How To Gain Competitive Advantage With Big Data Analytics And Visualization

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1 MKTG3000 Special Topics: Competitive Advantage with Big Data Marketing Analytics & Marketplace Visualization Strategy Fall 2015 Dr. Jared Hansen Associate Professor of Marketing

2 MKTG : Special Topics: Competitive Advantage with Big Data Marketing Analytics and Marketplace Visualization Class Sessions (see calendar at end of syllabi for more details): T 3:30pm to 6:15 pm, Room 122 Friday Building 1 Instructor Information Dr. Jared M. Hansen, Associate Professor of Marketing, Office: 250B Friday, jared<dot>hansen<at>uncc<dot>edu, Fall 2015 office hours are posted on class moodle page. Teaching Assistant contact information and hours will be posted on moodle when it becomes available. Course Description As defined by the American Marketing Association, marketing is the activity, set of institutions, and processes for creating, communicating, delivering, and exchanging offerings that have value for customers, clients, partners, and society at large. And within that scope, competitive advantage is what distinguishes an organization from its competitors in the minds of its customers. To gain such advantage it is now critical for organizations to combine the power of big data analytics and visualization with strategic decision making in marketing in order to drive organizational performance and remain relevant given changing competitive landscapes, disruptive technologies, and customer interests. Toward that critical combination, this course focuses on the integration of marketing analytics tools, including social media analytics and mobile analytics, with an understanding of how companies leverage Big Data to gain strategic advantage in strategic positioning of the firm and its market offerings. In particular, emphasis will be on identifying relevant competitive advantage strategies, business research questions, data and sampling frames, and on visualizing, analyzing, and using Big Data for the creation of competitive advantage and new value in the marketing contexts of branding and strategic positioning, new product strategy, business model innovation, customer micro segmentation, pricing, and promotion decisions. For each topic, current best practices are reviewed, latest tools are compared, demonstrated and then used by participants to gain hands on experience, and then participants are taught and practice how to more effectively and persuasively communicate data-driven strategies to nontechnical/managerial audiences. Course Objectives 1. Understand different approaches/strategies on sources of competitive advantage and which alternatives would be more advantageous given particular organizational circumstances 2. Interpret implications of the same big data analytics and visualization through the lens of difference competitive advantage strategies 3. Understand role of big data analytics and visualization in organizational strategy and how organizations can leverage useful data/information to gain competitive advantage in the market and acquire marketing insights 4. Gain experience using current business analytic techniques (such as Excel, Powerpoint, Tableau, SPSS, R, Python) to identify competitive advantage opportunities and how to translate them into executionable business strategies regarding customers and competitors 5. Think strategically about how to combine customer and competitor mobile, social, and internet data with other information from inside and outside of the organization to create competitive advantage and 1 The Friday building can be located at:

3 influence business decisions through data visualizations in real time that enable business insights and strategies 6. Estimate impact of different forms of digital data and analytics on brand performance and overall marketing strategy 7. Using actual business cases and examples of marketing topics such as strategic positioning, brand equity and pricing, market segmentation profiles, and digital/mobile marketing dashboards, develop data-driven strategies that enhance stakeholder relationships, open new market opportunities, and/or better position the organization for competitive advantage during industry transition 8. Improve the ability to present technical topics and business strategies effectively and persuasively in front of large, non-technical audiences Instructional Method This course will take a case & project approach, complemented by lectures, seminar style discussion and outside speakers. Students will be introduced to several topics and tools with emphasis through cases and projects on how to use them to generate firm value. Students should bring laptops with them to class for hands-on exercises. Credit Hours: This is a 3 credit hour course. Thus, the course has been designed to require about 10 hours/week (about 3 hours outside of class for every 1 credit hour) between readings, quizzes, and exercise/project work. If a student has limited backgrounds in certain topical areas, they might need to spend additional time to keep up with other students in the course. Required Text: Managerial Analytics: An Applied Guide to Principles, Methods, Tools, and Best Practices (2014), Authors = Michael Watson and Derek Nelson, Publisher = Pearson FT Press, ISBN-10: X, ISBN-13: Supplemental Texts: A list of supplemental texts (not mandatory) that students might find of additional value toward mastering the course topics is posted to the class Moodle page. Grading: The final grade will be determined on the following weights: Attendance Activities 50 points 5% Exercises and Cases 50 points 15% Exam points 20% Exam points 20% Exam 3 (Cumulative Final) 250 points 20% Final Group Project 200 points 20% Total 1000 points 100% Final letter grades will be based on the following totals: 900 and above A (Superior Performance) B (Good Performance) C (Average Performance) D (Below Average Performance) Below 600 F (Unsatisfactory) Attendance: Attendance is taken at the beginning of live class sessions. There will be a sign in sheet at the front of the classroom beginning the second week of class. No one may sign it after 2:05 pm, regardless of reason. Attendance is recorded for online sessions through participation in the associated online exercises

4 by the due dates/times listed online. Extensions will not be given. Students are expected to attend all class meetings. Class topics are integrated, with each week building on prior weeks. This applies to both in-person and online class sessions. Failure to attend or to arrive on time can adversely affect both individual performance, ability to contribute to the group project, and the earned letter grade. If a student misses 3 weeks of class or more, they will automatically receive an unsatisfactory grade in the course regardless of earned points to date on other activities. If a student misses a class it is their responsibility to get notes from peers. Exercises and Cases: There are weekly cases and exercises for most class sessions designed to encourage higher levels of content mastery. Some cases and exercises will involve the entire class discussing a situation while others will be team-based discussion/answers and others will be individual based. Links to the material for the cases/exercises will be posted online. The teams for the exercises/cases and the group semester project will be assigned in-class. If a student enrolls after assignments have been made, it is their responsibility to join a team. Team assignments will be listed in an Excel file posted to Moodle. Exercises and cases will generally be posted or announced at least one week in advance of the due date. The instructor will often randomly call upon individuals or teams to share their answers or ideas on exercises and cases during the class sessions. Final Group Term Project: The final group semester project is described in detail in a separate document posted online and discussed during the second class session. Extra Credit Opportunities: Descriptions of extra credit opportunities will be discussed in-class and posted to the online class resources/moodle. Civility: The University strives to create an inclusive academic climate in which the dignity of all individuals is respected and maintained. We celebrate diversity that is beneficial to both employers and society at large. Students are encouraged to actively appropriately share their views in class discussions. Academic Integrity/Honesty: Students have the responsibility to know and observe the requirements of The UNC Charlotte Code of Student Academic Integrity available online at This code forbids cheating, fabrication or falsification of information, multiple submissions of academic work without authorization, plagiarism (which includes viewing others work without instructor permission), abuse of academic materials, and complicity in academic dishonesty. This forbidding includes sharing/copying work between individuals or teams without permission of instructors. Any special requirements or permission regarding academic integrity in this course will be stated by the instructor, and are binding on the students. Students who violate the code can be expelled from UNC Charlotte. The normal penalty for a first offense is zero credit on the work involving dishonesty and further substantial reduction of the course grade. In almost all cases the course grade is reduced to failing. Students are expected to report cases of academic dishonesty to the course instructor. Other Information Students are responsible for all announcements made in class and on the class online resources. Students should check the online class resources throughout the semester. The instructors will send occasional s with important information to the class listing in the Banner system. It is the students responsibility to make sure that their addresses are accurate.

5 The instructors will discuss grades only in person (and not via telephone or ) and only with the student (not with parents, spouses, etc); student s other than related to scheduling appointments may not be answered by the instructors. Office hours for each week will be posted online each week. Class related questions should be asked during classes and during office hours or scheduled appointments. The instructors may modify the class schedule and all content in the syllabus during the course of the semester depending upon the progress of the class which includes which class sessions meet in person vs. which are online modules. By attending class beyond the first week, students agree to follow the framework and rules related to this course that are described above.

6 Date 25-Aug (T) Tentative Topics (Subject to Change Depending on Class Progress, etc). Note: Moodle contains the updated calendar + readings, exercises, and video links Course Introduction + Key Topics in Marketing Data Science and Big Data + Team Formation 1-Sep (T) Analytical Mindset + Thinking Deeper About Data + Measurement Theory Exploratory Analytics in SPSS 8-Sep (T) Analytical Mindset + Thinking Deeper About Data + Measurement Theory Exploratory Analytics in SPSS and R vs. Python 15-Sep(T) Competitive and Customer Data Security/Hacking Considerations + Business Intelligence Predictive Analytics in SPSS and R vs. Python 22-Sep (T) Statistical Formulas vs Data Algorithms for Complex and Large Scale Marketing Data Classification Analysis in SPSS and R vs. Python 29-Sep(T) Big Data Strategy of Social Media Sentiment Analysis of Marketing Data 6-Oct (T) Exam 1 13-Oct (T) University Fall Break No Classes 20 Oct (T) Best Practices and Strategies in Marketing Data Visualizations and Preattentive Processing Tableau Part 1 27 Oct (T) Real Time Marketing (RTM) Strategies and Tools 3-Nov (T) 10-Nov (T) Tableau Part 2 Additional Topics in Marketplace Visualization Tableau Part 3 Aligning Marketing Dashboards and Business Intelligence with Strategy and Key Research Questions Tableau Part 4 17-Nov (T) Exam 2 24-Nov (T) Best Practices in Presenting Data 1-Dec (T) Team Presentations 8-Dec (T) TBA Team Presentations Exam 3 Cumulative Final Exam (as listed in University Final Exam Calendar)