Partnership Discovery - Increased Discovery 25%
Experimentation and UX optimization
Problem
Partnerships had low awareness and discovery rates, with users not finding or engaging with partnership opportunities. The existing UI patterns and messaging weren't effective at surfacing partnerships. How might we improve partnership discovery through experimentation and UX optimization?
What I did
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Conducted experiments to improve partnership awareness and discovery using A/B testing and data-driven design
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Tested different UI patterns including placement, sizing, and visual treatment to optimize visibility
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Experimented with messaging strategies to improve clarity and appeal of partnership opportunities
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Tested various placement strategies to identify optimal locations for partnership discovery
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Analyzed user behavior data to understand discovery patterns and optimize accordingly
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Iterated on designs based on experiment results to continuously improve discovery rates
My key contribution
Conducted experiments to improve partnership discovery, increasing discovery by 25% and engagement by 40%.
Highlight:
I conducted systematic experiments testing different UI patterns, messaging, and placement strategies. Through data-driven design and A/B testing, I improved partnership discovery by 25% and increased engagement by 40%, identifying optimal strategies that became the foundation for ongoing optimization.
Results & Impact
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25% improvement in partnership discovery
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40% increase in partnership engagement
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Identified optimal placement and messaging strategies
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Established experimentation framework for ongoing optimization
Overview
The Partnerships department at Booking.com needed to increase awareness and discovery of partnership opportunities, but existing UI patterns and messaging weren't effectively reaching potential partners. The department operated in a data-driven environment where experimentation and A/B testing are standard practice for optimization. With limited visibility into which discovery patterns and messaging strategies were most effective, the team needed systematic experiments to identify optimal approaches. In a competitive partnerships market where discovery and engagement directly impact business growth, the department needed data-driven insights to optimize partnership awareness.
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 partnership discovery through experimentation and UX optimization?
How I structured the problem space to guide design decisions
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visibility through optimal placement and sizing
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clarity through effective messaging
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engagement through appealing UI patterns
Using experimentation and data analysis, I identified that the key was testing different approaches systematically to find what worked best.
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 analysts to analyze experiment results, and with engineers to implement experiment variations. I facilitated experiment planning sessions and reviewed results to inform design decisions. Regular syncs with stakeholders ensured experiments aligned with business goals.
Challenges and trade-offs
This project required systematic experimentation to identify what worked best for partnership discovery.
Key Challenges and Solutions
Testing Multiple Variables Systematically
We needed to test UI patterns, messaging, and placement, but testing everything at once would be unclear. I designed a systematic testing approach that tested one variable at a time, enabling clear insights into what drove improvements.
Balancing Experimentation with User Experience
Experiments could create inconsistent experiences for users. I designed experiments that maintained good user experience while testing variations, ensuring users always had a positive experience regardless of which variation they saw.
Translating Experiment Results into Design Decisions
Experiment results needed to inform ongoing design decisions. I created clear documentation of results and design recommendations that enabled the team to apply learnings beyond the initial experiments.
What I learnt
Conducting partnership discovery experiments taught me that experimentation is about systematic learning, not just testing. By designing controlled experiments and analyzing results carefully, we identified what actually drove improvements rather than guessing. The key was understanding that experimentation requires clear hypotheses, proper controls, and careful analysis to yield actionable insights. This experience reinforced the importance of data-driven design in optimization, where experiments provide evidence for design decisions rather than assumptions.
Feedback
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