I'm trying to build a model for eCommerce that will predict revenue of a click that comes via online-marketing channels (e.g. google shopping). Click goes directly to product detail page (so it's not click on keyword that would go to search pages).
Historical data consts of product details like: price, delivery time, category, manufacturer. Every historical click has attached revenue to it. The problem is that revenue equals zero for more that 99% clicks. My question is abut data representation before I try to apply model. I believe I have two choices:
- Apply regression directly to click data and hope that regression would do the right thing. In this case regression error would be pretty big on the end so it would be hard to tell how good the model actually is.
- Try to group data points(clicks) before applying model - group all data points that have the same features and calculate SUM(revenue)/COUNT(clicks) as revenue per click. With this approach I still have a lot of zeroes in revenue (products that got only few clicks) and sometimes there will be thousands of clicks that give you only one data point - which doesn't seem right.
Historical data would look like this:
click_id | manufacturer | category | delivery_time | price | revenue
1 |man1 | cat1 | 24 | 100 | 0
2 |man1 | cat1 | 24 | 100 | 0
3 |man1 | cat1 | 24 | 100 | 0
4 |man1 | cat1 | 24 | 100 | 120
5 |man2 | cat2 | 48 | 200 | 0
Any advice how to proceed with this problem is very welcomed.