We are looking to improve our service on Prep Air so want to calculate NPS from a survey and see how we compare.
Step 1 - Combine Data
First we want to combine the data from both of the files. We could use the union tool for this, but I have used the Wildcard Union in the input tool. You don't need to add any matching patterns as we want to bring all of the files through:
We should now have both of our inputs in a single table:
Step 2 - Classify Customers
Next we want to classify the responses that we want to compare, so the first step is to count the total number of customers for each Airline. To calculate this we can use a fixed LOD:
Number of Customers
After counting the customers, we can then filter to only airlines with more than 50 customers. You can filter this by using a range filter on Number of Customers:
Finally, to classify the customers responses we can use the following IF statement:
Classification
IF [How likely are you to recommend this airline?]<7
THEN "Detractor"
ELSEIF[How likely are you to recommend this airline?]<9
THEN "Passive"
ELSE "Promoter"
END
After the classification our data should look like this:
Step 3 - Calculate NPS
We can now turn to the main part of the challenge, calculating the NPS for each Airline.
First we need to count the number of customers for each classification and airline by using an aggregate tool:
After the aggregation, we only want to focus on Detractor and Promoter so we can exclude the Passive classifications. Also, to make things easier we can rename Number of Customers to Total Customers and Customer ID to Number of Customers.
Now we can calculate the % Total in each Airline & Classification using this calculation:
% Total
100*[Number of Customers] / [Total Customers]
Then make this a whole number and remove the Total Customer and Number of Customers fields.
Finally we are ready to pivot our table so that we have the Detractor and Promoter scores on a single row. The rows to columns pivot setup looks like this:
Now we are ready to calculate the NPS:
NPS [Promoter]-[Detractor]
Our table looks like this:
Step 4 - Calculate Z-Score
The last step is to calculate the Z-Score for each airline. We calculate this with the following calculation
[NPS]-[Average] / [Standard Deviation]
Before we get to this stage we first need to calculate the overall Average and Standard Dev across the data set. We can use a fixed LOD for this:
Average
Standard Dev
Now we're ready to calculate the Z-Score:
Z-Score
ROUND(
([NPS]-[Average])
/
[Standard Deviation]
,2)
Our data should now look like this:
Then finally we can filter for just Prep Air (filter for selected values) so our output will look like this:
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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