Skip to main content
cancel
Showing results for 
Search instead for 
Did you mean: 

Power BI is turning 10! Let’s celebrate together with dataviz contests, interactive sessions, and giveaways. Register now.

Reply
HeevaCh
Frequent Visitor

Creating a dynamic summarization table based on measure values

Hello everybody!

I have categorized my clients into 4 LRFM segments: Key, Frequent, Spender & Uncertain. Using measure.

ClientStatus
AKey
BUncertain
Uncertain
DFrequent
EKey
FKey
GFrequent
HKey
ISpender
JSpender
KFrequent
LUncertain

 I want a summarization table that shows something like this.

Status# Clients
Key4
Uncertain3
Frequent3
Spender2

Notice that my output in the first table is measure. But we can’t use measure as a legend. I don’t want to use columns because I want it to be dynamic and the summarization values change based on the slicers I have.

What should I do?

 

My lrfm measure:

LRFM Analysis LRFM = 

IF([If Normalize L LRFM]="High" && [If Normalize F LRFM]="High" && [If Normalize M LRFM]="High" && [If Normalize R LRFM]="High","Key",

IF([If Normalize L LRFM]="Low" && [If Normalize F LRFM]="High" && [If Normalize M LRFM]="High" && [If Normalize R LRFM]="High","Key",

IF([If Normalize L LRFM]="High" && [If Normalize F LRFM]="High" && [If Normalize M LRFM]="High" && [If Normalize R LRFM]="Low","Key",

IF([If Normalize L LRFM]="Low" && [If Normalize F LRFM]="High" && [If Normalize M LRFM]="High" && [If Normalize R LRFM]="Low","Key",

IF([If Normalize L LRFM]="High" && [If Normalize F LRFM]="Low" && [If Normalize M LRFM]="High" && [If Normalize R LRFM]="High","Spender",

IF([If Normalize L LRFM]="Low" && [If Normalize F LRFM]="Low" && [If Normalize M LRFM]="High" && [If Normalize R LRFM]="High","Spender",

IF([If Normalize L LRFM]="High" && [If Normalize F LRFM]="Low" && [If Normalize M LRFM]="High" && [If Normalize R LRFM]="Low","Spender",

IF([If Normalize L LRFM]="Low" && [If Normalize F LRFM]="Low" && [If Normalize M LRFM]="High" && [If Normalize R LRFM]="Low","Spender",

IF([If Normalize L LRFM]="High" && [If Normalize F LRFM]="High" && [If Normalize M LRFM]="Low" && [If Normalize R LRFM]="High","Frequent",

IF([If Normalize L LRFM]="Low" && [If Normalize F LRFM]="High" && [If Normalize M LRFM]="Low" && [If Normalize R LRFM]="High","Frequent",

IF([If Normalize L LRFM]="High" && [If Normalize F LRFM]="High" && [If Normalize M LRFM]="Low" && [If Normalize R LRFM]="Low","Frequent",

IF([If Normalize L LRFM]="Low" && [If Normalize F LRFM]="High" && [If Normalize M LRFM]="Low" && [If Normalize R LRFM]="Low","Frequent",

IF([If Normalize L LRFM]="High" && [If Normalize F LRFM]="Low" && [If Normalize M LRFM]="Low" && [If Normalize R LRFM]="High","Uncertain",

IF([If Normalize L LRFM]="Low" && [If Normalize F LRFM]="Low" && [If Normalize M LRFM]="Low" && [If Normalize R LRFM]="High","Uncertain",

IF([If Normalize L LRFM]="High" && [If Normalize F LRFM]="Low" && [If Normalize M LRFM]="Low" && [If Normalize R LRFM]="Low","Uncertain",

IF([If Normalize L LRFM]="Low" && [If Normalize F LRFM]="Low" && [If Normalize M LRFM]="Low" && [If Normalize R LRFM]="Low","Uncertain","Not Found"))))))))))))))))
1 ACCEPTED SOLUTION
xifeng_L
Super User
Super User

Hi @HeevaCh ,

 

You can try this solution.

 

Step 1: Use the following table expression to create an auxiliary table of customer categories to be used as row label fields.

 

CustomerCategory = 
DATATABLE (
    "Category",STRING,
    "Index",INTEGER,
    {
        {"Key",1},
        {"Uncertain",2},
        {"Frequent",3},
        {"Spender",4}
    }
)

 

Step 2: Create a measure named # Clients.

 

# Clients =
VAR TempTable =
    ADDCOLUMNS ( ALL ( 'Client'[Client] ), "Status", [LRFM Analysis LRFM] )
RETURN
    SUMX (
        VALUES ( 'CustomerCategory'[Category] ),
        COUNTROWS ( FILTER ( TempTable, [Status] = 'CustomerCategory'[Category] ) )
    )

 

Step 3: Use the category field of the CustomerCategory table created earlier as the matrix row label and the # Clients measure as the value field of the matrix. Then you can get the results you want.

 

 

 

Did I answer your question? If yes, pls mark my post as a solution and appreciate your Kudos !

 

Thank you~

 

 

View solution in original post

3 REPLIES 3
HeevaCh
Frequent Visitor

I edited the code a little bit, In case any user using this code:

# Clients = 

VAR TempTable =

    ADDCOLUMNS ( ALL ( 'VBI_JobProgressReport'[Client closed] ), "Status", [LRFM Analysis LRFM] )

RETURN

    sumx(

        VALUES ( 'CustomerCategory'[Category] ),

        CALCULATE(DISTINCTCOUNT(VBI_JobProgressReport[Client Closed]),FILTER ( TempTable, [Status] = 'CustomerCategory'[Category] ) )

    )


I used distinct count becuase I wanted to count my clients and in my dataset I had multiple rows with the same client.

xifeng_L
Super User
Super User

Hi @HeevaCh ,

 

You can try this solution.

 

Step 1: Use the following table expression to create an auxiliary table of customer categories to be used as row label fields.

 

CustomerCategory = 
DATATABLE (
    "Category",STRING,
    "Index",INTEGER,
    {
        {"Key",1},
        {"Uncertain",2},
        {"Frequent",3},
        {"Spender",4}
    }
)

 

Step 2: Create a measure named # Clients.

 

# Clients =
VAR TempTable =
    ADDCOLUMNS ( ALL ( 'Client'[Client] ), "Status", [LRFM Analysis LRFM] )
RETURN
    SUMX (
        VALUES ( 'CustomerCategory'[Category] ),
        COUNTROWS ( FILTER ( TempTable, [Status] = 'CustomerCategory'[Category] ) )
    )

 

Step 3: Use the category field of the CustomerCategory table created earlier as the matrix row label and the # Clients measure as the value field of the matrix. Then you can get the results you want.

 

 

 

Did I answer your question? If yes, pls mark my post as a solution and appreciate your Kudos !

 

Thank you~

 

 

Thanks!

Helpful resources

Announcements
Join our Fabric User Panel

Join our Fabric User Panel

This is your chance to engage directly with the engineering team behind Fabric and Power BI. Share your experiences and shape the future.

June 2025 Power BI Update Carousel

Power BI Monthly Update - June 2025

Check out the June 2025 Power BI update to learn about new features.

June 2025 community update carousel

Fabric Community Update - June 2025

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

Top Solution Authors