Helping users navigate complex choices and driving sustained nine-figure annual BRL revenue uplift

Without fully bundling, we were missing out on significant revenue gain potential in a moment financial challenges for the company.

Booking confusion eroded user confidence in Localiza’s transparency, putting long-term customer relationships at risk.

This wasn’t just about immediate revenue; we needed to lay a foundation for scalable cross-sell strategies across Localiza’s ecosystem.

Providing proof of concept, extra revenue and data for more complex future versions.

In user complaints about protections and removal requests at pickup counters.

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Coordinated multiple teams (enabling systems, pricing sources, mobile/web channels) under tight technical and timeline constraints.

When stakeholder confidence faltered, I pivoted to quantitative testing, turning a potential crisis into an opportunity for stronger validation.

Earlier attempts at grouping protections existed before I joined, but they were incomplete and still confusing to users. My role was to reframe the problem, align stakeholders on a viable V1, and turn a broad commercial initiative into a coherent product direction.

Prior to this innitiative only protections could be bundled together.

The project spanned multiple back-end systems with interdependencies and data touchpoints. Each system had its own logic and limitations, making cohesive design and data accuracy a constant challenge.

As a key initiative for 2024, bundling couldn’t wait. We needed to deliver quickly without compromising the clarity and reliability users needed.

With multiple teams involved, each with unique perspectives and constraints, success depended on constant alignment and active negotiation.

Our protection model was designed for operational flexibility, but users expected familiar insurance-like tiers. This mismatch created a gap between user expectations and how our products actually worked.

Mixing bundled and standalone items created unnecessary complexity, especially on mobile. Users felt overwhelmed and lost confidence in their decisions.

When users couldn’t see exactly what was included in protections, they assumed information was being hidden. This perception of opacity damaged trust.

Internally focused product names and terms didn’t resonate with users. The mismatch between what users saw and what they expected to see created friction at the point of decision.

Negotiating competing priorities between business, design, and engineering teams while staying laser-focused on user needs. The stakes were high, and every decision had to consider impact on revenue, experience, and future iterations.

Conducted rapid in-agency testing to validate assumptions and fill knowledge gaps efficiently.

Interviewed support agents who dealt with confused customers daily. Their frontline perspective was invaluable.

Designed components that could be built independently and assembled like Lego blocks, reducing dependencies and risk.

Created a clear hierarchy: what must ship in V1, what could follow quickly, and what belonged in the long-term vision.

Ensured that even if only core components shipped, the experience would still be functional and better than the status quo.

Proposed and led a rapid Maze test to gather quantitative evidence, turning skepticism into momentum for data-driven decisions.

Framed it as risk mitigation: "Let's get data that makes everyone confident, without disrupting development or risking revenue."

Information accessibility mattered more than card quantity. When users could easily find the "no pack" option only a single-digit share missed it vs. one-third when hidden

Pre-selection backfired: it pushed 7 to 14 percentage points more users toward the cheapest option, while mid and top tier packages fell by 4 to 16 percentage points. The financial result of pre-selection would have been negative.

The fourth card added minimal value while increasing complexity and development cost.

The "no pack" alert was our compromise, addressing business concerns while maintaining user agency. With around 1 in 5  conversion rate, it proved highly effective.

Streamlined the experience to help users make confident choices about protections, directly reducing confusion and complaints.

Drove a significant and sustained lift in the average revenue-per-transaction by continuously optimizing the add-on flows for clarity and value.

Established a repeatable framework for cross-sell opportunities and demonstrated how user research drives bottom-line results.

Refined interaction hierarchy in modals and flows, guiding attention and reducing cognitive load.

Qualitative research got us to the right design direction. But we only gained full organizational confidence once we added quantitative validation.

Some compromises were necessary. Some were only expedient. I'd push harder before launch on the ones that were already likely to create friction, comparison support being the clearest example.

Bundling components are now part of Localiza’s design system, enabling scalable future improvements.

The project shifted how teams at Localiza approach design-led decisions. One concrete signal: physical branch counter agents adopted the comparison table as an active sales tool, using it to walk customers through coverage tiers at pickup.