Marketing Analytics I
Develop quantitative models that leverage business data to forecast sales and support important marketing decisions.
Modules/Weeks
Weekly Effort
Discipline
Format
Cost
Course Description
- Develop statistical models and optimization tools to forecast sales and make informed marketing decisions.
- Create quantitative models using business data, statistical computation, and machine learning to understand customer behavior and take effective marketing actions.
- Explore customer perception, preference measurement, choice modeling, and market structure analysis, among other key topics.
- Utilize regression analysis, conjoint analysis, multidimensional scaling, clustering and segmentation tools, and finite mixture models for preference visualization and data-driven marketing strategies.
Course Prerequisites
- Recommended prior completion of undergraduate courses in probability, statistics, and calculus
- Expected familiarity with programming languages like R or similar
What You Will Learn
By the end of this course, learners will be able to:
Utilize statistical models and optimization tools to analyze customer purchase data and make informed marketing decisions.
Apply marketing analytics and customer analysis techniques to gain insights into consumer behavior.
Implement market segmentation, preference measurement, and consumer choice models to understand customer preferences.
Evaluate customer lifetime value and make data-driven decisions for new product development and marketing strategies.
Course Outline
Module 1: Introduction to marketing analytics and consumer analysis
Module 2: Market segmentation
Module 3: Perceptual mapping
Module 4: Consumer choice models
Module 5: Customer lifetime value
Module 6: New product decisions
Instructors
Kamel Jedidi is the Jerome A. Chazen Professor of Global Business at Columbia Business School, New York. He earned his bachelor's degree in Economics from the University of Tunis, and his master's and Ph.D. degrees in Marketing and Statistics from the Wharton School, University of Pennsylvania. Dr. Jedidi's research interests include market segmentation, product positioning, and pricing, and he has published extensively in top marketing and statistical journals. He received several awards for his work, including the 1998 IJRM Best Article Award and the 2000 Best Paper Award from the Marketing Science Institute. Dr. Jedidi serves as Senior Editor for the Consumer Needs and Solutions Journal and sits on the editorial board for the Journal of Business-to-Business Marketing. He is a member of the Faculty Steering Committee at Columbia Global Centers (Amman) and a Founding Trustee of the Marketing Accountability Standards Board (MASB). He has also conducted seminars at business conferences and universities and consulted with multinational companies.
Professor Ansari's research focuses on e-commerce personalization and targeting, Bayesian models of consumer actions, social network modeling, and customer relationship management. He is currently working on machine learning methods for Big-Data settings in marketing. Prior to joining Columbia, Professor Ansari worked at the University of British Columbia in Canada and has published extensively in leading marketing and allied journals, including the Journal of Marketing Research, Marketing Science, and Management Science. Professor Ansari has received several awards for his research, including the Paul Green Award from the American Marketing Association and nominations for the O'Dell Award, Paul Green Award, Long-Term Impact Award, Frank M. Bass Outstanding Dissertation Award, and John D. Little Award. Ansari serves on the editorial board of Marketing Science and the Journal of Marketing Research. He has taught the core marketing course for several years, an elective course on Pricing Strategies for MBA and EMBA students, and PhD courses on empirical modeling and Bayesian methods. Professor Ansari received the Dean's Award for Teaching Excellence in the Core in 2009.
Please note that there are no instructors or course assistants actively monitoring this course.