Background

Year Up United, a nonprofit dedicated to empowering underserved young adults through professional training and internships, aims to scale operations tenfold. Achieving this requires overhauling our internship "Matching" process to enhance efficiency and consistency.

Core Problems:

1. Operational Inefficiency: The time-intensive Matching process, coupled with inconsistent practices across markets, hampers scalability. Staff often resorted to offline methods like Excel, bypassing Salesforce.​

2. Ineffective Matching Tool: Our primary tool was criticized for being confusing and counterproductive. Its complexity, combined with Salesforce's learning curve, posed challenges for staff.

As part of its push for operational efficiency, Year Up United sought to enhance the Matching Tool with AI and machine learning.

To achieve this, we partnered with specialists from AdeptID, who acted as consultants and subject matter experts (SMEs) in AI, assisting in building an API for seamless integration into our product.

Project Structure

Our executives communicated to us the urgency for producing an updated Matching Tool to roll-out across the organization that would address current pain points of the platform and any business process recommendations for the full Matching timeline.

As such, my objective for this was to create a research study that delivered solutions for both UI design and user workflow improvements.

Matching Tool Research & Design Improvement

To address staff needs, I aimed to enhance the Matching tool's design by identifying and resolving key usability issues.​

Timeline

Research

I conducted the follow user research activities to achieve the goals set out for me in this project. Now, let’s break this down as to how this was all executed.

Identifying pain points with the Matching experience

My first priority was to understand:

• How staff use and respond to the Matching Tool

• Key challenges users face with the tool

• The end-to-end Matching process and its goals

• Overall user feedback and perception of Matching

We lacked quantitative data and KPIs on how Program staff experienced the Matching process, their perceptions of the tool, and overall efficiency.

To establish a baseline, I designed and launched two surveys—one before implementing the updated prototype and one after—to measure changes in usability and satisfaction.

Key questions included:

• Time spent in Matching meetings (in minutes).

• Total number of Matching meetings per week.

• What went well/what didn’t in Matching?

• Perceived value of the Matching Tool (1-5 scale).

• Satisfaction with the Matching Tool (1-5 scale).

Staff completed these surveys weekly over a 4-week Matching cycle, both before and after the prototype launch.

Survey responses revealed key sentiment scores and metrics:

Users also provided qualitative insights on Matching pain points through survey responses. While not highly detailed, these responses helped identify key areas for deeper investigation.

To substantiate my findings, I conducted user interviews and observation studies to better understand user behaviors, concerns, and the information they prepare and present during Matching meetings.

Over 9.5 hours of fly-on-the-wall observation sessions of Matching calls provided key insights into staff workflows. By having staff share their screens, I observed how they navigated the tool, categorized findings into Tasks, Process, and Pain Points, and documented key questions.

From these sessions, I discovered that users faced:

• Difficulty locating critical information within the Matching Tool.

• Disjointed collaboration between client-facing and student-facing staff.

• Lengthy discussions focused on evaluating a student’s “fit” before making a Match.

Preliminary insights from surveys and observation studies provided a clearer understanding of the Matching process. To deepen qualitative research, I conducted interviews with five Program staff members, focusing on key questions such as:

• How do you prepare for Matching?

• What are the main milestones in the process?

• What data is most useful for Matching?

• What challenges do you face?

• How do you currently use the Matching Tool?

• What improvements would make it more effective?

I then performed an affinity mapping exercise to uncover major themes and pain points. Several important insights emerged:

Process Issues

• Decentralized data access creates inconsistencies across departments

• Limited internship availability hinders Matching, with more Students than open positions.

• No standardized collaboration framework beyond Matching sessions, leading to siloed teamwork

Defining the workflow challenges of Matching

Before designing a new Matching Tool concept, I consulted with Business Services Manager Walter to address process-oriented challenges. To support our collaboration, I created a comprehensive end-to-end process flow that visualized the Matching timeline and key tasks for Program Managers.

With Walter’s help, we identified key pain points in the Matching process, using my research as a reference. Since Walter worked closely with Program Managers, I leveraged his insights to verify challenges within their workflow.

I then created a user journey map to visualize the Matching experience, emphasizing these critical issues:

• Information gaps disrupt the Matching process.

• Mid-cycle challenges arise due to poor collaboration between Program Managers and client-facing staff.

• Match quality suffers as teams spend excessive time fact-checking outside of Salesforce, rather than using it as the primary source of truth.

• The "seat gap" phenomenon creates pressure due to the limited pool of available internships.

I presented this journey map to the broader Agile team to highlight workflow inefficiencies and drive alignment on necessary improvements.

I recommended sharing these findings with the Program department to lay the groundwork for solutioning. Tara and Walter agreed to lead this effort while I shifted focus to designing the Tool.

Process change recommendations were tabled until after the prototype was developed and vetted.

Designing a new Matching Tool solution

Through a quick and iterative design process, I moved from producing wireframes, prototyping, and then usability testing with our target audience.

Let’s first explore how the Matching Tool operated.

1️⃣ Staff begin the Matching process by opening the Matching Interface in Salesforce, where they scan and verify available Internships. However, the current design presents challenges—small text sizes and truncation obscure key details, while excessive text creates information overload, making it easy to lose track of entries or misidentify Internships.

3️⃣ Despite being the most critical function, Matching is not immediately accessible. After selecting a qualified Student, users must navigate to the Matching tab, which opens a separate page. To fact-check or review details, they must switch back to previously opened tabs, adding unnecessary friction to the process.

The core user journey in the original Matching Tool involves three essential actions: finding internships, reviewing candidates, and making a Match. However, the existing system creates friction at every step.

1️⃣ Internship Discovery: Users begin by scanning available internships in the Matching Interface, but poor text hierarchy and visual clutter make it difficult to quickly identify relevant opportunities.

2️⃣ Candidate Review: To evaluate Students, users must open a new tab via the Work Site link and review each profile individually—requiring multiple clicks and constant tab switching to maintain context.

3️⃣ Making a Match: The actual Matching function is hidden behind an additional tab, forcing users to jump between views to cross-reference information and complete the process.

Ultimately, users struggle to perform what should be a streamlined workflow. Instead, they face a fragmented experience that requires excessive navigation, making even basic tasks feel time-consuming and inefficient.

My initial approach was to design a ‘one-stop shop’ layout, enabling users to access all necessary information and actions on a single page. I prioritized user feedback on enhancing visual cues and structuring content in a more digestible format.

Collaborating with my developer counterpart, Ajay, I ensured the new Matching tool was custom-built within Salesforce to meet our unique requirements.

I designed a three-panel interface integrating Internship, Student, and Matching functionalities into a single, comprehensive view to streamline user workflows. After confirming its technical feasibility with Ajay, I demoed the design to the broader product team, where it received an enthusiastic greenlight from our Product Manager, Tara.

The new design organizes Matching data into three distinct panels: Internship, Student, and Matching. Users can quickly scan high-level details through profile cards, while the Matching panel provides deeper insights. This structure allows for efficient data review while keeping the Matching action easily accessible

Users needed a fast way to assess Student-Internship fit. Program Managers already submit Internship Surveys with six quantitative ratings on student performance, but these were used only for record-keeping, requiring manual access.

I proposed repurposing these existing ratings by integrating AdeptID’s AI algorithms to generate visual data points within Salesforce, making them instantly accessible and actionable during Matching.

1️⃣ Profile Cards for Quick Scanning

• Both Seat and Student profiles are displayed as cards, offering a high-level overview

• Student photos humanize the process, making Matching more intuitive

2️⃣ Sorting by Performance Metrics

• 100% of users wanted a way to rank Students by the six performance categories from highest to lowest

• A sorting function allows for quick comparisons

3️⃣ Enhanced Search & Filtering

• Users can search and filter Student entries to refine results beyond sorting

4️⃣ One-Click Matching from the Main View

• Previously hidden in a separate tab, Matching can now be done directly within the main page while viewing all relevant data

5️⃣ Structured Data Breakdown

• Matching involves complex datasets across multiple documents

• Users can now easily access job descriptions, student details, and Internship Survey ratings in a structured format

6️⃣ Fit Scores for Data-Driven Matching

• Performance ratings from the Internship Survey are visualized into three categories for quick decision-making:
  🟢 Green – High Fit
  🟡 Yellow – Medium Fit
  🔴 Red – Low Fit

Constructing a working prototype on Salesforce Lightning App Builder

I worked closely with Ajay to recreate the Figma UI in Salesforce using Lightning App Builder. Through three working sessions, we refined element interactions and primary user flows, ensuring alignment with user needs. For additional insights, I collaborated with Walter, leveraging his expertise on the information Program Managers frequently use. Our efforts resulted in a working Salesforce prototype, which I planned to use for usability testing.

Our primary testing focus was Matching a Student to a Seat. In the sample task flow below, a user aims to fill an open Seat with a qualified Student—such as an available role at Apple.

In four simple steps, the user is able to complete the all-important task of Matching without having to navigate needlessly in and out of multiple pages. With our prototype live, I moved on to the next stage and prepared my usability research.

Testing the usability of the Predictive Matching prototype

I scheduled and conducted these tests virtually with our 5 participants, needing only their laptop or computer, the Zoom application, and the Salesforce prototype. On my end, I used the Userlytics platform to record, annotate, and proctor these sessions.

Synthesizing the data from the usability tests

The results of the usability test generally showed high favorability among our users.

Users indicated a high level of approval of the new Predictive Matching Tool in perceived ease of use, satisfaction, value, and confidence. I observed from these sessions that though there were cosmetic and minor critiques to improve the UI further, there were no critical usability issues that were severe enough to merit a significant redesign.

The last question that participants were asked during testing was:

What are your overall thoughts on this new interface? What can be improved?

To refine usability testing insights, I conducted an affinity mapping exercise. Feedback primarily focused on Fit Scores, the information display for Students and Seats, and the Matching Panel. Key findings included:

Incorporating user feedback into a finalized prototype

While the prototype was well-received by users, there were still areas of improvement to tackle. I incorporated the following design changes to address user concerns:

1️⃣ Enhanced Seat Profile Display – Replaced company logos with full internal ID numbers and contract length for clearer identification.

2️⃣ Student Progress at a Glance – Added a tagline under Student profiles to indicate academic progress.

3️⃣ Flexible Fit Score Sorting – Enabled users to sort by multiple Fit Score categories instead of just one.

4️⃣ Match Status Indicators & Icon Legend – Introduced visual cues to help users easily distinguish Matched Students and Seats.

5️⃣ "Possible Match" Option – Allows Program Managers to shortlist candidates for further review before finalizing a Match.

⚠️ Prototype Access Note:
Due to company restrictions and the proprietary nature of the Salesforce platform, I’m unable to share a live prototype for this project. Additionally, the tool handles sensitive information such as student demographics and personal data, which is protected under internal privacy protocols. This case study instead provides a detailed walkthrough of my UX process, design decisions, and key outcomes.

Shipping to production & revisiting workflow improvements

From a UX and design standpoint, the new Matching Tool was a resounding success. Staff confidence and enthusiasm reached an all-time high following the design updates.

I presented my full research findings to the Agile team, executive leadership, and AdeptID—highlighting the strong, positive results we had gathered.

We secured executive approval to move the new Matching Interface into production. I compiled my work into a comprehensive handoff document for Ajay and his development team to begin refinement and implementation.

In collaboration with Tara and Walter, we designed a future-state workflow to help Program Managers improve the Matching process.

Using the process flow and journey map I created, we identified key points to introduce standardized working sessions between Matching teams. The updated workflow also included recommendations on what information to prepare and the expected outcomes of each session.

The process was iterative, going through several rounds of review and refinement. The final version, approved by the Agile team, is now being prepared for implementation within the Program department.

Problem:

🚧 Limited Internship Availability – With fewer Internships, staff can’t Match all qualified Students, leading to suboptimal outcomes for both the business and Students.

Recommendation:

✅ Expand Outreach Through Headhunting – My research found that staff rely heavily on existing relationships to secure Internships. I recommended headhunting training to help staff proactively source new opportunities and grow the Internship pool.

Reflections

I emphasized to the internal team that optimizing the Matching Tool alone wasn’t enough—we needed process improvements to maximize its full potential.

To drive this forward, I reconnected with Tara and Walter to review key insights from their discussions with the Program department.

Reimagining the Matching business process

From my research, I identified three core challenges in the Matching business workflow:

• Decentralized data access – Teams had inconsistent access levels, creating information silos across departments.

• Limited internship availability – A surplus of Students compared to available Internships led to Matching bottlenecks.

• Lack of cross-team collaboration – Without a standardized framework, coordination outside of Matching sessions was fragmented and inefficient.

The updated Matching Tool resolved information centralization, providing a single repository with universal access for Matching staff. However, challenges remained—limited internship availability and the lack of a standardized cross-departmental collaboration framework still needed to be addressed.

I also recommended the following to address the disruption caused by the “seat gap” phenomenon and to maximize the Tool’s effectiveness among staff:

Compared to before:

Process-Oriented Next Steps

Research Client-Facing Staff Workflows

• Investigate their pain points and friction points with Program staff

• Identify opportunities to improve cross-team collaboration and workflow alignment

Secure Executive Buy-In for Standardized Processes

• Present research-backed insights on workflow inefficiencies affecting Matching

• Propose structured collaboration frameworks and pilot small-scale process changes

Refine Training & Change Management

• Develop training programs to onboard staff to workflow updates and Matching improvements

• Establish feedback loops to measure adoption and adjust as needed

Project Recap

The Predictive Matching project was a complex, high-visibility, year-long initiative. I led the end-to-end research and design process, delivering critical UI improvements to Year Up United’s primary CRM and proposing workflow enhancements to address Matching inefficiencies.

Let’s revisit the core challenges this project aimed to solve:

Design Problems

• Excessive Text & Limited Visual Cues – Information overload makes data interpretation difficult.

• Inefficient Navigation – Users must switch between multiple pages to review Student qualifications and complete a Match.

• Lack of Filtering & Categorization – No tools exist for organizing information efficiently.

• Read-Only Internship Surveys – Users want to leverage survey data in Salesforce to identify qualified candidates more effectively.

Matching Tool Design Changes

• Streamlined Information Architecture – Consolidated data into a single interface, reducing the need for multiple tabs

• Enhanced Filtering & Sorting – Minimized information overload with new organizational tools

• AI-Powered Fit Scores – Transformed Internship Survey data into visual insights for better decision-making

• Clear Content Categorization – Structured Student, Internship, and Matching info for easy navigation

• Accessible Matching Action – Made Matching functionality more intuitive and readily available

• Stronger Visual Cues – Improved UI clarity with enhanced visual hierarchy

Business Workflow Problems

• Decentralized Data Access – Staff have varying access levels, leading to information silos across departments.

• Limited Internship Availability – A shortage of internships prevents all qualified Students from being Matched.

• Lack of Standardized Collaboration – No structured framework exists for cross-team coordination outside of Matching sessions.

Matching Workflow Improvements

Restructuring a core business function requires time and resources. To support executive-led changes to the Matching process, I recommended:

• Standardized Workflow Implementation – Establishing regular touchpoints to improve cross-team collaboration and reduce friction.

• Expanding Internship Sourcing – Training staff in proactive lead generation instead of relying solely on existing client relationships.

• Mandatory Training Programs – Equipping staff with workflow and Matching Tool training to streamline change management.

Metrics & results after launching the new Matching Interface

After handing off materials to Ajay and his development team, we finalized the Matching prototype and launched it live in Salesforce for a multi-site pilot.

To evaluate its impact, we recruited 21 new Program Managers from New York, Texas, Massachusetts, and Arizona. Following the same guidelines as our baseline study, they used the tool in their Matching work and submitted weekly responses over a 4-week period.

The results revealed the following insights:

Evidenced from these studies, my updated design of the Matching Tool led to:

• Increased user satisfaction from 55% to 86%

• Increased perceived value of the platform from 40% to 91%

• Reduced the total amount of staff time spent in a Matching cycle by 16.3%

• Reduced the total amount of staff time spent in Matching meetings by 6.3%

• Increased the number of young adults we matched to an internship by 35.3%

• Reduced time spent on task by 47%

Next steps: enhancing Matching’s long-term transformation & success

While the updated Matching Interface has significantly improved efficiency and usability, the next challenge is implementing process improvements—a long-term effort requiring resources, strategic planning, and executive buy-in.

At the same time, working alongside AdeptID’s AI experts inspired me to think beyond one-to-one Matching. I began envisioning a future where the Matching Tool could automate large-scale batch Matching, efficiently pairing multiple students with available seats in a single, streamlined process.

With these challenges and opportunities in mind, my next steps focus on refining business workflows and exploring AI-powered solutions to further scale and optimize the Matching process.

Leading the Predictive Matching initiative at Year Up United required navigating complex stakeholder needs, process inefficiencies, and high delivery pressure while ensuring UX best practices remained central to our approach.

One of the biggest challenges was balancing speed with research integrity. Early in the project, there was pressure to push wireframes into production before conducting usability testing. Understanding the urgency, I engaged cross-functional stakeholders—product managers, business services, and leadership—by fostering high visibility and communication through demos and working sessions. This approach reinforced the importance of research-driven design and ultimately led to a more impactful, well-informed solution.

Throughout this project, trust and collaboration were key. By building strong relationships and advocating for an iterative UX process, I aligned the team around thoughtful decision-making, improving both workflow efficiency and end-user outcomes. This experience reinforced in me that great UX leadership isn’t just about designing better products—it’s about creating alignment, influencing strategy, and driving user-centered thinking across teams.