Optimizing marketing campaigns is one of the most common data science tasks. Among the many possible marketing tools, one of the most efficient is emails.

Emails are great because they are free, scalable, and can be easily personalized. Email optimization involves personalizing the text and/or the subject, who should receive it, when should be sent, etc. Machine Learning excels at this.

Challenge Description

The marketing team of an e-commerce site has launched an email campaign. This site has email addresses from all the users who created an account in the past.

They have chosen a random sample of users and emailed them. The email lets the user know about a new feature implemented on the site. From the marketing team perspective, success is if the user clicks on the link inside of the email. This link takes the user to the company site.

You are in charge of figuring out how the email campaign performed and were asked the following questions:


We have 3 tables downloadable by clicking here.

The 3 tables are:

email_table - info about each email that was sent


email_opened_table - the id of the emails that were opened at least once.


link_clicked_table - the id of the emails whose link inside was clicked at least once.


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