In this lesson, I walk through how to build a quadrant chart (4-quadrant scatterplot) in Tableau.

Quadrant charts are one of the most effective ways to compare performance across two measures and quickly identify outliers, opportunities, and risk areas.

In this example, we’re analyzing:

- Profit Ratio
- Average Discount

At first glance, a scatterplot shows the relationship.

But once you add quadrant segmentation, the insight becomes much clearer.

You can instantly see:

- High profit / Low discount performers
- High profit but heavily discounted items
- Low profit / Low discount products
- Low profit / High discount problem areas

Inside the video, I’ll walk you through:

- Creating a Profit Ratio calculation
- Building the base scatterplot
- Adding average reference lines
- Splitting the view into four quadrants
- Categorizing marks based on performance
- Two different ways to build the quadrant chart

This is a foundational analysis technique you can apply to product performance, customer segments, sales teams, marketing channels, and more.

Download the Workbook + Dataset

You can download the workbook and dataset used in this lesson here:
👉 https://nlt.kit.com/quadrant

If you want to go deeper than individual chart techniques, this is exactly the kind of real-world training we focus on inside Next-Level Tableau.

Live classes.
Real use cases.
Advanced techniques you can apply immediately.

Learn more here:
👉 https://www.nextleveltableau.com/

Cycle plots are a fantastic way to identify seasonality in a data set. And you can make them even more useful by adding indicators for whether they are above or below the average.

Here's how you can do that in Tableau in only 2 minutes.

Like this? You'll love Next-Level Tableau. Learn more at nextleveltableau.com.

A floating bar chart is similar to a Gantt chart, except it shows the range of two data points instead of two dates.

In this video, I show you how to resize marks (in this case circles) dependent on how many marks are in the view. For example, if all possible marks are in the view (100%), the circles should be small. If only 10% of possible marks are in the view, the circles should be large.

I show how to do this on a map, but it can be extended to scatter plots, box plots, dot plots, jittered bar chart, and more.

My goal this week was to correct my failures from last week, so I focused almost exclusively on building the starburst/sunburst chart. As promised to the viewers, I will also record a separate tip video for how to build them.

Here's the WatchMeViz and the viz is below that.

In this tip, I show you how to use a parameter to filter the rank across multiple worksheets or an entire dashboard.

Context is important in data visualization. For every chart you create, consider asking yourself "compared to what?" 

In this tip, I show you how to add context to a simple bar chart by showing whether a value is above or below the average.

DS22 is nearing the end of their training and today was a bit of a refresher. One of the questions I wanted them to answer was how many sub-categories in each region had sales above $40,000?

We then expanded that to include (1) the sales for those sub-categories and (2) the % of sales those sub-categories make up of the region sales. They were to complete this using LODs.

As they worked on the task, I thought that this, for sure, could be done with table calculations. This is perfect for the Data School Gym. 

If you know me, you know I love table calculations. And if you know Lorna Brown, you'll know she HATES table calculations. So this is your Data School Gym challenge Lorna.

Of course, everyone is welcome at the Data School Gym. It's actually not that hard and is a good way to help you learn about LODs vs. table calcs.

I'm not too fussed about making it look exactly the same. The point is to see if you can create the identical tables. Enjoy!

If you like a table calc challenge, this Workout Wednesday is for you. Get Ann's requirements here. On the surface it seems pretty simple:

1. Get the first order date for each customer.
2. Determine the number of quarters that elapsed since then.
3. Calculate the cumulative value of each cohort.

Next, calculate each cohort's cumulative lifetime value.

You should now have a view like this with the marks are filled in across the whole table

First, thank you to Tommaso Ferri for moderating Watch Me Viz. I enjoyed working with this data set and was able to build nine vizzes in about 40 minutes, then took another 40-50 working through formatting and some pesky table calcs. I got there in the end!

After the live stream ended, I created one more version that uses containers. I think I like this one best. Below you'll find the live stream recording as well as the two final visualizations.

Thanks for watching!