Forum Discussion

Jidnyasa2904's avatar
Jidnyasa2904
Helper I
2 years ago
Solved

Customer occurence with filter

I am trying to understand customer behavior. 
I have given an example below. 
I want to know how many customers first shopped with a 20% off coupon and returned to shop with 10%off or 5%off. 

Answer: 
1 customer returned to the shop with 10%off (customer id: ABC)

1 customer returned to the shop with 5%off (customer id: BCD)

 

Customer ID Order id Amount Coupon
ABC12020%off
BCD21220%off
EFG31220%off
ABC41010%off
BCD5355%off


Also, which visual would be best and easy to understand. 

Need Help
  • Kaviraj11's avatar
    Kaviraj11
    2 years ago

    Hi,

    I think bar chart is ideal in this scenarios because it's easy to compare different categories (in this case, the types of discount coupons) by visually representing the data with bars of varying lengths. Secondly, the visual separation of bars helps in quickly identifying which coupon type had more returning customers. Lastly, the differences in bar lengths immediately highlight the variations in the number of customers for each coupon type.

  • Anonymous's avatar
    Anonymous
    2 years ago

    Hi Kaviraj11 ,thanks for the quick reply, I'll add more.

    Hi Jidnyasa2904 ,

    The Table data is shown below:

    Use the following DAX expression to create  measures

    Total_returning_customers = 
VAR _table = SUMMARIZE('Table',[Customer ID ],"Returning Customers",COUNTROWS('Table'))
RETURN COUNTROWS(FILTER(_table,[Returning Customers] >= 2 ))
    which_coupon = 
VAR _coupon = SELECTEDVALUE('Table'[Coupon])
RETURN COUNTROWS(FILTER('Table','Table'[Coupon] <> "20%off" && 'Table'[Coupon] = _coupon))

    Final output

     

    Best Regards,
    Wenbin Zhou

4 Replies

  • Anonymous's avatar
    Anonymous
    Not applicable
    2 years ago

    Hi Kaviraj11 ,thanks for the quick reply, I'll add more.

    Hi Jidnyasa2904 ,

    The Table data is shown below:

    Use the following DAX expression to create  measures

    Total_returning_customers = 
VAR _table = SUMMARIZE('Table',[Customer ID ],"Returning Customers",COUNTROWS('Table'))
RETURN COUNTROWS(FILTER(_table,[Returning Customers] >= 2 ))
    which_coupon = 
VAR _coupon = SELECTEDVALUE('Table'[Coupon])
RETURN COUNTROWS(FILTER('Table','Table'[Coupon] <> "20%off" && 'Table'[Coupon] = _coupon))

    Final output

     

    Best Regards,
    Wenbin Zhou

  • Kaviraj11's avatar
    Kaviraj11
    Solution Sage
    2 years ago

    Hi,

    I think bar chart is ideal in this scenarios because it's easy to compare different categories (in this case, the types of discount coupons) by visually representing the data with bars of varying lengths. Secondly, the visual separation of bars helps in quickly identifying which coupon type had more returning customers. Lastly, the differences in bar lengths immediately highlight the variations in the number of customers for each coupon type.

  • Jidnyasa2904's avatar
    Jidnyasa2904
    Helper I
    2 years ago

    Hi,

    Can you also help me with the occurrence calculation? 
    I want to know, how customers buy and their patterns.

    Kaviraj11