ELLUCIAN
Higher-ed buyers do not casually shop for student information systems, finance platforms, AI tools, or CRM products. They compare, validate, worry, forward links, build consensus, and try not to make career-limiting decisions.
The new experience needed to make Ellucian easier to understand, easier to navigate, and easier to believe in. The strategic center was clear: what matters to students matters most. Our job was to make the site prove it.
The legacy site came with a significant amount of technical and design debt. Chief among these issues was the way that products and services were structured. An ineffective focus on branding individual offerings, rather than matching the outcomes users were looking for to content made it difficult for the uninitiated to understand what Ellucian could do to meet their needs. This type of thinking was common throughout the category, and gave us the opportunity to leverage a better approach.
The old experience had the classic enterprise problem: Too many doors. Too many labels. Too much internal logic. Too many important pages buried too deep. The site was not short on content. It was short on prioritization. Users needed faster answers to three questions:
What does Ellucian do?
Where do I fit?
Why should I trust this platform?
Everything else had to support those answers.
The taxonomy had to make a large portfolio feel usable. We created a cleaner top-level structure. Then we reorganized product discovery around families users could understand. This gave users multiple ways in without forcing them to know Ellucian’s product universe on arrival. The goal was not to hide complexity. The goal was to make it navigable.
The taxonomy rebuild translated a sprawling product ecosystem into a buyer-friendly structure.
The new IA was built for the way enterprise buyers actually behave. They do not move in a straight line. They compare, validate, read resources, check customer proof, look for security, share links, revisit from another device, and use the nav like a confidence test.
So the site needed multiple valid paths; for direct buyers, for explorers, for skeptics, for researchers, for future-looking teams, and for implementation confidence. The IA did not just help people find pages. It helped them feel oriented.
The optimized IA gave different buyer types different doors into the same platform story.
After testing several lines, we found a positioning statement that resonated both internally and with the target audience. The brand line gave the experience a spine: What matters to students matters most. It worked because it was simple, human, and hard to argue with. But enterprise software cannot live on sentiment. The UX had to connect that emotion to proof. The result felt less like a software catalog and more like a modern campus command center. The page system paired:
Student-centered messaging
Institutional outcomes
Product clarity
Platform credibility
Customer evidence
Conversion paths
Brand, product, and proof were brought into the same system instead of living as separate layers.
Moderated testing helped us understand confidence, language, and credibility. Automated testing helped validate task completion and path clarity. The biggest insight was simple: Users did not need Ellucian to feel smaller. They needed it to feel clearer. So we stopped trying to simplify the company and focused on simplifying the decision. Lo-fi wireframes and Figma Make prototypes were used to create quick menu and page-flow concepts. The goal was to test the decisions that mattered early: labels, grouping, hierarchy, and whether users could find the right thing without coaching.
Core tasks included:
Find Student Information Systems.
Find AI use cases.
Find implementation services.
Find analytics products.
Find customer proof.
Find the best next step from a product page.
Testing shifted the work from “reduce complexity” to “make complexity easier to choose.”
Early testing kept the team from polishing the wrong structure. Initial wires proved the model, but also showed where users needed help sooner. They liked the student-centered story. They just wanted to know faster where they fit inside it. So we added clearer entry points, stronger product-family logic, and better crosslinking. The first round made the answer obvious. The brand idea was strong. The IA needed to be even sharper. Users wanted less cleverness and more orientation. So we tightened the labels, elevated proof, grouped products harder, and made CTAs more intent-based.
We added deeper product templates, campaign pages, AI modules, platform storytelling, customer proof patterns, and resource-driven conversion paths. The content model became cleaner. High-level pages sold the problem. Mid-level pages explained the ecosystem. Product pages gave the specifics. Resources built confidence. CTAs matched intent. That gave the team a flexible system without turning every page into a one-off.
Low and high fidelity wires established the system and exposed where orientation needed to happen earlier. These turned the core flows into a scalable enterprise page system.
1 // Components
2 // Tokens
3 // Modules
4 // Reusable patterns
5 // AI-assisted code handoff
6 // Smoke testing
7 // Accessibility checks
8 // Batch QA
9 // Regression review
A parallel design/dev workflow helped the team move fast without abandoning quality control. AI helped speed up repetitive production and Figma-to-code translation, but judgment stayed human. The goal was speed without letting the system fall apart.
Feedback
This was a big-company project, which means the UX had to survive a lot of gravity. Product teams needed visibility, sales needed better conversion paths, brand needed the new story to land, demand gen needed campaign flexibility, customer teams needed support and resource access. In particular, AI and platform teams needed room for emerging capabilities.
All of which meant services needed to show up without competing with products.
Organizing around the institution, not the company shaped the taxonomy, navigation, page hierarchy, and content model. Stakeholder input was mapped against buyer needs so internal priorities could be represented without becoming the navigation model. Modeled results showed that users were finding relevant product areas, engaging with AI and platform content, converting more through both hard and soft channels, seeking demo content, and bouncing less at the front door.
That’s the difference between a website and a working sales asset.
Modeled lift indemo-start intent
Located product content in two clicks
Homepage bounce rate reduction
Increase in AI content engagement
Improvement in successful resource location
Average annual tier-onecontract value
Estimated year one increase in tier-one/two client engagements
Improvement in soft conversion through backstops
© 2026 Jon Gifford