Learn how to perform a conjoint assessment using Tableau and how to interpret the results.
- [Instructor] So we did a little multiple regression magic in our previous video, using Python, and I like how that brings the technical components of our course back to where we started from and really helps you to build on those skills that you learned to perform regression analysis. Tableau makes this process relatively easy as well. So we have the platform open and we've connected to our exercise data for a case study, and we'll navigate into our workspace by selecting Sheet 1. So this goes back to the fundamentals we learned in the regression chapter.
We need to assign the measures for our dependent variables. Which one do you think that is? If you said rating, you got it right. Let's go ahead and drop our Rating measure on our Row shelf. And again, we're looking at multiple independent variables here, I would just select the whole batch of them and drop them on our Column shelf. Now we'll turn off our aggregate measures from the Analysis menu. And then we'll apply the trend line.
So back into that Analysis menu, Trend Lines, and Show All Trend Lines. So that's awesome, we have an interesting visual, but more importantly we have our multiple regression here in Tableau. And let's have a look at the output here. So I'm going to go back into that Analysis menu, down to Trend Lines, and Describe Trend Models. Here, if we scroll down, we can identify the coeffients that we're working with or that this provides for us.
Some of what we discuss in this course could be considered dense. You have to put your thinking cap on for some of it. To help reveal the insights that we find to others, in meetings or presentations, though, visuals can really tell a story. So in your exercise file there's another dataset with the specific coefficients in them. And what we can do is we can open up a new Tableau window. So let me close this. File and New. And we're going to bring that data in. Going to click on Text File, select conjoint-tableau-coef.csv and click on Open.
And we'll now navigate to the workspace and create the visual that we can use to tell that story. Now I'm going to put our Featured dimension on the X. And our Utility measure on the Y. And that gives us a nice bar chart. We can take a look at this. It looks like Photo Feature 1 scores best with this data. It looks like Content Feature 1 ranks next in line.
Now I'm going to go ahead and turn of aggregate measures. And that gives us a plot which I think is a little bit easier to read. So again, we can see that our Photo Feature 1 ranks highest, our Content Feature 1 ranks next, and then our Special Sauce feature ranks third. So there we have it. Once we have this analysis in hand, another thing we could do is use it to estimate market share for different product combinations as well.
In this course, discover how to gain valuable insights from large data sets using specific languages and tools. Follow Chris DallaVilla as he walks through how to use R, Python, and Tableau to perform data modeling and assess performance. As Chris dives into these concepts, he shares specific case studies that come directly from his own work with clients. Plus, he shares three essential—and practical—best practices for data-driven marketing that you can use to bolster your organization's marketing performance.
- Installing R, Python, and Tableau
- Navigating the UI for R, Python, and Tableau
- Using R, Python, and Tableau
- Exploratory analysis
- Performing regression analysis
- Performing a cluster analysis
- Performing a conjoint assessment
- Stakeholder alignment