UX Research & Accessibility

Melissa Maykut

Senior User Experience Researcher · Certified Professional in Accessibility Core Competencies

My focus is qualitative research — especially with assistive technology users, underserved communities, and the credit invisible — that produces insights product and design teams can act on to make products accessible and usable for people of all abilities.

I'm currently a Senior User Experience Researcher at Experian where I run moderated and unmoderated studies, in-depth interviews, and an accessibility testing program. I then work with designers, engineers, and stakeholders to implement my research findings to make digital products more inclusive for consumers of all abilities and backgrounds.

Selected research

Case studies

Explore samples of my work! The below projects span accessibility testing, competitive benchmarking, a longitudinal diary study, and design comparison research. Product names have been generalized to protect confidential details.

Goal

Evaluate the accessibility of an AI chat assistant built into the account experience for screen reader and screen magnification users, and understand how it fit into their broader experience with the app.

Who

6 active account holders and 1 new user, all screen reader and/or screen magnification users, each completing a 45-minute session across desktop, mobile web, and native app.

Key findings

  • Critical: Some users couldn't locate the assistant at all within their account.
  • Critical: Screen readers interpreted the assistant's entry point inconsistently, with no confirmation of what users were interacting with.
  • Serious: After opening the assistant, users' screen reader focus stayed on the previous screen, leaving them disoriented.
  • Serious: The question text field was difficult to activate with a screen reader on native.
  • Minor: Character counts read aloud while typing interrupted the experience.
  • Minor: Users wanted responses organized under proper headings, and more personalized answers.
Outcome: Findings were prioritized for remediation with engineering and design, alongside a broader push to improve the assistant's personalization to match users' expectations of what the company already knows about them.

Relevant WCAG 2.2 Criteria

2.4.3 Focus Order 4.1.2 Name, Role, Value 2.1.1 Keyboard 4.1.3 Status Messages 1.3.1 Info & Relationships

Goal

Evaluate navigation accessibility across six popular consumer apps — Credit Karma, SoFi, Amazon, Amazon Music, Spotify, and Rocket Money — for screen reader and voice recognition users, to build a benchmark for our own product's accessibility standards.

Who

8 assistive technology users (7 screen reader, 1 voice recognition), mostly testing on smartphones, each spending their session in one or two apps they already used.

What helped users move faster

  • Positive: Clear heading structures and labeled tab bars.
  • Positive: Properly labeled search bars and interactive controls.

What slowed users down

  • Serious: Missing or inconsistent labels on page elements.
  • Serious: Carousels and tab bars that were hard to navigate with voice recognition software.
  • Serious: Focus that jumped unexpectedly, and inconsistent accessibility between iOS and Android.
Outcome: Findings shaped an eight-point accessibility recommendation set covering heading structure, tab order, control labeling, focus behavior, and voice command support — applied across both iOS and Android product work.

Relevant WCAG 2.2 Criteria

1.3.1 Info & Relationships 2.2.1 Timing Adjustable 2.4.2 Page Titled 2.4.6 Headings & Labels 3.2.2 On Input

Goal

Get a baseline read on the live experience of a debt payoff planning tool, and identify usability and accessibility issues before further investment.

Who

10 active members with at least $2,000 in credit card debt they were actively paying down. 5 of 10 were confirmed to be part of underserved or marginalized communities, including users with a cognitive or learning disability and screen reader users with visual impairments; 2 of 10 were confirmed very low income.

Design

Phase 1: a 45-minute moderated usability test. Phase 2–5: a 5-week diary study where users logged their debt payoff behaviors. Phase 6: a 30-minute follow-up interview.

Key findings

  • Critical: 4 of 10 users couldn't use the tool at all — it didn't recognize debts that were listed as closed accounts or in collections, even though users were still actively paying them off.
  • Serious: 6 of 10 users couldn't find the tool from the main dashboard, though they could find it elsewhere in the app.
  • Serious: Screen reader users struggled with inconsistent heading structure, poor alt text and ARIA labeling, and unpredictable focus behavior throughout the tool.
  • Minor: In the diary study, some users forgot the tool existed after their first session; others already tracked debt elsewhere.
  • Minor: Users wanted projected interest savings and the ability to model different repayment strategies.
Outcome: Recommendations included letting users with debts on closed or collections accounts still use the tool, improving its visibility from the main dashboard, and remediating the accessibility issues found across the broader account experience.

Relevant WCAG 2.2 Criteria

1.3.1 Info & Relationships 2.4.3 Focus Order 2.4.6 Headings & Labels 2.4.7 Focus Visible 4.1.2 Name, Role, Value 2.1.1 Keyboard

Goal

Determine which results-page design for a financial health assessment tool — pillars without scores, pillars with scores, or strengths/weaknesses — users found most understandable, helpful, and actionable.

Who

18 members, ages 18–50, household income under $100,000, tested on their phones in a between-subjects unmoderated study (6 users per design variation).

Key findings

  • Positive: The strengths/weaknesses version was the easiest for users to interpret and act on.
  • Serious: Users didn't know how to interpret numeric pillar scores.
  • Serious: Some users mistook the financial health score for their credit score.
  • Positive: Colored tags helped guide users toward where to focus.
Outcome: Recommended shipping the strengths/weaknesses design and reconsidering the numeric "score" framing altogether, since it invited confusion with users' credit scores.

Accessibility leadership

Building accessibility into how a company works

Beyond individual studies, I've worked to make accessibility a standing part of how products get built — not just something tested for after a new digital experience is built and launched.

CPACC certificate awarded to Melissa Maykut by the International Association of Accessibility Professionals, dated May 20, 2026, certificate number MACPACC14472.

Certified Professional in Accessibility Core Competencies (CPACC)

International Association of Accessibility Professionals · Awarded May 2026

Founding the first Accessibility Council at the largest credit bureau in the U.S.

An estimated 70 million people live with some sort of disability. This community and their families control an estimated $13 trillion in disposable income. Making products accessible is a market opportunity, but also just the right thing to do. I pitched and founded a cross-functional Accessibility Council that brings together Product, Engineering, Design, Research, and Legal & Compliance leaders. This Accessibility Council's objective is to provide a lasting governance model for ADA and WCAG compliance, instead of one-off fixes after launch.

Risk management Ensure products and services meet ADA and WCAG compliance standards, limiting legal exposure.
Operational efficiency Centralize accessibility efforts to cut the cost of late-stage rework and remediation.
Product quality Build accessibility into the product lifecycle to reduce defects and improve usability for consumers of all abilities.

AI & innovation

Building tools for the research team

I build purpose-built AI skills in ChatGPT Codex to speed up my and my research team's workflow — with clear guardrails on what the AI is allowed to decide.

Screenshot of the Qualtrics to GreatQuestion skill interface, showing a script that prepares panelist data for upload.
Research operations

Qualtrics → GreatQuestion panelist automation

My team recruits research participants through a panel that needs new panelist data reformatted for our research repository after every Qualtrics export — previously a manual process I ran in Excel. I rebuilt it as a Codex skill that automates the formatting and also flags panelists likely to be part of a disadvantaged or underserved community, based on household income and zip code, so recruiting for inclusive research is faster and more consistent.

Screenshot of the Accessibility WCAG Video Reel Identification skill, listing five AI use guardrails including 'AI executes, researchers decide.'
Accessibility research

WCAG criteria identification skill

My team and I regularly surface potential WCAG violations when we test with assistive technology users. The challenge is determining exactly which WCAG criteria to cite when sharing findings with engineers. This skill takes an uploaded highlight reel of user clips and the session transcript, analyzes them, and suggests the closest-matching WCAG 2.2 criteria for each finding. This skill has helped my team and I move faster in creating well-cited reports and guiding stakeholders on accessibility.

Built-in guardrail: "AI executes, researchers decide." Every output has to pass five rules I wrote into the skill: it applies documented frameworks rather than inventing findings, a researcher owns the study and its conclusions, a named researcher signs off before anything reaches a stakeholder, anything the AI infers (rather than reads directly) ships labeled as a hypothesis, and only data cleared for the tool's use ever goes in.

How I work

Research methods & tools

These are the methodologies and tools I reach for most when conducting research, analyzing findings, and collaborating with stakeholders.

Research methods

Moderated usability testing Unmoderated testing In-depth interviews Accessibility research Diary studies Card sorting & tree testing Competitor benchmarking AI-assisted usability testing Preference testing First click testing

Tools

Condens GreatQuestion Qualtrics Outset AI Lyssna TheyDo ChatGPT Codex Recollective Figma UserInterviews UserTesting

Outside of work

Muttventures with Mel

I love dogs (I have three rescues at home!) and believe each one deserves love and care. I am a weekly dog walking volunteer at the Cleveland Animal Protective League (APL), taking shelter dogs on walks and field trips — and sharing their stories on social media to help them find their forever homes.

Cleveland APL volunteer & founder of Muttventures with Mel

I started an Instagram and TikTok, Muttventures with Mel, to bring more visibility to the dogs waiting for homes at the Cleveland APL.

Dog's Day Out with Sega, a Cleveland APL field trip