Welcome to MKTG 475 - Marketing Analytics - at the University of Louisiana at Lafayette. In this course, we will learn how to select an analytical tool, apply it to a problem or issue, and communicate our conclusions. The required textbook remains Business Analytics: Communicating with Numbers by Jaggia, Kelly, Lertwachara, and Chen. We will frame our recommendations using the language associated with resource advantage theory. Of Theory, Competition, & Marketing provides a discussion of this theory.
You should find additional resources that aid your understanding and learning. I am working on curating resources for using Knime.
The reflection essay, homework assignments, topics, participation discussion, and student information sheet can be accessed by clicking this link.
The Tech Sales Reps and mini case assignment can be accessed by clicking on this link. The final exam assignment and attending dataset will be posted here between weeks six and seven of the term.
If you are looking for datasets to play with, then click on this link. More can be found through this curated list.
The course syllabus is available by clicking this link.
Students are strongly encouraged to consult these resources to help them succeed in the course.
Description & Objective
The primary objective of this course is to introduce you to advanced data analysis techniques. These techniques include correlation and regression, logit regression, cluster analysis, factor analysis, multidimensional scaling, and k-nearest neighbor. If time permits, we will review conjoint analysis.
Although various analytical techniques are available, the selected techniques represent tools used most frequently by marketing and management professionals. The tools you learn in the course go beyond simple data descriptors such as mean, median, mode, and variance. Such descriptors serve as a starting point for us to explore the data set. Techniques learned in this course will allow you to make a more informed decision and recommendation.
Data analysis represents a critical thinking tool. As consumers of knowledge, you will need to assess whether you have good or not-so-good knowledge. By understanding data analysis, you can assess whether the analysis is appropriate and whether you have good knowledge.
Finally, these data analysis techniques represent a more robust approach to understanding data. As producers of knowledge, you can furnish a more sophisticated analysis, which should establish your value to the firm and, in turn, help the firm be more efficient and/or effective.