The project involved setting up 42 automated email campaigns at key database entry points to drive web traffic and increase student engagement across our user base.
Email marketing, Email automation and user flow, copywriting, Looker Studio, user.com (email platform)
🧑⚖️ NDA disclaimer:
To comply with the NDA, I will only show/discuss the automation planning and structure at a high-level without going into the specifics of the content within the automation flows and email messages.
Project planning
Campaigns creation
Monitoring performance and improvement
⚡ Re-engage inactive users
📈 Grow traffic from email
🤝 Better content recommendations
The traffic % from email doubled
I took over the educations.com email marketing in June 2025. The scope of responsibilities included the strategy and brand ownership via the email channel.
In this project, I was in charge of:
Developing the strategy and logic for automated email campaigns
Planning, coordinating, monitoring, and documenting the project
Mentoring and training the team
As the Content Team Manager manager at educations.com, I inherited a problem with our email campaigns. There was no effective way to move users between the website and email, or to re-engage students who haven't interacted with our emails for a while.
A typical user flow looked like this: a user filled out a form, took an online quiz, or applied for a scholarship. That action added them to our database for email campaigns. After that, everyone received the same generic automated email, no matter where they were in the user journey. We had no way to guide them back to the core product: the site itself.
The first step in preparing the project was the identification of all email database "entry points". More specifically, which channel the students came from, what content they have already experienced, and what their goal is at that specific user journey stage.
This mapping can be done in various ways, but I chose Excel because of its simplicity. It also ensures that all employees involved in the project have access to the file, since the company already uses Microsoft Suite.
The process is straightforward: list every channel that adds students to the database and analyze each one. The table below illustrates the mapping inside the file without the actual contents due to NDA reasons.
The analysis showed that there were 3 general categories of 'entry points': quiz (10), form (14) and scholarship application (8).
Documentation played a central role in planning and creating these automations, since the project was completely new. In practice, this meant we had no established campaign model to follow, and each person had their own definition of a “good” campaign because we had no clear, consistent guidelines. Therefore, we needed to establish a clear standard and define what a “good” automated email campaign looks like. As project manager and lead, I led that work.
This process consisted of 3 phases:
Organizing information
Defining of technical requirements
Writing detailed guides on the email marketing process/workflow
To make information easy to access for all team members and colleagues, I created a Microsoft SharePoint site. I chose a site over a PDF so we can update content as needed and store all resources on the company cloud. Below is a screenshot from this site.
The guidelines cover maximum text length, brand identity elements, font sizes and colors for text and links, content structure, platform-specific technical requirements, and the UTM code structure for tracking campaign performance.
The screenshot below shows a snipped of the technical requirements page.
There were a few goals that we wanted to achieve with these automations:
Re-engage students
Diversify traffic sources to include email
Improve students' experience with our scholarships (reminding them to complete the second application form and sending an immediate rejection email to students who don't meet the degree level requirements).
Note: I cannot show the exact tool and automation due to NDA, so I will use sketches that recreate the logic of the algorithm.
Below is the sketch of the algorithm for the 'standard' automation we used for all emails except scholarship application emails.
Below is a simplified algorithm for the 'scholarship application' automation.
The purpose of this automation were:
Remind students who are eligible to complete the second half of the application with an instant follow-up email;
Keep students who have successfully applied in the loop by suggesting more scholarships;
Redirect students who don't meet the eligibility criteria towards scholarships that match their profile (for example, if a Bachelor's student applies for a Master's scholarship, they will receive an email explaining they are ineligible for the scholarship, along with a list of Bachelor's scholarships they can apply for).
Personalized email content plays a key role in keeping users engaged.
The content and approach of each message we created depended on the user’s goal. For example, someone who requested a motivational letter template has different needs than someone who completed a study methods quiz. Campaigns, tone and content were purpose-built to match those needs, interests and user journey stages.
The main questions we asked ourselves in the process of creating campaigns were:
What does the student want to achieve?
What information does the student need to make a decision?
What is their next step?
Do we have relevant content that could help the student?
Does the tone match the situation?
What is the age/maturity level of this student?
What are their concerns or problems? Can we address/remove them?
For the campaign that is sent out when students meet the eligibility criteria (left image below), we did a few A/B tests to see whether students prefered longer or shorter text, and if they engaged with additional resources on writing scholarship cover letters. After several iterations, we discovered that students converted the highest.
The 'rejection' campaign was a little more difficult, because we didn't want to immediately throw a wet blanket on the student's hope of winning a scholarship. So, we framed it more as a positive: you gain other Master's scholarships instead of losing the Bachelor's scholarship you're ineligible for.
With an average open rate of 67% and average click-to-open rate of 40% across 42 campaigns, the performance of this campaign is significantly above industry standard (the average OR for automated emails in the education industry is around 45%, exceptional is 66%. Average CTOR is 5%, and exceptional is 12%)
These automated emails helped double the amount of traffic we got from email in the first 2 months of the project. (The exact numbers and traffic sources in the Looker Studio screenshot were omitted for NDA compliance.)