Applications of Advanced Data Analysis in Marketing Research

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  • 0:03 Regression Analysis
  • 2:08 Cluster Analysis
  • 3:12 Discriminate Analysis
  • 4:45 Conjoint Analysis & Attributes
  • 5:22 Lesson Summary
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Lesson Transcript
Instructor: Scott Tuning

Scott has been a faculty member in higher education for over 10 years. He holds an MBA in Management, an MA in counseling, and an M.Div. in Academic Biblical Studies.

When research questions are complex, advanced statistical methods can answer tough questions with accuracy. This lesson will explore some of these methods.

Regression Analysis

Like many businesses, colleges and universities frequently track and monitor student performance by using benchmarks like graduation rates, attendance rates, or the length of time it takes for a student to get a degree. Every now and again, a college's performance on a particular benchmark (like student attendance) will drop suddenly without any obvious cause. The use of an advanced data analysis process known as regression analysis is a perfect choice when it is important to find the root cause of the problem.

A regression analysis is particularly useful when a market research question involves a significant number of variables, but it is not clear which variables are actually causing the outcome. In the college example, some of the variables that could play a role include:

  • Student's work status (full-time, part-time, casual, or unemployed)
  • Student's marital / parenting status (single parent, going through a divorce, domestic abuse concerns)
  • Student's economic status (poverty line income, middle-class, upper class)
  • Student's highest level of education prior to entering college (HS diploma, GED, undergraduate, graduate)

Without a regression analysis, the only thing that can be said definitively is that one or more of these factors might influence student performance and/or behavior. When a regression analysis is performed, a fixed value is assigned to all the variables except one. By testing one variable after another in this manner, it is possible isolate the variables that are actually influencing a process.

Fig 1

This figure depicts the patterns that occur when charting the results of a linear regression analysis when a statistically significant relationship exists. In the classroom example, the linear regression analysis is likely to reveal a strong negative relationship between working full-time and attending college full-time simultaneously: Negative because a high number of hours worked each week is the root cause of a decrease in performance.

Cluster Analysis

We've now learned that a regression can tell a researcher about individual variables in complex analysis, but there is another similar technique that may be more actionable for some organizations or industries. In Colorado, the recreational marijuana industry is 'budding' (pardon the bad pun). But opening up a recreational pot shop will be far more risky in certain Colorado regions than others. If an entrepreneur was lucky enough to obtain a recreational shop license, they would want to be extremely careful to place it in the best market possible. A cluster analysis is perfect for this situation.

Figure 2 Cluster

In a cluster analysis, researchers take multiple responses and place them in groups (clusters) based on similarities. Consistent with the definition of a cluster, we are not looking for identical responses; rather, we are looking for patterns that allow for the segmentation of a complex market. What variables do you think might cluster in this scenario? Take a look at this cluster chart, and see if you can identify the three distinct variable groups.

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