Marketing Analytics

This page serves as the landing page for MKTG 3850 - Marketing Analytics - at Otterbein University and includes content associated with this unique course. The following texts are recommended:

  • Statistics in Plain English by Timothy Urdan
  • Making Sense of Statistics by Fred Pyrczak
  • Marketing Research by Donal R. Lehmann, Sunil Gupta, and Joel H. Steckel
  • Marketing Research: An Applied Orientation by Naresh K. Malhotra

Other textbooks and websites exist. You should find resources that are assisting with your learning.

The IBM minicase and Kimberly-Clark final case can be accessed by clicking on this link. The folder contains the case, the assignment, and the datasets.

Cluster 1.gif

The homework and topics can be viewed by clicking on this link. For the homework, a few datasets are available. More can be found through this curated list. The required readings can be downloaded by clicking on this link.

The course syllabus is available by clicking this link.

Helpful Content

Additional resources devoted to:

Students are strongly encouraged to consult these resources to help them succeed in the course.

Name *

Description & Objective

The primary objective of this course is to introduce you to data analysis techniques. These techniques include cross tabulations, t-test and ANOVA, correlation and regression, cluster analysis, as well as multidimensional scaling. If time permits, we will review conjoint analysis.

Although a variety of analytical techniques are available, the selected techniques represent tools used most frequently by marketing and management professionals. The tools that 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, therefore, 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.