Want to analyze customer retention trends in a more insightful way? 

In this tutorial, I’ll show you how to create a Jump Plot in Tableau, a powerful visualization that helps track customer movement over time.

You’ll Learn:

✅ What a Jump Plot is and how it works

✅ How to structure your data for this visualization

✅ Step-by-step guide to building a Jump Plot in Tableau

✅ Key calculations to track customer retention effectively

The data sets, calculations, & steps are below the viz.

Create a free account to access the data:

📊 Download the sales dataset here

📊 Download the jump plot dataset here

Calculations you need:

1. Connect sales data source to 180 points and relate “1” to “1”

2. Compute Min Date by customer

   { FIXED [Customer Name] : MIN([Purchase Date]) }
   

3. Compute the Max Date by Customer

   { FIXED [Customer Name] : MAX([Purchase Date]) }
   

4. Filter customers that made more than one order (Max Date > Min Date)

   [Max Date]>[Min Date]

5. Create Customer Length calc

   DATEDIFF('day',[Min Date],[Max Date])
   

6. Create Columns calc (continuous dimension)

   DATE(
   ((COS([Point] * PI() / 180)) + 1 ) * (FLOAT([Max Date])-FLOAT([Min Date])) / 2
   + FLOAT([Min Date])
   )
   

7. Create Rows calc (continuous dimension)

   SIN((MIN([Point])) * PI() / 180)
   * SUM([Sales])
   

8. Add Columns and Rows to viz

9. Add Customer to Detail

10. Add Path to Path

11. Create Profitable calc and add to Color

    { FIXED [Customer Name] : SUM([Profit])}>0
    

• Consistent ordering of the countries within each diet type
• Colors are easy to distinguish from one another
• Arcs, while not best practice, are engaging and capture your attention
• Subtitle that explains what the viz is about

• Comparing the length of arc is difficult, especially across diet types.
• The icons are not needed since each diet is already labeled.
• The story in the data is lost as it's not included along with the charts.

• Simplify the visualisation; bar charts are a good place to start.
• Turn the text from the previous page that explains the findings into a story of some sort, probably long form and not story points.
• Remove the icons

With those goals in mind, here's my Makeover Monday week 39.

The topic for week 22 of Dear Data Two was our past. I immediately knew what I wanted to create in Tableau, and that was a visual resume (which you can view here). The inspiration for the resume comes from Ben Jones, who showed me his resume built in Tableau a couple years ago before he started working at Tableau.

From there, I started working on a couple of different draft versions of my postcard for Jeffrey. I thought I had settled on one and I showed it to my wife. When I asked for her impressions, she thought it was strange how I had stuff going left and right; she thought left looked negative and right positive, so in the final version I incorporated her feedback.

Data collection was pretty straight forward. I used:

• LinkedIn for the dates of my professional history
• Blogger for the dates when I started my various blogs
• The rest was by memory (or what is left of it)

From there, I simply plugged all of the dates into Tableau, categorized them, and built the resume in Tableau.