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Hi Everyone, Im stuck with a case that I have a fact_sale table as below
| order_date | order_id | product_id | customer_id |
| 10/07/2023 | 5346842804539 | 8374540599611 | 7179117920571 |
| 10/07/2023 | 5346853683515 | 8374539583803 | 7179162255675 |
| 17/07/2023 | 5359172845883 | 8374535618875 | 7179014635835 |
| 17/07/2023 | 5359152038203 | 8374536044859 | 7179014635835 |
| 15/08/2023 | 5454430503227 | 8374540599611 | 7179117920571 |
| 15/08/2023 | 5454430503227 | 8374540042555 | 717911792057 |
| 26/03/2024 | 5840225632571 | 8374534930747 | 7179014635835 |
| 26/03/2024 | 5840225632571 | 8951011639611 | 7179014635835 |
| 26/03/2024 | 5840225632571 | 8374535586107 | 7179014635835 |
| 26/03/2024 | 5840225632571 | 8374535717179 | 7179014635835 |
Now I want to have a measure that calculate the unique customer in the selected period and how many of them repurchase the after the selected period
for example if I select July 2023 I will have 4 unique customer, so I want to know if any of these 4 customers get back to us after July 2023. For example the table I need is like this
| Jul-23 | Aug-23 | Sep-23 | Oct-23 | Nov-23 | Dec-23 | Jan-24 |
| 4 | 1 | 2 | 1 | 1 | 0 | 1 |
If I select July-Sept 2023, it will calculate how many customer in these 3 months purcase with us and how many of them get back to to us from Oct-2023
Solved! Go to Solution.
Hi,
PBI file attached.
Hope this helps.
your sample data doesn't match your expected result.
I would go with a graphical solution - depending on how many customers you have.
Please provide sample data that fully covers your issue.
Please show the expected outcome based on the sample data you provided.
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