2024: Week 22 - Solution



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



Step 1 - Loyalty Points & Email

After inputting the table relating to Loyalty Points, we can then make sure that the DateTime_Out field is correctly formatted as a date. For this we can change the data type to a Date then rename the field to just Date. 

We can then focus on the loyalty points. We want to extract the numeric part from the Loyalty Points field. We can do this by using the Automatic Split functionality which will nicely bring out just the numeric part of the string. Then we can tidy the field by changing the data type to a decimal, removing the original Loyalty Points field, then renaming the Split field to Loyalty Points.

Next we want to extract the First and Last name from the Email Address field. For this we can use a custom split on the '@' and extract everything before the symbol.



We can then split this field again using an automatic split which will extract the first name and last name initial. Then we can remove the original Split field and rename the Name fields accordingly. At this stage the table should look like this: 



Step 2 - Join Tables

We can now join the Customer Details table with our existing workflow. For this we first need to extract the initial from the last name of each person using a string calculation: 

Last Name Initial 
LOWER(LEFT([Last Name],1))

We can then join the table just the First Name and Last Name Initial from both sides with an inner join: 



We can then remove the Last Name Initial, First Name-1, and Last Name Initial-1, then join the Store Data to our workflow.

For this we can use an inner join on Store ID:



Step 3 - Rank Customers

The final step is to remove the Store ID, Store ID-1 and all Null values within the Postcode field. 

We can then rank the customers per store based on their number of loyalty points. For this we can group by Store, then use a Dense rank on the Loyalty Points: 

Rank 


Then finally we want to only keep the top 5 so we can filter the table using the calculation [Rank]<=5

Our final output should look like this: 


You can view the output here.

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! 

Popular posts from this blog

2023: Week 1 The Data Source Bank

Image
 Created by: Carl Allchin Welcome to a New Year of Preppin' Data. These are weekly exercises to help you learn and develop data preparation skills. We publish the challenges on a Wednesday and share a solution the following Tuesday. You can take the challenges whenever you want and we love to see your solutions. With data preparation, there is never just one way to complete the tasks so sharing your solutions will help others learn too. Share on Twitter, LinkedIn, the Tableau Forums or wherever you want to too. Tag Jenny Martin, Tom Prowse or myself or just use the #PreppinData to share your solutions.  The challenges are designed for learning Tableau Prep but we have a broad community who complete the challenges in R, Python, SQL, DBT, EasyMorph and many other tools. We love seeing people learn new tools so feel free to use whatever tools you want to complete the challenges.  A New Year means we start afresh so January's challenges will be focused on beginners. We will use dif
Read more

2024: Week 1 - Prep Air's Flow Card

Image
Created by: Carl Allchin Welcome to a New Year of Preppin' Data challenges. For anyone new to the challenges then let us give you an overview how the weekly challenge works.  Each Wednesday the Preppin' crew (Jenny, myself or a guest contributor) drop a data set(s) that requires some reshaping and/or cleaning to get it ready for analysis.  You can use any tool or language you want to do the reshaping (we build the challenges in Tableau Prep but love seeing different tools being learnt / tried). Share your solution on LinkedIn, Twitter/X, GitHub or the Tableau Forums Fill out our tracker so you can monitor your progress and involvement The following Tuesday we will post a written solution in Tableau Prep (thanks Tom) and a video walkthrough too (thanks Jenny) As with each January for the last few years, we'll set a number of challenges aimed at beginners. This is a great way to learn a number of fundamental data preparation skills or a chance to learn a new tool — New Year&
Read more

How to...Handle Free Text

Image
Free isn't always a good thing. In data, Free text is the example to state when proving that statements correct. However, lots of benefit can be gained from understanding data that has been entered in Free Text fields. What do we mean by Free Text? Free Text is the string based data that comes from allowing people to type answers in to systems and forms. The resulting data is normally stored within one column, with one answer per cell. As Free Text means the answer could be anything, this is what you get - absolutely anything. From expletives to slang, the words you will find in the data may be a challenge to interpret but the text is the closest way to collect the voice of your customer / employee. The Free Text field is likely to contain long, rambling sentences that can simply be analysed. If you count these fields, you are likely to have one of each entry each. Therefore, simply counting the entries will not provide anything meaningful to your analysis. The value is in
Read more