2024: Week 21 - Solution


Solution by Tom Prowse and you can download the workflow here.


Step 1 - Loyalty Points

After inputting the data, we can first calculate the loyalty points for each customer: 

Loyalty Points
ROUND([Sale Total]/50,1)

Then we can categorise the customers based on the number of points they have: 

Category 
IF [Loyalty Points]>=7
THEN "MegaByte"
ELSEIF [Loyalty Points]>=5
THEN "Byte"
ELSE "No Byte"
END

At this stage our table has two new columns that look like this: 


Step 2 - Aggregate & Pivot

Next we want to total the number of customers who qualify for each category by using an aggregation step: 


Then once we have aggregated we need to pivot the data so that Male & Female genders have a separate column. For this we want to use a Rows to Columns pivot: 



The final task is to calculate the % of Total for Male & Female.

First we need to calculate the total of each by adding both the Male and Female totals together: 

Total 
[Male]+[Female]

Then we can calculate the % of total for each gender: 

Female 
ROUND([Female]*100/[Total],1)

Male 
ROUND([Male]*100/[Total],1)


We can then output our data that looks like this: 


After you finish the challenge make sure to fill in the participation tracker, then share your solution on Twitter using #PreppinData and tagging @Datajedininja, @JennyMartinDS14 & @TomProwse1

You can also post your solution on the Tableau Forum where we have a Preppin' Data community page. Post your solutions and ask questions if you need any help! 

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