Implementation of the Program Finder
UX Techniques: Analytics, Competitive/Comparative Analysis, Sketching, Prototyping, Mockups, Optimizely for Variant Testing
Three Questions a Prospective Students Asks a When Going Back to School:
- Do you have my program?
- How much does it cost?
- Where is the program held?
In this project we are focusing on the first question, but see my other projects for ‘How much does it cost’ and ‘Where is the program held’. There are 100+ programs that take place either online or on campus within a variety of fields of study.
In the old flow there was no filtering system. A user would have to find their program by navigating through school pages, then department, then program, which is not a good experience. Users do not know the intricacies of programs and the departments they fall under, let alone the school. It might make sense to faculty, which is who recommended this journey, but a prospective student lacks this background knowledge thus making the experience confusing.
We came up with two solutions to this problem: a program finder and a search bar on the homepage. Here we will focus on the program finder but check out the [search bar] project for a deeper dive. To fix this problem using the program finder, we implemented three main filters: Area of Study, Degree Level, and Format (online/on campus). All of the programs have to fit within these filters. For area of study there are eight categories which users better understand than what school a program lives under. The flow now is quite innate and feels very natural, not forcing the user to look hard for what they want to accomplish.
Card Sorting and Information Architecture
Our team conducted a card sorting activity in effort to develop the Information Architecture for the programs of the National University website. This method involved crawling the old site, then putting each program into a certain area of study.
It was immensely helpful to get all stakeholders in a room to take part in this method. This gave us a valuable opportunity to probe and to give rationales behind the decision-making process. This helped us to uncover key quantitative insights that couldn’t be deduced otherwise.
Tasks and User Flows
Now it was time to come up with user flows. We wanted to test our flows by creating different tasks for users to accomplish and then see how easy or difficult it was for them to complete based on our proposed IA. There were several tests that we asked users to complete. One of the most important tasks we set was:
You are a prospective student who is looking to go back to school, and want to see if NU has an MBA program. Go to the NU homepage and find the MBA program page.
The belief was that if prospective students could find their program faster, with less friction, via the program finder, then they could find the content they were looking for faster. In turn, this would generate more leads and thus, they would be more likely to enroll in the university resulting in higher revenue.
Wireframe of the Program Finder on desktop and a mobile device
Mockup of the Program Finder on a mobile device
Mockup of the Program Finder on a desktop device
After 118 days of splitting the traffic the program finder is outperforming the old navigation via ‘College of’ by 64.3% with a conversion rate of 4.7% which is up 67.8%.
Overall improvement to the program finder vs the control/original