Insurance Discovery - Increased Conversion 20%
Product discovery and UX experimentation
Problem
Insurance products had low discovery rates and conversion, with users not finding or engaging with insurance offerings. The existing discovery patterns and recommendation algorithms weren't effective. How might we improve insurance discovery and conversion through experimentation?
What I did
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Ran experiments to improve insurance product discovery and user engagement across app, web, and mobile
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Tested various discovery patterns including entry points, placement, and visual treatment
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Experimented with recommendation algorithms to optimize product suggestions and personalization
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Designed A/B tests to systematically compare different discovery approaches
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Analyzed conversion funnels to identify drop-off points and optimization opportunities
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Iterated on designs based on experiment results to continuously improve discovery and conversion
My key contribution
Conducted experiments to improve insurance discovery and conversion, increasing discovery by 30% and conversion by 20%.
Highlight:
I ran systematic experiments testing discovery patterns and recommendation algorithms across app, web, and mobile. Through data-driven design and A/B testing, I improved insurance discovery rate by 30% and increased conversion from discovery to purchase by 20%, optimizing recommendation algorithms and identifying effective discovery patterns.
Results & Impact
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30% improvement in insurance discovery rate
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20% increase in conversion from discovery to purchase
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Optimized recommendation algorithms for better personalization
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Identified effective discovery patterns across platforms
Overview
Ran experiments to improve insurance product discovery and user engagement. Tested various discovery patterns and recommendation algorithms using A/B testing across app, web, and mobile experiences.
To comply with my non-disclosure agreement, I have omitted and obfuscated confidential information in this case study. All information in this case study is my own and does not necessarily reflect the views of Booking.com.
How might we improve insurance discovery and conversion through experimentation?
How I structured the problem space to guide design decisions
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discovery through effective entry points and placement
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relevance through optimized recommendation algorithms
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conversion through improved user engagement
Using experimentation and funnel analysis, I identified that the key was testing different discovery approaches to find what resonated with users.
Information Architecture
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Designing modular, scalable components that integrate across the product ecosystem
Approach
Benefits
- •Reduces technical debt through reusable components
- •Enables faster iterations and scalability
- •Creates enterprise-scale solutions
Navigating complexity through cross-functional collaboration
I worked closely with product managers to define experiment hypotheses and success metrics, with data scientists to optimize recommendation algorithms, and with engineers to implement experiment variations. I facilitated experiment planning sessions and analyzed results to inform design and algorithm decisions. Regular syncs with stakeholders ensured experiments aligned with business goals.
Challenges and trade-offs
This project required systematic experimentation across multiple platforms to optimize insurance discovery and conversion.
Key Challenges and Solutions
Testing Across Multiple Platforms
We needed to test discovery patterns across app, web, and mobile, each with different constraints. I designed experiments that accounted for platform differences while maintaining consistent testing methodology, enabling cross-platform insights.
Optimizing Recommendation Algorithms
Recommendation algorithms needed to be optimized based on user behavior, but testing algorithm changes required careful design. I worked with data scientists to design experiments that tested algorithm variations while maintaining user experience quality.
Balancing Discovery with User Experience
We needed to improve discovery without being intrusive or disrupting the main user journey. I designed discovery patterns that felt natural and contextual, improving discovery while maintaining positive user experience.
What I learnt
Conducting insurance discovery experiments taught me that product discovery is about relevance and timing, not just visibility. By testing different discovery patterns and optimizing recommendation algorithms, we found that users engaged more when insurance was presented at the right moment with relevant recommendations. The key was understanding that discovery optimization requires both good UX design and effective algorithms working together. This experience reinforced the importance of cross-platform experimentation in product discovery, where testing across different platforms reveals platform-specific insights while maintaining overall strategy.
Feedback
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