Overview & Outcomes
This project became Localiza’s largest revenue driver in 2024, generating over R$100M in incremental revenue in under six months. We unlocked cross-sell potential by combining protections and optional items in streamlined bundles, tackling both the technical challenge of modernizing legacy systems and the user-facing confusion around protection options. This set the stage for an improved user experience, trust-building, and scalable growth.

Legacy systems weren’t designed for bundled offerings while confusing protection options caused decision fatigue, blocking critical revenue opportunities and eroding user trust during booking.
Why this mattered
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.
Key Outcomes
From mid-Q3/24 through November, becoming one of Localiza's biggest revenue highlights for Q3/24 despite being live for less than half the quarter.
Average increase in protection/optional uptake.
Providing proof of concept, extra revenue and data for more complex future versions.
In user complaints about protections and removal requests at pickup counters.
*data cannot be shared
My Approach
Led end-to-end design research, synthesizing historical data and fresh user insights to craft our approach.
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.
Timeline

Problem Space
Navigating this space meant balancing complex system dependencies and pressing timelines with a clear-eyed look at how unclear protection rules and overwhelming choices impacted users. Insights about mental models, cognitive load, and language clarity shaped every design decision.
Context & Constraints
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 Localiza’s key revenue 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.
Key Insights from Research
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.
Strategic Challenges
We had to balance the risk of patching legacy systems with the need for scalable, future-ready solutions. This tension was central to every design and technical 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.
Design Process & Decisions
Our focus was to prioritize what would drive immediate impact, while laying a modular foundation for future improvements. Along the way, we faced high-stakes stakeholder challenges that demanded quick pivots, like moving from qualitative to quantitative testing, to ensure decisions were data-backed and user-centered.
Research Foundation
Synthesized 2+ years of user feedback, support tickets, and NPS data to understand pain patterns.
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.
V1 Strategy & Tradeoffs
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.
Stakeholder Conflict
Directors questioned the validity of qualitative findings and demanded a risky production test to validate the hidden option.
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."
Quantitative Validation
Created 4 variants testing our key assumptions: 3 vs. 4 cards, with/without pre-selection.

Information accessibility mattered more than card quantity. When users could easily find the "no pack" option, only 6-11% missed it. When hidden, 34% proceeded unknowingly.
Pre-selection backfired: while basic pack selection increased 7-14%, premium pack selection dropped 11-16%—actually reducing revenue potential.
Final Design Decisions
Data proved that empowering choice led to higher-value selections, aligning user autonomy with business goals.

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. At 20% conversion, it proved highly effective.
Impact & Legacy
The results went beyond revenue: we saw meaningful improvements in user understanding and satisfaction. Early learnings from this project set the stage for ongoing iterations, with scalable patterns and a stronger foundation for cross-sell across Localiza’s ecosystem.
Final V1.1 Solution
Delivered a clean, simplified interface using modular components—clear separation between bundled and standalone options, and an alert system that supported transparency without overwhelming users.
Streamlined the experience to help users make confident choices about protections, directly reducing confusion and complaints.
Measurable Impact
The project became the largest revenue driver in 2024, generating over R$100M in incremental revenue in under six months.
Boosted daily revenue by R$2–5 per reservation through smarter bundling and clearer user decisions.
Established a repeatable framework for cross-sell opportunities and demonstrated how user research drives bottom-line results.
First Iteration Improvements
Introduced an interactive comparison table, helping users navigate choices more easily, especially on mobile.

Refined interaction hierarchy in modals and flows, guiding attention and reducing cognitive load.
Learnings
Early buy-in on scope and constraints sets the stage for successful delivery.
Combining qualitative research with live quantitative data strengthens decisions and earns trust.
Stakeholder pushback isn’t a blocker—it’s an opportunity to demonstrate leadership and rigor.
Next Steps & Legacy
V2 will focus on expanding comparison capabilities and reworking protection names for even clearer communication.
Bundling components are now part of Localiza’s design system, enabling scalable future improvements.
This project shifted how Localiza approaches design—proving that user research and data-led decisions create real business impact.