Below is a simplified version of the data. Daily transaction list for customer ID
SalesData =
DATATABLE (
"Customer ID", INTEGER,
"Date", DATETIME,
"Amount", INTEGER,
{
{ 101245, "2019/04/07", 500 },
{ 101245, "2018/08/05", 400 },
{ 100365, "2018/07/30", 900 },
{ 100365, "2018/02/22", 700 },
{ 104300, "2019/04/05", 300 },
{ 104300, "2019/04/03", 350 },
{ 104300, "2019/04/01", 310 },
{ 107804, "2018/11/08", 650 },
{ 107804, "2018/11/19", 640 },
{ 108040, "2019/01/02", 730 }
}
)
Objective: Calculate Reactivated and churned customers during the current period which in the example below is 1-7th April 2019.
Churned = Inactive for 90 days or more.
Reactivated = Inactive for 90 days or more prior to making the latest purchase.
In a matrix - as visualized below - the following measures work as expected for reactivated and churned in the current period, 1st to 7th of April.
churnedInCurrentPeriod =
VAR dayspassed =
DATEDIFF(
MAX(SalesData[Date]),
CALCULATE(
MAX(SalesData[Date]),
ALLEXCEPT(SalesData,SalesData[Date])),
DAY)
Return
IF(dayspassed >= 90 && dayspassed <= 97,1,0)
Please note that the "current period" in this case needs to be dynamic to the date, that's why the date slicer is there and I use an all expect on the date column to make it work. In the if statement it's 90 + 7 days, should be dynamic this as well.
ReactivatedInCurrentPeriod =
VAR differenceDays =
DATEDIFF(
CALCULATE(
MAX(SalesData[Date]),
FILTER(SalesData,SalesData[Date] <> MAX(SalesData[Date])
)
),
MAX(SalesData[Date]),
DAY
)
RETURN
IF(AND(differenceDays >= 90,MAX(SalesData[Date]) >= DATE(2019,4,1)),1,0)
As the screenshot shows the matrix works as expected. Not the totals. I've tried using calculate with distinct count to count the customers accordingly without success. Currently, I solve this in my real dataset by exporting the matrix and sum in excel(!).
Has to be a better way to make this work with DAX.
Many thanks for the help.