Case Study 1: eBay – Refurbished Reseller Dashboard

Project Overview: Led the user research for a dashboard that replaced a cumbersome reporting system, catering to refurbished resellers in the Motors category. The primary objective was to create a solution that provided real-time data for sales, item condition, and returns compliance. This addressed inefficiencies faced by resellers and the client services team.

Key Challenge: Resellers had to rely on client service reps to generate custom reports, leading to significant time waste and potential compliance risks. The lack of real-time data caused frustration and missed opportunities for efficiency.

UX Research Approach:

• Conducted interviews with resellers across the U.S., Germany, and the UK to understand their pain points with reporting and data accuracy.
• Facilitated cross-functional workshops to align product, design, and engineering teams on the user needs and priorities.
• Performed competitive analysis to ensure the dashboard would meet industry standards for usability and functionality.

Key Insights:

• Resellers needed a simple, actionable view of real-time metrics, including condition reports and return rates, all in one place.
• The complexity of the previous system was leading to user confusion and inefficiencies that hindered business performance.

Impact:

• The new dashboard saved each client services rep approximately 10 hours per week.
• Resellers could now independently access the data they needed for compliance and performance tracking.
• Streamlined operations, resulting in improved program efficiency and customer satisfaction.

Outcome: The dashboard received executive buy-in and was successfully implemented. The research findings also influenced product features and the overall design direction, with strong metrics on operational savings and user satisfaction.

Case Study 2: eBay – AI Product Listing & Item Recognition

• Project Overview: Conducted ad-hoc user research to improve the AI-powered product listing and item recognition features in eBay’s mobile app, aiming to increase the accuracy of listings and the efficiency of the product discovery process.
• Key Challenge: The AI-driven item recognition had room for improvement in terms of accuracy, which was negatively impacting the user experience—leading to frustration and mistrust in the app’s recommendations.
• UX Research Approach:
  • Collaborated with data scientists and product managers to identify the most pressing issues users encountered when AI-generated listings appeared inaccurate or incomplete.
  • Ran multiple rounds of usability testing and analyzed user feedback to pinpoint common pain points.
    • Participant Note: As this is proprietary in-house technology that is not ready for release or publicity, most of the advanced features were tested on eBay employees not affiliated with the design unit.
  • Prioritized recommendations based on user behavior and real-world use cases to improve the AI’s matching accuracy.
• Key Insights:
  • Users desired clearer indicators of AI-driven suggestions and more reliable item matches.
  • Users were often skeptical about the AI’s ability to accurately categorize items, especially when descriptions were vague or mismatched.
  • Clearer error messaging and more robust user feedback loops were necessary to build trust in AI-generated listings.
• Impact:
  • Led to continued investment in the AI’s performance, including further research into reducing listing inaccuracies and improving user confidence in the app.
  • Enhanced product discoverability, leading to better feedback scores and potentially greater sales for eBay.
• Outcome: The iterative changes to the AI recognition system have recently been integrated into the app, resulting in better user satisfaction and more accurate product listings.

Case Study 3: eBay – Buyer Flows in Motors App

• Project Overview: Focused on improving the user experience in the eBay Motors app, specifically the MyGarage and Guaranteed Fit sections, which were major sources of buyer frustration. This directly impacted purchase behavior and conversion rates.]
• Key Challenge: Users were abandoning the process at critical steps in their journey to purchase auto parts, often due to confusion about fitment and compatibility.
• UX Research Approach:
  • Conducted 20 remote user interviews via dScout with participants who had recently interacted with eBay Motors, particularly the MyGarage and Guaranteed Fit features.
  • Analyzed session recordings and heatmaps to identify where users struggled with navigation and decision-making.
  • Mapped out pain points and bottlenecks in the buyer flow, particularly in terms of clarity and confidence around product compatibility.
• Key Insights:
  • Users needed clearer visual indicators to easily verify whether a part was compatible with their vehicle, reducing cognitive load.
  • There were significant gaps in the information provided, leading to uncertainty during critical purchase decisions. Apparent logical inconsistencies in wording around blanket guarantees and individual items proved particularly worrisome.
• Impact:
  • Improved the flow design by simplifying key decision points and adding compatibility assurances.
  • Increased conversion rates and reduced drop-off during the purchasing process, particularly for users accessing the app through mobile devices.
• Outcome: The optimized flows were implemented, and data suggested a smoother, more confident user journey, ultimately boosting engagement and sales in the Motors division.

Case Study 3: Princeton – MAFE Online Digital Learning Strategy

• Project Overview: Led user research and digital transformation for Princeton’s Middle Ages for Educators (MAFE) platform, focusing on digital adoption and content development to increase user engagement.
• Problem: The MAFE program struggled with low user adoption due to limited digital content strategies and a lack of an engaging online presence.
• Research Methodology:
  • Conducted user interviews and surveys to understand the preferences and pain points of educators and students.
  • Analyzed user behavior data to identify which types of content were most engaging.
• Key Insights:
  • Users preferred interactive, multimedia-rich content that could be easily accessed across devices.
  • Adoption was hindered by a lack of cohesive, user-friendly content organization.
• Solution:
  • Developed a digital content strategy that included interactive lectures, downloadable resources, and mobile-optimized videos.
  • Simplified the user interface to make navigation more intuitive and content easier to discover.
• Impact:
  • Increased user adoption and platform engagement, resulting in 3,000 new visitors within a month, and enhanced user retention.
  • Boosted positive user feedback and reduced support queries related to content access.
  • Served as a platform for other digitally driven, project-managed initiatives both virtual and in-person.
• Outcome: The MAFE platform’s digital revamp met Princeton’s objectives for expanding reach and fostering sustained engagement through effective digital content strategies.

Case Study 4: Common Cause – Stakeholder Listening & Research Synthesis

• Project Overview: Applied advanced UX research techniques to support Common Cause’s advocacy initiatives, focusing on accessibility and transparency across various counties and municipalities in the U.S.
• Problem: Public access to crucial information varied greatly, with many municipalities lacking user-friendly interfaces and consistent practices, hindering transparency.
• Research Methodology:
  • Conducted extensive stakeholder listening sessions with community members, policy advocates, and public officials.
  • Synthesized data from multiple media sources, including public records, interviews, and third-party reports, to form a comprehensive understanding of accessibility issues.
  • Employed journey mapping to track user interactions with public information systems and highlight barriers.
• Key Insights:
  • Identified significant disparities in how counties managed and presented information, with accessibility gaps affecting public engagement.
  • Found that inconsistent digital experiences led to confusion and reduced participation in civic processes.
• Solution:
  • Compiled findings into the Paid Jailer report, which provided actionable insights and data to inform policy recommendations.
  • Presented synthesized research to stakeholders to support advocacy efforts and push for improvements in digital transparency.
• Impact:
  • Equipped Common Cause with well-researched evidence to advocate for better digital practices and more equitable information access.
  • Enhanced the organization’s strategic approach to engaging with policymakers and the public.
• Outcome: The research contributed to informed advocacy and highlighted the importance of accessible public information, aligning with Common Cause’s mission to promote transparency and civic engagement.