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Anonymous's avatar
Anonymous
Not applicable
8 years ago
Solved

Related Distinct Count

I'm trying to compute how many of our individuel product categories a customer has used, but I'm not getting the expected result.

Attached image is a good of the data model in a classic setup with Product-, Sales- and Customer Table.

The green columns are the ones I'm trying to compute, but current DAX formula is only returning a distinct count of all of the Product table, and not the table filtered by actual sales.

 

As far as I understand from Dax Patterns , all I need to do for each column is to use the following code:

Main Groups Used =
CALCULATE( 
  DISTINCTCOUNT(Product[ProductMainGroup]),
  Sales)

Sub Groups Used = 
CALCULATE(
   DISTINCTCOUNT(Product[Product Sub Group]),
   Sales)

Where CALCULATE should ensure that current CustomerKey row context is applied, and then filter the sales table accordingly, to only return a distinct count of the items the customer has bought.

My search lead me to a solution that suggested to apply "both" for crossfiltering between the tables, but my data model will not allow me do to so, complaining about only allowing one filtering path between tables.

 

So, how do I achieve my desired result without looking for a reconfiguration of my data model?

 

Need Help
  • Vvelarde's avatar
    Vvelarde
    8 years ago

    Anonymous

     

    Hi, you can use two measure like this:

     

    Main Groups Used =
CALCULATE (
    DISTINCTCOUNT ( 'Product'[ProductMainGroup] );
    CROSSFILTER ( 'Fact'[ProductID]; 'Product'[ProductId]; BOTH )
)

     

    Sub Groups Used =
CALCULATE (
    DISTINCTCOUNT ( 'Product'[Product Sub Group] );
    CROSSFILTER ( 'Fact'[ProductID]; 'Product'[ProductId]; BOTH )
)

     

    Regards 

     

    Victor

    Lima - Peru

     

8 Replies

  • Zubair_Muhammad's avatar
    Zubair_Muhammad
    Community Champion
    8 years ago

    Anonymous

     

    Try this calculated column in Customer Table

     

    Main Groups Used =
VAR mytable =
    ADDCOLUMNS (
        RELATEDTABLE ( Sales ),
        "MainGroup", CALCULATE ( VALUES ( 'Product'[ProductMainGroup] ) )
    )
RETURN
    CALCULATE ( DISTINCTCOUNT ( 'Product'[ProductMainGroup] ), mytable )
    • Anonymous's avatar
      Anonymous
      Not applicable
      8 years ago

      First, I didn't know you can pass tables as variables ? 

      Second, I think I understand what you're trying to do, but after having entered your suggestion but model is just "stuck" on working on it, so I don't think the solution is ideal.

       

      The dataset I'm working on has +120.000 Customers, around 1m sales rows distributed over 2000 products, if that has any relevance to performance.

      • Zubair_Muhammad's avatar
        Zubair_Muhammad
        Community Champion
        8 years ago
        Hi

        Try simplified version

        Calculate (distinctcount(product[Maingroup]), relatedtable(sales))
    • Zubair_Muhammad's avatar
      Zubair_Muhammad
      Community Champion
      8 years ago

      Hi Anonymous

       

      And this column one for Sub Groups used

       

      Sub Groups Used =
VAR mytable =
    ADDCOLUMNS (
        RELATEDTABLE ( Sales ),
        "MainGroup", CALCULATE ( VALUES ( 'Product'[Product Sub Group] ) )
    )
RETURN
    CALCULATE ( DISTINCTCOUNT ( 'Product'[Product Sub Group] ), mytable )
  • nickchobotar's avatar
    nickchobotar
    Skilled Sharer
    8 years ago

    Anonymous

     

    Here is another way to do this as a calculated column.

     

    You might be better off with measures if you are running this over a large dataset. You can use the option suggested by Vvelarde  or simply create a bi-directional relationship between sales and products and use a simple DISTINCTCOUNT() measure.

     

    MainGroup =
CALCULATE (
    CALCULATE (
        DISTINCTCOUNT ( Proucts[ProductMainGroup] ),
        SUMMARIZE ( Sales, Sales[CustomerKey], Proucts[ProductMainGroup] )
    )
)


SubGroup =
CALCULATE (
    CALCULATE (
        DISTINCTCOUNT ( Proucts[ProductSubGroup] ),
        SUMMARIZE ( Sales, Sales[CustomerKey], Proucts[ProductSubGroup] )
    )
)

     

    • Anonymous's avatar
      Anonymous
      Not applicable
      8 years ago

      I'm aware that a measure might be better for performance considerations, however currently I might use the calculated columns for filtering purposes and more importantly, I want to perform correlations on the results, which I have asked about in this thread:

      Granularity in correlation plot where I'm uncertain how PBI handles granularity when feed directlt as the input for the correlation plot.

       

      Anyone care to share the logic behind their approaches, and what should perform best?
      I really want to understand the logic, and not just implement it, for future purposes.

       

      Unfortunatly, I'm away from the model atm. so can't test it.