Course Description
The introduction of various retail analytics in the fashion/retail industry and the application of statistical tools to analyze consumer data and supervised and unsupervised methods
Additional Requirements for Graduate Students:
Graduate students will be required to create, distribute, collect, and analyze surveys (e.g., consumer data) to write a detailed report using statistical tools that they learned in class. A final report of the data analysis with interpretation will be required as well.
Athena Title
Retail Analytics
Undergraduate Prerequisite
(TXMI 3210 or TXMI 3210E) and (STAT 2000 or STAT 2000E or STAT 2100H)
Graduate Prerequisite
Permission of department
Grading System
A - F (Traditional)
Course Objectives
• Understand various technological applications that can be used to develop retail strategies; • Explore/Discuss challenges and decisions made in the retail environment related to data analytics; • Adapt evidence-based decision making in fashion retail; • Develop skills utilized in database marketing and data mining; • Integrate critical thinking and data literacy skills to decide on a team in the retail environment.
Topical Outline
Module I: Introduction to retail analytics - What is retail analytics? Module II: Key elements of retail analytics - Strategic planning - Relationship theories - Customer experience management (IDIC model: IDENTIFY, DIFFERENTIATE, INTERACT, CUSTOMIZE) - Retail touchpoints Module III. Technologies and retail analytics - Data mining overview - Artificial Intelligence and machine learning in retailing - Virtual reality and metaverse in retailing Module IV. Data mining practices - Survey design - Data presentation - Descriptive statistics - T-test and ANOVA - Regression - Decision tree - Market basket analysis