What is the best model for this data and question?

I have a dataset consisting of customer data of an e-commerce website and deals that have been shown to them for each item, the price of each item, the items they clicked on and the items they bought:(This data is from one session)

customer_id  item_id  deal  items_clicked  items_bought  price($)
     1          2     50%Off     1              0          2
     1          4     No-deal    0              0          5
     1          7     free-ship  0              0          1
     1          3     20%Off     1              1          7
     2          5     30%Off     1              1          6
     2          7     No-deal    1              0          2
     2          2     free-ship  0              0          4
     2          1     free-ship  1              1          1

I want to know are and which deals are having a positive effect on customers, and find out what are the deals that customers who buy items prefer.

My initial thought is to run a logistic regression with target variable items_bought and dependent variable as.facotr(deal).

In this case, I don't think customer aggregation is needed, and I can use data as is. Is this the right path? Then does this also give me the preferred deals by buying customers?