March 19, 2025

How to Analyze Customer Retention with a Jump Plot

arc , customer retention , data analysis , data visualization , how to , jump plot , relationship , tableau No comments

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:

Compute Min Date by customer

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

Compute the Max Date by Customer

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

Filter customers that made more than one order (Max Date > Min Date)
```
[Max Date]>[Min Date]
```
Create Customer Length calc
```
DATEDIFF('day',[Min Date],[Max Date])
```

Create Columns calc (continuous dimension)

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

Create Rows calc (continuous dimension)

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

Create Profitable calc and add to Color

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