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Srikantht
Helper II
Helper II

Sales Trend which is normalized

Hi guys,

For example, Staten Island opened in Week 8 of 2021, we would compare sales of that store from Week 9 2021 onwards with Hyannis that opened in Week 17 of 2022.   

 

I will defenitley appreciate can anyone give solution for this. 

 

Thanks,

Srikanth

 

 

1 ACCEPTED SOLUTION
Jihwan_Kim
Super User
Super User

Hi,

Please check the attached file and the below.

 

1. create a dim-calendar table

2. create a relationship between the sales table and dim-calendar table

3. create two calculated columns in the sales table

main point is to create a calculated column = Normalized Wk number CC

Normalized Wk number CC =
RANKX (
    SUMMARIZE (
        FILTER ( Sales, Sales[Store] = EARLIER ( Sales[Store] ) ),
        Sales[Startdate of Wk CC]
    ),
    Sales[Startdate of Wk CC],
    ,
    ASC
)

 

4. create a measure that shows sales total and put it into the visualization that uses one of the newly created calculated columns as the X-axis

 

 

 

Untitled.pngUntitled2.png

If this post helps, then please consider accepting it as the solution to help other members find it faster, and give a big thumbs up.


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View solution in original post

2 REPLIES 2
Jihwan_Kim
Super User
Super User

Hi,

Please check the attached file and the below.

 

1. create a dim-calendar table

2. create a relationship between the sales table and dim-calendar table

3. create two calculated columns in the sales table

main point is to create a calculated column = Normalized Wk number CC

Normalized Wk number CC =
RANKX (
    SUMMARIZE (
        FILTER ( Sales, Sales[Store] = EARLIER ( Sales[Store] ) ),
        Sales[Startdate of Wk CC]
    ),
    Sales[Startdate of Wk CC],
    ,
    ASC
)

 

4. create a measure that shows sales total and put it into the visualization that uses one of the newly created calculated columns as the X-axis

 

 

 

Untitled.pngUntitled2.png

If this post helps, then please consider accepting it as the solution to help other members find it faster, and give a big thumbs up.


Go to My LinkedIn Page


Thank you.

 

it was helpful

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