Simplifying B2B Insurance: Designing a Quoting Tool with 90% User Success
- Manuel Ortiz
- Nov 9, 2025
- 4 min read
Updated: 8 hours ago
A research-driven validation sprint for a new B2B insurance product, delivering a prototype with a 90%+ user success rate.
AI-Enhanced Research - Systemic Design - Strategic Design - Business Modeling
Summary
My Role: Lead UX Researcher & Strategist, responsible for validating the product concept, conducting all mixed-methods research, and defining the end-to-end user flow.
The Challenge: Munich RE, the world's largest re-insurer, needed to validate the viability of a new digital-first insurance product for the Colombian B2B (PYMES) market—a market that traditionally relies on in-person brokers.
The Outcome:
Delivered a high-fidelity prototype that achieved a 90%+ user success rate in unguided testing.
Validated the product's market desirability and usability, providing the client with the data-backed confidence to green-light the business model.
The Context and Challenge
The Business Problem: Munich RE saw an opportunity to enter the underserved small-to-medium enterprise (PYMES) market in Colombia. The risk was high: building a full-scale digital product for a traditionally analog, broker-based industry was a multi-million dollar gamble. We had to prove, with data, that a digital-first tool was viable, desirable, and usable.
The User Problem: Small business owners (PYMES) in Colombia perceived insurance as complex, opaque, and time-consuming. The existing broker-led process lacked transparency, and there was no simple way to get a quick, customizable quote.
Initial Constraints & Ambiguity: This was a rapid design sprint. We had five days to move from market research to a high-fidelity prototype and deliver a conclusive, data-backed "go/no-go" recommendation to the client.
How I Approach the Challenge
As the lead for this validation sprint, my role was to de-risk the client's concept and deliver a clear, actionable report. My key strategic contributions included:
Leading the end-to-end validation strategy, from initial context research to the final executive report.
Designing and executing a mixed-methods research plan: a 50-user Maze survey for quantitative data, and 5 deep-dive interviews for qualitative insights.
Building the complex, high-fidelity Figma prototype used for all unguided and guided user testing.
Analyzing and presenting the final data (heat maps, success metrics, card sorting) to stakeholders, proving the concept's viability.
The Process
Before building, we had to understand the competitive landscape and user expectations for digital insurance in Colombia. I conducted context research on the market and analyzed existing digital-first competitors (like luko, CHUNN, and Lemonade) to establish a usability baseline.
The Key Insight: We confirmed that while the concept of digital insurance existed, none were successfully serving the B2B (PYMES) market. This was a blue ocean opportunity, if we could overcome the complexity.
Finding the Solution
The Challenge: We needed to test for two things: 1) Could users technically complete the flow? (Usability) and 2) What did they actually want in their plan? (Desirability).
The Action: I designed a two-pronged test. First, a 50-user unguided Maze survey to gather quantitative heat maps and success metrics. Second, I conducted 5 in-depth, guided interviews with target users to understand the "why" behind their clicks.
The heat maps from Maze were a breakthrough. They showed that when users were presented with a wall of options, they became overwhelmed and "needed assistance". A purely self-serve tool would fail. This insight pivoted our design from a "catalog" to an "assistant."
Iteration Is Key to Success
The Challenge: We had to confirm our new "assistive" hypothesis was correct. We also had to define what services were most important to users.
The Action: I ran a "Card Sorting" exercise to allow users to rank and categorize the different insurance coverages. This gave us a clear, user-defined priority for the interface.
The card sorting was incredibly clear: users prioritized "Accidentes personales" and "Transporte de mercancias." The final validation came when we tested our new, assistive prototype: over 90% of users successfully followed the expected path.
The Solution
Feature 1: The "Smart Recommendation" Our research proved users were overwhelmed. To solve this, we designed an "assistive" flow that asked for their "actividad económica" (economic activity) and provided a smart recommendation, drastically reducing cognitive load and building trust.
Feature 2: The Prioritized Coverage Cards The card sorting exercise showed us what users cared about most. We used this data to design the UI hierarchy, putting the most-desired coverages (like "Personal Accidents") front and center, which directly led to the 90%+ success rate.
Impact and Reflections
The Impact:
90%+ User Success Rate: We successfully designed a prototype that proved a complex B2B quoting process could be made simple and intuitive.
Validated the Business Model: Our final report gave Munich RE the data-backed confidence to move forward, de-risking a multi-million dollar investment by proving the product's usability, desirability, and market fit.
Delivered Actionable Product Strategy: Our research on "assistive" design and feature prioritization (via card sorting) became the core of the product's go-to-market strategy.
Personal Reflection: This project was a masterclass in the power of rapid, mixed-methods validation. It proved that in just five days, a small team can answer a massive business question. It also taught me that for complex products, the designer's job isn't just to build the tool, but to build the user's confidence in using it.
Credits
Product Manager: Juan Esteban Chaparro
Business Analyst: Manuela Uribe
Design Tech Director: Alejandro Córdoba
Visual Designer: Daniela Santa Cruz
