Suppose I have a dataset as follows:
Code: 
Product      Week    Type             Num times ad shown      Purchases
1         2/2/13    young,old,hipster        100                    5
and I want understand what are characteristics of people that drive people to purchase a particular product(2 types). I have one column 'type' that shows the types of people who bought the product in that week. What would be a good modeling approach for this problem? Here is my approach. Transform the data set so that each level of the type variable gets its own row.
Code: 
 Product      Week       Type      Num times ad shown     Purchases
    1         2/2/13    young       100                    5
    1         2/2/13    old         100                    5
    1         2/2/13   hipster      100                    5
The purchases are rare so most weeks the number of purchases is 0, and the range of values for the purchase variable is 0-200. I could either model the raw number of purchases, the proportion of purchases to the number of ads shown, or transform purchases into a binary 0/1 variable and model the probability of a purchase. Respectively, I could either run a Poisson regression for counts, a CART model for the proportions, or a logistic regression for the binary outcome. I was just wondering if all three of these approaches are viable. Also is making the levels for the 'type' variable into its own row a viable option to capture what type of users buy the product and is type of transformation only good for logistic regression?