Course Schedule - Spring Semester 2024

     

Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).
Additional information available here.

BUSI 480 001 (CRN: 24292)

MARKETING ANALYTICS

Long Title: MARKETING ANALYTICS
Department: Business
Instructor: Lee, Jung Youn
Meeting: 11:00AM - 12:15PM MW (8-JAN-2024 - 19-APR-2024) 
Part of Term: Full Term
Grade Mode: Standard Letter
Course Type: Lecture
Language of Instruction: Taught in English
Method of Instruction: Face to Face
Credit Hours: 3
Course Syllabus:
Course Materials: Rice Campus Store
 
Restrictions:
Must be enrolled in one of the following Level(s):
Undergraduate Professional
Visiting Undergraduate
Undergraduate
Prerequisites: BUSI 380
Section Max Enrollment: 65
Section Enrolled: 23
Enrollment data as of: 17-APR-2024 10:50AM
 
Additional Fees: None
 
Final Exam: Scheduled Final Exam-Dept Room
Final Exam Time:
30-APR-2024  
9:00AM - 12:00PM T
 
Description: Marketing is evolving from an art to a science as data is now the key source of decision making. In this course students learn how to use data analytics to address decisions by marketing managers, with emphasis on pricing and promotion. Students will understand how different types of data can—or cannot—be used to answer managerial questions and how better planning can simplify the analytics and increase confidence in the findings. The course is organized around a hierarchy of topics. We begin with understanding pricing and promoting to an individual customer. We then move to more aggregate decisions, such as setting regular and promoted prices at the product level, managing category pricing, and store analytics. This course is practical and hands-on, analyzing data from real-world managerial problems, through collaborations with leading retailers and consulting firms. Working knowledge of statistics (e.g., t-test and regression analysis) is required. Students learn and use R for data analysis; no prior experience with R is necessary. The goal is not to train students to become experts in statistics or computer science; rather, students will learn to become a bridge between data scientists and managers.