Customer Journey- Email Campaign Optimization

 Email Campaign Optimization

Introduction:

Optimization of the design and delivery of customer journeys to predict each individual's engagement preference. 

The project aims to create a ML model to analyze and predict open rates, optimal send times. and probable churn based on historical engagements

The project will try to: 

• Predict send time to maximize opens
• Predict send time to maximize clicks
• Build a model to optimize in future email campaigns to maximize the probability of link clicked
• Logistic regression to understand feature importance for open emails and links clicked

Data:

1. Email_table - info about each email that was sent

• email_id : the Id of the email that was sent. It is unique by email
• email_text : there are two versions of the email: one has "long text" (i.e. has 4 paragraphs) and one has "short text" (just 2 paragraphs)
• email_version : some emails were "personalized" (i.e. they had the name of the user receiving the email in the incipit, such as "Hi John,"), while some emails were "generic" (the incipit was just "Hi,").
• hour : the user local time when the email was sent.
• weekday : the day when the email was sent.
• user_country : the country where the user receiving the email was based. It comes from the user ip address when the account was created the account.
• user_past_purchases : how many items in the past were bought by the user receiving the email

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

• email_id : the id of the emails that were opened, i.e. the user clicked on the email and, supposedly, read it.

3. Link_clicked_table - the id of the emails whose link inside was clicked at least once. This user was then brought to the site.

• email_id : if the user clicked on the link within the email, then the id of the email shows up on this table.

Project Details: