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Hello team,
After a quick research and a few attempts, I'm still struggling to find the solution I need.
Maybe it's a simple question for more experienced users, but I'm finding it hard to find the solutions to my problem.
My report has a single dataset (or table) that combines sales transactions from 3 clients.
Each row of my dataset is one item of an order, and the table structure is the same for those three clients.
The fields I have on my dataset are:
- reward_program_code (or client ID)
- account_id (which is the customer ID from my client side)
- member_uid (which is the customer ID from my side)
- account_reference (which is the customer ID that the system creates when combining the account_id + member_uid)
- order_number (unique identifier of each order)
- transaction_date (date of purchase)
- SKU (unique product identifier)
- product_name (product name from my client system)
- product_category (name of product category from m y client system)
- quantity (units sold for that product SKU, on that order)
- total_cost (cost of sales from my client)
- total_sell (total revenue per item on that order)
I'm creating a set of measures that will be used on a dashboard, such as:
- total revenue (sum of total_sell)
- order frequency (distinct count of order_id)
- revenue per order (total_sell divided by the distinct count of order_id)
- total quantity per order (sum of quantity per order_id)
I am struggling with two measures:
1) count the number of unique products sold per order (distinct count of SKU per order_id).
2) measure the number of days between the last 2 orders for each member_uid.
Can anyone help me with that? Would be great if I could add to those measures a filter by client (reward_program_code) as well.
Thanks in advance!
Hi @cperazza ,
Please share some sample data so that we could test the formula.
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