active customer by month
2 TopicsCalculate WAU/MAU Ratio ( Week numbers span across different months?)
Hello experts! I am trying to create a visual showing the weekly active user/monthly active user ratio with the following DAX. However, I realized later that e.g. week 31 has 5 days in August but 2 days in July, and in the graph seems the % is calculated using the number from August as the denominator. I wonder if anyone here knows a better way to calculate the WAU/MAU ratio. Thanks a lot! Weekly Active Users = DISTINCTCOUNT('Usage Logs'[user_Id]) Monthly Active User = calculate(distinctcount('Usage Logs'[user_Id]), filter(allselected(DateTable) , DateTable[Year Month] = Max( DateTable[Year Month] ) ) ) WAU/MAU = DIVIDE([Weekly Active Users],[Monthly Active User],0)963Views0likes1CommentCalculate Active Members
Hi! I am trying to calculate "active customers" in a given month. I am at a standsi Here is the data set. I want a distinct count of program numbers but only count those where Customer Enrolled = 1 for a given month. For example, the first two would be counted in Jan, Feb, March, April, May, June, July, August. The 7th one on the table would also count in september and in these other months. If customer enrolled is 0, that program number would not be in any month. I have tried the following. Cummulative Distinct Count = TOTALYTD(DISTINCTCOUNT('Table'[Program_No]),d_Date[CalendarDate]) Terminations Unitl Previous Month = TOTALYTD(CALCULATE(DISTINCTCOUNT('Table'[Program_No]),USERELATIONSHIP(d_Date[CalendarDate],'MHK Program'[Program End Date]),DATEADD(d_Date[CalendarDate],-1,MONTH)),d_Date[CalendarDate]) Active Members = [Cummulative Distinct Count]-[Terminations Unitl Previous Month] Program_No Program Start Date Program End Date Customer Enrolled P2200399039 1/3/2022 8/3/2022 1 P2200336838 1/3/2022 8/4/2022 1 P2200347181 1/3/2022 8/1/2022 1 P2200484315 1/4/2022 8/7/2022 1 P2200449729 1/4/2022 8/1/2022 1 P2200416122 1/4/2022 8/7/2022 1 P2201432207 1/14/2022 9/12/2022 1 P2201768200 1/17/2022 11/23/2022 1 P2201773512 1/17/2022 8/11/2022 1 P2201753833 1/17/2022 10/3/2022 1 P2201735989 1/17/2022 8/3/2022 1 P2201846268 1/18/2022 10/20/2022 1 P2201869106 1/18/2022 11/29/2022 1 P2201867142 1/18/2022 1/26/2023 0 P2201842158 1/18/2022 10/31/2022 0 P2201813974 1/18/2022 8/5/2022 1 P2201868596 1/18/2022 9/19/2022 1 P2201898799 1/18/2022 12/23/2022 1 P2201813261 1/18/2022 9/28/2022 1 P2201846294 1/18/2022 8/15/2022 1 P2202054448 1/20/2022 8/8/2022 0 P2202077917 1/20/2022 9/22/2022 1 P2202030467 1/20/2022 8/11/2022 1 P2202539602 1/25/2022 1/12/2023 0 P2202676340 1/26/2022 1/12/2023 0 P2202644106 1/26/2022 11/21/2022 1 P2203333550 2/2/2022 8/24/2022 1 P2203540012 2/4/2022 8/11/2022 0 P2204193603 2/10/2022 11/28/2022 1 P2204549564 2/14/2022 8/7/2022 1 P2204581262 2/14/2022 8/11/2022 1 P2204532390 2/14/2022 1/12/2023 0 P2204741062 2/16/2022 11/11/2022 1 P2204876074 2/17/2022 9/16/2022 1 P2205349649 2/22/2022 10/25/2022 1 P2205435050 2/23/2022 1/5/2023 0Solved1.3KViews0likes4Comments