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Add observer to remove returned/creditmemo products from orders
Clerk is a great relational data linker... but when the incoming data is incorrect and returned items are not removed from the order data .... the results can be
many in-statistical recommendations later on
You could argue that Clerk could "learn" what does NOT work well together
Currently returns and changed items are not removed from the sales product sync to Clerk. Our service dept sometimes updates orders adds/removes products before shipping and also customers return products (often 18-22%) ...... so we could have 22% statistical data that should not be there. And more important: the system is neglecting human feedback: people do not want the product.
I hope you see the statistical importance. Recommendations based on for instance 22% insignificant data would not seem very logical. Also imagine the following case: of all 5 occurences of product A and B being ordered in 1 sale, 100% of product B is returned. Maybe we should then not-suggest it.
Maybe an observer can be installed to monitor order changes and credit memos and update Clerk.
The process would then be
order made, sync data
(full) credit memo created, cancel order, create a new 1: sync data
3 or (partial) credit memo where 1 or 2 items in the order are returned/credited, removev items from the order: syn new data
I hope this helps
The text was updated successfully, but these errors were encountered:
Add observer to remove returned/creditmemo products from orders
Clerk is a great relational data linker... but when the incoming data is incorrect and returned items are not removed from the order data .... the results can be
Currently returns and changed items are not removed from the sales product sync to Clerk. Our service dept sometimes updates orders adds/removes products before shipping and also customers return products (often 18-22%) ...... so we could have 22% statistical data that should not be there. And more important: the system is neglecting human feedback: people do not want the product.
I hope you see the statistical importance. Recommendations based on for instance 22% insignificant data would not seem very logical. Also imagine the following case: of all 5 occurences of product A and B being ordered in 1 sale, 100% of product B is returned. Maybe we should then not-suggest it.
Maybe an observer can be installed to monitor order changes and credit memos and update Clerk.
The process would then be
3 or (partial) credit memo where 1 or 2 items in the order are returned/credited, removev items from the order: syn new data
I hope this helps
The text was updated successfully, but these errors were encountered: