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for eg:
Customer | date | product | |
---|---|---|---|
A | 12/10/2016 | ProductX | |
A | 04/10/2017 | ProductX | |
B | 11/07/2016 | ProductM | |
B | 10/06/2017 | ProductM | |
C | 05/10/2017 | ProductH | |
A | 01/01/2018 | ProductY |
I would like to come to a conclusion that Customer A bought different products. ( or just a count of how many people went on to purchase different products) Further on, I would also like to know how many went on to buy product Y from product X.
In SQL, it would be something like this:
Partition by Customer, Order by date,
select customer with count(product)>1
My objective is to analyse customer journey and I would like to hear any suggestions or recommendations on the best way to do it.
I used a table chart and added relevant fields and sorted by customer and date. I was wondering if a calculated field in the table would help answer my question. Or if I'll need to explore scripting to solve this. (using for each function)...
for this of data:
Customer | date | product | |
---|---|---|---|
A | 12/10/2016 | ProductX | |
A | 04/10/2017 | ProductX | |
B | 11/07/2016 | ProductM | |
B | 10/06/2017 | ProductM | |
C | 05/10/2017 | ProductH | |
A | 01/01/2018 | ProductY |
could u please show us what's the resulting table you're expecting to create?