Web Analytics: MIS 6344.501 Spring 2017 PROFESSOR AMIT MEHRA Associate Professor of Information Systems Email: Amit.Mehra@utdallas.edu Office: Room 3.433 in SM33 Class Hours: Wednesdays, 7 pm to 9.45 pm Class Location: JSOM 2.722 Office Hours: Mondays, from 10 am to 12pm. You can also write an email to the instructor and request for an appointment if you cannot meet during office hours for a genuine reason. Note that appointments are based on availability of the instructor. TA: Nisarg Shah TA email: nss150530@utdallas.edu Course Description The emergence of social and digital media has resulted in unprecedented access to vast amounts of data about products, firms, and consumer behavior. Due to this, some traditional strategies are now completely outdated, others have been deeply transformed, and new strategies are continuously evolving. Today, the fact is that all the core business activities such as new product design, advertising, sales and customer service have been affected. In this course, we aim to develop an understanding of the different types of data that can be collected from digital channels, and how this data can be used to formulate data driven strategies for the firm in order to optimize their business models, websites and digital marketing initiatives. Student Learning Objectives and Outcomes The objectives of the course are: 1. Appreciate different types of digital media and their role. 2. Understand how data drives a firm s deployment of digital media. 3. Learn data analytics techniques to analyze data generated from digital media. Course Website: On E-Learning Communication: For any communication, please use E-learning e-mails. Suggested Textbook and other materials Digital Marketing Essentials is the suggested textbook for this course. The book is available as an Internet book. An Internet book is a unique format where the whole book is made available over the Internet by the publisher. The advantage of this format is that the book is a living document which gets updated several times a year. As soon as the book gets updated, the students will have immediate access to the new content. The book is available at the website www.stukent.com.
In addition to the text book, we will be using several cases and other readings. All students are required to purchase these cases. Exact details on how to purchase the book and the cases will be specified in class. The cases that you will need to purchase are: 1) HighNote case 2) Air France case 3) RestaurantGrades case 4) Star Digital case 5) Progressive Insurance case In addition to these 5 cases, we will study 2 more cases, materials for which will be provided by the instructor. Student groups Students are required to form groups of 5-6 students as soon as possible. This group must remain the same throughout the semester for all group tasks. Group Assignments Several group assignments will be specified during the course of the semester. Some of these assignments may done in-class and the instructor may require the assignments to be submitted before the class is over. Students who are absent will miss out on the grade assigned for such assignments. The assignment in which you score your lowest points will not be considered towards your final course grade. Group Project: Students are required to do a group project. This project is the GOMC (Google Online Marketing Challenge). Details on GOMC will be specified in class and on the course website later. Quizzes To enforce more systematic approach to studying, there will be three quizzes. The quizzes are noncumulative. For each quiz, students will be responsible for making themselves familiar with the materials discussed in the lectures, the lecture slides, all assigned readings and any additional material discussed and/or posted on the E-learning site. Quiz 1 will be held on Feb 8 during class hours. Quiz 2 will be held on March 8 during class hours. Quiz 3 will be held on April 19 during class hours. Any change in the quiz timing will be notified in advance. Only your best two performances in the quizzes will count towards your final course grade. In other words, points in the quiz in which you have got your lowest score will not count towards your final grade.
Make-up quizzes will only be given for University approved absences. However, they may be in a different format than the original quiz. Required Software: We will use a software to do regression analysis. STATA and R will be used in class by the instructor, but you are free to use any software of your choosing for doing regression analysis. Topics 1. Introduction: The landscape of digital and web analytics and the different types of digital media Paid, owned and earned 2. Data driven business models: How data is changing existing business models and creating new ones 3. Customer tracking: How do firms track customers? How is this tracking data used? What can you do to avoid being tracked? What policies are in place or being considered to limit customer tracking? 4. Optimizing owned media: How do search engines create search results? What implication does this have for a website owner? How can search engine optimization (SEO) be used to optimize websites for discoverability by a search engine? 5. Optimizing paid media: a. How can search engine marketing (SEM) be used most effectively? b. What is the display advertising ecosystem and how can analytics be used to deploy display advertising most effectively? 6. Digital Experiments: How can A/B experiments be designed to analyze implications of digital media? When should multi-variable testing (MVT) be used in place of A/B experiments? What are quasi experiments and what is their use? How have experiments been used in practice? 7. Digital media analytics tools: There are several tools, both free and paid, that are available to collect and analyze digital data. We will study some such tools e.g. Google Analytics and Google Adwords etc. How is digital data collected by these tools, and what kinds of insights can be acquired to optimize the use of digital media? Schedule: (1) Some assignments may be given as in-class assignments. Students not in class will not be able to submit these assignments. (2) One session will be dedicated towards meeting the student teams for discussing the GOMC project s pre-campaign reports. More details will be announced in class. (3) Any reading will be specified before each class. (4) The schedule provided below is tentative and subject to change at the discretion of the instructor.
Assignments for extra credit: Session No. Topic 1 Introduction and data driven business models 2 Customer tracking + Display advertising 3 Display advertising + Search engine marketing 4 Search engine marketing + Regression review 5 Introduction to A/B experiments + Google Adwords 6 Design of A/B experiments + Metrics and KPIs 7 A/B experiments in Retail + Google Analytics 8 Google Analytics 9 A/B experiments + Multi variable experiments 10 Applications of A/B experiments + Search engine optimization 11 Group presentations 1. Students who complete the Google Analytics individual certification and upload a copy of their certification on elearning before 8 am on March 20, 2017 will get an extra 2 points. Students who miss this deadline but upload a copy of their certification before 12 pm on April 30, 2017 will get an extra 1 point. After this second deadline, the option of this extra grade will expire. More information about how to prepare and take the exam is available on the link: https://support.google.com/partners/answer/6089738?hl=en 2. Students who complete the Google Adwords Fundamentals certification and upload a copy of their certification on elearning before 8 am on March 20, 2017 will get an extra 2 points. Students who miss this deadline but upload a copy of their certification before 12 pm on April 30, 2017 will get an extra 1 point. After this second deadline, the option of this extra grade will expire. More information about how to prepare and take the exam is available on the link: https://support.google.com/partners/answer/3153810?hl=en Grading Policy: The grades will be based on performance in quizzes, assignments, group presentations and reports. Late submissions will not be graded. The grade distribution across different components will be as follows:
Three Quizzes Total of 60% Group Project 20% Cases and assignments in groups 20% Grading Criteria Your course grade will depend on your overall score relative to your peers. 1. Over 1 standard deviation of the median: Grade A 2. Between 1 standard deviation above the median and the median: Grade A- 3. Between the median and 0.5 standard deviation below the median: Grade B+ 4. Between 0.5 standard deviation below the median to 1 standard deviation below the median: Grade B 5. Between 1 standard deviation below the median to 1.5 standard deviation below the median: Grade B- 6. Below 1.5 standard deviation of the median: As decided by the instructor Other course policies Make-up Exams Extra Credit Late Work Special Assignments Class Attendance Classroom Citizenship A note from a medical doctor is required None other than the one specified above Any late assignment will not be graded No assignments will be given to make up for missed assignments etc. In person attendance is required in each class Students are encouraged to ask questions in class. However, sometimes in the interest of the class, the instructor may ask the students to discuss their doubts outside the class UT Dallas Syllabus Policies and Procedures: The information contained in the following link constitutes the University s policies and procedures segment of the course syllabus. Please go to http://go.utdallas.edu/syllabus-policies for these policies. The descriptions and timelines contained in this syllabus are subject to change at the discretion of the Professor.