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Syllabus for MKTG 474MARKETING ANALYTICShttp://www.personal.psu.edu/jxb14/MA/Fall 2020Hans BaumgartnerSmeal Chair Professor of MarketingOffice: 482 Business Bldg., Phone: 863-3559, E-mail: HansBaumgartner@psu.eduOffice hours: M/W 1:30-3:00 or by appointmentCourse objectives:This course will provide you with an introduction to marketing analytics. We will study varioustools for generating marketing insights from data in such areas as segmentation, targeting andpositioning, satisfaction management, customer lifetime analysis, customer choice, product andprice decisions using conjoint analysis, and text analysis and search analytics. This will be ahands-on course based on the Marketing Engineering (Enginius) approach and Excel software,in which you apply the tools studied to actual business situations. You will also complete agroup project in which you collect and analyze data or introduce your fellow students to amarketing analytics technique not studied in class.Course Details:Course materials. Lecture topics and assignments are listed under Course Schedule. Theoverheads used in class are available as Powerpoint and Adobe pdf files from the course website. The textbook for the course (referred to as LRB in the Course Schedule) is:Lilien, Gary L., Arvind Rangaswamy, and Arnaud de Bruyn (2017), Principles of MarketingEngineering and Analytics, 3rd edition, State College, PA: DecisionPro, Inc.You can download the relevant chapters of the textbook from the Enginius website(https://www.enginius.biz/). You have to purchase a license for the software for 28, which willalso give you access to the textbook and other materials. I will send you instructions about howto obtain the license via E-mail. Please complete your order as soon as possible.

Additional assignments (readings, exercises, software tutorials, etc.) can be obtained via thecourse website or from the Enginius portal.Preparation for class. The course will be a combination of lectures, discussions, exercises,case analyses, and presentations. For each class, read the relevant materials listed in theassignment column of the course schedule, download the overheads, and complete all otherassignments (see below). I expect you to attend class regularly and to take an active role in classdiscussions. I will monitor your attendance and participation in class discussions, and both willcontribute to your course grade. At the start of the semester I will randomly assign eachstudent to one of six teams, and each team will analyze six cases, present one case analysis, andcomplete a group project. These assignments are described in more detail below.In-class exercises. We will do several in-class exercises throughout the semester. Thepurpose of these exercises is to give you practice in applying marketing analytics tools to thesolution of practical business problems. The in-class exercises are listed in the assignmentcolumn of the Course Schedule. You do not have to hand in anything for these exercises, butyour contribution to the class discussion will count toward your participation grade.Case discussions. We will analyze six cases over the course of the semester. One team willbe randomly assigned to each case and will give a 20-minute presentation of their case analysisto the rest of the class. However, all teams have to analyze each case and, following thepresentation, there will be an open discussion of the case by the entire class. All teams have tosubmit a 5-page executive summary of their case analysis to the instructor (via E-mail) prior tothe beginning of the class in which the case is being analyzed; the group that is presenting thecase should also hand in a copy of their presentation (e.g., the Powerpoint slides).Team project. Each team will complete a group project, which has to be presented in class.The presentation dates are listed in the Course schedule, and teams will be randomly assignedto one of the three dates. Each team should prepare a 25-minute in-class presentation. A copyof the presentation and a 5-page executive summary have to be handed in to the instructor (viaE-mail) prior to the beginning of the class in which a team presents their project. The teamproject will be graded based on the quality of the oral presentation and the written materials.Team members will also rate each other’s contribution to the project. Two types of topics areappropriate for the team project. First, you can apply one of the tools studied in class to aparticular business problem. This will most likely involve the collection of primary data and anin-depth analysis of the data using the Enginius software. Second, you can choose a marketinganalytics technique that we did not cover in class and introduce your fellow students to thistechnique. The presentations will start after Thanksgiving break, but you should start thinkingabout possible topics for your project early in the semester and talk to your instructor aboutideas suitable for the project. A project proposal (3 pages maximum) is due on October 28th.The instructor will meet with each team via Zoom during the first week of November to finalizethe project idea.

Grading. Your course grade will be based on the six case analyses and the case presentation(60%), the group project (25%), and your attendance and participation in class, esp. during theexercises (15%). My grading policy is as follows:93 – 10090 – 9287 – 8983 – 8680 – 8277 – 7970 – 7660 – 690 – 59AAB BBC CDFAcademic integrity, affirmative action & sexual harassment, students with disabilities, andPenn State values. Please see the information at the end of this syllabus for details about theseissues.

Course ScheduleDATETOPICASSIGNMENT8/24Course f)8/26Excel Review (Excel.pptx, Excel.pdf)ExcelAnalysisToolPak.pdfLiquid Laundry exercise (ExcelReviewLiquidLaundry.pdf and Introduction to Marketing Analytics(Introduction.pptx, Introduction.pdf)LRB Chapter 1Allegro Exercise (Allegro (Smart Sheet).pdf,Allegro Data (Smart Sheet).xls)9/2Segmentation and targeting 1(Segmentation.pptx, Segmentation.pdf)LRB Chapter 3Segmentation Tutorial (Enginius)9/7Segmentation and targeting 2(Segmentation.pptx, Segmentation.pdf)LRB Chapter 3Segmentation Tutorial (Enginius)GE McKinsey Matrix Tutorial (Enginius)9/9Positioning 1(Positioning.pptx, Positioning.pdf)LRB Chapter 4Positioning Tutorial (Enginius)9/14Positioning 2(Positioning.pptx, Positioning.pdf)LRB Chapter 4Positioning Tutorial (Enginius)9/16Segmentation caseISBM (Positioning, Segmentation) Case Study (Enginius)9/21Analyzing customer satisfaction 1(Satisfaction.pptx, Satisfaction.pdf)Fornell et al., The American Customer SatisfactionIndex (available on Electronic Reserve)http://www.theacsi.org/ (explore the information onthis web site, esp. the material under About ASCI)9/23Positioning caseInfiniti G20 (Positioning) Case Study (Enginius)9/28Analyzing customer satisfaction 2(Satisfaction.pptx, Satisfaction.pdf)Fornell et al., The American Customer SatisfactionIndex (available on Electronic Reserve)http://www.theacsi.org/ (explore the information onthis web site, esp. the material under About ASCI)9/30Review session 1Review1.pdf10/5Satisfaction ment.pdfPleasureBoatSatisfaction.xlsx10/7Customer lifetime value 1(CLV.pptx, CLV.pdf)LRB Chapter 2 (esp. pp. 59-68)Lifetime Value Tutorial (Enginius)

10/12Customer lifetime value 2(CLV.pptx, CLV.pdf)LRB Chapter 2 (esp. pp. 59-68)Lifetime Value Tutorial (Enginius)10/14Customer choice 1(Choice.pptx, Choice.pdf)LRB Chapter 2 (esp. pp. 38-59), Chapter 3 (esp. pp.100-107)Predictive Modeling Tutorial (Enginius)10/19Customer choice 2(Choice.pptx, Choice.pdf)LRB Chapter 2 (esp. pp. 38-59), Chapter 3 (esp. pp.100-107)Predictive Modeling Tutorial (Enginius)10/21Customer lifetime value caseNorthern Aero (Lifetime) Case Study (Enginius)10/26Group meetings for project10/28Conjoint analysis 1(Conjoint.pptx, Conjoint.pdf)LRB Chapter 6 (esp. pp. 178-190)Conjoint Analysis Tutorial (Enginius)11/2Customer choice caseBookbinders Club (Predictive) Case Study (Enginius)QuestionsBookbindersCase.pdf11/4Conjoint analysis 2(Conjoint.pptx, Conjoint.pdf)LRB Chapter 6 (esp. pp. 178-190)Conjoint Analysis Tutorial (Enginius)11/9Text analysis and search analytics 1(Digital.pptx, Digital.pdf)LRB Chapter 8Sentiment Analysis Tutorial (Enginius)11/11Text analysis and search analytics 2(Digital.pptx, Digital.pdf)LRB Chapter 8Text analysis exercise (Ottos.pdf and Ottos.xlsx)11/16Conjoint analysis caseDürr (Conjoint, Segmentation) Case Study (Enginius)11/18Review session 2Review2.pdf11/23Thanksgiving break11/25Thanksgiving break11/30Presentations (Teams 1 and 2)12/2Presentations (Teams 3 and 4)12/7Presentations (Teams 5 and 6)12/9Implementing Marketing Engineeringand course wrap-up(Implementation.pptx,Implementation.pdf)LRB Chapter 9Germann et al., Performance Implications of DeployingMarketing Analytics (available on ElectronicReserve)

ACADEMIC INTEGRITYAccording to the Penn State Principles and University Code of Conduct:Academic integrity is a basic guiding principle for all academic activity at Penn State University,allowing the pursuit of scholarly activity in an open, honest, and responsible manner. Accordingto the University’s Code of Conduct, you must neither engage in nor tolerate academicdishonesty. This includes, but is not limited to cheating, plagiarism, fabrication of informationor citations, facilitating acts of academic dishonesty by others, unauthorized possession ofexaminations, submitting work of another person, or work previously used in another coursewithout informing the instructor, or tampering with the academic work of other students. Any violation of academic integrity will be investigated and, where warranted, correctiveacademic and/or disciplinary action will be taken. For every incident where a penalty isassessed, an Academic Integrity Incident Report form must be filed. The form can be foundon the Smeal College Honor and Integrity website: https://www.smeal.psu.edu/integrity.The report must be signed and dated by both the instructor and the student, and thensubmitted to Monica Snyder, 202 Business Building. University Policy G-9Once a student has been informed that academic misconduct is suspected, the student maynot drop the course during the adjudication process. The Dean of the College (UP) and/orthe Chancellor (campuses) or his or her representative is responsible for notifying the Officeof the University Registrar when academic misconduct is suspected in a course. Any drop orwithdrawal from the course during this time will be reversed. A student who has receivedan academic sanction as a result of a violation of academic integrity may not drop orwithdraw from the course at any time. These drop actions include regular drop, late drop,withdrawal, retroactive late drop and retroactive withdrawal. Any such drop action of thecourse will be reversed. This drop policy may be superseded in exceptional circumstances(i.e. trauma drop). In these cases, the Office of Student Conduct or the Student Conductdesignee will confer with the Dean of the College (UP) or the Chancellor (campuses) or hisor her representative to determine if the drop is warranted.University Policy G-9: ity.html.Smeal Honor Code:We, the Smeal College of Business Community, aspire to the highest ethical standards andwill hold each other accountable to them. We will not engage in any action that isimproper or that creates the appearance of impropriety in our academic lives, and weintend to hold to this standard in our future careers.