Title logic for 200,000+ pages

I created an H1 (title tag) logic for program listings pages based on available filters to help clarify which page the user is on, and at the same time, boost SEO visibility by ranking for a larger set of keywords.

Skills and tools

Content strategy, Excel, ChatGPT

Process

Came up with a simple matrix based on key search filters
Expand the number of filters the titles would apply to

Goals

🖥️ Improve UX

📊 Boost SEO

📈 Grow traffic

Results

More consistent title logic
Increase in search visibility
Better user clarity and experience

Context

My role

As the Content Team Manager at KEG, I owned the content strategy for educations.com, and worked together with the SEO team to optimize non-editorial pages like program listings and individial university and program landing pages.

The problem

The company had just completed a massive site merger, where 8 sites (which will be referred to as ”KAS” going forward) were merged into one, educations.com. As a part of the migration, educations.com also changed its CMS. Due to the size of the project, developers had to narrow down the scope to reduce the initial cost of the site migration and shorten the delivery timeline. One of the elements that weren’t in the initial scope were program listing H1 improvements.

There were 2 main issues with the existing version of the program listing H1s:

The H1s were inconsistent because they were manually written by many different authors. Titles were different in structure, content, length and overall quality.
If multiple filters were selected, H1s didn’t reflect that. The titles didn't update when a new filter was add3ed: the H1 would only display whatever filter the user selected first. This not only created user confusion, but also reduced visibility in search because the keywords used in the titles were limited by what could be written manually.

The process

My job was to map out a logic which could be applied to all program listing pages.

1. Coming up with a workable solution

Right from the get-go, I realized that the key thing I needed to account for the fact that users may combine one or multiple filters. So, the logic needed to work for all filter combinations.

For example:

I came up with 2 "rules" to account for these cases:

Below are a few examples of how it works in practice.

The mapping process itself was relatively straightforward: it was just a matrix I made in Excel (I chose Excel because it was easier to lay everything out in a spreadsheet).

2. Expanding the scope of the filters

The very first version just consisted of the location and majors, so it was a simple 2x2 matrix…

…which then grew into a 4x4 matrix:

Then the scope grew from there to account for all key filters.

We defined the "key filters" as those that bring in traffic (I collaborated with our SEO specialist to list them all out based on Google Search Console results).

These included:

Major (Business, Accounting, etc.)
Degree type/level (Bachelor's, Master's, Courses...)
Locations (continents, countries, cities)
Online learning (binary yes/no)
Part-time (binary yes/no)

So the logic needed to cover any combination of all those filters.

The mapping process itself was relatively straightforward: it was just a matrix I made in Excel (I chose Excel because it was easier to lay everything out in a spreadsheet).

The result

The final logic came in 2 parts: ‘basic’ filters and ‘expanded’ filters.

Basic filters (degree level, major, location)

Expanded filters (online, part-time)

Real-life examples

Of course, while this version was ‘final’, we’re continuously updating and improving the structure.

We’re now reusing this similar logic in other projects, like SERP (search engine result pages) text titles and scholarship directory H1s:

Google Sites

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