Integer (Value) based size groups?

Q&A
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1

I’d like to group our customer seat counts, so that we can analyze the differences and trends between the size groups. For example, I would like to create a 2-5 size group, 6-10 size group and so on.

We have a {total seat count} column which is a value in our data tables. However, I can’t figure out how to do this with calculated fields and there doesn’t appear to be a group button or function like there is with Tableau/Power BI.

2

Hi @DataDude1 - Welcome to AWS QuickSight community and thanks for posting the question. If I understood your question, you want to create group depending upon one value. You can create it by creating a calculated field with if else condition in QS. Please see the documentation for the same.

In parallel, if you can share sample data and number of groups, we can give a try as well.

Regards - Sanjeeb

3

Hi Sanjeeb. Appreciate the quick reply. The sample data would be the {total user seat count} value we have in the data. The groups I am trying to create are the following:

  • • 1 Seat – Individual
    • 2- 5 Seats
    • 6 – 10 Seats
    • 11-25 Seats
    • 26-50 Seats
    • 51- 100 Seats
    • 101 – 200 Seats
    • 200 + Seats
4

Hi @DataDude1 - Can you please create a calculated field with below logic

Change the {No} with your column name

ifelse(
{No} =1, "Seat – Individual",
{No} >1 AND {No} <=5, "2- 5 Seats",
{No} >5 AND {No} <=10, "6 – 10 Seats",
{No} >10 AND {No} <=25, "11-25 Seats",
{No} >25 AND {No} <=50, "26-50 Seats",
{No} >50 AND {No} <=100, "51- 100 Seats",
{No} >100 AND {No} <=200, "101 – 200 Seats",
"200 + Seats"
)

See the sample output

image
image843×561 19 KB

Regards - Sanjeeb

1 Like
5

Hey Dude. That worked! Thank you. I marked it as the solution. Follow up question (I can make a new post as well) : How would do this if I wanted to bucket by percentage. For example, the top 5 percent of customers in terms of {total user seats}

6

Hi @DataDude1 - Please check this out, possibly it will help you.

Regards - Sanjeeb

1 Like