Retail CDT (Category Decision Tree) in R

Question

I am trying to analyze a big data of many retail store transaction, within a particular category. My goal is to build a market structure tree, than will be based on shopper choices - what is the most/least important attribute (attaching a photo for example).

What is the right way to do it in r? I thought about choice modeling using mlogit, but I can't understand how to decide the ranking of the attributes.Pet treat category tree

Please provide enough code so others can better understand or reproduce the problem. — Community, Feb 02 '22 at 09:52
I am actually looking for code, so there is no code I can add /: — shir12688, Feb 03 '22 at 10:57

score 0 · Answer 1 · answered Feb 21 '22 at 11:54

0

If you have Customer or User Data I would recommend using a distance matrix based on odds ratio for product proximity in a category. This way you can see and interpret customer choices.

answered Feb 21 '22 at 11:54

MaDebu

11
4

Your answer could be improved with additional supporting information. Please [edit] to add further details, such as citations or documentation, so that others can confirm that your answer is correct. You can find more information on how to write good answers [in the help center](/help/how-to-answer). – Community Feb 22 '22 at 04:37

Retail CDT (Category Decision Tree) in R

1 Answers1