cancel
Showing results for
Did you mean:

Earn a 50% discount on the DP-600 certification exam by completing the Fabric 30 Days to Learn It challenge.

Helper I

## Trying to Calculate How Many Employees Worked on Multiple Customers in Given Time Period

I had asked a similar question, but decided to take a different route after re-working some data...

Table 1

Order ID

Order Date

Order Amount

Employee ID

Customer ID

I want to figure out how many Employee worked on a different customer idea on each date.

Example data:

Employee ID   Employee Name    Customer    Date

1212                Jon                         5133           1/1/2023

2455                Sam                       1344           1/2/2023

2455                Sara                       11111          1/2/2023

9559                Charles                  3553           1/1/2023

2455                Sam                       4949           1/1/2023

2455                Sam                       1344           1/1/2023

Finished product:

Date:                                                                   1/1/2023    1/2/2023

Employees working more than 1 customer        33%            0%

2 ACCEPTED SOLUTIONS
Helper I

Thank you for the quick response @Greg_Deckler!

This is giving me 98% across the board for every week (which I would expect to see around 30%). Something I accidentally omitted in my example data was that customer numbers will often repeat, and I can only count each unique customer once, sorry for the confusion!

Super User

@Spudder112 Well that certainly makes a difference but should be an easy fix. PBIX is attached with ammended data to demonstrate a duplicate.

``````Employees Working More Than 1 Customer Measure =
VAR __NumEmployees = COUNTROWS(DISTINCT('Table'[Employee ID]))
VAR __Table = SUMMARIZE('Table',[Date],[Employee ID],"__Count",COUNTROWS(DISTINCT(SELECTCOLUMNS('Table',"__Customer",[Customer]))))
VAR __Result = DIVIDE( COUNTROWS(FILTER(__Table, [__Count] > 1)), __NumEmployees) + 0
RETURN
__Result``````

Become an expert!: Enterprise DNA
External Tools: MSHGQM
Latest book!:
The Definitive Guide to Power Query (M)

DAX is easy, CALCULATE makes DAX hard...
4 REPLIES 4
Super User

@Spudder112 Your sample data won't give you the results you expect because you have duplicate ID's for Sam and Sara but if you correct that this will work:

``````Employees Working More Than 1 Customer Measure =
VAR __NumEmployees = COUNTROWS(DISTINCT('Table'[Employee ID]))
VAR __Table = SUMMARIZE('Table',[Date],[Employee ID],"__Count",COUNTROWS('Table'))
VAR __Result = DIVIDE( COUNTROWS(FILTER(__Table, [__Count] > 1)), __NumEmployees) + 0
RETURN
__Result``````

Become an expert!: Enterprise DNA
External Tools: MSHGQM
Latest book!:
The Definitive Guide to Power Query (M)

DAX is easy, CALCULATE makes DAX hard...
Helper I

Thank you for the quick response @Greg_Deckler!

This is giving me 98% across the board for every week (which I would expect to see around 30%). Something I accidentally omitted in my example data was that customer numbers will often repeat, and I can only count each unique customer once, sorry for the confusion!

Super User

@Spudder112 Well that certainly makes a difference but should be an easy fix. PBIX is attached with ammended data to demonstrate a duplicate.

``````Employees Working More Than 1 Customer Measure =
VAR __NumEmployees = COUNTROWS(DISTINCT('Table'[Employee ID]))
VAR __Table = SUMMARIZE('Table',[Date],[Employee ID],"__Count",COUNTROWS(DISTINCT(SELECTCOLUMNS('Table',"__Customer",[Customer]))))
VAR __Result = DIVIDE( COUNTROWS(FILTER(__Table, [__Count] > 1)), __NumEmployees) + 0
RETURN
__Result``````

Become an expert!: Enterprise DNA
External Tools: MSHGQM
Latest book!:
The Definitive Guide to Power Query (M)

DAX is easy, CALCULATE makes DAX hard...
Helper I

Thank you very much! I will use this logic in the future a ton.

Announcements

#### Fabric certifications survey

Certification feedback opportunity for the community.

#### Power BI Monthly Update - April 2024

Check out the April 2024 Power BI update to learn about new features.

#### Fabric Community Update - April 2024

Find out what's new and trending in the Fabric Community.

Top Solution Authors
Top Kudoed Authors