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Hi!
I have a dataset with the following tables (data is of grocery stores):
- sales (collumns of importance: date; customer id; receipt id; price; )
- customers (email, customer ID)
- receipts (ID; date)
I would like to be able to segment the customers in different groups of how active they are (>3 visits/week; >1 visit/week; >1 visit/month ...). In that way I would be able to compare basket size, revenue and so on for those types of customers. A customer can change from a group, so it has to be time sensitive. I would like to calculate of each customer which type of customer they were on a certain date in the past (based on the average sales data two months prior to that certain date).
So I assume i have to create a measure; But when I do I can't use the different groups of customers as a legend. Nor can I do percentage calculations. If I generate a new collumn, than I only have the customers group that is valid right now.
How should I solve this?
This is my current measurement
TypeCustomer=
if(calculate(DISTINCTCOUNT('public sales_receipt'[id]), DATESINPERIOD(sale[datetime].[Date],
max(sale[datetime]), -63, DAY))>27, "Frequent: >3/week",
if(calculate(DISTINCTCOUNT('public sales_receipt'[id]), DATESINPERIOD(sale[datetime].[Date], max(sale[datetime]), -63, DAY))>9, "ActiveClient: >1x/week",
if(calculate(DISTINCTCOUNT('public sales_receipt'[id]), DATESINPERIOD(sale[datetime].[Date], max(sale[datetime]), -63, DAY))>2, "SporadicClient: >1x/maand","")))
Thank you!
Corneel
Hi,
I cannot visualise the sructure of your report. Would you select a particular date in slicer and then see if for 10 weeks ended on that day, the buyer bought >3 times per week (average), > 1 time per week (average)?
Please clarify.
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