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Email Drip Campaign Strategy: Insights from Data Analysis

Definitions of Key Metrics: Before we dive in, let’s get the jargon sorted!

Parameter	Definition
Tour_Booking	Indicated interest in a specific location by selecting the Tour Booking option.
Tour_Booked	Completed the steps to book a Tour at a location.
Sessions	Unique visit by a lead.
Pageviews	Unit of engagement depth by our leads.
Email Key Group	A grouping of email keys serving a specific purpose (e.g., all email keys related to endorsements are in the endorsements email key group).
Groups	Discussions happening on social media groups linked to the specific member.
Startup	Forums or talks designed for startups and entrepreneurs.
nus_digest	Subscribed to news about the company and newsletter digests
locations	Locations page visited

**Description:**This project focuses on leveraging data insights to create an effective email drip campaign strategy for an industry that discovers and designs flexible spaces and solutions to move businesses forward.
The challenge? Sift through 500+ campaigns and their email key groups to identify the top performers that could drive more referrals, sessions, and pageviews.

Out of 500+ campaigns with diverse email key groups, identifying the most effective groups to focus on for the next email drip campaign is a significant challenge.
Balancing multiple metrics like pageviews, sessions, and conversions to arrive at a data-driven decision required meticulous analysis and modeling.

Metrics Considered:
- Session-to-Send Ratio: Sessions generated per email sent.
- Pageview-to-Send Ratio: Engagement depth per email sent.
- Incremental Pageviews Calculation: Projected future impact based on historical data.
Formulas Used:
- Session-to-Send Ratio: Sessions / Emails Sent
- Pageview-to-Send Ratio: Pageviews / Emails Sent
- Projected Incremental Pageviews: (Average Pageviews per Session) * (Incremental Sessions)
Decision-Making Approach:
- Email key groups were ranked based on these metrics to determine their effectiveness.
- Top performers like Tour_Booked and Tour_Booking were identified due to their high engagement and conversion potential.

Steps Taken:

Data Cleaning:
- Imputed zero for missing data instead of mean, median, or average.
- Why zero? To ensure that null values don’t skew results, as averages or medians could falsely inflate the numbers for categories with insufficient data.