The goal of this Project is to build a model that predicts conversion rate and based on the model, come up with ideas to improve revenue.
We have data about users who hit our site: whether they converted or not as well as some of their characteristics such as their country, the marketing channel, their age, whether they are repeat users and the number of pages visited during that session (as a proxy for site activity/time spent on site).
Goal of this project is to:
1) Predict conversion rate.
2) Come up with recommendations for the product team and the marketing team to improve conversion rate.
The table is "conversion_data". It has information about signed-in users during one session. Each row is a user session.
Columns:
country : user country based on the IP address
age : user age. Self-reported at sign-in step
new_user : whether the user created the account during this session or had already an account and simply came back to the site
source : marketing channel source
Ads: came to the site by clicking on an advertisement
Seo: came to the site by clicking on search results
Direct: came to the site by directly typing the URL on the browser
total_pages_visited: number of total pages visited during the session. This is a proxy for time spent on site and engagement during the session.
converted: this is our label. 1 means they converted within the session, 0 means they left without buying anything. The company goal is to increase conversion rate: # conversions/ total sessions.