Volkswagen & Audi

Volkswagen and Audi needed a centralized system to track dealer performance, training progress, and media engagement across regions and the nation. I led stakeholder interviews to identify gaps in data accessibility, usability, and reporting workflows.

Research showed users had to manually consolidate insights from different tools, slowing down reporting. They wanted a centralized dashboard to quickly compare performance across roles, regions, and metrics.

‍No existing competitor offered a platform that integrated training data, media metrics, and dealership insights at this scale.

The core issue was the lack of unified visibility for corporate teams trying to track sales and engagement KPIs.

Users needed to filter and compare metrics like video views, QR scans, and training completions across various business levels from individual dealers to entire regions.

The goal was to build a fast, clean, export-ready system that allowed teams to self-serve accurate data without needing IT support.

I designed four core dashboard views including Dealer Overview, Region Summary, Nation Summary, and KYVW Metrics to reflect how different roles accessed performance data.

The information architecture was modular, enabling scalable growth while keeping screens simple and navigable.

Using Adobe XD, I mapped flows and task scenarios based on actual user priorities to refine layouts that balanced clarity with control.

The UI was built for desktop-first clarity with sharp visual hierarchy, role-specific filters, and KPI modules that let users spot trends instantly.

I built a custom design system with reusable components, clear color logic for performance states, and consistent spacing to support enterprise accessibility.

The final prototype featured live filtering, real-time metric updates, and CSV exports giving execs instant access to actionable insights.

Allowing users to apply filters by date range and keyword, instantly updating the dashboard to reflect narrowed results. I paired it with toast feedback to confirm action and keep the experience smooth and predictable.

This enables users to isolate performance data for specific videos. Whether searching by name, model, or campaign, this filtering experience supports focused content analysis without overwhelming the user.

This flow mirrors the Volkswagen dashboard filter behavior but is applied to Audi IQ data. Maintaining this consistency across both brands ensures corporate users can quickly analyze metrics without needing to learn new interactions.

I let users drill into training metrics for a specific region. Once a region is selected, the table updates automatically, offering a targeted performance snapshot with no additional navigation required.

A short but effective functionality. This allows users to quickly return to a default state by clearing all applied filters in one action. Having the Reset button helps avoid unnecessary backtracking and supports exploration without commitment.

This flow demonstrates how users can minimize or expand data blocks to control how much content is visible on the screen. I introduced collapsible sections to make dense information more manageable and help reduce visual fatigue when viewing detailed metrics.

This lets users choose custom timeframes to generate reports. It’s subtle but essential. I made sure it handled both full and partial range selections smoothly. It connects directly to all filter panels and keeps the data updates responsive and fast.

I conducted scenario-based tests with corporate users who simulated real tasks like spotting low-performing regions and checking media engagement stats.

Users praised the system for cutting down reporting time and making data feel instantly understandable. Small refinements included renaming filters and adding navigation shortcuts.

Final iterations improved load speeds, data labeling, and the video analytics hierarchy which helped polish the experience before launch.

The dashboard is now used monthly by executives at Volkswagen and Audi and has become the primary platform for performance visibility across both brands.

This project taught me that enterprise UX is about reducing cognitive load, not reducing content.
Clarity is the true differentiator.

‍What's Next?

I see AI playing a role in surfacing smarter insights! Instead of just reporting data, the dashboard could start generating summaries, detecting unusual patterns, and predicting underperformance across regions or content. Features like natural language queries, trend alerts, and auto-generated highlights would help leadership get answers faster without digging through filters or charts.