I have a customer purchasing dataset and the data set is from a retailer having an online store and offline stores. So, customers have two options in their shopping channel, online or offline. In an online shopping, there is a shipping fee however if a basket size is larger than $50 there is no shipping fee.
I found pieces of evidence that customers are trying to add some of items to make their basket size larger than $50 when their baskets are near and a little bit below the $50, because their shipping fee can be waived by doing that.
- In this situation, I am trying to identify and characterize items that were purchased only because of the shipping threshold by using a machine learning algorithm.
If there is no shipping threshold, $50, the customers would not purchase the items, but they purchased some items to make their basket size larger than $50. I have not observed those kinds of items (added items because of the shipping threshold).
- Is there any machine learning algorithm that I can identify those kinds of items?
I think I need to use some of unsupervised machine learning algorithm.
Another challenging part is that each customer has different characteristics so I probably need to consider it as well. How can I detect those kinds of items??