session Topic Reading Case Discussion 3/23 Decision Calculus: Salesforce Response 3/25 GUEST LECTURE 1. Lodish, Building Marketing Models that Make Money In Class Exercise / Example Due 3/30 Sales Force Allocation Case Syntex Labs 4/1 Customer Analysis 4/6 Customer Case 1 4/8 Customer Case 2 4/13 Advertising budget decisions 1. Ofek, Customer Profitabilty and CLV 2. Davenport & Harris Competing on Analytics with External Processes Freeport Studio Duniya Finance LLC 4/15 Customer Case 3 Project Review 4/20 Advertising budgeting data 4/22 Course Review 4/27 Project Presentations Tuscan Lifestyles In-class estimation of Advertising response models Individual Tuscan Lifestyles 4/29 Project Presentations 5/11 FINAL EXAM 2 4:30 pm As specified in the USC Schedule of Classes See 7