Vehicle Quality Metrics

My Role: Product Manager

Following executive alignment on the concept, I owned the end-to-end delivery of the Metrics Dashboard MVP, a digital product that enables UX researchers and automotive clients to consistently measure, score, and analyze vehicle interface quality across dimensions such as Ease of Use, Capability, Brand Perception, Aesthetics, and Emotion. The outcomes informed how product teams, from Chief Engineers to Chief Evaluators, could make data-backed decisions on vehicle interface design and investment prioritization.

Project Objectives:

• Improve Decision-Making: Equip R&D teams with a centralized database to analyze user experience data across current and future summative evaluations.
• Reduce Reliance on Opinion: Replace subjective debates with objective data visualizations to support cost- and schedule-sensitive decisions.
• Enable Meaningful Stakeholder Conversations: Provide teams with more accurate, low-variance data than traditional IQS scores to drive clarity in discussions with senior evaluators.
• Build a Scalable Data Platform: Design a modular system that supports evolving UX research programs and broader cross-functional analysis.

Key Responsibilities:

• Product Requirements & Scope Management: Facilitated requirement-gathering sessions with Honda’s HMI and R&D teams to define functional and non-functional needs. Authored and maintained a detailed User & Design Requirements document to guide delivery, manage scope, and evaluate change requests based on cost and schedule impact.
• Stakeholder Alignment & Prioritization: Partnered with Honda’s Chief Evaluators, engineers, and analysts to ensure the platform addressed the highest-value pain points: lack of confidence in existing metrics, slow insights, and low decision-making transparency. Balanced priorities across technical feasibility, user needs, and business constraints to shape MVP scope.
• Dashboard & Data Architecture Definition: Collaborated with design and data teams to define the information architecture of both the database and interactive dashboard. Ensured that visualization tools supported both quick exploration and deeper performance insights across UX dimensions (Ease of Use, Aesthetics, Emotion, Brand, etc.).
• Cross-Functional Delivery: Worked alongside engineering and UX leads to guide the implementation of the database and dashboard features in phased releases. Managed backlog prioritization, sprint planning, and risk mitigation strategies throughout the project lifecycle.
• Development and Implementation: Worked closely with the development team to define technical requirements and prioritize delivery milestones. Managed sprints, resolved blockers, and kept the project on track through agile ceremonies and check-ins.
• Usability and Performance: Oversaw usability testing during and after development to validate ease of use, speed, and accuracy of insights. Iterated on feedback and performance metrics to refine core functionality post-MVP.

Sample UX Scenarios and Workflows:

Outcome:

The Metrics Dashboard  provides the company with a streamlined and efficient process for collecting, storing, and analyzing UX scorecard data. Researchers and engineers were able to make data-driven decisions, leading to improved vehicle interfaces and enhanced client satisfaction.

• Defined and delivered MVP requirements for a custom UX metrics database and dashboard, enabling faster insight generation and improved stakeholder buy-in for design changes.
• Reduced ambiguity in key R&D decisions by centralizing high-fidelity UX data previously spread across disparate sources or trapped in IQS summaries with wide confidence intervals.
• Established product foundation for future integration with simulation tools, benchmarking systems, and next-gen research programs.

Key Learning:

• Managing product scope in a highly regulated, schedule-sensitive automotive context required a deep understanding of organizational risk tolerance.
• Early validation of methodology builds trust and ensures stakeholder confidence in long-term data-driven tooling.
• Positioning data tools as enablers of speed (not just insights) was crucial to gaining stakeholder adoption in an environment where “schedule is never negotiable.”