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Salida esperada - first_shoppers = 3083
Salida esperada - avg_spend_first_d = 134.53
Agregar datos de ejemplo:
| report_date | canal | membership_num | demand_revenue |
| 3/1/2023 | OPIC | 6923297 | 473.89 |
| 12/1/2022 | OPIC | 6923297 | 95.57 |
| 3/1/2023 | OPIC | 4732483 | 220.59 |
| 3/1/2023 | OPIC | 7968799 | 54.09 |
| 3/1/2023 | OPIC | 7707891 | 99.94 |
| 3/1/2023 | OPIC | 2953797 | 67.22 |
| 3/1/2023 | OPIC | 2119246 | 173.53 |
| 3/1/2023 | OPIC | 2159933 | 159.96 |
| 3/1/2023 | OPIC | 2276172 | 260.8 |
| 3/1/2023 | OPIC | 5549575 | 99.13 |
| 3/1/2023 | OPIC | 2109264 | 76.79 |
| 3/1/2023 | OPIC | 9386426 | 126.91 |
| 3/1/2023 | OPIC | 6798980 | 82.36 |
| 3/1/2023 | OPIC | 4165529 | 151.66 |
| 3/1/2023 | OPIC | 9276831 | 298.21 |
| 3/1/2023 | OPIC | 4771072 | 106.87 |
| 11/1/2022 | OPIC | 4771072 | 304.89 |
| 3/1/2023 | OPIC | 3956433 | 153.84 |
| 3/1/2023 | OPIC | 1650323 | 100.91 |
| 3/1/2023 | OPIC | 9976085 | 76.52 |
| 3/1/2023 | OPIC | 1061082 | 115.94 |
Datos completos
Tenemos que convertir esta consulta ORACLE SQL (abajo) de un sistema heredado a dax. Cada report_date es dinámico en el sistema heredado.
Esta consulta obtiene compradores por primera vez que solo realizaron un pedido en un rango especificado y que no han realizado ningún pedido 6 meses antes a partir del día de inicio, de ahí la parte MENOS 180.
¿Cómo puedo volver a escribir la consulta en DAX que incluye la sección MENOS de -180 que excluye a los compradores que realizaron un pedido en Power BI?
Aquí está la consulta:
@PowerBIET Debería ser algo en este sentido. Consulte PBIX adjunto debajo de la firma. Difícil de probar porque solo tiene un mes de datos.
First Shoppers =
VAR __DaysAgo = 180
VAR __MinRange = MIN('Table'[report_date])
VAR __TableRange1 = SUMMARIZE('Table',[membership_num],"__Count",COUNTROWS('Table'))
VAR __TableRange2 = FILTER(__TableRange1, [__Count] = 1)
VAR __PastRange = FILTER(ALL('Table'),[report_date] < __MinRange && [report_date] >= __MinRange - 180)
VAR __FirstShoppers = DISTINCT(SELECTCOLUMNS(__TableRange2,"__MemberNum",[membership_num]))
VAR __PastShoppers = DISTINCT(SELECTCOLUMNS(__PastRange,"__MemeberNum",[membership_num]))
VAR __MembersOfInterest =
EXCEPT(
__FirstShoppers,
__PastShoppers
)
VAR __Result = COUNTROWS(__MembersOfInterest)
RETURN
__Result
Average Spend =
VAR __DaysAgo = 180
VAR __MinRange = MIN('Table'[report_date])
VAR __TableRange1 = SUMMARIZE('Table',[membership_num],"__Count",COUNTROWS('Table'))
VAR __TableRange2 = FILTER(__TableRange1, [__Count] = 1)
VAR __PastRange = FILTER(ALL('Table'),[report_date] < __MinRange && [report_date] >= __MinRange - 180)
VAR __FirstShoppers = DISTINCT(SELECTCOLUMNS(__TableRange2,"__MemberNum",[membership_num]))
VAR __PastShoppers = DISTINCT(SELECTCOLUMNS(__PastRange,"__MemeberNum",[membership_num]))
VAR __MembersOfInterest =
EXCEPT(
__FirstShoppers,
__PastShoppers
)
VAR __Result = AVERAGEX(FILTER('Table', [membership_num] IN __MembersOfInterest),[demand_revenue])
RETURN
__Result
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