With no dedicated researcher on the team, I owned the full discovery process end to end. I kicked things off with a cross-functional discovery sprint—pulling in PM, marketing, support, and the data team—to surface blind spots early and align on core assumptions.

We used a Lean UX Canvas to collaboratively frame the problem space, define hypotheses, identify key user segments, and align on desired outcomes. This gave the team a shared language and helped prioritize our research and design efforts around real user needs and business goals.

From there, I led mixed-methods research.

• Performed a heuristic evaluation of the current app that revealed major UX gaps around feedback, clarity, and hierarchy.
• Conducted interviews with current and churned users to understand how they think about and interact with the app.
• Distributed targeted surveys in collaboration with our marketing lead to validate hypotheses at scale.
• Mined support tickets and used GPT to cluster recurring pain points—turning qualitative noise into structured insights fast.

To turn research into action, I synthesized user quotes, behavior signals, and survey data into a FigJam board, then led a workshop with the team to surface core needs.

Three key themes emerged: reassurance (“Will my car be ready?”), transparency (“Why is it not charging now?”), and rewards visibility (“What am I earning?”). These reframed the problem—from usability to a trust and behavioral confidence gap.

To anchor strategy in real behavior, I mapped the customer journey end-to-end, identifying where friction emerged and trust broke down. This artifact became a shared tool across teams to align on pain points, opportunities, and success metrics.

From there, I co-led a feature ideation and prioritization session, mapping opportunities by user value versus effort.

I advocated for early wins that would immediately reduce opt-outs—like alerting users about the risk of missing a reward when opting out, and providing charging controls that felt empowering rather than risky. Heavier features, such as flexible time windows and usage history, were scoped for future releases. The result was a scrappy, retention-focused roadmap grounded in user trust—not feature volume.

Before diving into design, I zoomed out to rethink the app’s information architecture and interaction model. I mapped how users move through the experience—what they need to do, when, and why—to anticipate friction points and align workflows with real user intent.

In parallel, I mapped system behavior behind the scenes, ensuring backend logic could scale with future features like personalized incentives and smarter automation.

This early structure work brought clarity to the product vision, streamlined downstream design, and helped us proactively address edge cases before they became blockers.

I mapped both user flows (e.g., onboarding, opt-out, rewards check-in) and state flows (e.g., reward triggers, charge events) to help engineering reason through logic and edge cases. These diagrams became crucial communication tools that drove alignment and accelerated velocity.

With clarity from research and a lean strategy in place, I led an iterative, impact-driven design process—anchoring every decision in behavioral insights and user trust. Our redesign centered on four behavioral pillars: reassurance, transparency, control, and motivation. These pillars directly addressed the clarity and confidence gaps uncovered during research, forming the backbone of the new product experience.

Before diving into a full redesign, I focused on quick, high-leverage improvements that would build trust early and reduce churn. Two major friction points stood out: a fragmented onboarding experience and confusion around opt-outs. I tackled these first, rewriting onboarding content, clarifying the flow, and redesigning the opt-out confirmation to highlight consequences and reward loss. These lightweight changes delivered immediate gains in user retention and confidence, buying time for deeper product improvements.

Though onboarding wasn’t originally in scope, I stepped in after the marketing team flagged it as a drop-off point. I rewrote the content, restructured the flow, and swapped outdated illustrations for a cleaner, more consistent visual system aligned with the redesign.

This opportunistic fix made a strong first impression and reduced early churn.

A key friction point emerged around the “Charge Now” action. Users didn’t realize this actually opted them out of the program—leading to confusion, regret, and lost rewards.

The original interaction triggered only a subtle alert, making the consequences unclear. To solve this, I redesigned the opt-out confirmation flow with clearer, more intentional copy, a visual hierarchy that emphasized the outcomes, and inline messaging that showed how rewards would be affected. This small UX change significantly reduced accidental opt-outs and reinforced user trust through clarity and transparency.

The redesigned dashboard emphasizes clarity, confidence, and consistency. Clearer icons, refined copy, and contextual notifications help users understand their charging state at a glance. A new confirmation modal for "Override Schedule" reinforces the tradeoffs of opting out, using our updated visual system and language. These changes, informed by user feedback, contributed to stronger comprehension and trust.

The Statistics screen helps users make sense of their charging behavior over time, with an energy usage chart spanning weekly, monthly, and yearly views. Key metrics are presented in a clean card layout—prioritized based on what users told us they cared about most.

For the carbon impact indicator, I opted for a simple low/medium/high scale over a more complex gauge visualization which tested better for clarity during usability testing sessions. This choice reduced visual noise and made the data easier to interpret, staying true to the product’s focus on clarity.

A key user insight was the need for greater visibility into upcoming incentives. To keep motivation high, we made “Next Reward” a focal point on the Rewards screen.

We also introduced a gentle alert to warn users when they’re nearing their monthly opt-out limit—designed to inform without overwhelming.

To improve transparency, the rewards history section clearly labels each reward’s status: pending, claimed, or not eligible. When a reward isn’t earned, users can tap an info icon to open a drawer explaining why—closing the feedback loop and reinforcing trust in the system.

After onboarding, users often lacked a clear sense of what to do next. To address this, I designed just-in-time walkthroughs—lightweight, contextual prompts triggered by key events like logging in for the first time, earning a reward, or opting out. These cues helped guide users at the right moment, reducing confusion, lowering support tickets, and building early confidence—without adding clutter to the core experience.

As the redesign took shape, I proposed a referral flow to support user growth. Drawing from past experience with viral loops, I pitched the idea, aligned it with the product’s tone and visuals, and delivered a developer-ready spec with state logic—before it was even prioritized on the roadmap.

I prioritized theming support early while building the mobile design system—knowing dark mode would improve accessibility and visual comfort for many users. By designing for flexibility from the start, we were able to introduce dark mode later with minimal additional effort.

The beta launch saw a 46% drop in opt-outs and an average user trust rating of 4.7/5, signaling strong gains in engagement and confidence. Qualitative feedback echoed this—users felt more clarity and comfort in their charging experience. While we designed and validated a “Claim Reward” feature, we ultimately paused its release due to utility-side limitations. This decision reflected a broader theme: making strategic trade-offs to balance innovation with delivery speed.