Learn about how to perform a cluster analysis using Tableau and how to interpret the results.
- [Instructor] Tableau 10 introduced k-means clustering functionality to the platform and it's a great example of how the platform is constantly evolving and getting more powerful. I have Tableau open and have connected to our data from our exercise files so let's navigate into our workspace and we can see our list of measures includes three different behaviors. We also have a brand preference measure, CTA and some demographic information. Now in this case we're interested in creating a set of groups from CTA and age.
In other words, this data tells us the age for each response to a certain call to action. I'm going to select our CTA measure and drop that in the column shelf. I'm going to select the Demo-Age and drop that in the row shelf. Let's go ahead and disaggregate the data so drop down our Analysis menu and deselect Aggregate Measures. Now if you look towards the top left of Tableau you'll see a tab just to the right of the Data tab called Analytics.
See that there? Let's go ahead and click on that tab. Under the Model heading is an option called Cluster so let's select that and we're going to drag it out onto the plot. Automagically as they say, Tableau has not only generated our groups but it's also determined how many groups it assesses to be ideal based on our data. If you're using your own data with this technique you might have some other number of clusters here. Now if we had something different or had a different requirement for the number of groups we were looking to create then we could change the number of clusters by dropping the arrow down on our Clusters pill and Edit clusters.
Now we can enter in the number of clusters that we need in the configuration box that right now reads Automatic. I'll enter eight and we can see that Tableau's gone ahead and made that change for us. We now have eight groups. We can explore what these look like. If I drop that arrow down again on our Clusters pill go ahead and close out of this drop down our Clusters pill I can select Show Highlighter. Then we can drop down our Highlighter Clusters field and select Cluster 2 to isolate those points.
Then if we mouse over those data points on the plot we can see the combined age range and CTA value and if we want to convert this cluster into data for additional reference we can simply choose the pill in the color Marks card and we can drag that over to our Data Dimension pane. I'm going to select our Data tab going to select Clusters, and just drop that in right here. This is a great addition to Tableau and allows for us to rapidly explore applying our clustering algorithm to our data.
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