Google Analytics allows me to collect data about every web-session. For simplicity, let's assume for each user, we collect the number of pages and time spent on site for each session:
user_id visit_id page_views time_spent result
1 1 10 100 0
1 2 31 510 0
1 3 1 10 1
How would you model this data? What I would like the ML algorithm to do:
- Extract as much information as possible
- Have a flexible number of inputs (e.g. the number of sessions can go to infinity)
What I can think of:
- Aggregate the data per user e.g. average page_views or total page_views and feed it into a general algorithm e.g. random forrest (but I lose information with aggregation)
- Use LSTM and feed at most last 3 visits (will also lose information, but would this perform better than aggregation?)
Goal: To build a predictive model to analyse all user sessions and make a prediction whether the person will convert or not.