Technology serves people best when they participate in its design.

Context

Verizon was introducing multi-offer tiles to bring personalized offers together across the My Verizon app and website, and offer engagement had declined.

Approach

I paired behavioral telemetry with in-depth interviews, then synthesized findings across several offer studies, including a color study and tile usability testing run by my team. The data showed where people dropped off. The interviews explained why: problems with visual hierarchy, color and typography, including brand color combinations that added friction.

What changed

The synthesis became a set of experience principles for offer strategy, and design used it to rework tile layout, color and placement. Those design changes contributed to a 25% increase in offer acceptance.

Context

Verizon Assistant had no standard template for choosing a line or device in chat. The goal was to find the layout, controls and level of detail that worked best and turn them into a standard.

Approach

I ran unmoderated interaction tests with 193 participants, comparing vertical layouts with carousels, different selection controls, and levels of information density up to Level 4: name, number, device image, plan details and pricing.

What changed

Vertical layouts beat carousels on speed, and the most detailed rows gave people the most confidence. The findings led to a recommendation to standardize vertical layouts on mobile across plan management flows, and a baseline rule requiring a device image for every troubleshooting line selection. People preferred confirming a selection because it prevented mistakes, and the pattern with radio buttons and a confirm step scored 58% confidence and 41% ease of use.

Context

Verizon's Next Gen Digital mobile sales flow was being reskinned under the new Monarch brand. The study checked whether people using assistive technology could still complete a purchase.

Approach

I ran an unmoderated study with 27 assistive technology users, 10 prospects and 17 existing customers, on desktop and mobile. They included 16 screen reader users, 5 switch and voice control users, and 6 low-vision magnification users, each working through purchase tasks.

Findings

• Sign-in loops that sent authenticated customers back to the login screen
• A focus trap in the Estimated Next Bill modal
• Unlabeled trade-in and porting options that a screen reader announced as "Object Object"
• Visual density that went beyond what participants could comfortably take in

What changed

I turned the findings into prioritized fixes for engineering and design: stop authenticated sessions from resetting, remove the focus trap in the bill review dialog, add programmatic labels to radio buttons, and move dense specs into collapsible sections. Each fix was logged as a Jira ticket and remediated, clearing the blockers from checkout steps where high-intent customers had been dropping out.

Context

Product teams were detaching from components in the VDS central library and building their own. This study was part of VDS Promise, a research program testing whether the system delivers on consistency, inclusivity, and speed and efficiency.

Approach

I configured an AI design audit tool to analyze component analytics and find the 10 components with the highest detachment rates. I then interviewed 13 designers and developers on live product teams about adoption, workflow speed and the friction of integrating the system into their work.

Findings

• Teams using VDS components worked up to 5 times faster than teams building custom ones, saved about 25% of redesign time, shortened development cycles by more than a month, and got pre-vetted accessibility compliance
• A web-first engineering bias with outdated viewports that didn't support mobile-first layouts
• A rigid atomic structure that lacked complex, ready-made components
• Poor discoverability, and error-prone transitions between versions

What changed

The audit isolated specific gaps in the system and informed updates to the central library. The adoption study led to four recommendations: make VDS usage part of leadership acceptance criteria, set aside sprint time for visual quality defects, expand Figma and front-end coding training, and open contribution paths so teams can add to the system instead of relying on a central team alone. The VDS Promise research also shaped roadmap items, including a Figma bulk-updating plugin, low-fidelity wireframe libraries and motion packs.

Context

Creatability is an open-source set of experiments that use machine learning to make creative tools more accessible, built by Google Creative Lab with NYU's Ability Project.

Approach

I ran user research with people with diverse abilities and tested new interaction modes as they were built, working across research, design and engineering.

What changed

Research findings guided the design of the tools. The project went on to win the 2019 Cannes Lions Design Grand Prix.