MKAN1-UC 5103 Marketing Analytics
MKAN1-UC 5103 Marketing Analytics
General Course Information:
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Faculty name: |
Dongnanzi (Janet) Zheng |
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Faculty NYU email address: |
[email protected] |
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Semester/Year: |
Fall 2023 |
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Class Meeting Schedule: |
Wednesdays from 6:20pm - 8:50pm. |
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Class Location: |
Online via NYU Zoom |
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Office Hours: |
Upon request, NYU Zoom, Appointments via email |
Description:
Marketing is undergoing a data-driven transformation for developing strategies to acquire, manage, and retain business-to-business and business-to-customer relationships. Recent advancements in collecting, storing, and analyzing information have created new business opportunities for marketers tasked with strategic planning and execution responsibilities, resulting in significant awareness of the potential of marketing analytics. This course prepares students to turn business data into prescriptive recommendations for the marketing execution process.
Students will learn how to use SAS Enterprise Guide (analytic software technology) and will become comfortable with managing data, applying techniques to convert data into usable information, and analysis. An important consideration is students will learn how to apply analytic techniques to various business challenges. In addition, students will learn by doing: that is, guided by the instructorand using software and assigned readings/videos, they will take a hands-to-keys approach to developprescriptive recommendations for marketing and business.
Prerequisite:
- MATH1-UC1172 Statistical Methods
- MATH1-UC1171 Precalculus
- ISMM1-UC0742 Business Intelligence
Learning Outcomes:
Students who successfully complete the course requirements should be able to:
- Manage data
• Data access• Data cleansing• Data preparation
- Apply techniques for converting data to information
• Data exploration• Summarization• Introductory visualization
- Analyze data
• Exploratory analytics• Introductory predictive analytics
- Conduct marketing analytics related projects and reports
• Technical writing and reporting
Communication Methods:
All class work related communications must take place via the NYU Brightspace Assignments and Messaging system. Students must use the appropriate subject line related to the message in order to receive timely responses (within 1 to 3 business days).
Inquiries not related to class work should be sent via email to the professor using the NYU Email system. Communication inquires will be answered as soon as possible and prior to the start time ofthe next lecture.
If the inquiry is urgent, it must include the word URGENT in the subject line.
Announcements
Please be sure to check the “Announcements” module within the course site. This will be the main method I communicate with the class other than the course site Messages. This is the best way to post time-sensitive information critical to your success within the course.
Information that I might send out would be:
● Due dates for assignments and projects
● Changes to your syllabus
● Corrections/clarifications of materials
● Exam schedules
● Tips on how to improve your course performance
Please be sure to contact me directly via the course site messages system should you have anyquestions regarding any announcement.3
Structure | Method | Modality:
- Each session (except the first) will begin with a retrospective of the prior session. This will be followed by a lecture covering the weekly topic. The remainder of the class will consist ofan activity that shall reinforce and/or supplement the material covered during the lecture, when applicable.
- Attendance is required.If for some reason you cannot attend a lecture you must notifythe professor via email 24 hours prior to the class session. If you miss more than 4 scheduled lectures sessions you will receive an automatic Fail for the course, unless there is prior approval by the professor.
- Students must check NYU Brightspace within 24 and 48 hours after each lecture and atother times as requested.
- The course site on the NYU Brightspace learning management system (in addition to your @nyu.edu e-mail address) will serve as an extension of our in-person activities. The most up-to-date version of the course syllabus, links to the readings that supplement the required textbook, as well as labs can be found on this site. It is your responsibility to check NYU Brightspace (and your email) daily and be aware of any announcements that are sent. Please attempt to use the discussion board to ask questions regarding assignments as your peersmay benefit from having access to this exchange.
- Students are expected to attend all sessions from start to finish. If you fail to come to a session, arrive more than 20 minutes into a session, leave more than 20 minutes prior to theend of class or leave class for any other purpose for more than 20 minutes you will be considered absent fo that class session.
- Students are also expected to complete all assignments and to do so by the assigned due dates. The late policy is very simple, no assignments will be accepted after the duedate. Assignments must be submitted solely through the Assignments function on NYU Brightspace, unless instructed otherwise by the instructor.
Electronic Recording
Audio, video and/or screen recording is strictly prohibited.
Learning Modules/Units/Weeks/Chapters
Discussion Forums
Assignments
In this course you will discuss and understand how to conduct marketing analytics work using SAS.To foster comprehension, you will be asked throughout the semester to complete a number of activities to demonstrate your understanding of the concepts and skills introduced. These activities, or labs, will be presented weekly in the class session material. Unless indicated otherwise in Weekly materials (lecture slides and other material posted under thecourse site), you will submit all coursework assigned to its respective assignments area within the course site:
- Assignments are provided via NYU Brightspace and are due by the “Due date” noted on the assignment. No assignments or project work will be accepted if submitted past the due time (0 points) unless prior approval is provided via an NYU Brightspace message by the professor - no exceptions.
- Assigned work must be submitted solely through the Assignments function on NYU Brightspace, unless instructed otherwise by the professor. Assignments submitted via other methods (e.g. email) will receive 0 points.
- All submitted work is expected to be complete, labeled, of high quality and with proper citations and references.
Zoom Sessions
Expectations:
Learning Environment
Participation
Assignments and Deadlines
Course Technology Use
Feedback and Viewing Grades
Attendance
Textbook and Course Materials:
1. Exploring Marketing Research, 11th EditionBarry
J. Babin; William G. Zikmund
ISBN-10: 1-305-26352-9
ISBN-13: 978-1-305-26352-9
In addition, there will be a series of articles that will be assigned as required readings during theterm and will be available through NYU Brightspace. Additional reading material and practice exercises to be provided through slides, as appropriate. You are asked to complete assigned readings after the respective topics have been presented to solidify the material that was introduced.2.Learning SAS by Example : A Programmer’s Guide, 2 nd Edition
Cody RP, SAS Institute. Cary,
N.C.: SAS Institute; 2018.
Software
Grading | Assessment:
Grading in this course is based upon the following activities and assignments. Since all graded assignments are related directly to course objectives, failure to complete any assignment will result in an unsatisfactory course grade. All written assignments are to be completed using APA format and must be typed and double-spaced. Grammar, punctuation, and spelling will be considered in grading. Please carefully proof-read your written assignments before submitting them for a grade.
I will update the online grades each time a grading session has been complete — typically 14 days following the completion of an activity. You will see a visual indication of new grades posted on your NYU Brightspace home page under the link to this course.
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Description |
Percentage |
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Class Participation + Discussion |
18% |
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Assignments |
22% |
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Midterm |
15% |
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Final Project + Presentation |
25% |
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Final Exam |
20% |
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Total Possible |
100% |
Class Participation + Discussion (18% of final grade)
Participation is based on meaningful interaction in class and in the course discussion section and require reading assignments weekly for this synchronous course. It also means actively listening andbuilding on the questions and discussion points of your classmates.
You are expected to contribute productively and regularly to any classroom discussions and activities which will contribute 11% percent of your final course grade (with a maximum of 1% awarded for an individual class session). Note that participation will not be awarded for the first 2 class sessions (9/6/2023 & 9/13/2023) as some students may be joining the class during the drop add period. Participation will not be awarded on session 8 (10/25/2023) as it will be devoted to taking the midterm. Not showing up in class will receive 0 point unless you provide the medical proof.
- In discussion forums, you learn from one another by posing questions, justifying your comments, and providing multiple perspectives. When you prepare for discussions through thoughtful reflection, you contribute to your own successful learning experience as well as tothe experience of your peers.
- Log in to the course frequently (at least several times per week for long semesters and dailyfor summer sessions) and check the announcements. This will keep you apprised of any course updates, progress in discussions, assignment information, and messages requiringimmediate attention.
Assignments (22% of final grade)
All students must complete all course assignments. You should come to each class session fully prepared, having read the assigned readings and completed the assignment(s), and actively engage inclass discussions.7
Assignments will be provided at each applicable lecture and are graded on a 100% or pass-fail scale as noted in the assignment. Specific assignment topics and submission details will be announced viathe NYU Brightspace Assignments tool. All assignments must be uploaded to the “Assignments” tool in NYU Brightspace. Assignments will not be accepted through email. Each assignment sub- folder will include the detailed assignment description and grading criteria/rubric. The number of assignments and their due dates are in the course outline that follows. Be sure to pay close attention to deadlines—there will be no make-up assignments or quizzes, or late work accepted without approval. If you will be unable to meet a certain assignment’s deadline, please email me as soon as possible. Late submission will receive 0 points.
Exams (Midterm 15% & Final Exam 20% of final grade)
Exams cover all course topics discussed up to and including the previous session, unless otherwisenoted in the guidelines provided prior to the exam.
There will be no make-up exams (you will receive 0 points) unless prior approval is provided by the professor for a serious documented reason (approval at the discretion of the professor).
Final Project + Presentation (25% of final grade)
Writing Expectations
All students must have adequate writing skills to communicate content in a professional and concise manner. Students must follow [APA, MLA, Chicago, etc.] guidelines, use non-racist and non-sexist language, and include sufficient references to support their thesis and ideas.
Academic writing may incorporate references to published work to support (or possibly counter) a statement or argument. When stating the ideas or using the words of others your work must always clearly reveal its source and the extent to which that source is being used in your work.
You should therefore include a “References” section at the end of the work that you are preparing and cite any sources. The style to be used for this course is that of the Association for Computing Machinery (ACM), which is regarded in the United States of America as the pre-eminent professional body that deals with all aspects of information systems. The ACM is also well respected worldwide. Specifically usethe “Citation Style and Reference Formats” guidelines for authors which can be found online at: https://www.acm.org/publications/authors/reference-formatting .
User Manual ACM MAC MS Word Template( https://www.acm.org/binaries/content/assets/publications/consolidated-textemplate/mac2016- user-manual.pdf )
Unless otherwise noted on the instructions for an assignment, use the “Large Format Single Column” template to provide you with a guide as to: font size, font style, margin width, etc.
Viewing Grades and Feedback
Grades and feedback that you receive for graded activities will be posted to the NYU Brightspace Gradebook and/or via a course site message. Click on the Gradebook link on the left navigation to view your grades and feedback. I will update the online grades each time a grading session has been completed—typically 14 following the completion of an activity. You will see a visual indication of new grades posted on your course home page.
See the “Grades” section of Academic Policies for the complete grading policy, including the letter grade conversion, and the criteria for a grade of incomplete, taking a course on a pass/fail basis, and withdrawing from a course.
Course Outline:
Session 1, 9/6/2023Introduction, Syllabus review, Marketing Analytics Introduction, SAS overview and installation
Session 2, 9/13/2023
Chapter 1 The Role of Marketing Research/Analytics
SAS - Introduction to SAS
Session 3, 9/20/2023
SAS - Working with Data in a Project
Assignment 1 Posted
Session 4, 9/27/2023
Chapter 3 Marketing Research/Analytics ProcessMarketing Analytics Case StudiesSAS - Combining Data with QueriesAssignment 2 Posted
Session 5, 10/4/2023
Chapter 2 Harnessing Big Data into Better Decisions Chapter 6 Secondary Data Research in a Digital AgeMarketing Analytics Video and Discussion – Big Data for MarketingSAS - Transforming DataAssignment 3 Posted
Session 6, 10/11/2023
Chapter 13 Big Data Basics: Describing Samples and PopulationsSAS - Exploring Data with TasksAssignment 4 Posted
Session 7, 10/18/2023
Midterm preparation review topics and discussionGood and bad data visualization examples and discussionSAS - Visualizing DataAssignment 5 Posted
Session 8, 10/25/2023, Midterm Exam
Final Project Posted
Session 9, 11/1/2023
Feedback on midterm to studentsChapter 14 Basic Data AnalysisChapter 15 Testing for Differences Between Groups and for Predictive RelationshipsSAS - Performing Statistical Analyses with Marketing Analytics Tasks I
Session 10, 11/8/2023, Final Project Proposal Due by 6 pm
TBD - Tech Talk Workshop in Analytics – Learn how to present your analytics workSession 11, 11/15/2023Feedback on project proposal to studentsDiscussion on Critique of Tech TalkChapter 15 Testing for Differences Between Groups and for Predictive RelationshipsChapter 18 Advanced Topics in Linear AnalyticsSAS - Performing Statistical Analyses with Marketing Analytics Tasks II
11/22/2023 Fall Break No Class
Session 12, 11/29/2023
Academic Citation Format/DiscussionTopics in Marketing Analytics presentation, Analytics Research Project Sample/DiscussionChapter 16 Communicating Marketing Research/Analysis Results10SAS – Other advanced topics in marketing analytics
Session 13, 12/6/2023 Final Project Presentations Slides Due by 6 pm
Final Project Presentations with Q & ASAS Visual Analytics Introduction
Session 14, 12/13/2023
Course Review – key topics, Q & ASAS – Other advanced topics in marketing analytics
NOTE:
(TBD) On Wednesday, December 20th, 2023 from 6:20pm-8:10pm we will have our FinalExamination.Final Project Due by 6 pm
The syllabus may be modified to better meet the needs of students and to achieve the learning outcomes.
The School of Professional Studies (SPS) and its faculty celebrate and are committed to inclusion, diversity, belonging, equity, and accessibility (IDBEA), and seek to embody the IDBEA values. The School of Professional Studies (SPS), its faculty, staff, and students are committed to creating a mutually respectful and safe environment (from the SPS IDBEA Committee).
Guidelines, NYU SPS Policies and Procedures, and Student Affairs and Reporting.
2. Learning/Academic Accommodations - New York University is committed to providing equal educationalopportunity and participation for students who disclose their dis/ability to the Moses Center for Student Accessibility. If you are interested in applying for academic accommodations, contact the Moses Center asearly as possible in the semester. If you already receive accommodations through the Moses Center, request your accommodation letters through the Moses Center Portal as soon as possible ([email protected] | 212- 998-4980).
4. Student Support Resources - There are a range of resources at SPS and NYU to support your learning and professional growth. For a complete list of resources and services available to SPS students, visit the NYU SPS Office of Student Affairs site.
5. Religious Observance - As a nonsectarian, inclusive institution, NYU policy permits members of any religious group to absent themselves from classes without penalty when required for compliance with their religious obligations. Refer to the University Calendar Policy on Religious Holidays for the complete policy.
Moreover, you are expected to demonstrate how what you have learned incorporates an understanding of the research and expertise of scholars and other appropriate experts; and thus recognizing others' published work or teachings—whether that of authors, lecturers, or one's peers—is a required practice in all academic projects.
Plagiarism involves borrowing or using information from other sources without proper and full credit. You are subject to disciplinary actions for the following offenses which include but are not limited to cheating, plagiarism, forgery or unauthorized use of documents, and false form of identification
Turnitin, an originality detection service in NYU Brightspace, may be used in this course to check your work for plagiarism.
Read more about academic integrity policies at the NYU School of Professional Studies on the Academic Policies for NYU SPS Students page.
7. Use of Third-Party Tools - During this class, you may be required to use non-NYU apps/platforms/software as a part of course studies, and thus, will be required to agree to the “Terms of Use” (TOU) associated with such apps/platforms/software.
These services may require you to create an account but you can use a pseudonym (which may not identify you to the public community, but which may still identify you by IP address to the company and companies with whom it shares data).
You should carefully read those terms of use regarding the impact on your privacy rights and intellectual property rights. If you have any questions regarding those terms of use or the impact on the class, you are encouraged to ask the instructor prior to the add/drop deadline.
2023-10-14